From a7c1f1895de9b0c42d9ec3fcbe2414181a0de74b Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Thu, 1 Aug 2024 16:28:07 -0500 Subject: [PATCH 01/12] [188037693]: re-roxygen --- DESCRIPTION | 2 +- man/SubtotalsHeadings.Rd | 4 +- man/Transforms.Rd | 4 +- man/analysis-methods.Rd | 2 +- man/crunch-extract.Rd | 158 +++++++++++++++++++-------------------- man/derivations.Rd | 2 +- man/describe-entity.Rd | 2 +- man/geo.Rd | 2 +- man/hide.Rd | 6 +- man/weight.Rd | 2 +- 10 files changed, 92 insertions(+), 92 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index d3c893c37..c37a8dcc5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -49,7 +49,7 @@ Suggests: VignetteBuilder: knitr Language: en-US Encoding: UTF-8 -RoxygenNote: 7.2.3 +RoxygenNote: 7.3.2 Roxygen: list(markdown = TRUE) LazyData: true Collate: diff --git a/man/SubtotalsHeadings.Rd b/man/SubtotalsHeadings.Rd index d3defba18..f138abe0f 100644 --- a/man/SubtotalsHeadings.Rd +++ b/man/SubtotalsHeadings.Rd @@ -68,7 +68,7 @@ Heading(name, position = c("relative", "top", "bottom"), after = NULL) \S4method{subtotals}{CrunchVariable,ANY}(x) <- value -\S4method{subtotals}{CrunchVariable,`NULL`}(x) <- value +\S4method{subtotals}{CrunchVariable,NULL}(x) <- value Subtotal( name, @@ -99,7 +99,7 @@ Heading(name, position = c("relative", "top", "bottom"), after = NULL) \S4method{subtotals}{CrunchVariable,ANY}(x) <- value -\S4method{subtotals}{CrunchVariable,`NULL`}(x) <- value +\S4method{subtotals}{CrunchVariable,NULL}(x) <- value } \arguments{ \item{name}{character the name of the subtotal or heading} diff --git a/man/Transforms.Rd b/man/Transforms.Rd index d77f31208..e653afaf0 100644 --- a/man/Transforms.Rd +++ b/man/Transforms.Rd @@ -34,7 +34,7 @@ transforms(x) <- value \S4method{transforms}{CrunchVariable,Transforms}(x) <- value -\S4method{transforms}{CrunchVariable,`NULL`}(x) <- value +\S4method{transforms}{CrunchVariable,NULL}(x) <- value \S4method{transforms}{CrunchCube}(x) @@ -44,7 +44,7 @@ transforms(x) <- value \S4method{transforms}{CrunchCube,TransformsList}(x) <- value -\S4method{transforms}{CrunchCube,`NULL`}(x) <- value +\S4method{transforms}{CrunchCube,NULL}(x) <- value } \arguments{ \item{...}{For the constructor function \code{Transforms} you can pass diff --git a/man/analysis-methods.Rd b/man/analysis-methods.Rd index f94f4d372..414631b42 100644 --- a/man/analysis-methods.Rd +++ b/man/analysis-methods.Rd @@ -179,7 +179,7 @@ formulaToSlideQuery(query, dataset) \S4method{filters}{Analysis,CrunchFilter}(x) <- value -\S4method{filters}{Analysis,`NULL`}(x) <- value +\S4method{filters}{Analysis,NULL}(x) <- value \S4method{filters}{Analysis,list}(x) <- value diff --git a/man/crunch-extract.Rd b/man/crunch-extract.Rd index 1a3d0ffe0..cf6b24b99 100644 --- a/man/crunch-extract.Rd +++ b/man/crunch-extract.Rd @@ -10,9 +10,9 @@ % R/variable-update.R, R/variable.R \name{crunch-extract} \alias{crunch-extract} -\alias{[,AbstractCategories,ANY,ANY-method} -\alias{[,AbstractCategories,character,ANY-method} -\alias{[,AbstractCategories,numeric,ANY-method} +\alias{[,AbstractCategories,ANY,ANY,ANY-method} +\alias{[,AbstractCategories,character,ANY,ANY-method} +\alias{[,AbstractCategories,numeric,ANY,ANY-method} \alias{[<-,AbstractCategories,character,ANY,ANY-method} \alias{[[,AbstractCategories,character-method} \alias{[[<-,AbstractCategories,character,ANY,ANY-method} @@ -26,19 +26,19 @@ \alias{[[<-.CrunchDataFrame} \alias{$.CrunchDataFrame} \alias{$<-.CrunchDataFrame} -\alias{[,CubeDims,ANY,ANY-method} -\alias{[,CrunchCube,ANY,ANY-method} +\alias{[,CubeDims,ANY,ANY,ANY-method} +\alias{[,CrunchCube,ANY,ANY,ANY-method} \alias{[[<-,TransformsList,ANY,missing,NULL-method} \alias{[[,DatasetCatalog,numeric-method} \alias{[[<-,DatasetCatalog,character,missing,DatasetTuple-method} -\alias{[,CrunchDataset,ANY,ANY-method} -\alias{[,CrunchDataset,logical,missing-method} -\alias{[,CrunchDataset,character,ANY-method} -\alias{[,CrunchDataset,VariableGroup,ANY-method} -\alias{[,CrunchDataset,VariableOrder,ANY-method} -\alias{[,CrunchDataset,missing,ANY-method} -\alias{[,CrunchDataset,CrunchLogicalExpr,missing-method} -\alias{[,CrunchDataset,CrunchLogicalExpr,ANY-method} +\alias{[,CrunchDataset,ANY,ANY,ANY-method} +\alias{[,CrunchDataset,logical,missing,ANY-method} +\alias{[,CrunchDataset,character,ANY,ANY-method} +\alias{[,CrunchDataset,VariableGroup,ANY,ANY-method} +\alias{[,CrunchDataset,VariableOrder,ANY,ANY-method} +\alias{[,CrunchDataset,missing,ANY,ANY-method} +\alias{[,CrunchDataset,CrunchLogicalExpr,missing,ANY-method} +\alias{[,CrunchDataset,CrunchLogicalExpr,ANY,ANY-method} \alias{subset,CrunchDataset-method} \alias{[[,CrunchDataset,ANY-method} \alias{[[,CrunchDataset,character-method} @@ -55,10 +55,10 @@ \alias{[<-,CrunchDataset,ANY,missing,list-method} \alias{[<-,CrunchDataset,ANY,missing,CrunchDataset-method} \alias{[<-,CrunchDataset,CrunchExpr,ANY,ANY-method} -\alias{[,ShojiCatalog,character,ANY-method} -\alias{[,ShojiCatalog,numeric,ANY-method} -\alias{[,ShojiCatalog,logical,ANY-method} -\alias{[,ShojiCatalog,ANY,ANY-method} +\alias{[,ShojiCatalog,character,ANY,ANY-method} +\alias{[,ShojiCatalog,numeric,ANY,ANY-method} +\alias{[,ShojiCatalog,logical,ANY,ANY-method} +\alias{[,ShojiCatalog,ANY,ANY,ANY-method} \alias{[[,ShojiCatalog,ANY-method} \alias{[[,ShojiCatalog,character-method} \alias{$,ShojiCatalog-method} @@ -72,9 +72,9 @@ \alias{[[<-,AnalysisCatalog,ANY,missing,Analysis-method} \alias{[[,CrunchDeck,ANY-method} \alias{[[<-,CrunchDeck,ANY,ANY,ANY-method} -\alias{[,CrunchExpr,CrunchLogicalExpr,ANY-method} -\alias{[,CrunchExpr,logical,ANY-method} -\alias{[,CrunchExpr,numeric,ANY-method} +\alias{[,CrunchExpr,CrunchLogicalExpr,ANY,ANY-method} +\alias{[,CrunchExpr,logical,ANY,ANY-method} +\alias{[,CrunchExpr,numeric,ANY,ANY-method} \alias{[[,FilterCatalog,numeric-method} \alias{[[<-,FilterCatalog,character,missing,CrunchLogicalExpr-method} \alias{[[<-,FilterCatalog,numeric,missing,CrunchLogicalExpr-method} @@ -93,8 +93,8 @@ \alias{[[<-,ProjectFolder,character,missing,ProjectFolder-method} \alias{[[,ShojiFolder,numeric-method} \alias{[[,ShojiFolder,character-method} -\alias{[,ShojiOrder,ANY,ANY-method} -\alias{[,ShojiOrder,character,ANY-method} +\alias{[,ShojiOrder,ANY,ANY,ANY-method} +\alias{[,ShojiOrder,character,ANY,ANY-method} \alias{[[,ShojiOrder,ANY-method} \alias{[[,ShojiOrder,character-method} \alias{$,ShojiOrder-method} @@ -109,8 +109,8 @@ \alias{[[<-,ShojiOrder,character,missing,NULL-method} \alias{[[<-,ShojiOrder,character,missing,ShojiOrder-method} \alias{$<-,ShojiOrder-method} -\alias{[,OrderGroup,ANY,ANY-method} -\alias{[,OrderGroup,character,ANY-method} +\alias{[,OrderGroup,ANY,ANY,ANY-method} +\alias{[,OrderGroup,character,ANY,ANY-method} \alias{[[,OrderGroup,character-method} \alias{[[,OrderGroup,ANY-method} \alias{$,OrderGroup-method} @@ -132,7 +132,7 @@ \alias{[[<-,AnalysisCatalog,numeric,missing,list-method} \alias{[[,Subvariables,character-method} \alias{[[,Subvariables,numeric-method} -\alias{[,Subvariables,character,ANY-method} +\alias{[,Subvariables,character,ANY,ANY-method} \alias{[[<-,Subvariables,character,missing,CrunchVariable-method} \alias{[[<-,Subvariables,ANY,missing,CrunchVariable-method} \alias{[[<-,Subvariables,ANY,missing,NULL-method} @@ -140,9 +140,9 @@ \alias{[<-,Subvariables,character,missing,Subvariables-method} \alias{[<-,Subvariables,ANY,missing,Subvariables-method} \alias{[<-,Subvariables,ANY,missing,ANY-method} -\alias{[,ArrayVariable,character,ANY-method} -\alias{[,ArrayVariable,missing,ANY-method} -\alias{[,ArrayVariable,missing,character-method} +\alias{[,ArrayVariable,character,ANY,ANY-method} +\alias{[,ArrayVariable,missing,ANY,ANY-method} +\alias{[,ArrayVariable,missing,character,ANY-method} \alias{[[,ArrayVariable,ANY-method} \alias{[[,ArrayVariable,character-method} \alias{$,ArrayVariable-method} @@ -155,13 +155,13 @@ \alias{[[,TeamCatalog,numeric-method} \alias{[[<-,TeamCatalog,character,missing,list-method} \alias{[[<-,TeamCatalog,character,missing,CrunchTeam-method} -\alias{[,UserCatalog,character,ANY-method} +\alias{[,UserCatalog,character,ANY,ANY-method} \alias{[[,UserCatalog,character-method} \alias{[[,VariableCatalog,numeric-method} \alias{[[<-,VariableCatalog,character,missing,VariableTuple-method} \alias{[[<-,VariableCatalog,character,missing,CrunchVariable-method} -\alias{[,VariableCatalog,VariableOrder,ANY-method} -\alias{[,VariableCatalog,VariableGroup,ANY-method} +\alias{[,VariableCatalog,VariableOrder,ANY,ANY-method} +\alias{[,VariableCatalog,VariableGroup,ANY,ANY-method} \alias{[<-,VariableCatalog,VariableOrder,missing,VariableCatalog-method} \alias{[<-,VariableCatalog,VariableGroup,missing,VariableCatalog-method} \alias{[[<-,VariableOrder,character,missing,CrunchDataset-method} @@ -183,16 +183,16 @@ \alias{[<-,CategoricalArrayVariable,ANY,missing,factor-method} \alias{[<-,CrunchVariable,ANY,missing,logical-method} \alias{is.na<-,CrunchVariable,ANY-method} -\alias{[,CrunchVariable,CrunchExpr,ANY-method} -\alias{[,CrunchVariable,numeric,ANY-method} -\alias{[,CrunchVariable,logical,ANY-method} +\alias{[,CrunchVariable,CrunchExpr,ANY,ANY-method} +\alias{[,CrunchVariable,numeric,ANY,ANY-method} +\alias{[,CrunchVariable,logical,ANY,ANY-method} \title{Extract and modify Crunch objects} \usage{ -\S4method{[}{AbstractCategories,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{AbstractCategories,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{AbstractCategories,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{AbstractCategories,character,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{AbstractCategories,numeric,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{AbstractCategories,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[}{AbstractCategories,character,ANY,ANY}(x, i, j, ...) <- value @@ -220,31 +220,31 @@ \method{$}{CrunchDataFrame}(x, i) <- value -\S4method{[}{CubeDims,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CubeDims,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchCube,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchCube,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[[}{TransformsList,ANY,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{TransformsList,ANY,missing,NULL}(x, i, j) <- value \S4method{[[}{DatasetCatalog,numeric}(x, i, j, ...) \S4method{[[}{DatasetCatalog,character,missing,DatasetTuple}(x, i, j) <- value -\S4method{[}{CrunchDataset,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,logical,missing}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,logical,missing,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchDataset,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,character,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,VariableGroup,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,VariableGroup,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,VariableOrder,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,VariableOrder,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,missing,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,missing,ANY,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchDataset,CrunchLogicalExpr,missing}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,CrunchLogicalExpr,missing,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchDataset,CrunchLogicalExpr,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,CrunchLogicalExpr,ANY,ANY}(x, i, j, ..., drop = FALSE) \S4method{subset}{CrunchDataset}(x, ...) @@ -262,9 +262,9 @@ \S4method{[[}{CrunchDataset,ANY,ANY,ANY}(x, i) <- value -\S4method{[[}{CrunchDataset,character,missing,`NULL`}(x, i) <- value +\S4method{[[}{CrunchDataset,character,missing,NULL}(x, i) <- value -\S4method{[[}{CrunchDataset,ANY,missing,`NULL`}(x, i) <- value +\S4method{[[}{CrunchDataset,ANY,missing,NULL}(x, i) <- value \S4method{$}{CrunchDataset}(x, name) <- value @@ -278,13 +278,13 @@ \S4method{[}{CrunchDataset,CrunchExpr,ANY,ANY}(x, i, j) <- value -\S4method{[}{ShojiCatalog,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,character,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiCatalog,numeric,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiCatalog,logical,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,logical,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiCatalog,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{ShojiCatalog,ANY}(x, i, j, ...) @@ -312,11 +312,11 @@ \S4method{[[}{CrunchDeck,ANY,ANY,ANY}(x, i, j) <- value -\S4method{[}{CrunchExpr,CrunchLogicalExpr,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchExpr,CrunchLogicalExpr,ANY,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchExpr,logical,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchExpr,logical,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchExpr,numeric,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchExpr,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{FilterCatalog,numeric}(x, i, j, ...) @@ -332,7 +332,7 @@ \S4method{[[}{MemberCatalog,ANY,missing,ANY}(x, i, j) <- value -\S4method{[[}{MemberCatalog,character,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{MemberCatalog,character,missing,NULL}(x, i, j) <- value \S4method{[[}{MultitableCatalog,numeric}(x, i, j, ...) @@ -342,7 +342,7 @@ \S4method{[[}{MultitableCatalog,ANY,missing,Multitable}(x, i, j) <- value -\S4method{[[}{MultitableCatalog,ANY,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{MultitableCatalog,ANY,missing,NULL}(x, i, j) <- value \S4method{[[}{PermissionCatalog,character}(x, i, j, ...) @@ -354,9 +354,9 @@ \S4method{[[}{ShojiFolder,character}(x, i, ..., drop = FALSE) -\S4method{[}{ShojiOrder,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiOrder,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiOrder,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiOrder,character,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{ShojiOrder,ANY}(x, i, j, ...) @@ -378,17 +378,17 @@ \S4method{[[}{ShojiOrder,ANY,missing,ANY}(x, i, j) <- value -\S4method{[[}{ShojiOrder,ANY,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{ShojiOrder,ANY,missing,NULL}(x, i, j) <- value -\S4method{[[}{ShojiOrder,character,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{ShojiOrder,character,missing,NULL}(x, i, j) <- value \S4method{[[}{ShojiOrder,character,missing,ShojiOrder}(x, i, j) <- value \S4method{$}{ShojiOrder}(x, name) <- value -\S4method{[}{OrderGroup,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{OrderGroup,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{OrderGroup,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{OrderGroup,character,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{OrderGroup,character}(x, i, j, ...) @@ -406,9 +406,9 @@ \S4method{[[}{OrderGroup,ANY,missing,OrderGroup}(x, i, j) <- value -\S4method{[[}{OrderGroup,numeric,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{OrderGroup,numeric,missing,NULL}(x, i, j) <- value -\S4method{[[}{OrderGroup,character,missing,`NULL`}(x, i, j) <- value +\S4method{[[}{OrderGroup,character,missing,NULL}(x, i, j) <- value \S4method{$}{OrderGroup}(x, name) <- value @@ -432,13 +432,13 @@ \S4method{[[}{Subvariables,numeric}(x, i, j, ...) -\S4method{[}{Subvariables,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{Subvariables,character,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{Subvariables,character,missing,CrunchVariable}(x, i) <- value \S4method{[[}{Subvariables,ANY,missing,CrunchVariable}(x, i) <- value -\S4method{[[}{Subvariables,ANY,missing,`NULL`}(x, i) <- value +\S4method{[[}{Subvariables,ANY,missing,NULL}(x, i) <- value \S4method{[[}{Subvariables,ANY,missing,ANY}(x, i) <- value @@ -448,11 +448,11 @@ \S4method{[}{Subvariables,ANY,missing,ANY}(x, i) <- value -\S4method{[}{ArrayVariable,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ArrayVariable,character,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ArrayVariable,missing,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ArrayVariable,missing,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ArrayVariable,missing,character}(x, i, j, ..., drop = TRUE) +\S4method{[}{ArrayVariable,missing,character,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{ArrayVariable,ANY}(x, i, j, ...) @@ -478,7 +478,7 @@ \S4method{[[}{TeamCatalog,character,missing,CrunchTeam}(x, i, j) <- value -\S4method{[}{UserCatalog,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{UserCatalog,character,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{UserCatalog,character}(x, i, j, ...) @@ -488,9 +488,9 @@ \S4method{[[}{VariableCatalog,character,missing,CrunchVariable}(x, i, j) <- value -\S4method{[}{VariableCatalog,VariableOrder,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{VariableCatalog,VariableOrder,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{VariableCatalog,VariableGroup,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{VariableCatalog,VariableGroup,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[}{VariableCatalog,VariableOrder,missing,VariableCatalog}(x, i, j) <- value @@ -502,7 +502,7 @@ \S4method{[}{CrunchVariable,ANY,missing,ANY}(x, i, j) <- value -\S4method{[}{CrunchVariable,ANY,missing,`NULL`}(x, i, j) <- value +\S4method{[}{CrunchVariable,ANY,missing,NULL}(x, i, j) <- value \S4method{[}{TextVariable,ANY,missing,character}(x, i, j) <- value @@ -534,11 +534,11 @@ \S4method{is.na}{CrunchVariable,ANY}(x) <- value -\S4method{[}{CrunchVariable,CrunchExpr,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchVariable,CrunchExpr,ANY,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchVariable,numeric,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchVariable,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchVariable,logical,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchVariable,logical,ANY,ANY}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{a Crunch object (Dataset, Variable, \code{CrunchExpr}, Catalog, diff --git a/man/derivations.Rd b/man/derivations.Rd index 73e597151..1123dba36 100644 --- a/man/derivations.Rd +++ b/man/derivations.Rd @@ -25,7 +25,7 @@ is.derived(x) <- value \S4method{derivation}{CrunchVariable,ANY}(x) <- value -\S4method{derivation}{CrunchVariable,`NULL`}(x) <- value +\S4method{derivation}{CrunchVariable,NULL}(x) <- value \S4method{is.derived}{CrunchVariable}(x) diff --git a/man/describe-entity.Rd b/man/describe-entity.Rd index 426fbb50f..68b10a560 100644 --- a/man/describe-entity.Rd +++ b/man/describe-entity.Rd @@ -113,7 +113,7 @@ notes(x) <- value \S4method{name}{AbstractCategory}(x) <- value -\S4method{name}{`NULL`}(x) <- value +\S4method{name}{NULL}(x) <- value \S4method{id}{AbstractCategory}(x) diff --git a/man/geo.Rd b/man/geo.Rd index 0488e443f..40c3e8031 100644 --- a/man/geo.Rd +++ b/man/geo.Rd @@ -24,7 +24,7 @@ geo(x) <- value \S4method{geo}{CrunchVariable,CrunchGeography}(x) <- value -\S4method{geo}{CrunchVariable,`NULL`}(x) <- value +\S4method{geo}{CrunchVariable,NULL}(x) <- value availableGeodata(x = getAPIRoot()) } diff --git a/man/hide.Rd b/man/hide.Rd index 49661ba08..6cb2209d0 100644 --- a/man/hide.Rd +++ b/man/hide.Rd @@ -142,9 +142,9 @@ back in the main variable catalog). \item \code{hide()} / \code{privatize()} - take a \code{CrunchVariable} or \code{VariableCatalog} and make them hidden/private. (\code{unhide()} / \code{deprivatize()} put them back in the main variable catalog). -\item \code{hiddenFolder()} / \code{privateFolder()} / \code{publicFolder()} - take a dataset and return a folder that -contains the public/hidden/private variables. This folder is like other \code{CrunchFolder}s and -so you can use \code{\link[=mkdir]{mkdir()}} to create subfolders and \code{\link[=mv]{mv()}} to move them in/out. +\item \code{hiddenFolder()} / \code{privateFolder()} / \code{publicFolder()} - take a dataset and return a folder +that contains the public/hidden/private variables. This folder is like other \code{CrunchFolder}s +and so you can use \code{\link[=mkdir]{mkdir()}} to create subfolders and \code{\link[=mv]{mv()}} to move them in/out. \item \code{hiddenVariables()} / \code{privateVariabiles()} - return a character vector of variables that are hidden/private. You can assign into the catalog to add variables or assign to \code{NULL} to remove all of them. diff --git a/man/weight.Rd b/man/weight.Rd index dafb4915f..d03ae4f8b 100644 --- a/man/weight.Rd +++ b/man/weight.Rd @@ -20,7 +20,7 @@ weight(x) <- value \S4method{weight}{Analysis,CrunchVariable}(x) <- value -\S4method{weight}{Analysis,`NULL`}(x) <- value +\S4method{weight}{Analysis,NULL}(x) <- value \S4method{weight}{CrunchDataset}(x) From 07636ce4fe46946576d0bb90fc0b8b04c0c08c15 Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Fri, 2 Aug 2024 09:57:45 -0500 Subject: [PATCH 02/12] [188037693]: update fixture generation code so that it works --- .../fixture-creation/vegetables-dataset.R | 35 +++++++++++-------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/dev-misc/fixture-creation/vegetables-dataset.R b/dev-misc/fixture-creation/vegetables-dataset.R index 1de683563..b8564dc59 100644 --- a/dev-misc/fixture-creation/vegetables-dataset.R +++ b/dev-misc/fixture-creation/vegetables-dataset.R @@ -564,8 +564,11 @@ temp_dir <- tempfile() httpcache::clearCache() dir_create(temp_dir) +# Load by ID now that it's in a project +# This means that the fixtures from each aren't 100% complete, but that's okay +ds_url <- self(ds) start_capturing(temp_dir) -ds <- loadDataset("Vegetables example") +ds <- loadDataset(ds_url) mt <- multitables(ds)[[1]] tb <- tabBook(mt, ds[c("healthy_eater", "veg_enjoy_ca", "enjoy_mr", "age", "ratings_numa")]) @@ -618,14 +621,14 @@ stop_capturing() # File level modifications needed to scrub attributes that change over time stabilize_json_files( temp_dir, - list( - "app.crunch.io/api/datasets/by_name/Vegetables%20example.json", - list(list("index", 1, "current_editor_name"), "User"), - list(list("index", 1, "owner_name"), "User"), - list(list("index", 1, "creation_time"), "2021-01-01T21:25:59.791000"), - list(list("index", 1, "modification_time"), "2021-01-01T21:26:43.038000"), - list(list("index", 1, "access_time"), "2021-01-01T21:26:43.038000") - ), + # list( # No longer loading by id + # "app.crunch.io/api/datasets/by_name/Vegetables%20example.json", + # list(list("index", 1, "current_editor_name"), "User"), + # list(list("index", 1, "owner_name"), "User"), + # list(list("index", 1, "creation_time"), "2021-01-01T21:25:59.791000"), + # list(list("index", 1, "modification_time"), "2021-01-01T21:26:43.038000"), + # list(list("index", 1, "access_time"), "2021-01-01T21:26:43.038000") + # ), list( "app.crunch.io/api/datasets/veg.json", list(list("body", "current_editor_name"), "User"), @@ -667,11 +670,15 @@ path(temp_dir, "app.crunch.io/api/datasets/veg/multitables/mt_01") %>% file_delete() # Now move to the mocks folder -file_copy( - path(temp_dir, "app.crunch.io/api/datasets/by_name/Vegetables%20example.json"), - here("mocks/app.crunch.io/api/datasets/by_name/Vegetables%20example.json"), - overwrite = TRUE -) + +# Since we loaded by id, this isn't available (it comes from before we had a project folder) ---- +# But it should exist from the old fixtures +# file_copy( +# path(temp_dir, "app.crunch.io/api/datasets/by_name/Vegetables%20example.json"), +# here("mocks/app.crunch.io/api/datasets/by_name/Vegetables%20example.json"), +# overwrite = TRUE +# ) +# ---- file_copy( path(temp_dir, "app.crunch.io/api/datasets/veg.json"), From 0325843bdcca484e8be7cd7d43d62f2c14d2ab5e Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 5 Aug 2024 09:47:57 -0500 Subject: [PATCH 03/12] [188037693]: regenerate fixtures --- mocks/app.crunch.io/api/datasets/veg.json | 18 +- .../api/datasets/veg/cube-28013a.json | 3 +- .../api/datasets/veg/cube-331178.json | 3 +- .../api/datasets/veg/cube-a13010.json | 3 +- .../api/datasets/veg/decks/dk01.json | 9 +- .../veg/decks/dk01/slides/dk01s01.json | 4 +- .../veg/decks/dk01/slides/dk01s02.json | 4 +- .../api/datasets/veg/decks/dk02.json | 9 +- .../veg/decks/dk02/slides/dk02s01.json | 3 +- .../veg/decks/dk02/slides/dk02s02.json | 3 +- .../veg/decks/dk02/slides/dk02s03.json | 3 +- .../api/datasets/veg/export.json | 1 + .../api/datasets/veg/multitables/mt_01.json | 1 + .../veg/multitables/mt_01/cat-mr-tabbook.json | 3717 ++++++++++++----- .../app.crunch.io/api/datasets/veg/table.json | 125 +- .../api/datasets/veg/variables-d118fa.json | 12 +- .../api/datasets/veg/variables/var_02.json | 9 +- .../veg/variables/var_02/subvariables.json | 6 +- .../variables/var_02/subvariables/0001.json | 2 +- .../api/datasets/veg/variables/var_15.json | 86 +- .../veg/variables/var_15/subvariables.json | 12 +- .../api/datasets/veg/variables/var_17.json | 12 +- .../veg/variables/var_17/subvariables.json | 8 +- .../api/datasets/veg/variables/var_26.json | 31 +- mocks/cubes/numa-x-cat.json | 89 +- mocks/cubes/numa-x-mr.json | 128 +- mocks/cubes/numa.json | 87 +- mocks/dataset-fixtures/veg.csv | 422 +- tests/testthat/test-as-data-frame.R | 1 + 29 files changed, 3508 insertions(+), 1303 deletions(-) diff --git a/mocks/app.crunch.io/api/datasets/veg.json b/mocks/app.crunch.io/api/datasets/veg.json index d705ddac7..7198582df 100644 --- a/mocks/app.crunch.io/api/datasets/veg.json +++ b/mocks/app.crunch.io/api/datasets/veg.json @@ -55,9 +55,9 @@ } ], "brand": { - "message": "#722580", + "message": "#712480", "primary": "#0064a4", - "secondary": "#107f65" + "secondary": "#107e64" } }, "path": "Vegetables fixture|Vegetables example", @@ -65,16 +65,21 @@ "edit": true, "view": true }, + "project_inherited_settings": { + "disallow_dataset_shares": false, + "stop_dataset_shares": true + }, "size": { - "columns": 26, - "derivations": 0, + "columns": 32, + "derivations": 6, "rows": 210, "source_columns": 26, "unfiltered_rows": 210, - "variables": 26 + "variables": 32 }, "start_date": null, "streaming": "no", + "type": "dataset", "view_of": null }, "catalogs": { @@ -136,7 +141,8 @@ "export": "https://app.crunch.io/api/datasets/veg/export/", "second_order_analysis": "https://app.crunch.io/api/datasets/veg/second_order_analysis/", "sources": "https://app.crunch.io/api/datasets/veg/sources/", - "summary": "https://app.crunch.io/api/datasets/veg/summary/" + "summary": "https://app.crunch.io/api/datasets/veg/summary/", + "var_by_alias": "https://app.crunch.io/api/datasets/veg/var_by_alias/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/cube-28013a.json b/mocks/app.crunch.io/api/datasets/veg/cube-28013a.json index a8210d0f4..c10a4fa01 100644 --- a/mocks/app.crunch.io/api/datasets/veg/cube-28013a.json +++ b/mocks/app.crunch.io/api/datasets/veg/cube-28013a.json @@ -129,7 +129,8 @@ } } - } + }, + "n_missing": 5 } }, "missing": 5, diff --git a/mocks/app.crunch.io/api/datasets/veg/cube-331178.json b/mocks/app.crunch.io/api/datasets/veg/cube-331178.json index de3d5d76e..21ec03860 100644 --- a/mocks/app.crunch.io/api/datasets/veg/cube-331178.json +++ b/mocks/app.crunch.io/api/datasets/veg/cube-331178.json @@ -129,7 +129,8 @@ } } - } + }, + "n_missing": 5 } }, "missing": 5, diff --git a/mocks/app.crunch.io/api/datasets/veg/cube-a13010.json b/mocks/app.crunch.io/api/datasets/veg/cube-a13010.json index 6d13d4a69..16afa950d 100644 --- a/mocks/app.crunch.io/api/datasets/veg/cube-a13010.json +++ b/mocks/app.crunch.io/api/datasets/veg/cube-a13010.json @@ -104,7 +104,8 @@ } } - } + }, + "n_missing": 11 } }, "missing": 11, diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk01.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk01.json index 541b762a0..7973d12d6 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk01.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk01.json @@ -1,22 +1,27 @@ { "body": { + "can_convert": true, "creation_time": "2021-01-01T21:29:59.791000", "description": "", "id": "dk01", "is_public": false, + "is_scriptable": false, "name": "1 deck about transforms", "owner_id": "https://app.crunch.io/api/users/user_id/", "owner_name": "Greg", "team": null }, "catalogs": { - "slides": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/" + "slides": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/", + "variables": "https://app.crunch.io/api/datasets/veg/decks/dk01/variables/" }, "description": "Detail of one deck", "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk01/", "views": { - "export": "https://app.crunch.io/api/datasets/veg/decks/dk01/export/" + "deck": "https://app.crunch.io/api/datasets/veg/decks/dk01/deck/", + "export": "https://app.crunch.io/api/datasets/veg/decks/dk01/export/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk01/script/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01.json index 52f6b186d..d9422789f 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01.json @@ -86,7 +86,9 @@ "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01/", "views": { - "applied": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01/applied/" + "applied": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01/applied/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01/script/", + "slide": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s01/slide/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02.json index f59487695..3f0066c03 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02.json @@ -103,7 +103,9 @@ "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02/", "views": { - "applied": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02/applied/" + "applied": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02/applied/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02/script/", + "slide": "https://app.crunch.io/api/datasets/veg/decks/dk01/slides/dk01s02/slide/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk02.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk02.json index c3c01f3b2..f991d06f7 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk02.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk02.json @@ -1,22 +1,27 @@ { "body": { + "can_convert": false, "creation_time": "2021-01-02T21:29:59.792000", "description": "", "id": "dk02", "is_public": false, + "is_scriptable": false, "name": "2 deck about printing", "owner_id": "https://app.crunch.io/api/users/user_id/", "owner_name": "Greg", "team": null }, "catalogs": { - "slides": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/" + "slides": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/", + "variables": "https://app.crunch.io/api/datasets/veg/decks/dk02/variables/" }, "description": "Detail of one deck", "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk02/", "views": { - "export": "https://app.crunch.io/api/datasets/veg/decks/dk02/export/" + "deck": "https://app.crunch.io/api/datasets/veg/decks/dk02/deck/", + "export": "https://app.crunch.io/api/datasets/veg/decks/dk02/export/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk02/script/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01.json index 14d28cbf1..b148705c9 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01.json @@ -86,7 +86,8 @@ "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01/", "views": { - "applied": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01/applied/" + "applied": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01/applied/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s01/script/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02.json index 6cb11a598..a696bfe05 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02.json @@ -114,7 +114,8 @@ "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02/", "views": { - "applied": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02/applied/" + "applied": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02/applied/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s02/script/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03.json b/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03.json index 350459ce2..740769955 100644 --- a/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03.json +++ b/mocks/app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03.json @@ -13,7 +13,8 @@ "element": "shoji:entity", "self": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03/", "views": { - "applied": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03/applied/" + "applied": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03/applied/", + "script": "https://app.crunch.io/api/datasets/veg/decks/dk02/slides/dk02s03/script/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/export.json b/mocks/app.crunch.io/api/datasets/veg/export.json index d037707c8..08501862c 100644 --- a/mocks/app.crunch.io/api/datasets/veg/export.json +++ b/mocks/app.crunch.io/api/datasets/veg/export.json @@ -2,6 +2,7 @@ "element": "shoji:view", "self": "https://app.crunch.io/api/datasets/veg/export/", "views": { + "crunchcl": "https://app.crunch.io/api/datasets/veg/export/crunchcl/", "csv": "https://app.crunch.io/api/datasets/veg/export/csv/", "parquet": "https://app.crunch.io/api/datasets/veg/export/parquet/", "spss": "https://app.crunch.io/api/datasets/veg/export/spss/" diff --git a/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01.json b/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01.json index bb2d1dcad..94bd537b1 100644 --- a/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01.json +++ b/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01.json @@ -52,6 +52,7 @@ "views": { "applied": "https://app.crunch.io/api/datasets/veg/multitables/mt_01/applied/", "export": "https://app.crunch.io/api/datasets/veg/multitables/mt_01/export/", + "script": "https://app.crunch.io/api/datasets/veg/multitables/mt_01/script/", "tabbook": "https://app.crunch.io/api/datasets/veg/multitables/mt_01/tabbook/" } } diff --git a/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01/cat-mr-tabbook.json b/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01/cat-mr-tabbook.json index 759c0a6ca..3701c1736 100644 --- a/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01/cat-mr-tabbook.json +++ b/mocks/app.crunch.io/api/datasets/veg/multitables/mt_01/cat-mr-tabbook.json @@ -12,6 +12,7 @@ "col_percent" ], "name": "Age", + "notes": "How old are you?", "page_layout": { "columns": { "alias": false, @@ -32,7 +33,7 @@ } }, "subtitle": "Age of respondent", - "title": "Age", + "title": null, "weight": null }, { @@ -150,7 +151,8 @@ "measures": [ "col_percent" ], - "name": null, + "name": "Vegetable Ratings", + "notes": "On a scale of 0-100, how would you rate...?", "page_layout": { "columns": { "alias": false, @@ -170,7 +172,7 @@ "notes": false } }, - "subtitle": "", + "subtitle": "Rating of Vegetables: Scale of 0-100", "title": null, "weight": null } @@ -276,7 +278,7 @@ "measures": { "mean": { "data": [ - 41.8391959799 + 41.8391959798995 ], "metadata": { "derived": true, @@ -299,7 +301,7 @@ }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -307,11 +309,47 @@ } } - } + }, + "n_missing": 11 + }, + "median": { + "data": [ + 42 + ], + "metadata": { + "derived": false, + "references": { + "alias": "age", + "description": "Age of respondent", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Age", + "notes": "How old are you?", + "view": { + "column_width": null + } + }, + "type": { + "class": "numeric", + "missing_reasons": { + "No Data": -1 + }, + "missing_rules": { + + } + } + }, + "n_missing": 11 }, "stddev": { "data": [ - 13.5748680205 + 13.5748680205197 ], "metadata": { "derived": true, @@ -334,7 +372,7 @@ }, "type": { "class": "numeric", - "integer": null, + "integer": false, "missing_reasons": { "No Data": -1 }, @@ -342,7 +380,8 @@ } } - } + }, + "n_missing": 11 }, "sum": { "data": [ @@ -377,7 +416,8 @@ } } - } + }, + "n_missing": 11 }, "valid_count_unweighted": { "data": [ @@ -412,7 +452,8 @@ } } - } + }, + "n_missing": 11 } }, "n": 210, @@ -447,24 +488,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -478,7 +518,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -491,7 +532,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -504,7 +546,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -525,24 +568,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -611,13 +653,13 @@ "mean": { "data": [ 37.2125, - 45.8679245283, - 37.4615384615, - 44.4166666667, - 38.1692307692, - 36.7857142857, + 45.8679245283019, + 37.4615384615385, + 44.4166666666667, + 38.1692307692308, + 36.7857142857143, 42.09375, - 42.1212121212, + 42.1212121212121, 33.5 ], "metadata": { @@ -641,7 +683,7 @@ }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -649,19 +691,63 @@ } } - } + }, + "n_missing": 11 + }, + "median": { + "data": [ + 34, + 47, + 44, + 45.5, + 37, + 39, + 43, + 41, + 30 + ], + "metadata": { + "derived": false, + "references": { + "alias": "age", + "description": "Age of respondent", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Age", + "notes": "How old are you?", + "view": { + "column_width": null + } + }, + "type": { + "class": "numeric", + "missing_reasons": { + "No Data": -1 + }, + "missing_rules": { + + } + } + }, + "n_missing": 11 }, "stddev": { "data": [ - 14.153492882, - 11.6120425763, - 15.333054624, - 14.0255369336, - 11.3227774182, - 14.7865769245, - 13.4855137162, - 14.628220961, - 7.6615925238 + 14.153492881953, + 11.6120425763192, + 15.3330546239999, + 14.0255369335548, + 11.3227774181752, + 14.7865769244561, + 13.4855137161578, + 14.6282209610345, + 7.66159252375118 ], "metadata": { "derived": true, @@ -684,7 +770,7 @@ }, "type": { "class": "numeric", - "integer": null, + "integer": false, "missing_reasons": { "No Data": -1 }, @@ -692,7 +778,8 @@ } } - } + }, + "n_missing": 11 }, "sum": { "data": [ @@ -735,7 +822,8 @@ } } - } + }, + "n_missing": 11 }, "valid_count_unweighted": { "data": [ @@ -778,7 +866,8 @@ } } - } + }, + "n_missing": 11 } }, "n": 210, @@ -875,8 +964,8 @@ "measures": { "mean": { "data": [ - 41.641025641, - 42.188034188, + 41.6410256410256, + 42.1880341880342, 35.5 ], "metadata": { @@ -900,7 +989,7 @@ }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -908,13 +997,51 @@ } } - } + }, + "n_missing": 15 + }, + "median": { + "data": [ + 42, + 42, + 33.5 + ], + "metadata": { + "derived": false, + "references": { + "alias": "age", + "description": "Age of respondent", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Age", + "notes": "How old are you?", + "view": { + "column_width": null + } + }, + "type": { + "class": "numeric", + "missing_reasons": { + "No Data": -1 + }, + "missing_rules": { + + } + } + }, + "n_missing": 15 }, "stddev": { "data": [ - 12.8507661695, - 13.8948834691, - 19.9749843554 + 12.8507661694621, + 13.8948834690871, + 19.9749843554382 ], "metadata": { "derived": true, @@ -937,7 +1064,7 @@ }, "type": { "class": "numeric", - "integer": null, + "integer": false, "missing_reasons": { "No Data": -1 }, @@ -945,7 +1072,8 @@ } } - } + }, + "n_missing": 15 }, "sum": { "data": [ @@ -982,7 +1110,8 @@ } } - } + }, + "n_missing": 15 }, "valid_count_unweighted": { "data": [ @@ -1019,7 +1148,8 @@ } } - } + }, + "n_missing": 15 } }, "n": 210, @@ -1139,7 +1269,8 @@ } } - } + }, + "n_missing": 5 } }, "n": 210, @@ -1236,24 +1367,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -1267,7 +1397,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -1280,7 +1411,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -1293,7 +1425,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -1314,24 +1447,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -1442,7 +1574,8 @@ } } - } + }, + "n_missing": 5 } }, "n": 210, @@ -1614,7 +1747,8 @@ } } - } + }, + "n_missing": 5 } }, "n": 210, @@ -1653,24 +1787,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -1684,7 +1817,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -1697,7 +1831,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -1710,7 +1845,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -1731,24 +1867,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -1841,7 +1976,8 @@ } } - } + }, + "n_missing": 0 } }, "n": 210, @@ -1948,24 +2084,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -1979,7 +2114,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -1992,7 +2128,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -2005,7 +2142,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -2026,24 +2164,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -2087,24 +2224,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -2118,7 +2254,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -2131,7 +2268,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -2144,7 +2282,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -2165,24 +2304,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -2347,7 +2485,8 @@ } } - } + }, + "n_missing": 0 } }, "n": 210, @@ -2400,24 +2539,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -2431,7 +2569,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -2444,7 +2583,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -2457,7 +2597,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -2478,24 +2619,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -2650,7 +2790,8 @@ } } - } + }, + "n_missing": 5 } }, "n": 210, @@ -2704,29 +2845,29 @@ { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -2740,7 +2881,8 @@ "references": { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" } } }, @@ -2753,7 +2895,8 @@ "references": { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" } } }, @@ -2766,7 +2909,8 @@ "references": { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" } } }, @@ -2779,7 +2923,8 @@ "references": { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } } } @@ -2800,29 +2945,29 @@ { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -2948,7 +3093,8 @@ } } - } + }, + "n_missing": 0 } }, "n": 210, @@ -3190,29 +3336,29 @@ { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -3226,7 +3372,8 @@ "references": { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" } } }, @@ -3239,7 +3386,8 @@ "references": { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" } } }, @@ -3252,7 +3400,8 @@ "references": { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" } } }, @@ -3265,7 +3414,8 @@ "references": { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } } } @@ -3286,29 +3436,29 @@ { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -3370,24 +3520,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -3401,7 +3550,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -3414,7 +3564,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -3427,7 +3578,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -3448,24 +3600,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -3765,7 +3916,8 @@ } } - } + }, + "n_missing": 0 } }, "n": 210, @@ -3863,29 +4015,29 @@ { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -3899,7 +4051,8 @@ "references": { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" } } }, @@ -3912,7 +4065,8 @@ "references": { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" } } }, @@ -3925,7 +4079,8 @@ "references": { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" } } }, @@ -3938,7 +4093,8 @@ "references": { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } } } @@ -3959,29 +4115,29 @@ { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -4199,7 +4355,8 @@ } } - } + }, + "n_missing": 5 } }, "n": 210, @@ -4255,42 +4412,42 @@ "measures": { "covariance": { "data": [ - 185.7836409205, - 25.1570745044, - 30.3133287765, - -0.4192754614, - -32.0922305764, - 9.487992709, - 25.1570745044, - 500.7643882433, - -6.240328093, - 0.6217361586, - -48.5099794942, - -76.6578263841, - 30.3133287765, - -6.240328093, - 278.3125085441, - 7.573342447, - -54.6950102529, - 2.0743677375, - -0.4192754614, - 0.6217361586, - 7.573342447, - 389.437593985, - -4.5230348599, - -23.4550922761, - -32.0922305764, - -48.5099794942, - -54.6950102529, - -4.5230348599, - 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point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -4350,16 +4579,17 @@ "0006" ] } - } + }, + "n_missing": 0 }, "mean": { "data": [ - 72.312195122, - 62.3970588235, - 75.8719211823, + 72.3121951219512, + 62.3970588235294, + 75.871921182266, 72.54, - 68.0251256281, - 86.6734693878 + 68.0251256281407, + 86.6734693877551 ], "metadata": { "derived": true, @@ -4372,39 +4602,111 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -4420,16 +4722,19 @@ "0006" ] } - } + }, + "n_missing": 0 }, - "stddev": { + "median": { "data": [ - 8.076235871, - 20.0942162886, - 9.7530573908, - 12.5369163416, - 11.2566196847, - 4.080662365 + [ + 71, + 60, + 75, + 75, + 67, + 86 + ] ], "metadata": { "derived": true, @@ -4442,39 +4747,110 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false }, "type": { "class": "numeric", - "integer": null, "missing_reasons": { "No Data": -1 }, @@ -4490,16 +4866,17 @@ "0006" ] } - } + }, + "n_missing": 0 }, - "sum": { + "stddev": { "data": [ - 14824, - 12729, - 15402, - 14508, - 13537, - 16988 + 8.07623587100412, + 20.0942162885911, + 9.75305739083927, + 12.5369163416202, + 11.2566196847261, + 4.08066236504806 ], "metadata": { "derived": true, @@ -4512,32 +4889,247 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_eggplant", + "description": "Eggplant Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_fennel", + "description": "Fennel Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } + } + ], + "uniform_basis": false + }, + "type": { + "class": "numeric", + "integer": false, + "missing_reasons": { + "No Data": -1 + }, + "missing_rules": { + + }, + "subvariables": [ + "0001", + "0002", + "0003", + "0004", + "0005", + "0006" + ] + } + }, + "n_missing": 0 + }, + "sum": { + "data": [ + 14824, + 12729, + 15402, + 14508, + 13537, + 16988 + ], + "metadata": { + "derived": true, + "references": { + "alias": "ratings_numa", + "description": "Rating of Vegetables: Scale of 0-100", + "name": "Vegetable Ratings", + "notes": "On a scale of 0-100, how would you rate...?", + "subreferences": [ + { + "alias": "ratings_numa_avocado", + "description": "Avocado Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_brussel_sprout", + "description": "Brussel Sprout Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_carrot", + "description": "Carrot Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_daikon", + "description": "Daikon Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -4560,7 +5152,8 @@ "0006" ] } - } + }, + "n_missing": 0 }, "valid_count_unweighted": { "data": [ @@ -4582,32 +5175,104 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -4630,7 +5295,8 @@ "0006" ] } - } + }, + "n_missing": 0 } }, "n": 210, @@ -4665,24 +5331,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -4696,7 +5361,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -4709,7 +5375,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -4722,7 +5389,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -4743,24 +5411,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -4828,312 +5495,312 @@ "measures": { "covariance": { "data": [ - 126.481121751, - 38.3361149111, - 12.883994528, - -6.5913816689, - -18.7997264022, - 40.3508891929, - 38.3361149111, - 475.7582763338, - -14.6834473324, - 33.6471956224, - -85.6629274966, - -127.2986320109, - 12.883994528, - -14.6834473324, - 337.7860465116, - -8.1761969904, - -62.0905608755, - -20.3031463748, - -6.5913816689, - 33.6471956224, - -8.1761969904, - 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"ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -5947,22 +7259,23 @@ "0006" ] } - } + }, + "n_missing": 5 }, "mean": { "data": [ - 71.6235294118, - 62.6666666667, - 78.6172839506, - 69.3780487805, - 67.4268292683, + 71.6235294117647, + 62.6666666666667, + 78.6172839506173, + 69.3780487804878, + 67.4268292682927, 86.95, - 72.7913043478, - 62.1271186441, - 74.2905982906, - 74.4513274336, - 68.5178571429, - 86.4910714286, + 72.7913043478261, + 62.1271186440678, + 74.2905982905983, + 74.4513274336283, + 68.5178571428571, + 86.4910714285714, 73, 64.4, 68.4, @@ -5981,39 +7294,111 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -6029,28 +7414,189 @@ "0006" ] } - } + }, + "n_missing": 5 + }, + "median": { + "data": [ + [ + 70, + 60, + 80, + 69, + 67, + 87 + ], + [ + 71, + 57.5, + 74, + 78, + 67.5, + 86 + ], + [ + 75, + 59, + 70, + 80, + 66, + 85 + ] + ], + "metadata": { + "derived": true, + "references": { + "alias": "ratings_numa", + "description": "Rating of Vegetables: Scale of 0-100", + "name": "Vegetable Ratings", + "notes": "On a scale of 0-100, how would you rate...?", + "subreferences": [ + { + "alias": "ratings_numa_avocado", + "description": "Avocado Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_brussel_sprout", + "description": "Brussel Sprout Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_carrot", + "description": "Carrot Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_daikon", + "description": "Daikon Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_eggplant", + "description": "Eggplant Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } + }, + { + "alias": "ratings_numa_fennel", + "description": "Fennel Rating (100 point scale)", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } + } + ], + "uniform_basis": false + }, + "type": { + "class": "numeric", + "missing_reasons": { + "No Data": -1 + }, + "missing_rules": { + + }, + "subvariables": [ + "0001", + "0002", + "0003", + "0004", + "0005", + "0006" + ] + } + }, + "n_missing": 5 }, "stddev": { "data": [ - 7.5403304061, - 19.4261421801, - 10.0206884759, - 12.3214559865, - 11.1388880441, - 4.0624866115, - 8.4225008289, - 20.6466183196, - 9.2289460573, - 12.3945488814, - 11.3431188355, - 4.0536837561, - 9.6695398029, - 21.6055548413, - 5.4589376256, - 6.4575537164, - 13.1605471011, - 6.0207972894 + 7.54033040609317, + 19.4261421800624, + 10.0206884758915, + 12.3214559864985, + 11.1388880441173, + 4.06248661146772, + 8.42250082894489, + 20.6466183196446, + 9.2289460573035, + 12.3945488813505, + 11.3431188355031, + 4.05368375614724, + 9.66953980290686, + 21.6055548412902, + 5.45893762558247, + 6.45755371638518, + 13.1605471010897, + 6.02079728939615 ], "metadata": { "derived": true, @@ -6063,39 +7609,111 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false }, "type": { "class": "numeric", - "integer": null, + "integer": false, "missing_reasons": { "No Data": -1 }, @@ -6111,7 +7729,8 @@ "0006" ] } - } + }, + "n_missing": 5 }, "sum": { "data": [ @@ -6145,32 +7764,104 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -6193,7 +7884,8 @@ "0006" ] } - } + }, + "n_missing": 5 }, "valid_count_unweighted": { "data": [ @@ -6227,32 +7919,104 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -6275,7 +8039,8 @@ "0006" ] } - } + }, + "n_missing": 5 } }, "n": 210, diff --git a/mocks/app.crunch.io/api/datasets/veg/table.json b/mocks/app.crunch.io/api/datasets/veg/table.json index 780910e20..f98319050 100644 --- a/mocks/app.crunch.io/api/datasets/veg/table.json +++ b/mocks/app.crunch.io/api/datasets/veg/table.json @@ -44,17 +44,20 @@ "0001": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, "0002": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, "0003": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } }, "subvariables": [ @@ -270,7 +273,7 @@ "derived": true, "description": "Rating of Vegetables: Scale of 0-100", "missing_reasons": { - + "No Data": -1 }, "name": "Vegetable Ratings", "notes": "On a scale of 0-100, how would you rate...?", @@ -278,32 +281,104 @@ "0001": { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, "0002": { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, "0003": { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, "0004": { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, "0005": { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, "0006": { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } }, "subvariables": [ @@ -375,22 +450,26 @@ "0001": { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, "0002": { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, "0003": { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, "0004": { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } }, "subvariables": [ @@ -766,12 +845,14 @@ "0001": { "alias": "funnel_aware_mr_1", "description": "Awareness MR - Jicama", - "name": "Jicama" + "name": "Jicama", + "notes": "Have you ever heard of the vegetable Jicama?" }, "0002": { "alias": "funnel_aware_mr_2", "description": "Awareness MR - Kohlrabi", - "name": "Kohlrabi" + "name": "Kohlrabi", + "notes": "Have you ever heard of the vegetable Kohlrabi?" } }, "subvariables": [ @@ -885,12 +966,14 @@ "0001": { "alias": "funnel_buy_mr_1", "description": "Purchase MR - Jicama", - "name": "Jicama" + "name": "Jicama", + "notes": "Have you ever bought Jicama?" }, "0002": { "alias": "funnel_buy_mr_2", "description": "Purchase MR - Kohlrabi", - "name": "Kohlrabi" + "name": "Kohlrabi", + "notes": "Have you ever bought Kohlrabi?" } }, "subvariables": [ @@ -1004,12 +1087,14 @@ "0001": { "alias": "funnel_consider_mr_1", "description": "Consideration MR - Jicama", - "name": "Jicama" + "name": "Jicama", + "notes": "Have you ever consdidered buying Jicama?" }, "0002": { "alias": "funnel_consider_mr_2", "description": "Consideration MR - Kohlrabi", - "name": "Kohlrabi" + "name": "Kohlrabi", + "notes": "Have you ever consdidered buying Kohlrabi" } }, "subvariables": [ diff --git a/mocks/app.crunch.io/api/datasets/veg/variables-d118fa.json b/mocks/app.crunch.io/api/datasets/veg/variables-d118fa.json index 8ab9392da..da90b53dd 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables-d118fa.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables-d118fa.json @@ -1,7 +1,8 @@ { "catalogs": { "materialize": "https://app.crunch.io/api/datasets/veg/variables/materialize/", - "private": "https://app.crunch.io/api/datasets/veg/variables/private/" + "private": "https://app.crunch.io/api/datasets/veg/variables/private/", + "suggest_alias": "https://app.crunch.io/api/datasets/veg/variables/suggest_alias/" }, "description": "List of Variables of this dataset", "element": "shoji:catalog", @@ -35,7 +36,7 @@ ], "subvariables_catalog": "var_02/subvariables/", "type": "multiple_response", - "uniform_basis": true + "uniform_basis": false }, "var_03/": { "alias": "enjoy_savory_food", @@ -229,6 +230,7 @@ "id": "var_17", "name": "Reasons for Enjoying Vegetables", "notes": "To what extent do you enjoy vegetables because of...?", + "scale": "interval", "secure": false, "subvariables": [ "var_17/subvariables/0001/", @@ -357,7 +359,7 @@ ], "subvariables_catalog": "var_26/subvariables/", "type": "multiple_response", - "uniform_basis": true + "uniform_basis": false }, "var_27/": { "alias": "funnel_buy_1", @@ -401,7 +403,7 @@ ], "subvariables_catalog": "var_29/subvariables/", "type": "multiple_response", - "uniform_basis": true + "uniform_basis": false }, "var_30/": { "alias": "funnel_consider_1", @@ -445,7 +447,7 @@ ], "subvariables_catalog": "var_32/subvariables/", "type": "multiple_response", - "uniform_basis": true + "uniform_basis": false } }, "orders": { diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_02.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_02.json index 2144455f5..c7f104c68 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_02.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_02.json @@ -108,17 +108,20 @@ "https://app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0001/": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, "https://app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0002/": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, "https://app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0003/": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } }, "subvariables": [ diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables.json index 80a95544d..e718a5586 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables.json @@ -10,7 +10,7 @@ "hidden": false, "id": "0001", "name": "Savory", - "notes": "", + "notes": "Do you typically enjoy food that is savory?", "scale": "interval", "secure": false, "type": "categorical" @@ -23,7 +23,7 @@ "hidden": false, "id": "0002", "name": "Spicy", - "notes": "", + "notes": "Do you typically enjoy food that is spicy?", "scale": "interval", "secure": false, "type": "categorical" @@ -36,7 +36,7 @@ "hidden": false, "id": "0003", "name": "Sweet", - "notes": "", + "notes": "Do you typically enjoy food that is sweet?", "scale": "interval", "secure": false, "type": "categorical" diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0001.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0001.json index 1556b90e4..d995af655 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0001.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_02/subvariables/0001.json @@ -37,7 +37,7 @@ "No Data": -1 }, "name": "Savory", - "notes": "", + "notes": "Do you typically enjoy food that is savory?", "owner": null, "private": false, "secure": false, diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_15.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_15.json index 0cdc0885b..f79dfd3a3 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_15.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_15.json @@ -91,7 +91,7 @@ "hidden": false, "id": "var_15", "missing_reasons": { - + "No Data": -1 }, "name": "Vegetable Ratings", "notes": "On a scale of 0-100, how would you rate...?", @@ -102,32 +102,104 @@ "https://app.crunch.io/api/datasets/veg/variables/var_15/subvariables/0001/": { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, "https://app.crunch.io/api/datasets/veg/variables/var_15/subvariables/0002/": { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, "https://app.crunch.io/api/datasets/veg/variables/var_15/subvariables/0003/": { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, "https://app.crunch.io/api/datasets/veg/variables/var_15/subvariables/0004/": { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, "https://app.crunch.io/api/datasets/veg/variables/var_15/subvariables/0005/": { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, "https://app.crunch.io/api/datasets/veg/variables/var_15/subvariables/0006/": { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } }, "subvariables": [ diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_15/subvariables.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_15/subvariables.json index df8a16b38..5c1178ea7 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_15/subvariables.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_15/subvariables.json @@ -10,7 +10,7 @@ "hidden": false, "id": "0001", "name": "Avocado", - "notes": "", + "notes": "What is your rating between 0 and 100 of: Avocado", "secure": false, "type": "numeric" }, @@ -22,7 +22,7 @@ "hidden": false, "id": "0002", "name": "Brussel Sprout", - "notes": "", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", "secure": false, "type": "numeric" }, @@ -34,7 +34,7 @@ "hidden": false, "id": "0003", "name": "Carrot", - "notes": "", + "notes": "What is your rating between 0 and 100 of: Carrot", "secure": false, "type": "numeric" }, @@ -46,7 +46,7 @@ "hidden": false, "id": "0004", "name": "Daikon", - "notes": "", + "notes": "What is your rating between 0 and 100 of: Daikon", "secure": false, "type": "numeric" }, @@ -58,7 +58,7 @@ "hidden": false, "id": "0005", "name": "Eggplant", - "notes": "", + "notes": "What is your rating between 0 and 100 of: Eggplant", "secure": false, "type": "numeric" }, @@ -70,7 +70,7 @@ "hidden": false, "id": "0006", "name": "Fennel", - "notes": "", + "notes": "What is your rating between 0 and 100 of: Fennel", "secure": false, "type": "numeric" } diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_17.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_17.json index 377790352..c3632284d 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_17.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_17.json @@ -123,22 +123,26 @@ "https://app.crunch.io/api/datasets/veg/variables/var_17/subvariables/0001/": { "alias": "veg_enjoy_ca_healthy", "description": "Vegetables are healthy (5 point scale)", - "name": "Healthy" + "name": "Healthy", + "notes": "Do you eat vegetables because: they are healthy" }, "https://app.crunch.io/api/datasets/veg/variables/var_17/subvariables/0002/": { "alias": "veg_enjoy_ca_tasty", "description": "Vegetables are Tasty (5 point scale)", - "name": "Tasty" + "name": "Tasty", + "notes": "Do you eat vegetables because: they taste good" }, "https://app.crunch.io/api/datasets/veg/variables/var_17/subvariables/0003/": { "alias": "veg_enjoy_ca_filling", "description": "Vegetables are filling (5 point scale)", - "name": "Filling" + "name": "Filling", + "notes": "Do you eat vegetables because: they are filling" }, "https://app.crunch.io/api/datasets/veg/variables/var_17/subvariables/0004/": { "alias": "veg_enjoy_ca_env", "description": "Vegetables are environmental (5 point scale)", - "name": "Environmental" + "name": "Environmental", + "notes": "Do you eat vegetables because: eating them is good for the environment" } }, "subvariables": [ diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_17/subvariables.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_17/subvariables.json index 64d0fcaf3..4eb21e964 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_17/subvariables.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_17/subvariables.json @@ -10,7 +10,7 @@ "hidden": false, "id": "0001", "name": "Healthy", - "notes": "", + "notes": "Do you eat vegetables because: they are healthy", "scale": "interval", "secure": false, "type": "categorical" @@ -23,7 +23,7 @@ "hidden": false, "id": "0002", "name": "Tasty", - "notes": "", + "notes": "Do you eat vegetables because: they taste good", "scale": "interval", "secure": false, "type": "categorical" @@ -36,7 +36,7 @@ "hidden": false, "id": "0003", "name": "Filling", - "notes": "", + "notes": "Do you eat vegetables because: they are filling", "scale": "interval", "secure": false, "type": "categorical" @@ -49,7 +49,7 @@ "hidden": false, "id": "0004", "name": "Environmental", - "notes": "", + "notes": "Do you eat vegetables because: eating them is good for the environment", "scale": "interval", "secure": false, "type": "categorical" diff --git a/mocks/app.crunch.io/api/datasets/veg/variables/var_26.json b/mocks/app.crunch.io/api/datasets/veg/variables/var_26.json index 94856ee08..d11113bcb 100644 --- a/mocks/app.crunch.io/api/datasets/veg/variables/var_26.json +++ b/mocks/app.crunch.io/api/datasets/veg/variables/var_26.json @@ -81,7 +81,30 @@ "alias": "funnel_aware_mr", "description": "Awareness of Vegetables: Funnel", "name": "Awareness of Vegetables", - "notes": "Have you ever heard of the vegetable...?" + "notes": "Have you ever heard of the vegetable...?", + "view": { + "column_width": null, + "include_missing": false, + "include_noneoftheabove": false, + "show_counts": false, + "transform": { + "insertions": [ + { + "anchor": "top", + "function": "any_non_missing_selected", + "id": 1, + "kwargs": { + "subvariable_ids": [ + "funnel_aware_mr_1", + "funnel_aware_mr_2" + ], + "variable": "funnel_aware_mr" + }, + "name": "Jicama or Kohlrabi" + } + ] + } + } } }, "derived": true, @@ -107,12 +130,14 @@ "https://app.crunch.io/api/datasets/veg/variables/var_26/subvariables/0001/": { "alias": "funnel_aware_mr_1", "description": "Awareness MR - Jicama", - "name": "Jicama" + "name": "Jicama", + "notes": "Have you ever heard of the vegetable Jicama?" }, "https://app.crunch.io/api/datasets/veg/variables/var_26/subvariables/0002/": { "alias": "funnel_aware_mr_2", "description": "Awareness MR - Kohlrabi", - "name": "Kohlrabi" + "name": "Kohlrabi", + "notes": "Have you ever heard of the vegetable Kohlrabi?" } }, "subvariables": [ diff --git a/mocks/cubes/numa-x-cat.json b/mocks/cubes/numa-x-cat.json index 46ce3f52c..36c63e54b 100644 --- a/mocks/cubes/numa-x-cat.json +++ b/mocks/cubes/numa-x-cat.json @@ -142,39 +142,111 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -190,7 +262,8 @@ "0006" ] } - } + }, + "n_missing": 5 } }, "missing": 5, diff --git a/mocks/cubes/numa-x-mr.json b/mocks/cubes/numa-x-mr.json index ab0af886c..879caec37 100644 --- a/mocks/cubes/numa-x-mr.json +++ b/mocks/cubes/numa-x-mr.json @@ -70,24 +70,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "class": "enum", @@ -101,7 +100,8 @@ "references": { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" } } }, @@ -114,7 +114,8 @@ "references": { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" } } }, @@ -127,7 +128,8 @@ "references": { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } } } @@ -148,24 +150,23 @@ { "alias": "enjoy_mr_savory", "description": "Enjoyment of savory food (binary)", - "name": "Savory" + "name": "Savory", + "notes": "Do you typically enjoy food that is savory?" }, { "alias": "enjoy_mr_spicy", "description": "Enjoyment of spicy food (binary)", - "name": "Spicy" + "name": "Spicy", + "notes": "Do you typically enjoy food that is spicy?" }, { "alias": "enjoy_mr_sweet", "description": "Enjoyment of sweet food (binary)", - "name": "Sweet" + "name": "Sweet", + "notes": "Do you typically enjoy food that is sweet?" } ], - "uniform_basis": false, - "view": { - "include_missing": false, - "show_counts": false - } + "uniform_basis": false }, "type": { "categories": [ @@ -300,39 +301,111 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false }, "type": { "class": "numeric", - "integer": true, + "integer": null, "missing_reasons": { "No Data": -1 }, @@ -348,7 +421,8 @@ "0006" ] } - } + }, + "n_missing": 0 } }, "missing": 0, diff --git a/mocks/cubes/numa.json b/mocks/cubes/numa.json index 5af21dde7..6f3b72067 100644 --- a/mocks/cubes/numa.json +++ b/mocks/cubes/numa.json @@ -83,32 +83,104 @@ { "alias": "ratings_numa_avocado", "description": "Avocado Rating (100 point scale)", - "name": "Avocado" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Avocado", + "notes": "What is your rating between 0 and 100 of: Avocado", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_brussel_sprout", "description": "Brussel Sprout Rating (100 point scale)", - "name": "Brussel Sprout" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Brussel Sprout", + "notes": "What is your rating between 0 and 100 of: Brussel Sprout", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_carrot", "description": "Carrot Rating (100 point scale)", - "name": "Carrot" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Carrot", + "notes": "What is your rating between 0 and 100 of: Carrot", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_daikon", "description": "Daikon Rating (100 point scale)", - "name": "Daikon" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Daikon", + "notes": "What is your rating between 0 and 100 of: Daikon", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_eggplant", "description": "Eggplant Rating (100 point scale)", - "name": "Eggplant" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Eggplant", + "notes": "What is your rating between 0 and 100 of: Eggplant", + "view": { + "column_width": null + } }, { "alias": "ratings_numa_fennel", "description": "Fennel Rating (100 point scale)", - "name": "Fennel" + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "Fennel", + "notes": "What is your rating between 0 and 100 of: Fennel", + "view": { + "column_width": null + } } ], "uniform_basis": false @@ -131,7 +203,8 @@ "0006" ] } - } + }, + "n_missing": 0 } }, "missing": 0, diff --git a/mocks/dataset-fixtures/veg.csv b/mocks/dataset-fixtures/veg.csv index 230c09182..9503970d4 100644 --- a/mocks/dataset-fixtures/veg.csv +++ b/mocks/dataset-fixtures/veg.csv @@ -1,211 +1,211 @@ -wave,age,healthy_eater,enjoy_mr_savory,enjoy_mr_spicy,enjoy_mr_sweet,veg_enjoy_ca_healthy,veg_enjoy_ca_tasty,veg_enjoy_ca_filling,veg_enjoy_ca_env,ratings_numa_avocado,ratings_numa_brussel_sprout,ratings_numa_carrot,ratings_numa_daikon,ratings_numa_eggplant,ratings_numa_fennel,funnel_aware_mr_1,funnel_aware_mr_2,funnel_consider_mr_1,funnel_consider_mr_2,funnel_buy_mr_1,funnel_buy_mr_2,weight,last_vegetable,last_vegetable_date,enjoy_savory_food,enjoy_spicy_food,enjoy_sweet_food,veg_healthy,veg_tasty,veg_filling,veg_environmental,rating_avocado,rating_brussel_sprout,rating_carrot,rating_daikon,rating_eggplant,rating_fennel,funnel_aware_1,funnel_aware_2,funnel_consider_1,funnel_consider_2,funnel_buy_1,funnel_buy_2,resp_id -1,25.0,2,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,No Data,95.0,1,1,-1,1,-1,1,0.8,Carrot,2019-01-04,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,No Data,95.0,1,1,-1,1,-1,1,1.0 -1,43.0,1,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,1.2,Avocado,2019-11-25,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,2.0 -1,20.0,1,1,2,1,1,1,1,-1,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,1.2,Pepper,2019-04-04,1,2,1,1,1,1,-1,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,3.0 -1,39.0,1,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,1.2,Onion,2019-03-29,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,No Data -1,31.0,2,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,No Data,1,1,1,1,1,1,0.8,Green beans,2019-11-16,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,No Data,1,1,1,1,1,1,5.0 -1,50.0,2,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,-1,1,-1,0.8,Green beans,No Data,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,-1,1,-1,6.0 -1,33.0,2,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,0.8,Pepper,2019-01-03,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,7.0 -1,29.0,2,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,3,1,0.8,Carrot,2019-09-11,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,3,1,8.0 -1,52.0,1,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,1.2,Avocado,2019-02-08,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,9.0 -1,39.0,1,1,1,1,1,4,3,-1,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,1.2,Avocado,2019-12-22,1,1,1,1,4,3,-1,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,10.0 -1,62.0,1,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,3,1,3,1.2,Onion,2019-05-22,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,3,1,3,11.0 -1,39.0,1,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,1.2,Avocado,2019-04-16,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,12.0 -1,18.0,2,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,No Data,1,1,1,1,1,1,0.8,Avocado,2019-08-25,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,No Data,1,1,1,1,1,1,13.0 -1,56.0,2,1,1,1,1,5,5,-1,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,-1,0.8,Avocado,No Data,1,1,1,1,5,5,-1,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,-1,14.0 -1,64.0,2,2,1,1,4,4,3,1,64.0,34.0,65.0,No Data,84.0,94.0,1,1,1,1,2,1,0.8,Green beans,2019-08-21,2,1,1,4,4,3,1,64.0,34.0,65.0,No Data,84.0,94.0,1,1,1,1,2,1,15.0 -1,41.0,2,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,-1,1,-1,1,-1,1,0.8,Avocado,2019-03-23,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,-1,1,-1,1,-1,1,16.0 -1,No Data,1,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,No Data,1,1,1,1,-1,1,1.2,Carrot,No Data,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,No Data,1,1,1,1,-1,1,17.0 -1,65.0,1,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,1.2,Onion,2019-12-26,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,18.0 -1,34.0,2,1,1,1,4,4,3,1,67.0,No Data,82.0,60.0,65.0,85.0,1,1,1,-1,1,-1,0.8,No Data,2019-07-25,1,1,1,4,4,3,1,67.0,No Data,82.0,60.0,65.0,85.0,1,1,1,-1,1,-1,19.0 -1,64.0,2,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-08-20,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,3,3,3,20.0 -1,26.0,2,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,0.8,Onion,2019-02-15,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,21.0 -1,59.0,2,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-06-26,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,-1,1,-1,1,22.0 -1,45.0,2,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,0.8,Lettuce,2019-03-26,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,23.0 -1,60.0,1,2,2,1,4,1,3,3,73.0,48.0,No Data,53.0,80.0,84.0,1,2,1,3,2,3,1.2,Carrot,2019-08-02,2,2,1,4,1,3,3,73.0,48.0,No Data,53.0,80.0,84.0,1,2,1,3,2,3,24.0 -1,47.0,1,1,-1,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,1.2,Tomato,2019-12-27,1,-1,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,25.0 -1,23.0,1,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,1.2,Tomato,2019-11-03,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,26.0 -1,57.0,2,1,-1,2,4,1,5,-1,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,3,2,3,0.8,Avocado,2019-01-13,1,-1,2,4,1,5,-1,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,3,2,3,27.0 -1,59.0,1,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,-1,3,-1,3,1.2,Carrot,2019-06-11,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,-1,3,-1,3,28.0 -1,38.0,1,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,3,1,1.2,Onion,2019-09-08,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,3,1,29.0 -1,50.0,2,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,-1,1,-1,1,-1,0.8,Avocado,No Data,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,-1,1,-1,1,-1,30.0 -2,35.0,2,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,3,3,3,0.8,Green beans,2019-03-26,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,3,3,3,31.0 -2,29.0,1,1,2,-1,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,3,1,3,1.2,No Data,No Data,1,2,-1,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,3,1,3,32.0 -2,36.0,1,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,3,2,3,1.2,Avocado,2019-04-18,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,3,2,3,33.0 -2,21.0,2,1,1,1,2,4,-1,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,3,2,3,0.8,Pepper,2019-03-04,1,1,1,2,4,-1,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,3,2,3,No Data -2,60.0,1,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,3,3,3,1.2,Lettuce,2019-01-25,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,3,3,3,35.0 -2,34.0,2,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,0.8,Pepper,2019-05-02,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,No Data -2,22.0,2,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,No Data,81.0,2,1,3,1,3,1,0.8,Onion,2019-09-25,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,No Data,81.0,2,1,3,1,3,1,37.0 -2,48.0,2,-1,2,2,5,5,3,3,No Data,62.0,92.0,63.0,78.0,93.0,2,2,3,3,3,3,0.8,Green beans,2019-09-23,-1,2,2,5,5,3,3,No Data,62.0,92.0,63.0,78.0,93.0,2,2,3,3,3,3,38.0 -2,57.0,1,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,3,1,3,1.2,Onion,2019-11-04,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,3,1,3,39.0 -2,28.0,1,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,1.2,Lettuce,2019-10-19,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,40.0 -2,42.0,1,2,1,1,1,1,1,4,68.0,No Data,78.0,64.0,52.0,83.0,1,2,1,3,1,3,1.2,Pepper,2019-10-20,2,1,1,1,1,1,4,68.0,No Data,78.0,64.0,52.0,83.0,1,2,1,3,1,3,41.0 -2,31.0,1,2,2,1,-1,5,3,3,66.0,No Data,76.0,52.0,67.0,89.0,1,2,1,3,2,3,1.2,Carrot,2019-10-28,2,2,1,-1,5,3,3,66.0,No Data,76.0,52.0,67.0,89.0,1,2,1,3,2,3,42.0 -2,45.0,2,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,3,1,3,1,0.8,Green beans,2019-01-07,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,3,1,3,1,43.0 -2,35.0,2,2,2,1,2,-1,3,3,83.0,70.0,71.0,66.0,83.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-02-15,2,2,1,2,-1,3,3,83.0,70.0,71.0,66.0,83.0,No Data,1,2,2,3,3,3,44.0 -2,45.0,1,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,-1,1.2,Tomato,2019-10-22,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,-1,45.0 -2,No Data,2,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,No Data,91.0,1,1,1,1,2,1,0.8,Green beans,2019-05-11,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,No Data,91.0,1,1,1,1,2,1,46.0 -2,44.0,1,-1,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,3,3,3,1.2,Pepper,2019-10-20,-1,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,3,3,3,47.0 -2,60.0,2,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,0.8,Green beans,2019-04-09,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,48.0 -2,48.0,2,2,2,-1,-1,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,3,1,3,0.8,Carrot,2019-03-09,2,2,-1,-1,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,3,1,3,49.0 -2,36.0,1,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,3,2,3,1.2,Pepper,2019-02-20,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,3,2,3,No Data -2,58.0,1,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,3,1,3,1,1.2,Pepper,2019-05-07,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,3,1,3,1,51.0 -2,No Data,1,1,1,1,-1,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,-1,1.2,Lettuce,2019-08-03,1,1,1,-1,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,-1,52.0 -2,54.0,2,1,1,1,5,1,4,3,68.0,38.0,70.0,No Data,67.0,83.0,1,2,1,3,2,3,0.8,Tomato,2019-12-28,1,1,1,5,1,4,3,68.0,38.0,70.0,No Data,67.0,83.0,1,2,1,3,2,3,53.0 -2,41.0,2,1,1,2,4,-1,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,3,3,3,0.8,Onion,2019-05-08,1,1,2,4,-1,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,3,3,3,54.0 -2,No Data,2,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,3,2,3,0.8,Green beans,2019-10-19,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,3,2,3,55.0 -2,41.0,2,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,3,1,0.8,Tomato,2019-04-06,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,3,1,56.0 -2,53.0,2,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,3,3,3,0.8,Avocado,2019-09-11,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,3,3,3,No Data -2,No Data,1,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,3,1,3,1.2,Onion,2019-08-03,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,3,1,3,58.0 -2,24.0,2,1,-1,1,4,1,-1,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,3,3,3,0.8,Tomato,2019-10-01,1,-1,1,4,1,-1,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,3,3,3,59.0 -2,60.0,2,2,-1,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,3,2,3,0.8,Pepper,2019-10-12,2,-1,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,3,2,3,60.0 -3,60.0,2,1,1,1,3,5,-1,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,3,1,3,0.8,Tomato,2019-04-24,1,1,1,3,5,-1,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,3,1,3,61.0 -3,19.0,2,1,1,1,1,1,4,-1,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,3,-1,3,0.8,Lettuce,2019-08-14,1,1,1,1,1,4,-1,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,3,-1,3,62.0 -3,65.0,2,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,3,3,0.8,Green beans,2019-05-26,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,3,3,63.0 -3,53.0,2,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,3,3,3,3,0.8,No Data,2019-05-09,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,3,3,3,3,64.0 -3,No Data,1,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,3,1,1.2,Green beans,2019-06-28,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,3,1,65.0 -3,49.0,2,-1,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,3,3,3,3,0.8,No Data,2019-08-10,-1,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,3,3,3,3,66.0 -3,25.0,2,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,No Data,No Data,1,1,1,1,-1,1,0.8,No Data,2019-07-11,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,No Data,No Data,1,1,1,1,-1,1,67.0 -3,34.0,1,1,1,2,-1,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,3,2,3,1.2,Avocado,2019-07-14,1,1,2,-1,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,3,2,3,68.0 -3,21.0,1,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,3,1,3,1.2,Onion,2019-12-27,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,3,1,3,69.0 -3,64.0,1,1,1,1,-1,-1,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,3,3,3,1.2,No Data,2019-04-13,1,1,1,-1,-1,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,3,3,3,70.0 -3,36.0,2,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,No Data,86.0,2,-1,3,-1,3,-1,0.8,Onion,2019-01-15,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,No Data,86.0,2,-1,3,-1,3,-1,71.0 -3,35.0,2,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,3,1,3,1,0.8,Tomato,2019-09-02,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,3,1,3,1,72.0 -3,41.0,1,1,-1,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,3,2,3,1.2,Pepper,2019-02-02,1,-1,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,3,2,3,73.0 -3,19.0,2,-1,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,3,3,3,0.8,Green beans,2019-12-31,-1,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,3,3,3,74.0 -3,63.0,2,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,-1,3,-1,3,-1,0.8,Carrot,2019-12-31,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,-1,3,-1,3,-1,75.0 -3,64.0,2,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,0.8,Tomato,2019-02-21,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,76.0 -3,45.0,2,1,1,1,-1,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,3,3,3,0.8,Tomato,2019-04-25,1,1,1,-1,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,3,3,3,77.0 -3,55.0,1,-1,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,3,1,3,1.2,Lettuce,2019-11-06,-1,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,3,1,3,78.0 -3,33.0,2,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-05-09,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,3,3,3,79.0 -3,28.0,1,1,-1,1,3,5,3,5,66.0,48.0,No Data,80.0,65.0,92.0,1,2,2,3,3,3,1.2,Onion,2019-07-09,1,-1,1,3,5,3,5,66.0,48.0,No Data,80.0,65.0,92.0,1,2,2,3,3,3,80.0 -3,34.0,2,2,2,1,1,4,-1,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,3,1,3,0.8,Lettuce,2019-04-07,2,2,1,1,4,-1,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,3,1,3,No Data -3,39.0,1,2,1,1,1,1,3,4,67.0,No Data,86.0,72.0,68.0,87.0,1,1,2,1,3,-1,1.2,Tomato,2019-07-27,2,1,1,1,1,3,4,67.0,No Data,86.0,72.0,68.0,87.0,1,1,2,1,3,-1,82.0 -3,57.0,1,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,1.2,No Data,2019-07-10,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,83.0 -3,48.0,2,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,3,3,3,0.8,Avocado,2019-11-19,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,3,3,3,84.0 -3,No Data,1,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,3,1,3,1,1.2,Avocado,2019-12-19,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,3,1,3,1,85.0 -3,18.0,1,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,1.2,Carrot,2019-12-16,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,86.0 -3,25.0,2,2,1,1,4,-1,3,4,No Data,45.0,No Data,58.0,85.0,86.0,1,2,1,3,2,3,0.8,Carrot,2019-01-04,2,1,1,4,-1,3,4,No Data,45.0,No Data,58.0,85.0,86.0,1,2,1,3,2,3,87.0 -3,40.0,2,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,3,0.8,Avocado,2019-11-14,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,3,88.0 -3,43.0,2,2,1,1,1,5,5,-1,74.0,68.0,68.0,84.0,70.0,85.0,2,2,3,3,3,3,0.8,Pepper,2019-07-02,2,1,1,1,5,5,-1,74.0,68.0,68.0,84.0,70.0,85.0,2,2,3,3,3,3,89.0 -3,22.0,2,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,3,3,3,3,0.8,Onion,2019-12-13,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,3,3,3,3,90.0 -4,48.0,1,2,1,1,-1,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,3,3,3,1.2,Pepper,2019-12-04,2,1,1,-1,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,3,3,3,91.0 -4,26.0,1,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,3,1,3,1,1.2,Pepper,2019-12-28,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,3,1,3,1,92.0 -4,37.0,1,2,1,1,1,4,3,-1,62.0,90.0,78.0,60.0,88.0,87.0,2,-1,3,-1,3,-1,1.2,Onion,2019-12-26,2,1,1,1,4,3,-1,62.0,90.0,78.0,60.0,88.0,87.0,2,-1,3,-1,3,-1,93.0 -4,28.0,1,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,-1,2,1.2,Tomato,2019-07-25,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,-1,2,94.0 -4,56.0,1,-1,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-07-29,-1,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,3,1,95.0 -4,18.0,2,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,0.8,Lettuce,2019-05-26,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,96.0 -4,47.0,2,2,1,1,5,4,3,4,70.0,49.0,67.0,No Data,53.0,85.0,1,-1,2,-1,3,-1,0.8,Tomato,2019-09-14,2,1,1,5,4,3,4,70.0,49.0,67.0,No Data,53.0,85.0,1,-1,2,-1,3,-1,97.0 -4,37.0,2,2,-1,1,5,1,-1,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,3,2,3,0.8,Avocado,2019-06-20,2,-1,1,5,1,-1,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,3,2,3,98.0 -4,22.0,1,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,-1,-1,-1,1.2,Tomato,2019-07-08,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,-1,-1,-1,No Data -4,24.0,2,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,3,3,3,0.8,Green beans,2019-02-06,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,3,3,3,100.0 -4,54.0,2,2,2,1,4,4,3,-1,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,3,2,3,0.8,Lettuce,2019-11-24,2,2,1,4,4,3,-1,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,3,2,3,101.0 -4,34.0,2,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,-1,1,-1,2,-1,0.8,Carrot,2019-03-07,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,-1,1,-1,2,-1,102.0 -4,48.0,2,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,3,3,3,3,0.8,Green beans,2019-12-11,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,3,3,3,3,103.0 -4,No Data,-1,1,1,1,1,-1,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,3,1,0.8,Tomato,2019-07-04,1,1,1,1,-1,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,3,1,104.0 -4,21.0,2,2,-1,1,4,4,3,3,69.0,54.0,70.0,No Data,84.0,92.0,1,1,1,2,1,3,0.8,No Data,2019-11-27,2,-1,1,4,4,3,3,69.0,54.0,70.0,No Data,84.0,92.0,1,1,1,2,1,3,105.0 -4,42.0,1,2,2,1,-1,-1,3,3,68.0,55.0,67.0,No Data,82.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-11-16,2,2,1,-1,-1,3,3,68.0,55.0,67.0,No Data,82.0,84.0,1,1,2,1,3,1,106.0 -4,32.0,2,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,3,1,3,1,0.8,Tomato,2019-08-18,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,3,1,3,1,107.0 -4,30.0,2,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,3,2,3,0.8,Lettuce,2019-01-17,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,3,2,3,108.0 -4,46.0,2,1,2,2,3,5,3,5,64.0,72.0,61.0,No Data,61.0,No Data,1,1,-1,1,-1,1,0.8,Avocado,2019-02-25,1,2,2,3,5,3,5,64.0,72.0,61.0,No Data,61.0,No Data,1,1,-1,1,-1,1,109.0 -4,24.0,2,-1,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,-1,3,-1,0.8,Avocado,2019-03-27,-1,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,-1,3,-1,110.0 -4,53.0,1,1,1,2,2,4,5,4,71.0,73.0,No Data,87.0,83.0,83.0,1,2,2,3,3,3,1.2,Onion,2019-10-06,1,1,2,2,4,5,4,71.0,73.0,No Data,87.0,83.0,83.0,1,2,2,3,3,3,111.0 -4,27.0,1,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,-1,2,-1,3,-1,3,1.2,Lettuce,2019-09-24,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,-1,2,-1,3,-1,3,112.0 -4,65.0,2,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,3,1,3,0.8,Pepper,2019-01-14,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,3,1,3,113.0 -4,36.0,1,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,3,1,3,1,1.2,Carrot,2019-12-16,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,3,1,3,1,114.0 -4,48.0,-1,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,No Data,1,2,2,3,3,3,1.2,Avocado,2019-12-23,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,No Data,1,2,2,3,3,3,115.0 -4,47.0,1,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,3,1.2,Onion,2019-12-19,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,3,116.0 -4,30.0,2,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,3,1,3,0.8,Green beans,No Data,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,3,1,3,117.0 -4,20.0,2,-1,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,3,2,0.8,Lettuce,2019-12-05,-1,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,3,2,118.0 -4,21.0,1,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,3,3,3,3,1.2,Green beans,2019-07-31,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,3,3,3,3,119.0 -4,34.0,1,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,3,1,3,2,1.2,Onion,2019-09-02,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,3,1,3,2,120.0 -5,23.0,2,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,3,3,3,0.8,Lettuce,2019-11-06,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,3,3,3,121.0 -5,55.0,2,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,3,3,3,3,0.8,Pepper,No Data,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,3,3,3,3,122.0 -5,41.0,2,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,0.8,Onion,2019-06-24,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,123.0 -5,64.0,2,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,3,3,3,3,0.8,Lettuce,2019-03-14,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,3,3,3,3,124.0 -5,27.0,2,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,3,2,3,0.8,Pepper,No Data,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,3,2,3,125.0 -5,44.0,1,2,-1,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,3,1,1.2,Lettuce,2019-10-27,2,-1,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,3,1,126.0 -5,41.0,1,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,No Data,80.0,1,1,1,1,1,2,1.2,Avocado,2019-03-29,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,No Data,80.0,1,1,1,1,1,2,127.0 -5,54.0,2,2,1,1,4,2,5,4,No Data,68.0,60.0,84.0,54.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-11-26,2,1,1,4,2,5,4,No Data,68.0,60.0,84.0,54.0,92.0,1,2,2,3,3,3,128.0 -5,59.0,2,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,3,2,3,0.8,Lettuce,2019-12-04,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,3,2,3,129.0 -5,34.0,1,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,3,1,3,1,1.2,Tomato,2019-05-05,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,3,1,3,1,130.0 -5,No Data,2,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,0.8,Onion,2019-02-28,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,131.0 -5,28.0,1,1,2,-1,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,3,1,3,1,1.2,Carrot,2019-06-15,1,2,-1,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,3,1,3,1,No Data -5,18.0,-1,1,-1,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,-1,2,-1,3,-1,1.2,Carrot,2019-02-12,1,-1,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,-1,2,-1,3,-1,133.0 -5,26.0,1,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,3,1.2,Green beans,2019-10-30,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,3,134.0 -5,62.0,2,1,1,1,4,1,-1,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,3,3,3,3,0.8,Carrot,2019-03-29,1,1,1,4,1,-1,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,3,3,3,3,135.0 -5,64.0,2,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-07-02,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,3,3,3,136.0 -5,22.0,1,1,1,1,4,4,1,3,74.0,No Data,77.0,63.0,68.0,87.0,1,2,2,3,3,3,1.2,Pepper,2019-05-28,1,1,1,4,4,1,3,74.0,No Data,77.0,63.0,68.0,87.0,1,2,2,3,3,3,137.0 -5,46.0,1,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,3,3,3,3,1.2,Carrot,2019-10-06,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,3,3,3,3,138.0 -5,45.0,2,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,0.8,Avocado,2019-12-21,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,139.0 -5,34.0,2,1,2,2,-1,3,3,5,79.0,51.0,No Data,81.0,78.0,89.0,1,-1,2,-1,3,-1,0.8,Onion,2019-12-10,1,2,2,-1,3,3,5,79.0,51.0,No Data,81.0,78.0,89.0,1,-1,2,-1,3,-1,140.0 -5,33.0,1,2,2,1,-1,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,3,3,3,1.2,Carrot,2019-06-03,2,2,1,-1,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,3,3,3,141.0 -5,45.0,1,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,3,3,3,1.2,Avocado,2019-07-14,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,3,3,3,No Data -5,24.0,2,-1,2,1,4,5,4,3,86.0,50.0,73.0,No Data,52.0,90.0,1,1,2,1,3,2,0.8,No Data,2019-07-02,-1,2,1,4,5,4,3,86.0,50.0,73.0,No Data,52.0,90.0,1,1,2,1,3,2,143.0 -5,28.0,1,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,3,3,3,3,1.2,Green beans,2019-11-11,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,3,3,3,3,144.0 -5,19.0,-1,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,3,2,0.8,Lettuce,2019-07-17,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,3,2,145.0 -5,60.0,2,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,3,2,3,0.8,Lettuce,2019-07-17,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,3,2,3,146.0 -5,44.0,1,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,3,3,3,1.2,Lettuce,2019-07-26,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,3,3,3,147.0 -5,59.0,2,2,1,1,5,4,4,-1,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,3,3,3,0.8,Carrot,2019-02-16,2,1,1,5,4,4,-1,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,3,3,3,148.0 -5,18.0,2,-1,-1,1,-1,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,-1,2,-1,3,-1,0.8,Avocado,2019-12-10,-1,-1,1,-1,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,-1,2,-1,3,-1,149.0 -5,50.0,2,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,No Data,1,2,2,3,3,3,0.8,No Data,2019-04-24,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,No Data,1,2,2,3,3,3,150.0 -6,61.0,2,2,2,2,4,1,1,3,No Data,57.0,87.0,87.0,65.0,80.0,1,2,1,3,1,3,0.8,Green beans,2019-09-21,2,2,2,4,1,1,3,No Data,57.0,87.0,87.0,65.0,80.0,1,2,1,3,1,3,151.0 -6,31.0,2,1,2,-1,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,3,3,3,0.8,Pepper,2019-04-13,1,2,-1,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,3,3,3,152.0 -6,57.0,-1,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,3,-1,3,0.8,Tomato,2019-09-27,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,3,-1,3,153.0 -6,51.0,1,1,2,1,-1,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-03-29,1,2,1,-1,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,3,3,3,3,154.0 -6,64.0,1,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,No Data,1,1,1,1,2,1,1.2,Green beans,2019-04-05,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,No Data,1,1,1,1,2,1,155.0 -6,47.0,1,2,-1,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,3,1,1.2,Green beans,2019-10-22,2,-1,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,3,1,156.0 -6,64.0,2,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,No Data,93.0,1,1,1,1,1,1,0.8,Lettuce,2019-10-04,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,No Data,93.0,1,1,1,1,1,1,157.0 -6,37.0,2,2,1,1,1,4,5,5,63.0,No Data,74.0,92.0,71.0,88.0,1,2,1,3,2,3,0.8,Pepper,2019-05-29,2,1,1,1,4,5,5,63.0,No Data,74.0,92.0,71.0,88.0,1,2,1,3,2,3,158.0 -6,41.0,2,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,3,3,3,3,0.8,Tomato,2019-07-13,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,3,3,3,3,159.0 -6,53.0,2,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,3,3,3,0.8,Green beans,2019-05-14,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,3,3,3,160.0 -6,20.0,2,1,1,1,4,2,3,4,89.0,85.0,No Data,55.0,60.0,90.0,2,1,3,1,3,1,0.8,Tomato,2019-03-27,1,1,1,4,2,3,4,89.0,85.0,No Data,55.0,60.0,90.0,2,1,3,1,3,1,161.0 -6,53.0,1,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-09-24,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,3,3,3,3,162.0 -6,61.0,2,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,No Data,87.0,2,1,3,1,3,1,0.8,Avocado,2019-08-26,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,No Data,87.0,2,1,3,1,3,1,163.0 -6,21.0,2,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,No Data,No Data,2,2,3,3,3,3,0.8,Green beans,2019-03-18,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,No Data,No Data,2,2,3,3,3,3,164.0 -6,53.0,2,1,2,1,4,2,3,4,79.0,67.0,86.0,No Data,73.0,89.0,-1,1,-1,1,-1,1,0.8,Green beans,2019-09-17,1,2,1,4,2,3,4,79.0,67.0,86.0,No Data,73.0,89.0,-1,1,-1,1,-1,1,165.0 -6,35.0,2,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-12-09,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,3,3,3,166.0 -6,No Data,1,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,3,-1,1.2,Green beans,2019-11-19,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,3,-1,167.0 -6,No Data,1,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,3,3,3,1.2,Pepper,2019-02-19,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,3,3,3,168.0 -6,40.0,2,1,1,2,-1,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,3,3,3,3,0.8,Carrot,No Data,1,1,2,-1,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,3,3,3,3,No Data -6,64.0,1,2,1,1,1,1,1,4,62.0,71.0,83.0,No Data,51.0,90.0,1,2,2,3,3,3,1.2,Tomato,2019-08-30,2,1,1,1,1,1,4,62.0,71.0,83.0,No Data,51.0,90.0,1,2,2,3,3,3,170.0 -6,59.0,1,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,3,1,1.2,Onion,2019-08-05,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,3,1,171.0 -6,48.0,2,2,1,1,3,5,-1,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,3,3,3,3,0.8,Tomato,2019-09-28,2,1,1,3,5,-1,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,3,3,3,3,172.0 -6,51.0,2,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,-1,1,-1,1,-1,0.8,Pepper,2019-04-20,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,-1,1,-1,1,-1,No Data -6,38.0,2,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,3,3,3,3,0.8,Onion,2019-07-24,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,3,3,3,3,174.0 -6,60.0,1,1,1,1,-1,1,5,4,82.0,56.0,75.0,65.0,53.0,No Data,2,1,3,1,3,2,1.2,Onion,2019-01-26,1,1,1,-1,1,5,4,82.0,56.0,75.0,65.0,53.0,No Data,2,1,3,1,3,2,175.0 -6,29.0,2,1,2,1,-1,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,-1,-1,-1,-1,-1,-1,0.8,Lettuce,2019-08-06,1,2,1,-1,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,-1,-1,-1,-1,-1,-1,176.0 -6,33.0,1,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,-1,3,1.2,Carrot,2019-06-18,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,-1,3,177.0 -6,37.0,2,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-22,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,3,3,3,3,178.0 -6,37.0,1,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,3,3,3,1.2,Onion,2019-08-12,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,3,3,3,179.0 -6,57.0,2,-1,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,3,1,3,0.8,Carrot,2019-05-31,-1,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,3,1,3,180.0 -7,29.0,2,-1,2,-1,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,3,1,3,1,0.8,Avocado,2019-08-31,-1,2,-1,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,3,1,3,1,181.0 -7,48.0,2,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,3,2,0.8,No Data,2019-10-09,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,3,2,182.0 -7,47.0,1,2,1,1,1,1,4,3,82.0,40.0,No Data,84.0,67.0,81.0,1,2,2,3,3,3,1.2,Pepper,2019-01-29,2,1,1,1,1,4,3,82.0,40.0,No Data,84.0,67.0,81.0,1,2,2,3,3,3,183.0 -7,25.0,1,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,3,1,3,1,1.2,Pepper,2019-06-30,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,3,1,3,1,184.0 -7,50.0,1,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,No Data,2,2,3,3,3,3,1.2,Pepper,2019-11-15,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,No Data,2,2,3,3,3,3,185.0 -7,53.0,1,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,3,3,3,3,1.2,Onion,2019-01-18,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,3,3,3,3,186.0 -7,37.0,2,2,2,1,4,1,5,-1,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,0.8,Lettuce,2019-07-24,2,2,1,4,1,5,-1,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,187.0 -7,43.0,2,2,1,2,-1,1,-1,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,3,3,3,0.8,Green beans,2019-02-12,2,1,2,-1,1,-1,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,3,3,3,No Data -7,22.0,1,1,-1,1,1,1,3,4,75.0,83.0,82.0,77.0,No Data,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-02-28,1,-1,1,1,1,3,4,75.0,83.0,82.0,77.0,No Data,83.0,2,2,3,3,3,3,189.0 -7,47.0,2,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,0.8,Carrot,No Data,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,190.0 -7,27.0,1,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-10-30,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,3,3,3,3,191.0 -7,43.0,1,2,2,1,4,4,1,-1,70.0,91.0,61.0,55.0,57.0,85.0,2,2,3,3,3,3,1.2,Onion,2019-04-26,2,2,1,4,4,1,-1,70.0,91.0,61.0,55.0,57.0,85.0,2,2,3,3,3,3,192.0 -7,47.0,1,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,3,3,3,3,1.2,Onion,2019-09-25,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,3,3,3,3,193.0 -7,56.0,2,1,1,2,-1,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-02-15,1,1,2,-1,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,-1,1,-1,1,194.0 -7,51.0,2,2,-1,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,3,2,3,3,0.8,Green beans,2019-12-13,2,-1,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,3,2,3,3,195.0 -7,35.0,2,1,1,2,4,4,5,3,No Data,61.0,66.0,88.0,58.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-05-02,1,1,2,4,4,5,3,No Data,61.0,66.0,88.0,58.0,No Data,1,2,2,3,3,3,196.0 -7,54.0,1,2,1,1,4,1,1,5,61.0,85.0,70.0,No Data,50.0,80.0,1,2,2,3,3,3,1.2,Green beans,2019-09-01,2,1,1,4,1,1,5,61.0,85.0,70.0,No Data,50.0,80.0,1,2,2,3,3,3,197.0 -7,42.0,2,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,-1,3,-1,3,0.8,Avocado,2019-05-02,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,-1,3,-1,3,198.0 -7,44.0,2,2,2,1,2,-1,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,3,3,3,0.8,Green beans,2019-07-25,2,2,1,2,-1,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,3,3,3,199.0 -7,60.0,2,1,2,2,4,1,3,-1,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,3,1,3,0.8,Avocado,2019-03-14,1,2,2,4,1,3,-1,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,3,1,3,200.0 -7,41.0,2,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-29,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,3,3,3,3,201.0 -7,36.0,2,1,2,-1,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,3,3,3,0.8,Carrot,2019-03-12,1,2,-1,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,3,3,3,202.0 -7,59.0,2,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,3,2,3,0.8,Onion,2019-10-09,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,3,2,3,203.0 -7,37.0,1,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,No Data,No Data,-1,2,-1,3,-1,3,1.2,Pepper,2019-11-03,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,No Data,No Data,-1,2,-1,3,-1,3,204.0 -7,50.0,2,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-09-08,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,3,3,3,205.0 -7,54.0,1,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-04-21,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,3,3,3,3,206.0 -7,54.0,2,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,3,3,3,3,0.8,Lettuce,2019-01-05,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,3,3,3,3,207.0 -7,44.0,1,-1,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,3,1,1.2,Avocado,2019-02-16,-1,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,3,1,208.0 -7,27.0,2,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,3,3,3,0.8,Avocado,2019-04-28,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,3,3,3,209.0 -7,50.0,2,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,3,3,3,3,0.8,Lettuce,2019-06-18,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,3,3,3,3,210.0 +wave,age,healthy_eater,enjoy_mr_savory,enjoy_mr_spicy,enjoy_mr_sweet,veg_enjoy_ca_healthy,veg_enjoy_ca_tasty,veg_enjoy_ca_filling,veg_enjoy_ca_env,ratings_numa_avocado,ratings_numa_brussel_sprout,ratings_numa_carrot,ratings_numa_daikon,ratings_numa_eggplant,ratings_numa_fennel,funnel_aware_mr_1,funnel_aware_mr_2,funnel_consider_mr_1,funnel_consider_mr_2,funnel_buy_mr_1,funnel_buy_mr_2,weight,last_vegetable,last_vegetable_date,funnel_aware_1,veg_environmental,enjoy_savory_food,rating_avocado,enjoy_spicy_food,veg_tasty,rating_fennel,veg_healthy,enjoy_sweet_food,funnel_consider_2,funnel_buy_1,funnel_aware_2,funnel_consider_1,rating_brussel_sprout,rating_eggplant,resp_id,rating_daikon,rating_carrot,veg_filling,funnel_buy_2 +1,25.0,2,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,No Data,95.0,1,1,-1,1,-1,1,0.8,Carrot,2019-01-04,1,3,1,69.0,2,5,95.0,3,2,1,-1,1,-1,53.0,No Data,1.0,69.0,88.0,1,1 +1,43.0,1,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,1.2,Avocado,2019-11-25,1,2,2,62.0,1,1,88.0,1,1,1,1,1,1,59.0,65.0,2.0,80.0,94.0,1,1 +1,20.0,1,1,2,1,1,1,1,-1,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,1.2,Pepper,2019-04-04,1,-1,1,61.0,2,1,85.0,1,1,1,1,1,1,57.0,71.0,3.0,75.0,87.0,1,1 +1,39.0,1,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,1.2,Onion,2019-03-29,1,3,2,68.0,1,1,90.0,1,1,1,1,1,1,32.0,69.0,No Data,51.0,94.0,3,1 +1,31.0,2,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,No Data,1,1,1,1,1,1,0.8,Green beans,2019-11-16,1,4,1,70.0,2,1,No Data,5,1,1,1,1,1,45.0,67.0,5.0,93.0,86.0,4,1 +1,50.0,2,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,-1,1,-1,0.8,Green beans,No Data,1,3,2,63.0,2,4,83.0,5,1,-1,1,1,1,34.0,78.0,6.0,78.0,63.0,3,-1 +1,33.0,2,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,0.8,Pepper,2019-01-03,1,1,2,69.0,2,3,87.0,2,1,1,1,1,1,31.0,71.0,7.0,66.0,78.0,5,1 +1,29.0,2,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,3,1,0.8,Carrot,2019-09-11,1,4,2,67.0,2,5,83.0,4,2,1,3,1,2,31.0,60.0,8.0,91.0,83.0,3,1 +1,52.0,1,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,1.2,Avocado,2019-02-08,1,2,1,75.0,1,1,92.0,2,1,1,1,1,1,46.0,56.0,9.0,89.0,91.0,3,1 +1,39.0,1,1,1,1,1,4,3,-1,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,1.2,Avocado,2019-12-22,1,-1,1,81.0,1,4,93.0,1,1,1,1,1,1,41.0,81.0,10.0,57.0,80.0,3,1 +1,62.0,1,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,3,1,3,1.2,Onion,2019-05-22,1,1,2,78.0,1,1,92.0,2,1,3,1,2,1,36.0,70.0,11.0,64.0,91.0,3,3 +1,39.0,1,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,1.2,Avocado,2019-04-16,1,2,2,69.0,1,1,86.0,2,1,1,1,1,1,35.0,58.0,12.0,58.0,91.0,1,1 +1,18.0,2,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,No Data,1,1,1,1,1,1,0.8,Avocado,2019-08-25,1,4,1,74.0,1,3,No Data,4,1,1,1,1,1,96.0,84.0,13.0,89.0,82.0,3,1 +1,56.0,2,1,1,1,1,5,5,-1,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,-1,0.8,Avocado,No Data,1,-1,1,70.0,1,5,84.0,1,1,1,1,1,1,94.0,67.0,14.0,78.0,83.0,5,-1 +1,64.0,2,2,1,1,4,4,3,1,64.0,34.0,65.0,No Data,84.0,94.0,1,1,1,1,2,1,0.8,Green beans,2019-08-21,1,1,2,64.0,1,4,94.0,4,1,1,2,1,1,34.0,84.0,15.0,No Data,65.0,3,1 +1,41.0,2,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,-1,1,-1,1,-1,1,0.8,Avocado,2019-03-23,-1,3,2,60.0,2,1,84.0,1,1,1,-1,1,-1,33.0,88.0,16.0,57.0,60.0,5,1 +1,No Data,1,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,No Data,1,1,1,1,-1,1,1.2,Carrot,No Data,1,1,1,66.0,2,4,No Data,1,1,1,-1,1,1,60.0,87.0,17.0,57.0,70.0,3,1 +1,65.0,1,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,1.2,Onion,2019-12-26,1,1,2,67.0,1,1,89.0,1,1,1,1,1,1,42.0,62.0,18.0,51.0,85.0,3,1 +1,34.0,2,1,1,1,4,4,3,1,67.0,No Data,82.0,60.0,65.0,85.0,1,1,1,-1,1,-1,0.8,No Data,2019-07-25,1,1,1,67.0,1,4,85.0,4,1,-1,1,1,1,No Data,65.0,19.0,60.0,82.0,3,-1 +1,64.0,2,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-08-20,1,1,2,64.0,1,1,91.0,4,2,3,3,2,2,45.0,69.0,20.0,88.0,82.0,3,3 +1,26.0,2,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,0.8,Onion,2019-02-15,1,3,2,71.0,1,1,84.0,5,2,1,1,1,1,41.0,60.0,21.0,69.0,83.0,5,1 +1,59.0,2,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-06-26,1,1,2,71.0,1,4,83.0,1,2,1,-1,1,-1,41.0,64.0,22.0,58.0,75.0,5,1 +1,45.0,2,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,0.8,Lettuce,2019-03-26,1,4,2,75.0,2,5,91.0,4,2,1,1,1,1,31.0,86.0,23.0,80.0,74.0,3,1 +1,60.0,1,2,2,1,4,1,3,3,73.0,48.0,No Data,53.0,80.0,84.0,1,2,1,3,2,3,1.2,Carrot,2019-08-02,1,3,2,73.0,2,1,84.0,4,1,3,2,2,1,48.0,80.0,24.0,53.0,No Data,3,3 +1,47.0,1,1,-1,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,1.2,Tomato,2019-12-27,1,3,1,64.0,-1,2,87.0,3,1,1,1,1,1,86.0,83.0,25.0,53.0,68.0,3,1 +1,23.0,1,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,1.2,Tomato,2019-11-03,1,4,1,80.0,2,4,91.0,4,1,1,1,1,1,64.0,62.0,26.0,76.0,70.0,3,1 +1,57.0,2,1,-1,2,4,1,5,-1,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,3,2,3,0.8,Avocado,2019-01-13,1,-1,1,79.0,-1,1,85.0,4,2,3,2,2,1,41.0,84.0,27.0,60.0,81.0,5,3 +1,59.0,1,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,-1,3,-1,3,1.2,Carrot,2019-06-11,1,2,2,62.0,1,1,81.0,4,1,3,-1,2,-1,36.0,84.0,28.0,93.0,63.0,3,3 +1,38.0,1,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,3,1,1.2,Onion,2019-09-08,1,3,1,73.0,2,1,81.0,4,2,1,3,1,2,71.0,86.0,29.0,55.0,86.0,3,1 +1,50.0,2,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,-1,1,-1,1,-1,0.8,Avocado,No Data,1,3,2,79.0,1,3,89.0,4,1,-1,1,-1,1,33.0,89.0,30.0,66.0,83.0,5,-1 +2,35.0,2,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,3,3,3,0.8,Green beans,2019-03-26,1,1,2,64.0,1,4,86.0,2,2,3,3,2,2,43.0,72.0,31.0,67.0,69.0,3,3 +2,29.0,1,1,2,-1,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,3,1,3,1.2,No Data,No Data,1,1,1,74.0,2,1,80.0,2,-1,3,1,2,1,95.0,58.0,32.0,58.0,67.0,4,3 +2,36.0,1,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,3,2,3,1.2,Avocado,2019-04-18,1,3,2,66.0,1,4,80.0,4,1,3,2,2,1,53.0,64.0,33.0,95.0,68.0,3,3 +2,21.0,2,1,1,1,2,4,-1,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,3,2,3,0.8,Pepper,2019-03-04,1,4,1,80.0,1,4,82.0,2,1,3,2,2,1,35.0,75.0,No Data,85.0,61.0,-1,3 +2,60.0,1,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,3,3,3,1.2,Lettuce,2019-01-25,1,4,2,71.0,1,3,87.0,4,1,3,3,2,2,90.0,63.0,35.0,75.0,90.0,3,3 +2,34.0,2,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,0.8,Pepper,2019-05-02,1,2,2,75.0,2,4,84.0,4,2,1,2,1,1,63.0,81.0,No Data,61.0,81.0,3,1 +2,22.0,2,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,No Data,81.0,2,1,3,1,3,1,0.8,Onion,2019-09-25,2,4,1,83.0,2,4,81.0,1,1,1,3,1,3,70.0,No Data,37.0,75.0,94.0,3,1 +2,48.0,2,-1,2,2,5,5,3,3,No Data,62.0,92.0,63.0,78.0,93.0,2,2,3,3,3,3,0.8,Green beans,2019-09-23,2,3,-1,No Data,2,5,93.0,5,2,3,3,2,3,62.0,78.0,38.0,63.0,92.0,3,3 +2,57.0,1,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,3,1,3,1.2,Onion,2019-11-04,1,2,2,66.0,1,1,88.0,1,1,3,1,2,1,85.0,52.0,39.0,82.0,90.0,1,3 +2,28.0,1,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,1.2,Lettuce,2019-10-19,1,4,1,72.0,1,3,86.0,5,1,1,1,1,1,85.0,65.0,40.0,77.0,76.0,3,1 +2,42.0,1,2,1,1,1,1,1,4,68.0,No Data,78.0,64.0,52.0,83.0,1,2,1,3,1,3,1.2,Pepper,2019-10-20,1,4,2,68.0,1,1,83.0,1,1,3,1,2,1,No Data,52.0,41.0,64.0,78.0,1,3 +2,31.0,1,2,2,1,-1,5,3,3,66.0,No Data,76.0,52.0,67.0,89.0,1,2,1,3,2,3,1.2,Carrot,2019-10-28,1,3,2,66.0,2,5,89.0,-1,1,3,2,2,1,No Data,67.0,42.0,52.0,76.0,3,3 +2,45.0,2,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,3,1,3,1,0.8,Green beans,2019-01-07,2,3,2,61.0,2,5,81.0,1,1,1,3,1,3,82.0,68.0,43.0,78.0,62.0,3,1 +2,35.0,2,2,2,1,2,-1,3,3,83.0,70.0,71.0,66.0,83.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-02-15,1,3,2,83.0,2,-1,No Data,2,1,3,3,2,2,70.0,83.0,44.0,66.0,71.0,3,3 +2,45.0,1,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,-1,1.2,Tomato,2019-10-22,1,4,1,65.0,1,1,89.0,1,1,1,2,1,1,68.0,88.0,45.0,70.0,66.0,1,-1 +2,No Data,2,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,No Data,91.0,1,1,1,1,2,1,0.8,Green beans,2019-05-11,1,4,2,76.0,1,4,91.0,4,1,1,2,1,1,63.0,No Data,46.0,85.0,85.0,3,1 +2,44.0,1,-1,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,3,3,3,1.2,Pepper,2019-10-20,1,2,-1,86.0,1,5,88.0,4,1,3,3,2,2,61.0,79.0,47.0,58.0,76.0,3,3 +2,60.0,2,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,0.8,Green beans,2019-04-09,1,3,2,83.0,1,1,87.0,1,1,1,1,1,1,84.0,61.0,48.0,78.0,68.0,5,1 +2,48.0,2,2,2,-1,-1,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,3,1,3,0.8,Carrot,2019-03-09,1,4,2,71.0,2,2,82.0,-1,-1,3,1,2,1,53.0,73.0,49.0,83.0,64.0,3,3 +2,36.0,1,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,3,2,3,1.2,Pepper,2019-02-20,1,2,2,84.0,1,1,85.0,4,1,3,2,2,1,30.0,55.0,No Data,68.0,61.0,3,3 +2,58.0,1,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,3,1,3,1,1.2,Pepper,2019-05-07,2,4,2,70.0,1,2,86.0,4,1,1,3,1,3,33.0,70.0,51.0,67.0,85.0,3,1 +2,No Data,1,1,1,1,-1,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,-1,1.2,Lettuce,2019-08-03,1,4,1,70.0,1,2,84.0,-1,1,1,1,1,1,44.0,83.0,52.0,71.0,82.0,3,-1 +2,54.0,2,1,1,1,5,1,4,3,68.0,38.0,70.0,No Data,67.0,83.0,1,2,1,3,2,3,0.8,Tomato,2019-12-28,1,3,1,68.0,1,1,83.0,5,1,3,2,2,1,38.0,67.0,53.0,No Data,70.0,4,3 +2,41.0,2,1,1,2,4,-1,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,3,3,3,0.8,Onion,2019-05-08,1,4,1,87.0,1,-1,81.0,4,2,3,3,2,2,93.0,87.0,54.0,84.0,81.0,5,3 +2,No Data,2,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,3,2,3,0.8,Green beans,2019-10-19,1,4,2,80.0,2,1,82.0,4,1,3,2,2,1,46.0,68.0,55.0,84.0,64.0,3,3 +2,41.0,2,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,3,1,0.8,Tomato,2019-04-06,1,4,2,72.0,1,2,93.0,3,1,1,3,1,2,31.0,52.0,56.0,72.0,89.0,5,1 +2,53.0,2,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,3,3,3,0.8,Avocado,2019-09-11,1,4,2,63.0,2,1,81.0,5,1,3,3,2,2,46.0,72.0,No Data,72.0,68.0,1,3 +2,No Data,1,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,3,1,3,1.2,Onion,2019-08-03,1,1,2,61.0,1,1,86.0,4,1,3,1,2,1,45.0,71.0,58.0,71.0,85.0,1,3 +2,24.0,2,1,-1,1,4,1,-1,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,3,3,3,0.8,Tomato,2019-10-01,1,4,1,62.0,-1,1,81.0,4,1,3,3,2,2,35.0,66.0,59.0,75.0,63.0,-1,3 +2,60.0,2,2,-1,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,3,2,3,0.8,Pepper,2019-10-12,1,4,2,65.0,-1,5,84.0,2,1,3,2,2,1,34.0,62.0,60.0,92.0,79.0,3,3 +3,60.0,2,1,1,1,3,5,-1,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,3,1,3,0.8,Tomato,2019-04-24,1,4,1,71.0,1,5,83.0,3,1,3,1,2,1,41.0,73.0,61.0,53.0,73.0,-1,3 +3,19.0,2,1,1,1,1,1,4,-1,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,3,-1,3,0.8,Lettuce,2019-08-14,1,-1,1,62.0,1,1,86.0,1,1,3,-1,2,1,54.0,76.0,62.0,65.0,71.0,4,3 +3,65.0,2,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,3,3,0.8,Green beans,2019-05-26,1,4,2,75.0,1,4,90.0,2,1,2,3,1,2,38.0,66.0,63.0,75.0,85.0,3,3 +3,53.0,2,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,3,3,3,3,0.8,No Data,2019-05-09,2,5,2,75.0,2,3,81.0,4,1,3,3,2,3,55.0,80.0,64.0,79.0,94.0,3,3 +3,No Data,1,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,3,1,1.2,Green beans,2019-06-28,1,3,1,65.0,2,3,89.0,4,1,1,3,1,2,55.0,87.0,65.0,75.0,75.0,1,1 +3,49.0,2,-1,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,3,3,3,3,0.8,No Data,2019-08-10,2,4,-1,77.0,1,4,90.0,4,1,3,3,2,3,55.0,89.0,66.0,91.0,84.0,3,3 +3,25.0,2,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,No Data,No Data,1,1,1,1,-1,1,0.8,No Data,2019-07-11,1,1,1,75.0,1,5,No Data,1,1,1,-1,1,1,81.0,No Data,67.0,85.0,73.0,3,1 +3,34.0,1,1,1,2,-1,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,3,2,3,1.2,Avocado,2019-07-14,1,4,1,84.0,1,4,84.0,-1,2,3,2,2,1,68.0,86.0,68.0,74.0,81.0,3,3 +3,21.0,1,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,3,1,3,1.2,Onion,2019-12-27,1,4,1,70.0,1,1,93.0,1,1,3,1,2,1,93.0,57.0,69.0,66.0,65.0,3,3 +3,64.0,1,1,1,1,-1,-1,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,3,3,3,1.2,No Data,2019-04-13,1,1,1,66.0,1,-1,93.0,-1,1,3,3,2,2,60.0,56.0,70.0,54.0,73.0,3,3 +3,36.0,2,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,No Data,86.0,2,-1,3,-1,3,-1,0.8,Onion,2019-01-15,2,4,2,82.0,2,5,86.0,4,1,-1,3,-1,3,78.0,No Data,71.0,87.0,65.0,5,-1 +3,35.0,2,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,3,1,3,1,0.8,Tomato,2019-09-02,2,4,2,63.0,1,4,88.0,4,1,1,3,1,3,84.0,90.0,72.0,90.0,65.0,3,1 +3,41.0,1,1,-1,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,3,2,3,1.2,Pepper,2019-02-02,1,3,1,76.0,-1,4,82.0,4,1,3,2,2,1,65.0,67.0,73.0,75.0,93.0,1,3 +3,19.0,2,-1,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,3,3,3,0.8,Green beans,2019-12-31,1,4,-1,64.0,1,3,90.0,4,1,3,3,2,2,45.0,88.0,74.0,89.0,71.0,4,3 +3,63.0,2,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,-1,3,-1,3,-1,0.8,Carrot,2019-12-31,2,1,1,70.0,1,4,90.0,4,1,-1,3,-1,3,40.0,80.0,75.0,75.0,70.0,3,-1 +3,64.0,2,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,0.8,Tomato,2019-02-21,1,2,2,63.0,1,1,81.0,4,1,1,2,1,1,74.0,50.0,76.0,86.0,69.0,3,2 +3,45.0,2,1,1,1,-1,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,3,3,3,0.8,Tomato,2019-04-25,1,3,1,61.0,1,2,91.0,-1,1,3,3,2,2,34.0,79.0,77.0,85.0,62.0,5,3 +3,55.0,1,-1,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,3,1,3,1.2,Lettuce,2019-11-06,1,4,-1,77.0,1,3,91.0,4,1,3,1,2,1,66.0,58.0,78.0,85.0,80.0,4,3 +3,33.0,2,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-05-09,1,4,2,82.0,1,2,92.0,2,1,3,3,2,2,49.0,59.0,79.0,80.0,91.0,5,3 +3,28.0,1,1,-1,1,3,5,3,5,66.0,48.0,No Data,80.0,65.0,92.0,1,2,2,3,3,3,1.2,Onion,2019-07-09,1,5,1,66.0,-1,5,92.0,3,1,3,3,2,2,48.0,65.0,80.0,80.0,No Data,3,3 +3,34.0,2,2,2,1,1,4,-1,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,3,1,3,0.8,Lettuce,2019-04-07,1,3,2,67.0,2,4,84.0,1,1,3,1,2,1,83.0,55.0,No Data,54.0,62.0,-1,3 +3,39.0,1,2,1,1,1,1,3,4,67.0,No Data,86.0,72.0,68.0,87.0,1,1,2,1,3,-1,1.2,Tomato,2019-07-27,1,4,2,67.0,1,1,87.0,1,1,1,3,1,2,No Data,68.0,82.0,72.0,86.0,3,-1 +3,57.0,1,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,1.2,No Data,2019-07-10,1,4,1,62.0,1,1,87.0,2,1,1,2,1,1,86.0,68.0,83.0,77.0,70.0,3,1 +3,48.0,2,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,3,3,3,0.8,Avocado,2019-11-19,1,2,2,78.0,1,4,88.0,4,1,3,3,2,2,50.0,59.0,84.0,52.0,79.0,3,3 +3,No Data,1,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,3,1,3,1,1.2,Avocado,2019-12-19,2,4,1,66.0,1,4,89.0,4,1,1,3,1,3,64.0,69.0,85.0,83.0,89.0,3,1 +3,18.0,1,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,1.2,Carrot,2019-12-16,1,4,1,75.0,2,3,91.0,4,1,1,1,1,1,73.0,84.0,86.0,64.0,82.0,3,1 +3,25.0,2,2,1,1,4,-1,3,4,No Data,45.0,No Data,58.0,85.0,86.0,1,2,1,3,2,3,0.8,Carrot,2019-01-04,1,4,2,No Data,1,-1,86.0,4,1,3,2,2,1,45.0,85.0,87.0,58.0,No Data,3,3 +3,40.0,2,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,3,0.8,Avocado,2019-11-14,1,4,2,83.0,1,5,86.0,4,1,2,1,1,1,74.0,56.0,88.0,80.0,84.0,3,3 +3,43.0,2,2,1,1,1,5,5,-1,74.0,68.0,68.0,84.0,70.0,85.0,2,2,3,3,3,3,0.8,Pepper,2019-07-02,2,-1,2,74.0,1,5,85.0,1,1,3,3,2,3,68.0,70.0,89.0,84.0,68.0,5,3 +3,22.0,2,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,3,3,3,3,0.8,Onion,2019-12-13,2,5,1,78.0,1,5,88.0,4,1,3,3,2,3,62.0,86.0,90.0,68.0,63.0,5,3 +4,48.0,1,2,1,1,-1,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,3,3,3,1.2,Pepper,2019-12-04,1,4,2,75.0,1,2,85.0,-1,1,3,3,2,2,38.0,68.0,91.0,75.0,94.0,3,3 +4,26.0,1,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,3,1,3,1,1.2,Pepper,2019-12-28,2,4,1,84.0,1,5,91.0,4,1,1,3,1,3,60.0,73.0,92.0,86.0,91.0,3,1 +4,37.0,1,2,1,1,1,4,3,-1,62.0,90.0,78.0,60.0,88.0,87.0,2,-1,3,-1,3,-1,1.2,Onion,2019-12-26,2,-1,2,62.0,1,4,87.0,1,1,-1,3,-1,3,90.0,88.0,93.0,60.0,78.0,3,-1 +4,28.0,1,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,-1,2,1.2,Tomato,2019-07-25,1,4,1,74.0,1,1,90.0,1,1,1,-1,1,1,50.0,72.0,94.0,68.0,72.0,3,2 +4,56.0,1,-1,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-07-29,1,1,-1,61.0,1,1,84.0,4,1,1,3,1,2,51.0,55.0,95.0,87.0,75.0,1,1 +4,18.0,2,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,0.8,Lettuce,2019-05-26,1,4,2,65.0,2,4,82.0,5,2,1,1,1,1,80.0,62.0,96.0,64.0,66.0,3,1 +4,47.0,2,2,1,1,5,4,3,4,70.0,49.0,67.0,No Data,53.0,85.0,1,-1,2,-1,3,-1,0.8,Tomato,2019-09-14,1,4,2,70.0,1,4,85.0,5,1,-1,3,-1,2,49.0,53.0,97.0,No Data,67.0,3,-1 +4,37.0,2,2,-1,1,5,1,-1,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,3,2,3,0.8,Avocado,2019-06-20,1,1,2,64.0,-1,1,83.0,5,1,3,2,2,1,53.0,65.0,98.0,59.0,76.0,-1,3 +4,22.0,1,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,-1,-1,-1,1.2,Tomato,2019-07-08,1,4,1,65.0,2,5,90.0,4,1,-1,-1,1,1,38.0,84.0,No Data,53.0,88.0,3,-1 +4,24.0,2,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,3,3,3,0.8,Green beans,2019-02-06,1,5,2,63.0,1,5,87.0,4,2,3,3,2,2,98.0,76.0,100.0,65.0,62.0,3,3 +4,54.0,2,2,2,1,4,4,3,-1,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,3,2,3,0.8,Lettuce,2019-11-24,1,-1,2,69.0,2,4,92.0,4,1,3,2,2,1,81.0,71.0,101.0,53.0,75.0,3,3 +4,34.0,2,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,-1,1,-1,2,-1,0.8,Carrot,2019-03-07,1,3,1,85.0,2,4,87.0,5,1,-1,2,-1,1,57.0,52.0,102.0,63.0,77.0,5,-1 +4,48.0,2,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,3,3,3,3,0.8,Green beans,2019-12-11,2,4,2,62.0,2,5,87.0,4,1,3,3,2,3,84.0,78.0,103.0,54.0,70.0,3,3 +4,No Data,-1,1,1,1,1,-1,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,3,1,0.8,Tomato,2019-07-04,1,4,1,84.0,1,-1,94.0,1,1,1,3,1,2,57.0,52.0,104.0,73.0,71.0,3,1 +4,21.0,2,2,-1,1,4,4,3,3,69.0,54.0,70.0,No Data,84.0,92.0,1,1,1,2,1,3,0.8,No Data,2019-11-27,1,3,2,69.0,-1,4,92.0,4,1,2,1,1,1,54.0,84.0,105.0,No Data,70.0,3,3 +4,42.0,1,2,2,1,-1,-1,3,3,68.0,55.0,67.0,No Data,82.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-11-16,1,3,2,68.0,2,-1,84.0,-1,1,1,3,1,2,55.0,82.0,106.0,No Data,67.0,3,1 +4,32.0,2,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,3,1,3,1,0.8,Tomato,2019-08-18,2,4,2,63.0,1,3,94.0,3,1,1,3,1,3,56.0,73.0,107.0,79.0,75.0,4,1 +4,30.0,2,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,3,2,3,0.8,Lettuce,2019-01-17,1,4,2,72.0,1,4,94.0,4,1,3,2,2,1,89.0,86.0,108.0,90.0,78.0,3,3 +4,46.0,2,1,2,2,3,5,3,5,64.0,72.0,61.0,No Data,61.0,No Data,1,1,-1,1,-1,1,0.8,Avocado,2019-02-25,1,5,1,64.0,2,5,No Data,3,2,1,-1,1,-1,72.0,61.0,109.0,No Data,61.0,3,1 +4,24.0,2,-1,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,-1,3,-1,0.8,Avocado,2019-03-27,1,3,-1,89.0,1,4,85.0,4,1,-1,3,1,2,32.0,78.0,110.0,86.0,76.0,5,-1 +4,53.0,1,1,1,2,2,4,5,4,71.0,73.0,No Data,87.0,83.0,83.0,1,2,2,3,3,3,1.2,Onion,2019-10-06,1,4,1,71.0,1,4,83.0,2,2,3,3,2,2,73.0,83.0,111.0,87.0,No Data,5,3 +4,27.0,1,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,-1,2,-1,3,-1,3,1.2,Lettuce,2019-09-24,-1,1,2,76.0,2,2,85.0,2,1,3,-1,2,-1,85.0,60.0,112.0,54.0,62.0,4,3 +4,65.0,2,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,3,1,3,0.8,Pepper,2019-01-14,1,4,2,65.0,1,4,87.0,1,1,3,1,2,1,44.0,74.0,113.0,79.0,65.0,5,3 +4,36.0,1,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,3,1,3,1,1.2,Carrot,2019-12-16,2,3,1,68.0,1,2,88.0,2,1,1,3,1,3,57.0,72.0,114.0,76.0,71.0,3,1 +4,48.0,-1,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,No Data,1,2,2,3,3,3,1.2,Avocado,2019-12-23,1,4,2,63.0,1,2,No Data,1,1,3,3,2,2,59.0,72.0,115.0,80.0,65.0,3,3 +4,47.0,1,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,3,1.2,Onion,2019-12-19,1,4,2,77.0,2,4,90.0,4,1,2,2,1,1,87.0,75.0,116.0,66.0,66.0,3,3 +4,30.0,2,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,3,1,3,0.8,Green beans,No Data,1,3,1,70.0,2,1,81.0,4,1,3,1,2,1,98.0,56.0,117.0,81.0,63.0,3,3 +4,20.0,2,-1,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,3,2,0.8,Lettuce,2019-12-05,1,2,-1,89.0,1,1,81.0,4,1,1,3,1,2,45.0,71.0,118.0,81.0,81.0,3,2 +4,21.0,1,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,3,3,3,3,1.2,Green beans,2019-07-31,2,2,1,88.0,2,4,93.0,1,1,3,3,2,3,74.0,87.0,119.0,59.0,80.0,3,3 +4,34.0,1,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,3,1,3,2,1.2,Onion,2019-09-02,2,5,1,64.0,2,5,91.0,1,1,1,3,1,3,39.0,72.0,120.0,54.0,74.0,3,2 +5,23.0,2,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,3,3,3,0.8,Lettuce,2019-11-06,1,4,2,71.0,2,2,89.0,4,1,3,3,2,2,55.0,72.0,121.0,65.0,76.0,3,3 +5,55.0,2,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,3,3,3,3,0.8,Pepper,No Data,2,4,2,70.0,2,2,86.0,1,1,3,3,2,3,81.0,58.0,122.0,72.0,88.0,3,3 +5,41.0,2,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,0.8,Onion,2019-06-24,1,2,2,70.0,2,1,83.0,2,1,1,1,1,1,87.0,74.0,123.0,72.0,70.0,5,1 +5,64.0,2,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,3,3,3,3,0.8,Lettuce,2019-03-14,2,5,1,64.0,1,1,82.0,4,2,3,3,2,3,78.0,79.0,124.0,94.0,77.0,5,3 +5,27.0,2,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,3,2,3,0.8,Pepper,No Data,1,4,1,74.0,1,4,91.0,1,1,3,2,2,1,48.0,84.0,125.0,87.0,67.0,3,3 +5,44.0,1,2,-1,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,3,1,1.2,Lettuce,2019-10-27,1,4,2,74.0,-1,1,88.0,3,1,1,3,1,2,76.0,63.0,126.0,85.0,94.0,3,1 +5,41.0,1,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,No Data,80.0,1,1,1,1,1,2,1.2,Avocado,2019-03-29,1,3,2,76.0,2,1,80.0,2,1,1,1,1,1,43.0,No Data,127.0,51.0,91.0,3,2 +5,54.0,2,2,1,1,4,2,5,4,No Data,68.0,60.0,84.0,54.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-11-26,1,4,2,No Data,1,2,92.0,4,1,3,3,2,2,68.0,54.0,128.0,84.0,60.0,5,3 +5,59.0,2,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,3,2,3,0.8,Lettuce,2019-12-04,1,4,1,66.0,1,1,85.0,4,1,3,2,2,1,58.0,64.0,129.0,61.0,63.0,1,3 +5,34.0,1,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,3,1,3,1,1.2,Tomato,2019-05-05,2,1,1,65.0,1,5,93.0,4,1,1,3,1,3,65.0,66.0,130.0,77.0,84.0,3,1 +5,No Data,2,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,0.8,Onion,2019-02-28,1,4,2,75.0,1,5,81.0,4,1,1,2,1,1,54.0,74.0,131.0,67.0,71.0,3,2 +5,28.0,1,1,2,-1,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,3,1,3,1,1.2,Carrot,2019-06-15,2,5,1,79.0,2,1,86.0,1,-1,1,3,1,3,83.0,65.0,No Data,58.0,81.0,4,1 +5,18.0,-1,1,-1,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,-1,2,-1,3,-1,1.2,Carrot,2019-02-12,1,3,1,80.0,-1,1,82.0,4,2,-1,3,-1,2,36.0,86.0,133.0,80.0,61.0,5,-1 +5,26.0,1,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,3,1.2,Green beans,2019-10-30,1,4,1,76.0,1,4,91.0,4,1,2,2,1,1,60.0,52.0,134.0,71.0,92.0,5,3 +5,62.0,2,1,1,1,4,1,-1,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,3,3,3,3,0.8,Carrot,2019-03-29,2,4,1,80.0,1,1,80.0,4,1,3,3,2,3,86.0,88.0,135.0,79.0,68.0,-1,3 +5,64.0,2,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-07-02,1,4,2,81.0,1,1,91.0,5,2,3,3,2,2,84.0,64.0,136.0,64.0,68.0,5,3 +5,22.0,1,1,1,1,4,4,1,3,74.0,No Data,77.0,63.0,68.0,87.0,1,2,2,3,3,3,1.2,Pepper,2019-05-28,1,3,1,74.0,1,4,87.0,4,1,3,3,2,2,No Data,68.0,137.0,63.0,77.0,1,3 +5,46.0,1,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,3,3,3,3,1.2,Carrot,2019-10-06,2,4,1,83.0,1,3,88.0,2,1,3,3,2,3,57.0,74.0,138.0,56.0,94.0,3,3 +5,45.0,2,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,0.8,Avocado,2019-12-21,1,4,2,88.0,2,1,80.0,4,1,1,1,1,1,76.0,64.0,139.0,57.0,92.0,3,2 +5,34.0,2,1,2,2,-1,3,3,5,79.0,51.0,No Data,81.0,78.0,89.0,1,-1,2,-1,3,-1,0.8,Onion,2019-12-10,1,5,1,79.0,2,3,89.0,-1,2,-1,3,-1,2,51.0,78.0,140.0,81.0,No Data,3,-1 +5,33.0,1,2,2,1,-1,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,3,3,3,1.2,Carrot,2019-06-03,1,4,2,64.0,2,2,82.0,-1,1,3,3,2,2,63.0,54.0,141.0,55.0,90.0,3,3 +5,45.0,1,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,3,3,3,1.2,Avocado,2019-07-14,1,1,1,65.0,2,4,85.0,5,1,3,3,2,2,58.0,69.0,No Data,61.0,77.0,3,3 +5,24.0,2,-1,2,1,4,5,4,3,86.0,50.0,73.0,No Data,52.0,90.0,1,1,2,1,3,2,0.8,No Data,2019-07-02,1,3,-1,86.0,2,5,90.0,4,1,1,3,1,2,50.0,52.0,143.0,No Data,73.0,4,2 +5,28.0,1,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,3,3,3,3,1.2,Green beans,2019-11-11,2,2,1,70.0,1,3,92.0,4,1,3,3,2,3,39.0,79.0,144.0,65.0,66.0,4,3 +5,19.0,-1,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,3,2,0.8,Lettuce,2019-07-17,1,4,1,63.0,1,4,88.0,2,2,1,3,1,2,93.0,58.0,145.0,91.0,70.0,5,2 +5,60.0,2,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,3,2,3,0.8,Lettuce,2019-07-17,1,3,2,77.0,1,2,83.0,5,1,3,2,2,1,74.0,81.0,146.0,73.0,86.0,5,3 +5,44.0,1,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,3,3,3,1.2,Lettuce,2019-07-26,1,4,2,87.0,1,3,95.0,1,1,3,3,2,2,90.0,75.0,147.0,78.0,66.0,3,3 +5,59.0,2,2,1,1,5,4,4,-1,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,3,3,3,0.8,Carrot,2019-02-16,1,-1,2,69.0,1,4,86.0,5,1,3,3,2,2,71.0,64.0,148.0,91.0,86.0,4,3 +5,18.0,2,-1,-1,1,-1,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,-1,2,-1,3,-1,0.8,Avocado,2019-12-10,1,4,-1,61.0,-1,4,86.0,-1,1,-1,3,-1,2,86.0,61.0,149.0,58.0,65.0,3,-1 +5,50.0,2,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,No Data,1,2,2,3,3,3,0.8,No Data,2019-04-24,1,2,1,71.0,1,3,No Data,4,1,3,3,2,2,94.0,55.0,150.0,76.0,63.0,3,3 +6,61.0,2,2,2,2,4,1,1,3,No Data,57.0,87.0,87.0,65.0,80.0,1,2,1,3,1,3,0.8,Green beans,2019-09-21,1,3,2,No Data,2,1,80.0,4,2,3,1,2,1,57.0,65.0,151.0,87.0,87.0,1,3 +6,31.0,2,1,2,-1,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,3,3,3,0.8,Pepper,2019-04-13,1,4,1,86.0,2,1,85.0,2,-1,3,3,2,2,92.0,59.0,152.0,89.0,71.0,5,3 +6,57.0,-1,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,3,-1,3,0.8,Tomato,2019-09-27,1,4,2,75.0,1,3,81.0,4,1,3,-1,2,1,77.0,66.0,153.0,82.0,75.0,5,3 +6,51.0,1,1,2,1,-1,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-03-29,2,4,1,75.0,2,4,92.0,-1,1,3,3,2,3,99.0,63.0,154.0,58.0,67.0,3,3 +6,64.0,1,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,No Data,1,1,1,1,2,1,1.2,Green beans,2019-04-05,1,4,2,67.0,1,1,No Data,4,1,1,2,1,1,93.0,70.0,155.0,68.0,84.0,3,1 +6,47.0,1,2,-1,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,3,1,1.2,Green beans,2019-10-22,1,4,2,84.0,-1,1,83.0,3,1,1,3,1,2,63.0,53.0,156.0,59.0,85.0,3,1 +6,64.0,2,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,No Data,93.0,1,1,1,1,1,1,0.8,Lettuce,2019-10-04,1,4,1,77.0,1,1,93.0,3,1,1,1,1,1,99.0,No Data,157.0,54.0,78.0,5,1 +6,37.0,2,2,1,1,1,4,5,5,63.0,No Data,74.0,92.0,71.0,88.0,1,2,1,3,2,3,0.8,Pepper,2019-05-29,1,5,2,63.0,1,4,88.0,1,1,3,2,2,1,No Data,71.0,158.0,92.0,74.0,5,3 +6,41.0,2,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,3,3,3,3,0.8,Tomato,2019-07-13,2,2,2,83.0,1,4,93.0,4,1,3,3,2,3,60.0,63.0,159.0,91.0,88.0,5,3 +6,53.0,2,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,3,3,3,0.8,Green beans,2019-05-14,1,4,2,61.0,1,3,81.0,4,1,3,3,2,2,42.0,61.0,160.0,80.0,63.0,3,3 +6,20.0,2,1,1,1,4,2,3,4,89.0,85.0,No Data,55.0,60.0,90.0,2,1,3,1,3,1,0.8,Tomato,2019-03-27,2,4,1,89.0,1,2,90.0,4,1,1,3,1,3,85.0,60.0,161.0,55.0,No Data,3,1 +6,53.0,1,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-09-24,2,4,1,65.0,1,1,83.0,4,1,3,3,2,3,72.0,73.0,162.0,88.0,64.0,3,3 +6,61.0,2,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,No Data,87.0,2,1,3,1,3,1,0.8,Avocado,2019-08-26,2,2,2,88.0,1,1,87.0,2,1,1,3,1,3,40.0,No Data,163.0,52.0,83.0,5,1 +6,21.0,2,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,No Data,No Data,2,2,3,3,3,3,0.8,Green beans,2019-03-18,2,3,1,82.0,1,5,No Data,1,1,3,3,2,3,97.0,No Data,164.0,81.0,85.0,5,3 +6,53.0,2,1,2,1,4,2,3,4,79.0,67.0,86.0,No Data,73.0,89.0,-1,1,-1,1,-1,1,0.8,Green beans,2019-09-17,-1,4,1,79.0,2,2,89.0,4,1,1,-1,1,-1,67.0,73.0,165.0,No Data,86.0,3,1 +6,35.0,2,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-12-09,1,4,1,63.0,1,1,91.0,4,2,3,3,2,2,66.0,58.0,166.0,62.0,60.0,5,3 +6,No Data,1,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,3,-1,1.2,Green beans,2019-11-19,1,4,2,60.0,1,2,83.0,1,1,1,3,1,2,88.0,60.0,167.0,69.0,73.0,1,-1 +6,No Data,1,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,3,3,3,1.2,Pepper,2019-02-19,1,4,1,61.0,1,1,83.0,1,1,3,3,2,2,54.0,53.0,168.0,63.0,73.0,5,3 +6,40.0,2,1,1,2,-1,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,3,3,3,3,0.8,Carrot,No Data,2,4,1,62.0,1,4,86.0,-1,2,3,3,2,3,89.0,83.0,No Data,85.0,72.0,3,3 +6,64.0,1,2,1,1,1,1,1,4,62.0,71.0,83.0,No Data,51.0,90.0,1,2,2,3,3,3,1.2,Tomato,2019-08-30,1,4,2,62.0,1,1,90.0,1,1,3,3,2,2,71.0,51.0,170.0,No Data,83.0,1,3 +6,59.0,1,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,3,1,1.2,Onion,2019-08-05,1,4,2,76.0,1,1,82.0,1,1,1,3,1,2,93.0,59.0,171.0,53.0,87.0,3,1 +6,48.0,2,2,1,1,3,5,-1,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,3,3,3,3,0.8,Tomato,2019-09-28,2,5,2,71.0,1,5,88.0,3,1,3,3,2,3,74.0,55.0,172.0,68.0,69.0,-1,3 +6,51.0,2,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,-1,1,-1,1,-1,0.8,Pepper,2019-04-20,1,1,2,69.0,1,1,88.0,4,1,-1,1,-1,1,99.0,55.0,No Data,54.0,78.0,3,-1 +6,38.0,2,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,3,3,3,3,0.8,Onion,2019-07-24,2,3,1,79.0,2,4,91.0,5,1,3,3,2,3,79.0,76.0,174.0,88.0,92.0,3,3 +6,60.0,1,1,1,1,-1,1,5,4,82.0,56.0,75.0,65.0,53.0,No Data,2,1,3,1,3,2,1.2,Onion,2019-01-26,2,4,1,82.0,1,1,No Data,-1,1,1,3,1,3,56.0,53.0,175.0,65.0,75.0,5,2 +6,29.0,2,1,2,1,-1,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,-1,-1,-1,-1,-1,-1,0.8,Lettuce,2019-08-06,-1,3,1,66.0,2,4,84.0,-1,1,-1,-1,-1,-1,43.0,52.0,176.0,93.0,74.0,3,-1 +6,33.0,1,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,-1,3,1.2,Carrot,2019-06-18,1,5,2,69.0,2,4,94.0,4,1,2,-1,1,1,46.0,70.0,177.0,51.0,60.0,3,3 +6,37.0,2,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-22,2,3,2,86.0,2,3,93.0,5,1,3,3,2,3,76.0,69.0,178.0,61.0,77.0,3,3 +6,37.0,1,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,3,3,3,1.2,Onion,2019-08-12,1,4,2,80.0,1,1,85.0,4,1,3,3,2,2,62.0,63.0,179.0,93.0,67.0,3,3 +6,57.0,2,-1,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,3,1,3,0.8,Carrot,2019-05-31,1,4,-1,74.0,2,4,90.0,4,2,3,1,2,1,46.0,73.0,180.0,83.0,63.0,3,3 +7,29.0,2,-1,2,-1,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,3,1,3,1,0.8,Avocado,2019-08-31,2,4,-1,88.0,2,5,90.0,3,-1,1,3,1,3,93.0,61.0,181.0,79.0,89.0,3,1 +7,48.0,2,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,3,2,0.8,No Data,2019-10-09,1,4,2,64.0,2,4,80.0,2,1,1,3,1,2,86.0,59.0,182.0,67.0,64.0,5,2 +7,47.0,1,2,1,1,1,1,4,3,82.0,40.0,No Data,84.0,67.0,81.0,1,2,2,3,3,3,1.2,Pepper,2019-01-29,1,3,2,82.0,1,1,81.0,1,1,3,3,2,2,40.0,67.0,183.0,84.0,No Data,4,3 +7,25.0,1,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,3,1,3,1,1.2,Pepper,2019-06-30,2,3,1,78.0,1,3,93.0,4,1,1,3,1,3,45.0,75.0,184.0,90.0,83.0,3,1 +7,50.0,1,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,No Data,2,2,3,3,3,3,1.2,Pepper,2019-11-15,2,4,1,90.0,1,1,No Data,1,1,3,3,2,3,97.0,60.0,185.0,80.0,64.0,3,3 +7,53.0,1,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,3,3,3,3,1.2,Onion,2019-01-18,2,4,2,76.0,1,1,81.0,4,1,3,3,2,3,69.0,51.0,186.0,76.0,84.0,3,3 +7,37.0,2,2,2,1,4,1,5,-1,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,0.8,Lettuce,2019-07-24,1,-1,2,89.0,2,1,83.0,4,1,1,2,1,1,91.0,51.0,187.0,50.0,89.0,5,1 +7,43.0,2,2,1,2,-1,1,-1,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,3,3,3,0.8,Green beans,2019-02-12,1,4,2,65.0,1,1,85.0,-1,2,3,3,2,2,88.0,54.0,No Data,75.0,61.0,-1,3 +7,22.0,1,1,-1,1,1,1,3,4,75.0,83.0,82.0,77.0,No Data,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-02-28,2,4,1,75.0,-1,1,83.0,1,1,3,3,2,3,83.0,No Data,189.0,77.0,82.0,3,3 +7,47.0,2,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,0.8,Carrot,No Data,1,3,2,63.0,1,5,94.0,1,1,1,2,1,1,71.0,86.0,190.0,64.0,62.0,3,1 +7,27.0,1,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-10-30,2,4,2,64.0,1,5,92.0,4,1,3,3,2,3,42.0,53.0,191.0,86.0,87.0,3,3 +7,43.0,1,2,2,1,4,4,1,-1,70.0,91.0,61.0,55.0,57.0,85.0,2,2,3,3,3,3,1.2,Onion,2019-04-26,2,-1,2,70.0,2,4,85.0,4,1,3,3,2,3,91.0,57.0,192.0,55.0,61.0,1,3 +7,47.0,1,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,3,3,3,3,1.2,Onion,2019-09-25,2,3,2,74.0,1,1,86.0,4,1,3,3,2,3,33.0,52.0,193.0,82.0,67.0,4,3 +7,56.0,2,1,1,2,-1,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-02-15,1,4,1,63.0,1,1,84.0,-1,2,1,-1,1,-1,33.0,52.0,194.0,86.0,66.0,3,1 +7,51.0,2,2,-1,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,3,2,3,3,0.8,Green beans,2019-12-13,2,5,2,83.0,-1,1,84.0,4,1,2,3,1,3,55.0,50.0,195.0,88.0,75.0,3,3 +7,35.0,2,1,1,2,4,4,5,3,No Data,61.0,66.0,88.0,58.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-05-02,1,3,1,No Data,1,4,No Data,4,2,3,3,2,2,61.0,58.0,196.0,88.0,66.0,5,3 +7,54.0,1,2,1,1,4,1,1,5,61.0,85.0,70.0,No Data,50.0,80.0,1,2,2,3,3,3,1.2,Green beans,2019-09-01,1,5,2,61.0,1,1,80.0,4,1,3,3,2,2,85.0,50.0,197.0,No Data,70.0,1,3 +7,42.0,2,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,-1,3,-1,3,0.8,Avocado,2019-05-02,1,4,2,83.0,1,2,86.0,4,2,3,-1,2,-1,46.0,51.0,198.0,82.0,79.0,5,3 +7,44.0,2,2,2,1,2,-1,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,3,3,3,0.8,Green beans,2019-07-25,1,4,2,73.0,2,-1,83.0,2,1,3,3,2,2,87.0,58.0,199.0,52.0,73.0,3,3 +7,60.0,2,1,2,2,4,1,3,-1,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,3,1,3,0.8,Avocado,2019-03-14,1,-1,1,63.0,2,1,83.0,4,2,3,1,2,1,31.0,57.0,200.0,63.0,71.0,3,3 +7,41.0,2,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-29,2,4,1,88.0,1,1,83.0,2,1,3,3,2,3,56.0,56.0,201.0,80.0,76.0,3,3 +7,36.0,2,1,2,-1,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,3,3,3,0.8,Carrot,2019-03-12,1,4,1,83.0,2,4,92.0,4,-1,3,3,2,2,57.0,62.0,202.0,80.0,77.0,3,3 +7,59.0,2,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,3,2,3,0.8,Onion,2019-10-09,1,3,2,76.0,1,1,85.0,2,2,3,2,2,1,68.0,56.0,203.0,81.0,67.0,5,3 +7,37.0,1,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,No Data,No Data,-1,2,-1,3,-1,3,1.2,Pepper,2019-11-03,-1,4,1,81.0,1,3,No Data,2,1,3,-1,2,-1,48.0,No Data,204.0,84.0,86.0,4,3 +7,50.0,2,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-09-08,1,3,2,77.0,1,5,91.0,3,1,3,3,2,2,88.0,54.0,205.0,67.0,75.0,5,3 +7,54.0,1,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-04-21,2,3,2,66.0,1,4,83.0,1,1,3,3,2,3,93.0,64.0,206.0,69.0,92.0,3,3 +7,54.0,2,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,3,3,3,3,0.8,Lettuce,2019-01-05,2,5,2,74.0,1,4,85.0,2,1,3,3,2,3,46.0,71.0,207.0,88.0,79.0,5,3 +7,44.0,1,-1,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,3,1,1.2,Avocado,2019-02-16,1,4,-1,72.0,2,2,81.0,1,1,1,3,1,2,42.0,50.0,208.0,69.0,80.0,3,1 +7,27.0,2,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,3,3,3,0.8,Avocado,2019-04-28,1,3,1,65.0,2,5,86.0,2,2,3,3,2,2,42.0,65.0,209.0,91.0,78.0,5,3 +7,50.0,2,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,3,3,3,3,0.8,Lettuce,2019-06-18,2,4,2,70.0,2,5,90.0,5,1,3,3,2,3,87.0,72.0,210.0,64.0,72.0,4,3 diff --git a/tests/testthat/test-as-data-frame.R b/tests/testthat/test-as-data-frame.R index 660867c65..092796061 100644 --- a/tests/testthat/test-as-data-frame.R +++ b/tests/testthat/test-as-data-frame.R @@ -101,6 +101,7 @@ with_mock_crunch({ '{"filter":null,"options":{"use_category_ids":true}}' ) }) + test_that("csvToDataFrame produces the correct data frame", { csv_df <- read.csv(datasetFixturePath("veg.csv"), stringsAsFactors = FALSE) expected <- readRDS(datasetFixturePath("veg_df.rds")) From ab152661d09da11dba83e081f092ad034e4b3de2 Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 5 Aug 2024 14:07:59 -0500 Subject: [PATCH 04/12] [188037693]: improve as.data.frame behavior --- R/as-data-frame.R | 174 ++++++++++++++++++++++++++++++++---------- R/variable-metadata.R | 1 + R/variable-type.R | 2 + R/variable.R | 1 + man/crunch-extract.Rd | 136 ++++++++++++++++----------------- man/dataset-to-R.Rd | 12 +++ 6 files changed, 219 insertions(+), 107 deletions(-) diff --git a/R/as-data-frame.R b/R/as-data-frame.R index 66ec3c087..c3bc7042a 100644 --- a/R/as-data-frame.R +++ b/R/as-data-frame.R @@ -46,6 +46,14 @@ #' @param categorical.mode what mode should categoricals be pulled as? One of #' factor, numeric, id (default: factor) #' @param include.hidden logical: should hidden variables be included? (default: `TRUE`) +#' @param array_strategy Strategy to import array variables: "alias" (the default) +#' reads them as flat variables with the subvariable aliases, unless there are duplicate +#' aliases in which case they are qualified in brackets after the array alias, +#' like "array_alias\[subvar_alias\]". "qualified_alias" always uses the bracket notation. +#' "packed" reads them in what the tidyverse calls "packed" data.frame columns, with the +#' alias from the array variable, and subvariables as the columns of the data.frame. +#' @param verbose Whether to output a message to the console when subvariable aliases +#' are qualified when array_strategy="alias" (defaults to TRUE) #' @param ... additional arguments passed to `as.data.frame` (default method). #' @return When called on a `CrunchDataset`, the method returns an object of #' class `CrunchDataFrame` unless `force = TRUE`, in which case the return is a @@ -70,7 +78,7 @@ as.data.frame.CrunchDataset <- function(x, include.hidden = include.hidden ) if (force) { - out <- as.data.frame(out) + out <- as.data.frame(out, ...) } return(out) } @@ -82,63 +90,151 @@ as.data.frame.CrunchDataFrame <- function(x, row.names = NULL, optional = FALSE, include.hidden = attr(x, "include.hidden"), + array_strategy = c("alias", "qualified_alias", "packed"), + verbose = TRUE, ...) { + array_strategy <- match.arg(array_strategy) ds <- attr(x, "crunchDataset") tmp <- tempfile() on.exit(unlink(tmp)) - write.csv(ds, tmp, categorical = "id", include.hidden = include.hidden) - # TODO: use variableMetadata to provide all `colClasses`? - # meta <- variableMetadata(ds) - ds_out <- read.csv(tmp, stringsAsFactors = FALSE, check.names = FALSE) - return(csvToDataFrame(ds_out, x)) + write.csv( + ds, + tmp, + categorical = "id", + header_field = "qualified_alias", + missing_values = "", + include.hidden = include.hidden + ) + + parsing_info <- csvColInfo(ds, verbose = verbose && array_strategy == "alias") + + # guessing has been good enough (and distinguishes between Date and POSIXct class for us) + # except for text variables, so continue to guess the parsing info for all columns besides text + col_classes <- setNames( + ifelse(parsing_info$var_type == "text", "character", NA_character_), + parsing_info$qualified_alias + ) + + ds_out <- read.csv( + tmp, + stringsAsFactors = FALSE, + check.names = FALSE, + colClasses = col_classes, + na.strings = "" + ) + dup_csv_names <- duplicated(names(ds_out)) + if (any(dup_csv_names)) { + stop( + "csv has duplicate column headers, cannot parse: ", + paste0(unique(names(ds_out)[dup_csv_names]), collapse = ", ") + ) + } + return(csvToDataFrame(ds_out, x, parsing_info, array_strategy, categorical.mode = attr(x, "mode"))) +} + +csvColInfo <- function(ds, verbose = TRUE) { + # Get variable metadata for variables included in the export + meta <- variableMetadata(ds)[urls(allVariables(ds))] + flattened_meta <- flattenVariableMetadata(meta) + + orig_aliases <- aliases(flattened_meta) + parent_aliases <- vapply(flattened_meta, function(x) x$parent_alias %||% NA_character_, character(1)) + qualified_aliases <- ifelse( + is.na(parent_aliases), + orig_aliases, + paste0(parent_aliases, "[", orig_aliases, "]") + ) + # cond_qualified_aliases are only qualified if there are duplicates + dup_aliases <- orig_aliases[duplicated(orig_aliases)] + cond_qualified_aliases <- ifelse(orig_aliases %in% dup_aliases, qualified_aliases, orig_aliases) + out <- data.frame( + orig_alias = orig_aliases, + parent_alias = parent_aliases, + qualified_alias = qualified_aliases, + cond_qualified_alias = cond_qualified_aliases, + var_type = types(flattened_meta) + ) + out <- out[!out$var_type %in% ARRAY_TYPES, ] + + if (verbose) { + msg_rows <- out$cond_qualified_alias != out$orig_alias + if (any(msg_rows)) { + alias_info <- paste0(out$orig_alias[msg_rows], " -> ", out$cond_qualified_alias[msg_rows]) + message( + "Some column names are qualified because there were duplicate aliases ", + "in dataset: ", paste0(alias_info, collapse = ", ") + ) + } + } + + attr(out, "meta") <- meta + out } -csvToDataFrame <- function(csv_df, crdf) { - ds <- attr(crdf, "crunchDataset") - mode <- attr(crdf, "mode") - ## Use `variableMetadata` to avoid a GET on each variable entity for - ## categories and subvariables - ## Subset variableMetadata on the urls of the variables in the ds in case - ## `ds` has only a subset of variables - ds@variables <- variableMetadata(ds)[urls(allVariables(ds))] +csvToDataFrame <- function(csv_df, + cr_data, + parsing_info, + array_strategy = c("alias", "qualified_alias", "packed"), + categorical.mode = "factor") { + array_strategy <- match.arg(array_strategy) + meta <- attr(parsing_info, "meta") ## CrunchDataFrames contain both server variables and local variables. - ## Iterate over the names of crdf to preserve the desired order. - ## Nest individual columns in a list and then unlist all because array - ## variables can return multiple columns - out <- unlist(lapply(names(crdf), function(a) { - v <- ds[[a]] + var_order <- if (inherits(cr_data, "CrunchDataFrame")) names(cr_data) else aliases(allVariables(cr_data)) + ## Iterate over the names of cr_data to preserve the desired order. + ## Nest everything in an extra layer of lists because one layer is removed + out <- unlist(lapply(var_order, function(a) { + meta_idx <- match(a, aliases(meta)) + v <- if (!is.na(meta_idx)) meta[[meta_idx[1]]] else NULL if (is.null(v)) { ## Not in the dataset, so it exists only in the CRDF. Get it there. - return(structure(list(crdf[[a]]), .Names = a)) - } else if (is.Array(v)) { + return(structure(list(cr_data[[a]]), .Names = a)) + } else if (type(v) %in% ARRAY_TYPES) { ## Find the subvar columns in the csv_df and parse them as categorical - if (is.NumericArray(v)) { - cp <- columnParser("numeric") + if (type(v) == "numeric_array") { + cp <- numericCsvParser } else { cp <- columnParser("categorical") } - sub_a <- aliases(subvariables(v)) - return(structure(lapply(csv_df[sub_a], cp, v, mode), .Names = sub_a)) - } else if (is.Numeric(v)) { - # When data is downloaded using write.csv it includes the name of - # the No Data category instead of a missing value, and this is read - # into R as a character vector. The data needs to be downloaded in - # this form to preserve the missing categories for categorical data. - # We use as.numeric to convert this to numeric and coerce the "No - # Data" elements to NA. So c("1", "No Data", "2.7") becomes c(1, NA, - # 2.7). as.numeric issues a warning when coercion creates NAs, and - # because we expect that, we suppress the warning. - df_vect <- suppressWarnings(as.numeric(csv_df[[a]])) - return(structure(list(df_vect), .Names = a)) + subvar_info <- parsing_info[!is.na(parsing_info$parent_alias) & parsing_info$parent_alias == alias(v), ] + cols <- csv_df[, subvar_info$qualified_alias] + if (array_strategy == "alias"){ + return(structure(lapply(cols, cp, v, categorical.mode), .Names = subvar_info$cond_qualified_alias)) + } else if (array_strategy == "qualified_alias") { + return(structure(lapply(cols, cp, v, categorical.mode), .Names = subvar_info$qualified_alias)) + } else { # array_strategy==packed + # Extra list layer to hold the array variable's alias + return(structure( + list( + structure( + lapply(cols, cp, v, categorical.mode), + class = "data.frame", + .Names = subvar_info$orig_alias, + row.names = c(NA, -nrow(csv_df)) + ) + ), + .Names = alias(v) + )) + } } else { - cp <- columnParser(type(v)) - return(structure(list(cp(csv_df[[a]], v, mode)), .Names = a)) + type <- type(v) + cp <- switch(type, "numeric" = numericCsvParser, "text" = textCsvParser, columnParser(type)) + return(structure(list(cp(csv_df[[a]], v, categorical.mode)), .Names = a)) } }), recursive = FALSE) + ## Wrap that list of columns in a data.frame structure - return(structure(out, class = "data.frame", row.names = c(NA, -nrow(ds)))) + return(structure(out, class = "data.frame", row.names = c(NA, -nrow(csv_df)))) } +# We pass missing_values to export so no longer have to worry about finding text +# in a numeric variable +numericCsvParser <- function(col, ...) col + +# When data comes from a csv it should already be text (and definitely won't be +# a list with missing reasons included like JSON's text columnParser) +textCsvParser <- function(col, ...) col + + #' as.data.frame method for catalog objects #' #' This method gives you a view of a catalog, such as a `VariableCatalog`, as a diff --git a/R/variable-metadata.R b/R/variable-metadata.R index 5570a73ee..55dedb525 100644 --- a/R/variable-metadata.R +++ b/R/variable-metadata.R @@ -56,6 +56,7 @@ flattenVariableMetadata <- function(vm) { ## Add the parent ref x$parent <- u x$parent_alias <- this$alias + x$type <- if (this$type == "numeric_array") "numeric" else "categorical" return(x) }) return(out) diff --git a/R/variable-type.R b/R/variable-type.R index b29f138da..572914c96 100644 --- a/R/variable-type.R +++ b/R/variable-type.R @@ -65,6 +65,8 @@ is.subvariable <- function(x) { CASTABLE_TYPES <- c("numeric", "text", "categorical") ## Add datetime when server supports +ARRAY_TYPES <- c("categorical_array", "multiple_response", "numeric_array") + #' Change Crunch variable types #' #' Numeric, text, and categorical variables can be cast to one another by diff --git a/R/variable.R b/R/variable.R index e28e59530..2ce29dcf2 100644 --- a/R/variable.R +++ b/R/variable.R @@ -1,3 +1,4 @@ +setMethod("tuple", "VariableTuple", function(x) x) setMethod("tuple", "CrunchVariable", function(x) x@tuple) setMethod("tuple<-", "CrunchVariable", function(x, value) { x@tuple <- value diff --git a/man/crunch-extract.Rd b/man/crunch-extract.Rd index cf6b24b99..5f908481a 100644 --- a/man/crunch-extract.Rd +++ b/man/crunch-extract.Rd @@ -10,9 +10,9 @@ % R/variable-update.R, R/variable.R \name{crunch-extract} \alias{crunch-extract} -\alias{[,AbstractCategories,ANY,ANY,ANY-method} -\alias{[,AbstractCategories,character,ANY,ANY-method} -\alias{[,AbstractCategories,numeric,ANY,ANY-method} +\alias{[,AbstractCategories,ANY,ANY-method} +\alias{[,AbstractCategories,character,ANY-method} +\alias{[,AbstractCategories,numeric,ANY-method} \alias{[<-,AbstractCategories,character,ANY,ANY-method} \alias{[[,AbstractCategories,character-method} \alias{[[<-,AbstractCategories,character,ANY,ANY-method} @@ -26,19 +26,19 @@ \alias{[[<-.CrunchDataFrame} \alias{$.CrunchDataFrame} \alias{$<-.CrunchDataFrame} -\alias{[,CubeDims,ANY,ANY,ANY-method} -\alias{[,CrunchCube,ANY,ANY,ANY-method} +\alias{[,CubeDims,ANY,ANY-method} +\alias{[,CrunchCube,ANY,ANY-method} \alias{[[<-,TransformsList,ANY,missing,NULL-method} \alias{[[,DatasetCatalog,numeric-method} \alias{[[<-,DatasetCatalog,character,missing,DatasetTuple-method} -\alias{[,CrunchDataset,ANY,ANY,ANY-method} -\alias{[,CrunchDataset,logical,missing,ANY-method} -\alias{[,CrunchDataset,character,ANY,ANY-method} -\alias{[,CrunchDataset,VariableGroup,ANY,ANY-method} -\alias{[,CrunchDataset,VariableOrder,ANY,ANY-method} -\alias{[,CrunchDataset,missing,ANY,ANY-method} -\alias{[,CrunchDataset,CrunchLogicalExpr,missing,ANY-method} -\alias{[,CrunchDataset,CrunchLogicalExpr,ANY,ANY-method} +\alias{[,CrunchDataset,ANY,ANY-method} +\alias{[,CrunchDataset,logical,missing-method} +\alias{[,CrunchDataset,character,ANY-method} +\alias{[,CrunchDataset,VariableGroup,ANY-method} +\alias{[,CrunchDataset,VariableOrder,ANY-method} +\alias{[,CrunchDataset,missing,ANY-method} +\alias{[,CrunchDataset,CrunchLogicalExpr,missing-method} +\alias{[,CrunchDataset,CrunchLogicalExpr,ANY-method} \alias{subset,CrunchDataset-method} \alias{[[,CrunchDataset,ANY-method} \alias{[[,CrunchDataset,character-method} @@ -55,10 +55,10 @@ \alias{[<-,CrunchDataset,ANY,missing,list-method} \alias{[<-,CrunchDataset,ANY,missing,CrunchDataset-method} \alias{[<-,CrunchDataset,CrunchExpr,ANY,ANY-method} -\alias{[,ShojiCatalog,character,ANY,ANY-method} -\alias{[,ShojiCatalog,numeric,ANY,ANY-method} -\alias{[,ShojiCatalog,logical,ANY,ANY-method} -\alias{[,ShojiCatalog,ANY,ANY,ANY-method} +\alias{[,ShojiCatalog,character,ANY-method} +\alias{[,ShojiCatalog,numeric,ANY-method} +\alias{[,ShojiCatalog,logical,ANY-method} +\alias{[,ShojiCatalog,ANY,ANY-method} \alias{[[,ShojiCatalog,ANY-method} \alias{[[,ShojiCatalog,character-method} \alias{$,ShojiCatalog-method} @@ -72,9 +72,9 @@ \alias{[[<-,AnalysisCatalog,ANY,missing,Analysis-method} \alias{[[,CrunchDeck,ANY-method} \alias{[[<-,CrunchDeck,ANY,ANY,ANY-method} -\alias{[,CrunchExpr,CrunchLogicalExpr,ANY,ANY-method} -\alias{[,CrunchExpr,logical,ANY,ANY-method} -\alias{[,CrunchExpr,numeric,ANY,ANY-method} +\alias{[,CrunchExpr,CrunchLogicalExpr,ANY-method} +\alias{[,CrunchExpr,logical,ANY-method} +\alias{[,CrunchExpr,numeric,ANY-method} \alias{[[,FilterCatalog,numeric-method} \alias{[[<-,FilterCatalog,character,missing,CrunchLogicalExpr-method} \alias{[[<-,FilterCatalog,numeric,missing,CrunchLogicalExpr-method} @@ -93,8 +93,8 @@ \alias{[[<-,ProjectFolder,character,missing,ProjectFolder-method} \alias{[[,ShojiFolder,numeric-method} \alias{[[,ShojiFolder,character-method} -\alias{[,ShojiOrder,ANY,ANY,ANY-method} -\alias{[,ShojiOrder,character,ANY,ANY-method} +\alias{[,ShojiOrder,ANY,ANY-method} +\alias{[,ShojiOrder,character,ANY-method} \alias{[[,ShojiOrder,ANY-method} \alias{[[,ShojiOrder,character-method} \alias{$,ShojiOrder-method} @@ -109,8 +109,8 @@ \alias{[[<-,ShojiOrder,character,missing,NULL-method} \alias{[[<-,ShojiOrder,character,missing,ShojiOrder-method} \alias{$<-,ShojiOrder-method} -\alias{[,OrderGroup,ANY,ANY,ANY-method} -\alias{[,OrderGroup,character,ANY,ANY-method} +\alias{[,OrderGroup,ANY,ANY-method} +\alias{[,OrderGroup,character,ANY-method} \alias{[[,OrderGroup,character-method} \alias{[[,OrderGroup,ANY-method} \alias{$,OrderGroup-method} @@ -132,7 +132,7 @@ \alias{[[<-,AnalysisCatalog,numeric,missing,list-method} \alias{[[,Subvariables,character-method} \alias{[[,Subvariables,numeric-method} -\alias{[,Subvariables,character,ANY,ANY-method} +\alias{[,Subvariables,character,ANY-method} \alias{[[<-,Subvariables,character,missing,CrunchVariable-method} \alias{[[<-,Subvariables,ANY,missing,CrunchVariable-method} \alias{[[<-,Subvariables,ANY,missing,NULL-method} @@ -140,9 +140,9 @@ \alias{[<-,Subvariables,character,missing,Subvariables-method} \alias{[<-,Subvariables,ANY,missing,Subvariables-method} \alias{[<-,Subvariables,ANY,missing,ANY-method} -\alias{[,ArrayVariable,character,ANY,ANY-method} -\alias{[,ArrayVariable,missing,ANY,ANY-method} -\alias{[,ArrayVariable,missing,character,ANY-method} +\alias{[,ArrayVariable,character,ANY-method} +\alias{[,ArrayVariable,missing,ANY-method} +\alias{[,ArrayVariable,missing,character-method} \alias{[[,ArrayVariable,ANY-method} \alias{[[,ArrayVariable,character-method} \alias{$,ArrayVariable-method} @@ -155,13 +155,13 @@ \alias{[[,TeamCatalog,numeric-method} \alias{[[<-,TeamCatalog,character,missing,list-method} \alias{[[<-,TeamCatalog,character,missing,CrunchTeam-method} -\alias{[,UserCatalog,character,ANY,ANY-method} +\alias{[,UserCatalog,character,ANY-method} \alias{[[,UserCatalog,character-method} \alias{[[,VariableCatalog,numeric-method} \alias{[[<-,VariableCatalog,character,missing,VariableTuple-method} \alias{[[<-,VariableCatalog,character,missing,CrunchVariable-method} -\alias{[,VariableCatalog,VariableOrder,ANY,ANY-method} -\alias{[,VariableCatalog,VariableGroup,ANY,ANY-method} +\alias{[,VariableCatalog,VariableOrder,ANY-method} +\alias{[,VariableCatalog,VariableGroup,ANY-method} \alias{[<-,VariableCatalog,VariableOrder,missing,VariableCatalog-method} \alias{[<-,VariableCatalog,VariableGroup,missing,VariableCatalog-method} \alias{[[<-,VariableOrder,character,missing,CrunchDataset-method} @@ -183,16 +183,16 @@ \alias{[<-,CategoricalArrayVariable,ANY,missing,factor-method} \alias{[<-,CrunchVariable,ANY,missing,logical-method} \alias{is.na<-,CrunchVariable,ANY-method} -\alias{[,CrunchVariable,CrunchExpr,ANY,ANY-method} -\alias{[,CrunchVariable,numeric,ANY,ANY-method} -\alias{[,CrunchVariable,logical,ANY,ANY-method} +\alias{[,CrunchVariable,CrunchExpr,ANY-method} +\alias{[,CrunchVariable,numeric,ANY-method} +\alias{[,CrunchVariable,logical,ANY-method} \title{Extract and modify Crunch objects} \usage{ -\S4method{[}{AbstractCategories,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{AbstractCategories,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{AbstractCategories,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{AbstractCategories,character,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{AbstractCategories,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{AbstractCategories,numeric,ANY}(x, i, j, ..., drop = TRUE) \S4method{[}{AbstractCategories,character,ANY,ANY}(x, i, j, ...) <- value @@ -220,9 +220,9 @@ \method{$}{CrunchDataFrame}(x, i) <- value -\S4method{[}{CubeDims,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CubeDims,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchCube,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchCube,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{TransformsList,ANY,missing,NULL}(x, i, j) <- value @@ -230,21 +230,21 @@ \S4method{[[}{DatasetCatalog,character,missing,DatasetTuple}(x, i, j) <- value -\S4method{[}{CrunchDataset,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,logical,missing,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,logical,missing}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchDataset,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,character,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,VariableGroup,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,VariableGroup,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,VariableOrder,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchDataset,VariableOrder,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchDataset,missing,ANY,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,missing,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchDataset,CrunchLogicalExpr,missing,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,CrunchLogicalExpr,missing}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchDataset,CrunchLogicalExpr,ANY,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchDataset,CrunchLogicalExpr,ANY}(x, i, j, ..., drop = FALSE) \S4method{subset}{CrunchDataset}(x, ...) @@ -278,13 +278,13 @@ \S4method{[}{CrunchDataset,CrunchExpr,ANY,ANY}(x, i, j) <- value -\S4method{[}{ShojiCatalog,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,character,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiCatalog,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,numeric,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiCatalog,logical,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,logical,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiCatalog,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiCatalog,ANY,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{ShojiCatalog,ANY}(x, i, j, ...) @@ -312,11 +312,11 @@ \S4method{[[}{CrunchDeck,ANY,ANY,ANY}(x, i, j) <- value -\S4method{[}{CrunchExpr,CrunchLogicalExpr,ANY,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchExpr,CrunchLogicalExpr,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchExpr,logical,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchExpr,logical,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchExpr,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchExpr,numeric,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{FilterCatalog,numeric}(x, i, j, ...) @@ -354,9 +354,9 @@ \S4method{[[}{ShojiFolder,character}(x, i, ..., drop = FALSE) -\S4method{[}{ShojiOrder,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiOrder,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ShojiOrder,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ShojiOrder,character,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{ShojiOrder,ANY}(x, i, j, ...) @@ -386,9 +386,9 @@ \S4method{$}{ShojiOrder}(x, name) <- value -\S4method{[}{OrderGroup,ANY,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{OrderGroup,ANY,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{OrderGroup,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{OrderGroup,character,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{OrderGroup,character}(x, i, j, ...) @@ -432,7 +432,7 @@ \S4method{[[}{Subvariables,numeric}(x, i, j, ...) -\S4method{[}{Subvariables,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{Subvariables,character,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{Subvariables,character,missing,CrunchVariable}(x, i) <- value @@ -448,11 +448,11 @@ \S4method{[}{Subvariables,ANY,missing,ANY}(x, i) <- value -\S4method{[}{ArrayVariable,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ArrayVariable,character,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ArrayVariable,missing,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ArrayVariable,missing,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{ArrayVariable,missing,character,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{ArrayVariable,missing,character}(x, i, j, ..., drop = TRUE) \S4method{[[}{ArrayVariable,ANY}(x, i, j, ...) @@ -478,7 +478,7 @@ \S4method{[[}{TeamCatalog,character,missing,CrunchTeam}(x, i, j) <- value -\S4method{[}{UserCatalog,character,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{UserCatalog,character,ANY}(x, i, j, ..., drop = TRUE) \S4method{[[}{UserCatalog,character}(x, i, j, ...) @@ -488,9 +488,9 @@ \S4method{[[}{VariableCatalog,character,missing,CrunchVariable}(x, i, j) <- value -\S4method{[}{VariableCatalog,VariableOrder,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{VariableCatalog,VariableOrder,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{VariableCatalog,VariableGroup,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{VariableCatalog,VariableGroup,ANY}(x, i, j, ..., drop = TRUE) \S4method{[}{VariableCatalog,VariableOrder,missing,VariableCatalog}(x, i, j) <- value @@ -534,11 +534,11 @@ \S4method{is.na}{CrunchVariable,ANY}(x) <- value -\S4method{[}{CrunchVariable,CrunchExpr,ANY,ANY}(x, i, j, ..., drop = FALSE) +\S4method{[}{CrunchVariable,CrunchExpr,ANY}(x, i, j, ..., drop = FALSE) -\S4method{[}{CrunchVariable,numeric,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchVariable,numeric,ANY}(x, i, j, ..., drop = TRUE) -\S4method{[}{CrunchVariable,logical,ANY,ANY}(x, i, j, ..., drop = TRUE) +\S4method{[}{CrunchVariable,logical,ANY}(x, i, j, ..., drop = TRUE) } \arguments{ \item{x}{a Crunch object (Dataset, Variable, \code{CrunchExpr}, Catalog, diff --git a/man/dataset-to-R.Rd b/man/dataset-to-R.Rd index 3cd8c0aa7..b6ea130d9 100644 --- a/man/dataset-to-R.Rd +++ b/man/dataset-to-R.Rd @@ -22,6 +22,8 @@ row.names = NULL, optional = FALSE, include.hidden = attr(x, "include.hidden"), + array_strategy = c("alias", "qualified_alias", "packed"), + verbose = TRUE, ... ) } @@ -45,6 +47,16 @@ Crunch Dataset order will be used.} \item{include.hidden}{logical: should hidden variables be included? (default: \code{TRUE})} \item{...}{additional arguments passed to \code{as.data.frame} (default method).} + +\item{array_strategy}{Strategy to import array variables: "alias" (the default) +reads them as flat variables with the subvariable aliases, unless there are duplicate +aliases in which case they are qualified in brackets after the array alias, +like "array_alias[subvar_alias]". "qualified_alias" always uses the bracket notation. +"packed" reads them in what the tidyverse calls "packed" data.frame columns, with the +alias from the array variable, and subvariables as the columns of the data.frame.} + +\item{verbose}{Whether to output a message to the console when subvariable aliases +are qualified when array_strategy="alias" (defaults to TRUE)} } \value{ When called on a \code{CrunchDataset}, the method returns an object of From 883d578edff46602ef64fe77d9353d3f14e60f70 Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 5 Aug 2024 14:08:52 -0500 Subject: [PATCH 05/12] [188037693]: fixture generation code for new as.data.frame behavior --- dev-misc/fixture-creation/dup-dataset.R | 111 ++++++++++++++++++ dev-misc/fixture-creation/redactors.R | 5 +- .../fixture-creation/vegetables-dataset.R | 47 +++++++- 3 files changed, 158 insertions(+), 5 deletions(-) create mode 100644 dev-misc/fixture-creation/dup-dataset.R diff --git a/dev-misc/fixture-creation/dup-dataset.R b/dev-misc/fixture-creation/dup-dataset.R new file mode 100644 index 000000000..37372c4db --- /dev/null +++ b/dev-misc/fixture-creation/dup-dataset.R @@ -0,0 +1,111 @@ +library(crunch) +library(here) +library(fs) +library(httptest) +library(purrr) + +setupCrunchAuth("team") + +source(here("dev-misc/fixture-creation/redactors.R")) + +# Make a dataset with duplicate aliases (in subvariables) +ds <- newDataset(data.frame( + x1 = 1:3, + x2 = 2:4, + y1 = factor(letters[1:3], levels = letters[1:5]), + y2 = factor(letters[2:4], levels = letters[1:5]), + z = factor(letters[11:13], levels = letters[11:15]) +), "dup test") + +ds$x <- deriveArray( + list( + VarDef(ds$x1, name = "x1", alias = "x1"), + VarDef(ds$x2, name = "x2_derived", alias = "x2_derived") + ), + name = "x", + numeric = TRUE +) + +ds$y <- deriveArray( + list( + VarDef(ds$y1, name = "y1", alias = "y1"), + VarDef(ds$y2, name = "z", alias = "z") + ), + name = "y", + numeric = FALSE +) + +mv(projects()[["Vegetables fixture"]], ds, projects()[["Vegetables fixture"]]) +ds <- refresh(ds) +ds_url <- self(ds) + +# Capture fixtures ---- +set_redactor(response_redactor(ds, "dup")) +set_requester(request_redactor(ds, "dup")) +## Capture dataset fixtures ---- +### General dataset capture ---- +temp_dir <- tempfile() +httpcache::clearCache() +dir_create(temp_dir) +start_capturing(temp_dir) + +ds <- loadDataset(ds_url) +aliases(allVariables(ds)) +# Don't actually export because httptest doesn't get it right +# but we do need the export views and metadata +exporters <- crGET(shojiURL(ds, "views", "export")) +var_meta <- variableMetadata(ds) + +stop_capturing() + +stabilize_json_files( + temp_dir, + list( + "app.crunch.io/api/datasets/dup.json", + list(list("body", "current_editor_name"), "User"), + list(list("body", "owner_name"), "User"), + list(list("body", "creation_time"), "2024-01-01T21:25:59.791000"), + list(list("body", "modification_time"), "2024-01-01T21:26:43.038000"), + list(list("body", "access_time"), "2024-01-01T21:26:43.038000"), + list( + # --- Only keep the palettes from the project folder so changes to crunch org + # --- don't affect fixtures. Maybe it'd be better to ask for a rcrunch test + # --- account, but this is okay for now + list("body", "palette", "analysis"), + function(x) { + purrr::keep(x, ~.$name %in% c("Default green palette for fixture", "purple palette for fixture")) + } + ), + list(list("urls", "owner_url"), "https://app.crunch.io/api/projects/pid/") + ) +) + +dir_delete(here("mocks/app.crunch.io/api/datasets/dup/")) +file_copy( + path(temp_dir, "app.crunch.io/api/datasets/dup.json"), + here("mocks/app.crunch.io/api/datasets/dup.json"), + overwrite = TRUE +) +dir_copy( + path(temp_dir, "app.crunch.io/api/datasets/dup/"), + here("mocks/app.crunch.io/api/datasets/dup/"), + overwrite = TRUE +) + + +write.csv( + ds, + here("mocks", "dataset-fixtures", "dup.csv"), + categorical = "id", + include.hidden = TRUE, + missing_values = ""#, + # header_field = "qualified_alias" # This will only work after #188045851 ships +) + +# Mock what header_field="qualified_alias" will look like after #188045851 ships +lines <- readLines(here("mocks", "dataset-fixtures", "dup.csv")) +lines[1] <- "x1,x2,y1,y2,z,x[x1],x[x2_derived],y[y1],y[z]" +writeLines(lines, here("mocks", "dataset-fixtures", "dup.csv")) + + +with_consent(delete(ds)) diff --git a/dev-misc/fixture-creation/redactors.R b/dev-misc/fixture-creation/redactors.R index a58d4b317..118dbbdd6 100644 --- a/dev-misc/fixture-creation/redactors.R +++ b/dev-misc/fixture-creation/redactors.R @@ -178,6 +178,8 @@ ids_from_ds <- function(ds, desired_ds_id) { stable_var_alias_order <- function(ds) { + if (name(ds) != "Vegetables example") return(aliases(allVariables(ds))) + saved_order_path <- here::here("dev-misc/fixture-creation/var_order.csv") saved_var_order <- suppressWarnings(try(read.csv(saved_order_path, stringsAsFactors = FALSE)[[1]], silent = TRUE)) if (inherits(saved_var_order, "try-error")) { @@ -203,6 +205,7 @@ ids_from_folders <- function(ds) { ) out <- unlist(out) + if (length(out) == 0) return() setNames(out, sprintf("vdir_%02d", seq_along(out))) } @@ -214,7 +217,7 @@ ids_below <- function(folder) { } ids_from_decks <- function(ds) { - if (length(decks(ds)) == 0) return + if (length(decks(ds)) == 0) return() deck_ids <- lapply(seq_along(decks(ds)), function(deck_num) { deck <- refresh(decks(ds)[[deck_num]]) slide_ids <- lapply(seq_along(refresh(slides(deck))), function(slide_num) { diff --git a/dev-misc/fixture-creation/vegetables-dataset.R b/dev-misc/fixture-creation/vegetables-dataset.R index b8564dc59..2258d829c 100644 --- a/dev-misc/fixture-creation/vegetables-dataset.R +++ b/dev-misc/fixture-creation/vegetables-dataset.R @@ -686,7 +686,7 @@ file_copy( overwrite = TRUE ) -dir_delete(here("mocks/app.crunch.io/api/datasets/veg/")) +dir_delete(here("mocks/app.crunch.io/api/datasets/veg/"), ) dir_copy( path(temp_dir, "app.crunch.io/api/datasets/veg/"), here("mocks/app.crunch.io/api/datasets/veg/"), @@ -708,15 +708,54 @@ write.csv( ds, here("mocks", "dataset-fixtures", "veg.csv"), categorical = "id", - include.hidden = TRUE + include.hidden = TRUE, + missing_values = ""#, + # header_field = "qualified_alias" # This will only work after #188045851 ships ) write.csv( ds, here("mocks", "dataset-fixtures", "veg-no-hidden.csv"), categorical = "id", - include.hidden = FALSE -) + include.hidden = FALSE, + missing_values = ""#, + # header_field = "qualified_alias" # This will only work after #188045851 ships +) + +# Mock what header_field="qualified_alias" will look like after #188045851 ships +lines <- readLines(here("mocks", "dataset-fixtures", "veg.csv")) +lines[1] <- paste0( + "wave,age,healthy_eater,enjoy_mr[enjoy_mr_savory],enjoy_mr[enjoy_mr_spicy],", + "enjoy_mr[enjoy_mr_sweet],veg_enjoy_ca[veg_enjoy_ca_healthy],veg_enjoy_ca[veg_enjoy_ca_tasty],", + "veg_enjoy_ca[veg_enjoy_ca_filling],veg_enjoy_ca[veg_enjoy_ca_env],", + "ratings_numa[ratings_numa_avocado],ratings_numa[ratings_numa_brussel_sprout],", + "ratings_numa[ratings_numa_carrot],ratings_numa[ratings_numa_daikon],", + "ratings_numa[ratings_numa_eggplant],ratings_numa[ratings_numa_fennel],", + "funnel_aware_mr[funnel_aware_mr_1],funnel_aware_mr[funnel_aware_mr_2],", + "funnel_consider_mr[funnel_consider_mr_1],funnel_consider_mr[funnel_consider_mr_2],", + "funnel_consider_mr[funnel_buy_mr_1],funnel_consider_mr[funnel_buy_mr_2],", + "weight,last_vegetable,last_vegetable_date,rating_daikon,funnel_aware_1,funnel_consider_1,", + "funnel_buy_2,veg_environmental,funnel_aware_2,funnel_consider_2,enjoy_savory_food,", + "resp_id,veg_tasty,rating_fennel,rating_carrot,enjoy_sweet_food,veg_filling,", + "rating_brussel_sprout,rating_eggplant,funnel_buy_1,enjoy_spicy_food,rating_avocado,veg_healthy" +) +writeLines(lines,here("mocks", "dataset-fixtures", "veg.csv")) + +lines <- readLines(here("mocks", "dataset-fixtures", "veg-no-hidden.csv")) +lines[1] <- paste0( + "wave,age,healthy_eater,enjoy_mr[enjoy_mr_savory],enjoy_mr[enjoy_mr_spicy],", + "enjoy_mr[enjoy_mr_sweet],veg_enjoy_ca[veg_enjoy_ca_healthy],veg_enjoy_ca[veg_enjoy_ca_tasty],", + "veg_enjoy_ca[veg_enjoy_ca_filling],veg_enjoy_ca[veg_enjoy_ca_env],", + "ratings_numa[ratings_numa_avocado],ratings_numa[ratings_numa_brussel_sprout],", + "ratings_numa[ratings_numa_carrot],ratings_numa[ratings_numa_daikon],", + "ratings_numa[ratings_numa_eggplant],ratings_numa[ratings_numa_fennel],", + "funnel_aware_mr[funnel_aware_mr_1],funnel_aware_mr[funnel_aware_mr_2],", + "funnel_consider_mr[funnel_consider_mr_1],funnel_consider_mr[funnel_consider_mr_2],", + "funnel_buy_mr[funnel_buy_mr_1],funnel_buy_mr[funnel_buy_mr_2],", + "weight,last_vegetable,last_vegetable_date" +) +writeLines(lines,here("mocks", "dataset-fixtures", "veg-no-hidden.csv")) + ## Generate cube fixtures ---- ### Numeric array alone (numa.json) ---- From ed91176939a16f9dcb1a4dce9d42c708a9e26152 Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 5 Aug 2024 14:09:09 -0500 Subject: [PATCH 06/12] [188037693]: update fixtures for new as.data.frame behavior --- mocks/app.crunch.io/api/datasets/dup.json | 148 ++++++ .../api/datasets/dup/export.json | 11 + .../app.crunch.io/api/datasets/dup/table.json | 283 ++++++++++++ .../api/datasets/dup/variables-d118fa.json | 116 +++++ .../datasets/dup/variables/hier-d118fa.json | 15 + mocks/dataset-fixtures/dup.csv | 4 + mocks/dataset-fixtures/veg-no-hidden.csv | 422 +++++++++--------- mocks/dataset-fixtures/veg.csv | 422 +++++++++--------- mocks/dataset-fixtures/veg_df.rds | Bin 2197 -> 4688 bytes mocks/dataset-fixtures/veg_hidden_df.rds | Bin 2311 -> 4769 bytes 10 files changed, 999 insertions(+), 422 deletions(-) create mode 100644 mocks/app.crunch.io/api/datasets/dup.json create mode 100644 mocks/app.crunch.io/api/datasets/dup/export.json create mode 100644 mocks/app.crunch.io/api/datasets/dup/table.json create mode 100644 mocks/app.crunch.io/api/datasets/dup/variables-d118fa.json create mode 100644 mocks/app.crunch.io/api/datasets/dup/variables/hier-d118fa.json create mode 100644 mocks/dataset-fixtures/dup.csv diff --git a/mocks/app.crunch.io/api/datasets/dup.json b/mocks/app.crunch.io/api/datasets/dup.json new file mode 100644 index 000000000..5deccca8d --- /dev/null +++ b/mocks/app.crunch.io/api/datasets/dup.json @@ -0,0 +1,148 @@ +{ + "body": { + "access_time": "2024-01-01T21:26:43.038000", + "account": "https://app.crunch.io/api/accounts/00001/", + "app_settings": { + "report_issue_url": "" + }, + "archived": false, + "creation_time": "2024-01-01T21:25:59.791000", + "current_editor": "https://app.crunch.io/api/users/user_id/", + "current_editor_name": "User", + "description": "", + "end_date": null, + "id": "dup", + "is_published": true, + "logo": { + "favicon": "", + "large": "", + "small": "" + }, + "maintainer": "https://app.crunch.io/api/users/user_id/", + "modification_time": "2024-01-01T21:26:43.038000", + "name": "dup test", + "notes": "", + "owner": "https://app.crunch.io/api/projects/proj_id/", + "owner_name": "User", + "palette": { + "analysis": [ + { + "default": true, + "name": "Default green palette for fixture", + "palette": [ + "#4fc3f7", + "#4dd0e1", + "#4db6ac", + "#81c783", + "#aed581", + "#dce775", + "#cddc39", + "#fdae6b" + ], + "type": "qualitative" + }, + { + "default": false, + "name": "purple palette for fixture", + "palette": [ + "#340043", + "#640083", + "#9100bf", + "#c300ff", + "#e17fff" + ], + "type": "qualitative" + } + ], + "brand": { + "message": "#712480", + "primary": "#0064a4", + "secondary": "#107e64" + } + }, + "path": "Vegetables fixture|dup test", + "permissions": { + "edit": true, + "view": true + }, + "project_inherited_settings": { + "disallow_dataset_shares": false, + "stop_dataset_shares": true + }, + "size": { + "columns": 7, + "derivations": 2, + "rows": 3, + "source_columns": 5, + "unfiltered_rows": 3, + "variables": 7 + }, + "start_date": null, + "streaming": "no", + "type": "dataset", + "view_of": null + }, + "catalogs": { + "actions": "https://app.crunch.io/api/datasets/dup/actions/", + "batches": "https://app.crunch.io/api/datasets/dup/batches/", + "boxdata": "https://app.crunch.io/api/datasets/dup/boxdata/", + "dashboards": "https://app.crunch.io/api/datasets/dup/dashboards/", + "decks": "https://app.crunch.io/api/datasets/dup/decks/", + "filters": "https://app.crunch.io/api/datasets/dup/filters/", + "folders": "https://app.crunch.io/api/datasets/dup/folders/", + "forks": "https://app.crunch.io/api/datasets/dup/forks/", + "multitables": "https://app.crunch.io/api/datasets/dup/multitables/", + "parent": "https://app.crunch.io/api/projects/proj_id/datasets/", + "permissions": "https://app.crunch.io/api/datasets/dup/permissions/", + "project": "https://app.crunch.io/api/projects/proj_id/", + "savepoints": "https://app.crunch.io/api/datasets/dup/savepoints/", + "scripts": "https://app.crunch.io/api/datasets/dup/scripts/", + "tags": "https://app.crunch.io/api/datasets/dup/tags/", + "teams": "https://app.crunch.io/api/datasets/dup/teams/", + "users": "https://app.crunch.io/api/datasets/dup/users/", + "variables": "https://app.crunch.io/api/datasets/dup/variables/", + "variables_by_type": "https://app.crunch.io/api/datasets/dup/variables/?type={type}", + "variables_private": "https://app.crunch.io/api/datasets/dup/variables/private/", + "views": "https://app.crunch.io/api/datasets/dup/views/" + }, + "description": "Detail for a given dataset", + "element": "shoji:entity", + "folder_trees": { + "hidden": "folders/hidden/", + "personal": "folders/personal/", + "public": "folders/public/", + "secure": "folders/secure/" + }, + "fragments": { + "exclusion": "https://app.crunch.io/api/datasets/dup/exclusion/", + "pk": "https://app.crunch.io/api/datasets/dup/pk/", + "preferences": "https://app.crunch.io/api/datasets/dup/preferences/", + "publish": "https://app.crunch.io/api/datasets/dup/publish/", + "schema": "https://app.crunch.io/api/datasets/dup/schema/", + "settings": "https://app.crunch.io/api/datasets/dup/settings/", + "state": "https://app.crunch.io/api/datasets/dup/state/", + "stream": "https://app.crunch.io/api/datasets/dup/stream/", + "table": "https://app.crunch.io/api/datasets/dup/table/" + }, + "orders": { + "favorites": "https://app.crunch.io/api/datasets/dup/variables/favorites/", + "variables_hier": "https://app.crunch.io/api/datasets/dup/variables/hier/", + "variables_weights": "https://app.crunch.io/api/datasets/dup/variables/weights/" + }, + "self": "https://app.crunch.io/api/datasets/dup/", + "urls": { + "editor_url": "https://app.crunch.io/api/users/user_id/", + "owner_url": "https://app.crunch.io/api/projects/pid/", + "user_url": "https://app.crunch.io/api/users/user_id/" + }, + "views": { + "applied_filters": "https://app.crunch.io/api/datasets/dup/filters/applied/", + "cube": "https://app.crunch.io/api/datasets/dup/cube/", + "export": "https://app.crunch.io/api/datasets/dup/export/", + "second_order_analysis": "https://app.crunch.io/api/datasets/dup/second_order_analysis/", + "sources": "https://app.crunch.io/api/datasets/dup/sources/", + "summary": "https://app.crunch.io/api/datasets/dup/summary/", + "var_by_alias": "https://app.crunch.io/api/datasets/dup/var_by_alias/" + } +} + diff --git a/mocks/app.crunch.io/api/datasets/dup/export.json b/mocks/app.crunch.io/api/datasets/dup/export.json new file mode 100644 index 000000000..2e06912a5 --- /dev/null +++ b/mocks/app.crunch.io/api/datasets/dup/export.json @@ -0,0 +1,11 @@ +{ + "element": "shoji:view", + "self": "https://app.crunch.io/api/datasets/dup/export/", + "views": { + "crunchcl": "https://app.crunch.io/api/datasets/dup/export/crunchcl/", + "csv": "https://app.crunch.io/api/datasets/dup/export/csv/", + "parquet": "https://app.crunch.io/api/datasets/dup/export/parquet/", + "spss": "https://app.crunch.io/api/datasets/dup/export/spss/" + } +} + diff --git a/mocks/app.crunch.io/api/datasets/dup/table.json b/mocks/app.crunch.io/api/datasets/dup/table.json new file mode 100644 index 000000000..63a4f45b9 --- /dev/null +++ b/mocks/app.crunch.io/api/datasets/dup/table.json @@ -0,0 +1,283 @@ +{ + "description": "A Crunch Table of data for this dataset.", + "element": "crunch:table", + "metadata": { + "var_01": { + "alias": "x1", + "derived": false, + "description": "", + "missing_reasons": { + "No Data": -1 + }, + "name": "x1", + "notes": "", + "type": "numeric" + }, + "var_02": { + "alias": "x2", + "derived": false, + "description": "", + "missing_reasons": { + "No Data": -1 + }, + "name": "x2", + "notes": "", + "type": "numeric" + }, + "var_03": { + "alias": "y1", + "categories": [ + { + "id": 1, + "missing": false, + "name": "a", + "numeric_value": 1 + }, + { + "id": 2, + "missing": false, + "name": "b", + "numeric_value": 2 + }, + { + "id": 3, + "missing": false, + "name": "c", + "numeric_value": 3 + }, + { + "id": 4, + "missing": false, + "name": "d", + "numeric_value": 4 + }, + { + "id": 5, + "missing": false, + "name": "e", + "numeric_value": 5 + }, + { + "id": -1, + "missing": true, + "name": "No Data", + "numeric_value": null + } + ], + "derived": false, + "description": "", + "name": "y1", + "notes": "", + "type": "categorical" + }, + "var_04": { + "alias": "y2", + "categories": [ + { + "id": 1, + "missing": false, + "name": "a", + "numeric_value": 1 + }, + { + "id": 2, + "missing": false, + "name": "b", + "numeric_value": 2 + }, + { + "id": 3, + "missing": false, + "name": "c", + "numeric_value": 3 + }, + { + "id": 4, + "missing": false, + "name": "d", + "numeric_value": 4 + }, + { + "id": 5, + "missing": false, + "name": "e", + "numeric_value": 5 + }, + { + "id": -1, + "missing": true, + "name": "No Data", + "numeric_value": null + } + ], + "derived": false, + "description": "", + "name": "y2", + "notes": "", + "type": "categorical" + }, + "var_05": { + "alias": "z", + "categories": [ + { + "id": 1, + "missing": false, + "name": "k", + "numeric_value": 1 + }, + { + "id": 2, + "missing": false, + "name": "l", + "numeric_value": 2 + }, + { + "id": 3, + "missing": false, + "name": "m", + "numeric_value": 3 + }, + { + "id": 4, + "missing": false, + "name": "n", + "numeric_value": 4 + }, + { + "id": 5, + "missing": false, + "name": "o", + "numeric_value": 5 + }, + { + "id": -1, + "missing": true, + "name": "No Data", + "numeric_value": null + } + ], + "derived": false, + "description": "", + "name": "z", + "notes": "", + "type": "categorical" + }, + "var_06": { + "alias": "x", + "derived": true, + "description": "", + "missing_reasons": { + "No Data": -1 + }, + "name": "x", + "notes": "", + "subreferences": { + "0001": { + "alias": "x1", + "description": "", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "x1", + "notes": "", + "view": { + "column_width": null + } + }, + "0002": { + "alias": "x2_derived", + "description": "", + "format": { + "data": { + "digits": 2 + }, + "summary": { + "digits": 2 + } + }, + "name": "x2_derived", + "notes": "", + "view": { + "column_width": null + } + } + }, + "subvariables": [ + "0001", + "0002" + ], + "type": "numeric_array" + }, + "var_07": { + "alias": "y", + "categories": [ + { + "id": 1, + "missing": false, + "name": "a", + "numeric_value": 1 + }, + { + "id": 2, + "missing": false, + "name": "b", + "numeric_value": 2 + }, + { + "id": 3, + "missing": false, + "name": "c", + "numeric_value": 3 + }, + { + "id": 4, + "missing": false, + "name": "d", + "numeric_value": 4 + }, + { + "id": 5, + "missing": false, + "name": "e", + "numeric_value": 5 + }, + { + "id": -1, + "missing": true, + "name": "No Data", + "numeric_value": null + } + ], + "derived": true, + "description": "", + "name": "y", + "notes": "", + "subreferences": { + "0001": { + "alias": "y1", + "description": "", + "name": "y1", + "notes": "" + }, + "0002": { + "alias": "z", + "description": "", + "name": "z", + "notes": "" + } + }, + "subvariables": [ + "0001", + "0002" + ], + "type": "categorical_array" + } + }, + "self": "https://app.crunch.io/api/datasets/dup/table/" +} + diff --git a/mocks/app.crunch.io/api/datasets/dup/variables-d118fa.json b/mocks/app.crunch.io/api/datasets/dup/variables-d118fa.json new file mode 100644 index 000000000..366054fe8 --- /dev/null +++ b/mocks/app.crunch.io/api/datasets/dup/variables-d118fa.json @@ -0,0 +1,116 @@ +{ + "catalogs": { + "materialize": "https://app.crunch.io/api/datasets/dup/variables/materialize/", + "private": "https://app.crunch.io/api/datasets/dup/variables/private/", + "suggest_alias": "https://app.crunch.io/api/datasets/dup/variables/suggest_alias/" + }, + "description": "List of Variables of this dataset", + "element": "shoji:catalog", + "index": { + "var_01/": { + "alias": "x1", + "derived": false, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_01", + "name": "x1", + "notes": "", + "secure": false, + "type": "numeric" + }, + "var_02/": { + "alias": "x2", + "derived": false, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_02", + "name": "x2", + "notes": "", + "secure": false, + "type": "numeric" + }, + "var_03/": { + "alias": "y1", + "derived": false, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_03", + "name": "y1", + "notes": "", + "scale": "interval", + "secure": false, + "type": "categorical" + }, + "var_04/": { + "alias": "y2", + "derived": false, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_04", + "name": "y2", + "notes": "", + "scale": "interval", + "secure": false, + "type": "categorical" + }, + "var_05/": { + "alias": "z", + "derived": false, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_05", + "name": "z", + "notes": "", + "scale": "interval", + "secure": false, + "type": "categorical" + }, + "var_06/": { + "alias": "x", + "derived": true, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_06", + "name": "x", + "notes": "", + "secure": false, + "subvariables": [ + "var_06/subvariables/0001/", + "var_06/subvariables/0002/" + ], + "subvariables_catalog": "var_06/subvariables/", + "type": "numeric_array" + }, + "var_07/": { + "alias": "y", + "derived": true, + "description": "", + "discarded": false, + "hidden": false, + "id": "var_07", + "name": "y", + "notes": "", + "scale": "interval", + "secure": false, + "subvariables": [ + "var_07/subvariables/0001/", + "var_07/subvariables/0002/" + ], + "subvariables_catalog": "var_07/subvariables/", + "type": "categorical_array" + } + }, + "orders": { + "favorites": "https://app.crunch.io/api/datasets/dup/variables/favorites/", + "hier": "https://app.crunch.io/api/datasets/dup/variables/hier/", + "weights": "https://app.crunch.io/api/datasets/dup/variables/weights/" + }, + "self": "https://app.crunch.io/api/datasets/dup/variables/?relative=on" +} + diff --git a/mocks/app.crunch.io/api/datasets/dup/variables/hier-d118fa.json b/mocks/app.crunch.io/api/datasets/dup/variables/hier-d118fa.json new file mode 100644 index 000000000..54b4c532b --- /dev/null +++ b/mocks/app.crunch.io/api/datasets/dup/variables/hier-d118fa.json @@ -0,0 +1,15 @@ +{ + "description": "Hierarchical order from folder structure of dataset variables", + "element": "shoji:order", + "graph": [ + "../var_01/", + "../var_02/", + "../var_03/", + "../var_04/", + "../var_05/", + "../var_06/", + "../var_07/" + ], + "self": "https://app.crunch.io/api/datasets/dup/variables/hier/?relative=on" +} + diff --git a/mocks/dataset-fixtures/dup.csv b/mocks/dataset-fixtures/dup.csv new file mode 100644 index 000000000..d99eadb10 --- /dev/null +++ b/mocks/dataset-fixtures/dup.csv @@ -0,0 +1,4 @@ +x1,x2,y1,y2,z,x[x1],x[x2_derived],y[y1],y[z] +1.0,2.0,1,2,1,1.0,2.0,1,2 +2.0,3.0,2,3,2,2.0,3.0,2,3 +3.0,4.0,3,4,3,3.0,4.0,3,4 diff --git a/mocks/dataset-fixtures/veg-no-hidden.csv b/mocks/dataset-fixtures/veg-no-hidden.csv index 7ae0aa9df..8d861f68e 100644 --- a/mocks/dataset-fixtures/veg-no-hidden.csv +++ b/mocks/dataset-fixtures/veg-no-hidden.csv @@ -1,211 +1,211 @@ -wave,age,healthy_eater,enjoy_mr_savory,enjoy_mr_spicy,enjoy_mr_sweet,veg_enjoy_ca_healthy,veg_enjoy_ca_tasty,veg_enjoy_ca_filling,veg_enjoy_ca_env,ratings_numa_avocado,ratings_numa_brussel_sprout,ratings_numa_carrot,ratings_numa_daikon,ratings_numa_eggplant,ratings_numa_fennel,funnel_aware_mr_1,funnel_aware_mr_2,funnel_consider_mr_1,funnel_consider_mr_2,funnel_buy_mr_1,funnel_buy_mr_2,weight,last_vegetable,last_vegetable_date -1,25.0,2,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,No Data,95.0,1,1,-1,1,-1,1,0.8,Carrot,2019-01-04 -1,43.0,1,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,1.2,Avocado,2019-11-25 -1,20.0,1,1,2,1,1,1,1,-1,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,1.2,Pepper,2019-04-04 -1,39.0,1,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,1.2,Onion,2019-03-29 -1,31.0,2,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,No Data,1,1,1,1,1,1,0.8,Green beans,2019-11-16 -1,50.0,2,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,-1,1,-1,0.8,Green beans,No Data -1,33.0,2,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,0.8,Pepper,2019-01-03 -1,29.0,2,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,3,1,0.8,Carrot,2019-09-11 -1,52.0,1,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,1.2,Avocado,2019-02-08 -1,39.0,1,1,1,1,1,4,3,-1,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,1.2,Avocado,2019-12-22 -1,62.0,1,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,3,1,3,1.2,Onion,2019-05-22 -1,39.0,1,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,1.2,Avocado,2019-04-16 -1,18.0,2,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,No Data,1,1,1,1,1,1,0.8,Avocado,2019-08-25 -1,56.0,2,1,1,1,1,5,5,-1,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,-1,0.8,Avocado,No Data -1,64.0,2,2,1,1,4,4,3,1,64.0,34.0,65.0,No Data,84.0,94.0,1,1,1,1,2,1,0.8,Green beans,2019-08-21 -1,41.0,2,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,-1,1,-1,1,-1,1,0.8,Avocado,2019-03-23 -1,No Data,1,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,No Data,1,1,1,1,-1,1,1.2,Carrot,No Data -1,65.0,1,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,1.2,Onion,2019-12-26 -1,34.0,2,1,1,1,4,4,3,1,67.0,No Data,82.0,60.0,65.0,85.0,1,1,1,-1,1,-1,0.8,No Data,2019-07-25 -1,64.0,2,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-08-20 -1,26.0,2,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,0.8,Onion,2019-02-15 -1,59.0,2,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-06-26 -1,45.0,2,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,0.8,Lettuce,2019-03-26 -1,60.0,1,2,2,1,4,1,3,3,73.0,48.0,No Data,53.0,80.0,84.0,1,2,1,3,2,3,1.2,Carrot,2019-08-02 -1,47.0,1,1,-1,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,1.2,Tomato,2019-12-27 -1,23.0,1,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,1.2,Tomato,2019-11-03 -1,57.0,2,1,-1,2,4,1,5,-1,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,3,2,3,0.8,Avocado,2019-01-13 -1,59.0,1,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,-1,3,-1,3,1.2,Carrot,2019-06-11 -1,38.0,1,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,3,1,1.2,Onion,2019-09-08 -1,50.0,2,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,-1,1,-1,1,-1,0.8,Avocado,No Data -2,35.0,2,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,3,3,3,0.8,Green beans,2019-03-26 -2,29.0,1,1,2,-1,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,3,1,3,1.2,No Data,No Data -2,36.0,1,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,3,2,3,1.2,Avocado,2019-04-18 -2,21.0,2,1,1,1,2,4,-1,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,3,2,3,0.8,Pepper,2019-03-04 -2,60.0,1,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,3,3,3,1.2,Lettuce,2019-01-25 -2,34.0,2,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,0.8,Pepper,2019-05-02 -2,22.0,2,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,No Data,81.0,2,1,3,1,3,1,0.8,Onion,2019-09-25 -2,48.0,2,-1,2,2,5,5,3,3,No Data,62.0,92.0,63.0,78.0,93.0,2,2,3,3,3,3,0.8,Green beans,2019-09-23 -2,57.0,1,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,3,1,3,1.2,Onion,2019-11-04 -2,28.0,1,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,1.2,Lettuce,2019-10-19 -2,42.0,1,2,1,1,1,1,1,4,68.0,No Data,78.0,64.0,52.0,83.0,1,2,1,3,1,3,1.2,Pepper,2019-10-20 -2,31.0,1,2,2,1,-1,5,3,3,66.0,No Data,76.0,52.0,67.0,89.0,1,2,1,3,2,3,1.2,Carrot,2019-10-28 -2,45.0,2,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,3,1,3,1,0.8,Green beans,2019-01-07 -2,35.0,2,2,2,1,2,-1,3,3,83.0,70.0,71.0,66.0,83.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-02-15 -2,45.0,1,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,-1,1.2,Tomato,2019-10-22 -2,No Data,2,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,No Data,91.0,1,1,1,1,2,1,0.8,Green beans,2019-05-11 -2,44.0,1,-1,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,3,3,3,1.2,Pepper,2019-10-20 -2,60.0,2,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,0.8,Green beans,2019-04-09 -2,48.0,2,2,2,-1,-1,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,3,1,3,0.8,Carrot,2019-03-09 -2,36.0,1,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,3,2,3,1.2,Pepper,2019-02-20 -2,58.0,1,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,3,1,3,1,1.2,Pepper,2019-05-07 -2,No Data,1,1,1,1,-1,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,-1,1.2,Lettuce,2019-08-03 -2,54.0,2,1,1,1,5,1,4,3,68.0,38.0,70.0,No Data,67.0,83.0,1,2,1,3,2,3,0.8,Tomato,2019-12-28 -2,41.0,2,1,1,2,4,-1,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,3,3,3,0.8,Onion,2019-05-08 -2,No Data,2,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,3,2,3,0.8,Green beans,2019-10-19 -2,41.0,2,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,3,1,0.8,Tomato,2019-04-06 -2,53.0,2,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,3,3,3,0.8,Avocado,2019-09-11 -2,No Data,1,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,3,1,3,1.2,Onion,2019-08-03 -2,24.0,2,1,-1,1,4,1,-1,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,3,3,3,0.8,Tomato,2019-10-01 -2,60.0,2,2,-1,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,3,2,3,0.8,Pepper,2019-10-12 -3,60.0,2,1,1,1,3,5,-1,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,3,1,3,0.8,Tomato,2019-04-24 -3,19.0,2,1,1,1,1,1,4,-1,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,3,-1,3,0.8,Lettuce,2019-08-14 -3,65.0,2,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,3,3,0.8,Green beans,2019-05-26 -3,53.0,2,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,3,3,3,3,0.8,No Data,2019-05-09 -3,No Data,1,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,3,1,1.2,Green beans,2019-06-28 -3,49.0,2,-1,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,3,3,3,3,0.8,No Data,2019-08-10 -3,25.0,2,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,No Data,No Data,1,1,1,1,-1,1,0.8,No Data,2019-07-11 -3,34.0,1,1,1,2,-1,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,3,2,3,1.2,Avocado,2019-07-14 -3,21.0,1,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,3,1,3,1.2,Onion,2019-12-27 -3,64.0,1,1,1,1,-1,-1,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,3,3,3,1.2,No Data,2019-04-13 -3,36.0,2,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,No Data,86.0,2,-1,3,-1,3,-1,0.8,Onion,2019-01-15 -3,35.0,2,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,3,1,3,1,0.8,Tomato,2019-09-02 -3,41.0,1,1,-1,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,3,2,3,1.2,Pepper,2019-02-02 -3,19.0,2,-1,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,3,3,3,0.8,Green beans,2019-12-31 -3,63.0,2,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,-1,3,-1,3,-1,0.8,Carrot,2019-12-31 -3,64.0,2,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,0.8,Tomato,2019-02-21 -3,45.0,2,1,1,1,-1,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,3,3,3,0.8,Tomato,2019-04-25 -3,55.0,1,-1,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,3,1,3,1.2,Lettuce,2019-11-06 -3,33.0,2,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-05-09 -3,28.0,1,1,-1,1,3,5,3,5,66.0,48.0,No Data,80.0,65.0,92.0,1,2,2,3,3,3,1.2,Onion,2019-07-09 -3,34.0,2,2,2,1,1,4,-1,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,3,1,3,0.8,Lettuce,2019-04-07 -3,39.0,1,2,1,1,1,1,3,4,67.0,No Data,86.0,72.0,68.0,87.0,1,1,2,1,3,-1,1.2,Tomato,2019-07-27 -3,57.0,1,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,1.2,No Data,2019-07-10 -3,48.0,2,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,3,3,3,0.8,Avocado,2019-11-19 -3,No Data,1,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,3,1,3,1,1.2,Avocado,2019-12-19 -3,18.0,1,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,1.2,Carrot,2019-12-16 -3,25.0,2,2,1,1,4,-1,3,4,No Data,45.0,No Data,58.0,85.0,86.0,1,2,1,3,2,3,0.8,Carrot,2019-01-04 -3,40.0,2,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,3,0.8,Avocado,2019-11-14 -3,43.0,2,2,1,1,1,5,5,-1,74.0,68.0,68.0,84.0,70.0,85.0,2,2,3,3,3,3,0.8,Pepper,2019-07-02 -3,22.0,2,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,3,3,3,3,0.8,Onion,2019-12-13 -4,48.0,1,2,1,1,-1,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,3,3,3,1.2,Pepper,2019-12-04 -4,26.0,1,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,3,1,3,1,1.2,Pepper,2019-12-28 -4,37.0,1,2,1,1,1,4,3,-1,62.0,90.0,78.0,60.0,88.0,87.0,2,-1,3,-1,3,-1,1.2,Onion,2019-12-26 -4,28.0,1,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,-1,2,1.2,Tomato,2019-07-25 -4,56.0,1,-1,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-07-29 -4,18.0,2,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,0.8,Lettuce,2019-05-26 -4,47.0,2,2,1,1,5,4,3,4,70.0,49.0,67.0,No Data,53.0,85.0,1,-1,2,-1,3,-1,0.8,Tomato,2019-09-14 -4,37.0,2,2,-1,1,5,1,-1,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,3,2,3,0.8,Avocado,2019-06-20 -4,22.0,1,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,-1,-1,-1,1.2,Tomato,2019-07-08 -4,24.0,2,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,3,3,3,0.8,Green beans,2019-02-06 -4,54.0,2,2,2,1,4,4,3,-1,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,3,2,3,0.8,Lettuce,2019-11-24 -4,34.0,2,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,-1,1,-1,2,-1,0.8,Carrot,2019-03-07 -4,48.0,2,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,3,3,3,3,0.8,Green beans,2019-12-11 -4,No Data,-1,1,1,1,1,-1,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,3,1,0.8,Tomato,2019-07-04 -4,21.0,2,2,-1,1,4,4,3,3,69.0,54.0,70.0,No Data,84.0,92.0,1,1,1,2,1,3,0.8,No Data,2019-11-27 -4,42.0,1,2,2,1,-1,-1,3,3,68.0,55.0,67.0,No Data,82.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-11-16 -4,32.0,2,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,3,1,3,1,0.8,Tomato,2019-08-18 -4,30.0,2,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,3,2,3,0.8,Lettuce,2019-01-17 -4,46.0,2,1,2,2,3,5,3,5,64.0,72.0,61.0,No Data,61.0,No Data,1,1,-1,1,-1,1,0.8,Avocado,2019-02-25 -4,24.0,2,-1,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,-1,3,-1,0.8,Avocado,2019-03-27 -4,53.0,1,1,1,2,2,4,5,4,71.0,73.0,No Data,87.0,83.0,83.0,1,2,2,3,3,3,1.2,Onion,2019-10-06 -4,27.0,1,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,-1,2,-1,3,-1,3,1.2,Lettuce,2019-09-24 -4,65.0,2,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,3,1,3,0.8,Pepper,2019-01-14 -4,36.0,1,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,3,1,3,1,1.2,Carrot,2019-12-16 -4,48.0,-1,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,No Data,1,2,2,3,3,3,1.2,Avocado,2019-12-23 -4,47.0,1,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,3,1.2,Onion,2019-12-19 -4,30.0,2,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,3,1,3,0.8,Green beans,No Data -4,20.0,2,-1,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,3,2,0.8,Lettuce,2019-12-05 -4,21.0,1,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,3,3,3,3,1.2,Green beans,2019-07-31 -4,34.0,1,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,3,1,3,2,1.2,Onion,2019-09-02 -5,23.0,2,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,3,3,3,0.8,Lettuce,2019-11-06 -5,55.0,2,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,3,3,3,3,0.8,Pepper,No Data -5,41.0,2,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,0.8,Onion,2019-06-24 -5,64.0,2,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,3,3,3,3,0.8,Lettuce,2019-03-14 -5,27.0,2,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,3,2,3,0.8,Pepper,No Data -5,44.0,1,2,-1,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,3,1,1.2,Lettuce,2019-10-27 -5,41.0,1,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,No Data,80.0,1,1,1,1,1,2,1.2,Avocado,2019-03-29 -5,54.0,2,2,1,1,4,2,5,4,No Data,68.0,60.0,84.0,54.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-11-26 -5,59.0,2,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,3,2,3,0.8,Lettuce,2019-12-04 -5,34.0,1,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,3,1,3,1,1.2,Tomato,2019-05-05 -5,No Data,2,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,0.8,Onion,2019-02-28 -5,28.0,1,1,2,-1,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,3,1,3,1,1.2,Carrot,2019-06-15 -5,18.0,-1,1,-1,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,-1,2,-1,3,-1,1.2,Carrot,2019-02-12 -5,26.0,1,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,3,1.2,Green beans,2019-10-30 -5,62.0,2,1,1,1,4,1,-1,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,3,3,3,3,0.8,Carrot,2019-03-29 -5,64.0,2,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-07-02 -5,22.0,1,1,1,1,4,4,1,3,74.0,No Data,77.0,63.0,68.0,87.0,1,2,2,3,3,3,1.2,Pepper,2019-05-28 -5,46.0,1,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,3,3,3,3,1.2,Carrot,2019-10-06 -5,45.0,2,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,0.8,Avocado,2019-12-21 -5,34.0,2,1,2,2,-1,3,3,5,79.0,51.0,No Data,81.0,78.0,89.0,1,-1,2,-1,3,-1,0.8,Onion,2019-12-10 -5,33.0,1,2,2,1,-1,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,3,3,3,1.2,Carrot,2019-06-03 -5,45.0,1,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,3,3,3,1.2,Avocado,2019-07-14 -5,24.0,2,-1,2,1,4,5,4,3,86.0,50.0,73.0,No Data,52.0,90.0,1,1,2,1,3,2,0.8,No Data,2019-07-02 -5,28.0,1,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,3,3,3,3,1.2,Green beans,2019-11-11 -5,19.0,-1,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,3,2,0.8,Lettuce,2019-07-17 -5,60.0,2,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,3,2,3,0.8,Lettuce,2019-07-17 -5,44.0,1,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,3,3,3,1.2,Lettuce,2019-07-26 -5,59.0,2,2,1,1,5,4,4,-1,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,3,3,3,0.8,Carrot,2019-02-16 -5,18.0,2,-1,-1,1,-1,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,-1,2,-1,3,-1,0.8,Avocado,2019-12-10 -5,50.0,2,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,No Data,1,2,2,3,3,3,0.8,No Data,2019-04-24 -6,61.0,2,2,2,2,4,1,1,3,No Data,57.0,87.0,87.0,65.0,80.0,1,2,1,3,1,3,0.8,Green beans,2019-09-21 -6,31.0,2,1,2,-1,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,3,3,3,0.8,Pepper,2019-04-13 -6,57.0,-1,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,3,-1,3,0.8,Tomato,2019-09-27 -6,51.0,1,1,2,1,-1,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-03-29 -6,64.0,1,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,No Data,1,1,1,1,2,1,1.2,Green beans,2019-04-05 -6,47.0,1,2,-1,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,3,1,1.2,Green beans,2019-10-22 -6,64.0,2,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,No Data,93.0,1,1,1,1,1,1,0.8,Lettuce,2019-10-04 -6,37.0,2,2,1,1,1,4,5,5,63.0,No Data,74.0,92.0,71.0,88.0,1,2,1,3,2,3,0.8,Pepper,2019-05-29 -6,41.0,2,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,3,3,3,3,0.8,Tomato,2019-07-13 -6,53.0,2,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,3,3,3,0.8,Green beans,2019-05-14 -6,20.0,2,1,1,1,4,2,3,4,89.0,85.0,No Data,55.0,60.0,90.0,2,1,3,1,3,1,0.8,Tomato,2019-03-27 -6,53.0,1,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-09-24 -6,61.0,2,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,No Data,87.0,2,1,3,1,3,1,0.8,Avocado,2019-08-26 -6,21.0,2,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,No Data,No Data,2,2,3,3,3,3,0.8,Green beans,2019-03-18 -6,53.0,2,1,2,1,4,2,3,4,79.0,67.0,86.0,No Data,73.0,89.0,-1,1,-1,1,-1,1,0.8,Green beans,2019-09-17 -6,35.0,2,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-12-09 -6,No Data,1,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,3,-1,1.2,Green beans,2019-11-19 -6,No Data,1,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,3,3,3,1.2,Pepper,2019-02-19 -6,40.0,2,1,1,2,-1,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,3,3,3,3,0.8,Carrot,No Data -6,64.0,1,2,1,1,1,1,1,4,62.0,71.0,83.0,No Data,51.0,90.0,1,2,2,3,3,3,1.2,Tomato,2019-08-30 -6,59.0,1,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,3,1,1.2,Onion,2019-08-05 -6,48.0,2,2,1,1,3,5,-1,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,3,3,3,3,0.8,Tomato,2019-09-28 -6,51.0,2,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,-1,1,-1,1,-1,0.8,Pepper,2019-04-20 -6,38.0,2,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,3,3,3,3,0.8,Onion,2019-07-24 -6,60.0,1,1,1,1,-1,1,5,4,82.0,56.0,75.0,65.0,53.0,No Data,2,1,3,1,3,2,1.2,Onion,2019-01-26 -6,29.0,2,1,2,1,-1,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,-1,-1,-1,-1,-1,-1,0.8,Lettuce,2019-08-06 -6,33.0,1,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,-1,3,1.2,Carrot,2019-06-18 -6,37.0,2,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-22 -6,37.0,1,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,3,3,3,1.2,Onion,2019-08-12 -6,57.0,2,-1,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,3,1,3,0.8,Carrot,2019-05-31 -7,29.0,2,-1,2,-1,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,3,1,3,1,0.8,Avocado,2019-08-31 -7,48.0,2,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,3,2,0.8,No Data,2019-10-09 -7,47.0,1,2,1,1,1,1,4,3,82.0,40.0,No Data,84.0,67.0,81.0,1,2,2,3,3,3,1.2,Pepper,2019-01-29 -7,25.0,1,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,3,1,3,1,1.2,Pepper,2019-06-30 -7,50.0,1,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,No Data,2,2,3,3,3,3,1.2,Pepper,2019-11-15 -7,53.0,1,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,3,3,3,3,1.2,Onion,2019-01-18 -7,37.0,2,2,2,1,4,1,5,-1,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,0.8,Lettuce,2019-07-24 -7,43.0,2,2,1,2,-1,1,-1,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,3,3,3,0.8,Green beans,2019-02-12 -7,22.0,1,1,-1,1,1,1,3,4,75.0,83.0,82.0,77.0,No Data,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-02-28 -7,47.0,2,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,0.8,Carrot,No Data -7,27.0,1,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-10-30 -7,43.0,1,2,2,1,4,4,1,-1,70.0,91.0,61.0,55.0,57.0,85.0,2,2,3,3,3,3,1.2,Onion,2019-04-26 -7,47.0,1,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,3,3,3,3,1.2,Onion,2019-09-25 -7,56.0,2,1,1,2,-1,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-02-15 -7,51.0,2,2,-1,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,3,2,3,3,0.8,Green beans,2019-12-13 -7,35.0,2,1,1,2,4,4,5,3,No Data,61.0,66.0,88.0,58.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-05-02 -7,54.0,1,2,1,1,4,1,1,5,61.0,85.0,70.0,No Data,50.0,80.0,1,2,2,3,3,3,1.2,Green beans,2019-09-01 -7,42.0,2,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,-1,3,-1,3,0.8,Avocado,2019-05-02 -7,44.0,2,2,2,1,2,-1,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,3,3,3,0.8,Green beans,2019-07-25 -7,60.0,2,1,2,2,4,1,3,-1,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,3,1,3,0.8,Avocado,2019-03-14 -7,41.0,2,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-29 -7,36.0,2,1,2,-1,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,3,3,3,0.8,Carrot,2019-03-12 -7,59.0,2,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,3,2,3,0.8,Onion,2019-10-09 -7,37.0,1,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,No Data,No Data,-1,2,-1,3,-1,3,1.2,Pepper,2019-11-03 -7,50.0,2,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-09-08 -7,54.0,1,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-04-21 -7,54.0,2,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,3,3,3,3,0.8,Lettuce,2019-01-05 -7,44.0,1,-1,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,3,1,1.2,Avocado,2019-02-16 -7,27.0,2,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,3,3,3,0.8,Avocado,2019-04-28 -7,50.0,2,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,3,3,3,3,0.8,Lettuce,2019-06-18 +wave,age,healthy_eater,enjoy_mr[enjoy_mr_savory],enjoy_mr[enjoy_mr_spicy],enjoy_mr[enjoy_mr_sweet],veg_enjoy_ca[veg_enjoy_ca_healthy],veg_enjoy_ca[veg_enjoy_ca_tasty],veg_enjoy_ca[veg_enjoy_ca_filling],veg_enjoy_ca[veg_enjoy_ca_env],ratings_numa[ratings_numa_avocado],ratings_numa[ratings_numa_brussel_sprout],ratings_numa[ratings_numa_carrot],ratings_numa[ratings_numa_daikon],ratings_numa[ratings_numa_eggplant],ratings_numa[ratings_numa_fennel],funnel_aware_mr[funnel_aware_mr_1],funnel_aware_mr[funnel_aware_mr_2],funnel_consider_mr[funnel_consider_mr_1],funnel_consider_mr[funnel_consider_mr_2],funnel_buy_mr[funnel_buy_mr_1],funnel_buy_mr[funnel_buy_mr_2],weight,last_vegetable,last_vegetable_date +1,25.0,2,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,,95.0,1,1,,1,,1,0.8,Carrot,2019-01-04 +1,43.0,1,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,1.2,Avocado,2019-11-25 +1,20.0,1,1,2,1,1,1,1,,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,1.2,Pepper,2019-04-04 +1,39.0,1,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,1.2,Onion,2019-03-29 +1,31.0,2,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,,1,1,1,1,1,1,0.8,Green beans,2019-11-16 +1,50.0,2,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,,1,,0.8,Green beans, +1,33.0,2,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,0.8,Pepper,2019-01-03 +1,29.0,2,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,,1,0.8,Carrot,2019-09-11 +1,52.0,1,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,1.2,Avocado,2019-02-08 +1,39.0,1,1,1,1,1,4,3,,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,1.2,Avocado,2019-12-22 +1,62.0,1,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,,1,,1.2,Onion,2019-05-22 +1,39.0,1,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,1.2,Avocado,2019-04-16 +1,18.0,2,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,,1,1,1,1,1,1,0.8,Avocado,2019-08-25 +1,56.0,2,1,1,1,1,5,5,,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,,0.8,Avocado, +1,64.0,2,2,1,1,4,4,3,1,64.0,34.0,65.0,,84.0,94.0,1,1,1,1,2,1,0.8,Green beans,2019-08-21 +1,41.0,2,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,,1,,1,,1,0.8,Avocado,2019-03-23 +1,,1,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,,1,1,1,1,,1,1.2,Carrot, +1,65.0,1,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,1.2,Onion,2019-12-26 +1,34.0,2,1,1,1,4,4,3,1,67.0,,82.0,60.0,65.0,85.0,1,1,1,,1,,0.8,,2019-07-25 +1,64.0,2,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,,,,0.8,Green beans,2019-08-20 +1,26.0,2,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,0.8,Onion,2019-02-15 +1,59.0,2,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,,1,,1,0.8,Lettuce,2019-06-26 +1,45.0,2,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,0.8,Lettuce,2019-03-26 +1,60.0,1,2,2,1,4,1,3,3,73.0,48.0,,53.0,80.0,84.0,1,2,1,,2,,1.2,Carrot,2019-08-02 +1,47.0,1,1,,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,1.2,Tomato,2019-12-27 +1,23.0,1,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,1.2,Tomato,2019-11-03 +1,57.0,2,1,,2,4,1,5,,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,,2,,0.8,Avocado,2019-01-13 +1,59.0,1,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,,,,,1.2,Carrot,2019-06-11 +1,38.0,1,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,,1,1.2,Onion,2019-09-08 +1,50.0,2,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,,1,,1,,0.8,Avocado, +2,35.0,2,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,,,,0.8,Green beans,2019-03-26 +2,29.0,1,1,2,,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,,1,,1.2,, +2,36.0,1,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,,2,,1.2,Avocado,2019-04-18 +2,21.0,2,1,1,1,2,4,,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,,2,,0.8,Pepper,2019-03-04 +2,60.0,1,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,,,,1.2,Lettuce,2019-01-25 +2,34.0,2,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,0.8,Pepper,2019-05-02 +2,22.0,2,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,,81.0,2,1,,1,,1,0.8,Onion,2019-09-25 +2,48.0,2,,2,2,5,5,3,3,,62.0,92.0,63.0,78.0,93.0,2,2,,,,,0.8,Green beans,2019-09-23 +2,57.0,1,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,,1,,1.2,Onion,2019-11-04 +2,28.0,1,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,1.2,Lettuce,2019-10-19 +2,42.0,1,2,1,1,1,1,1,4,68.0,,78.0,64.0,52.0,83.0,1,2,1,,1,,1.2,Pepper,2019-10-20 +2,31.0,1,2,2,1,,5,3,3,66.0,,76.0,52.0,67.0,89.0,1,2,1,,2,,1.2,Carrot,2019-10-28 +2,45.0,2,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,,1,,1,0.8,Green beans,2019-01-07 +2,35.0,2,2,2,1,2,,3,3,83.0,70.0,71.0,66.0,83.0,,1,2,2,,,,0.8,Carrot,2019-02-15 +2,45.0,1,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,,1.2,Tomato,2019-10-22 +2,,2,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,,91.0,1,1,1,1,2,1,0.8,Green beans,2019-05-11 +2,44.0,1,,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,,,,1.2,Pepper,2019-10-20 +2,60.0,2,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,0.8,Green beans,2019-04-09 +2,48.0,2,2,2,,,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,,1,,0.8,Carrot,2019-03-09 +2,36.0,1,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,,2,,1.2,Pepper,2019-02-20 +2,58.0,1,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,,1,,1,1.2,Pepper,2019-05-07 +2,,1,1,1,1,,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,,1.2,Lettuce,2019-08-03 +2,54.0,2,1,1,1,5,1,4,3,68.0,38.0,70.0,,67.0,83.0,1,2,1,,2,,0.8,Tomato,2019-12-28 +2,41.0,2,1,1,2,4,,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,,,,0.8,Onion,2019-05-08 +2,,2,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,,2,,0.8,Green beans,2019-10-19 +2,41.0,2,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,,1,0.8,Tomato,2019-04-06 +2,53.0,2,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,,,,0.8,Avocado,2019-09-11 +2,,1,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,,1,,1.2,Onion,2019-08-03 +2,24.0,2,1,,1,4,1,,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,,,,0.8,Tomato,2019-10-01 +2,60.0,2,2,,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,,2,,0.8,Pepper,2019-10-12 +3,60.0,2,1,1,1,3,5,,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,,1,,0.8,Tomato,2019-04-24 +3,19.0,2,1,1,1,1,1,4,,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,,,,0.8,Lettuce,2019-08-14 +3,65.0,2,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,,,0.8,Green beans,2019-05-26 +3,53.0,2,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,,,,,0.8,,2019-05-09 +3,,1,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,,1,1.2,Green beans,2019-06-28 +3,49.0,2,,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,,,,,0.8,,2019-08-10 +3,25.0,2,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,,,1,1,1,1,,1,0.8,,2019-07-11 +3,34.0,1,1,1,2,,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,,2,,1.2,Avocado,2019-07-14 +3,21.0,1,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,,1,,1.2,Onion,2019-12-27 +3,64.0,1,1,1,1,,,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,,,,1.2,,2019-04-13 +3,36.0,2,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,,86.0,2,,,,,,0.8,Onion,2019-01-15 +3,35.0,2,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,,1,,1,0.8,Tomato,2019-09-02 +3,41.0,1,1,,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,,2,,1.2,Pepper,2019-02-02 +3,19.0,2,,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,,,,0.8,Green beans,2019-12-31 +3,63.0,2,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,,,,,,0.8,Carrot,2019-12-31 +3,64.0,2,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,0.8,Tomato,2019-02-21 +3,45.0,2,1,1,1,,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,,,,0.8,Tomato,2019-04-25 +3,55.0,1,,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,,1,,1.2,Lettuce,2019-11-06 +3,33.0,2,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,,,,0.8,Green beans,2019-05-09 +3,28.0,1,1,,1,3,5,3,5,66.0,48.0,,80.0,65.0,92.0,1,2,2,,,,1.2,Onion,2019-07-09 +3,34.0,2,2,2,1,1,4,,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,,1,,0.8,Lettuce,2019-04-07 +3,39.0,1,2,1,1,1,1,3,4,67.0,,86.0,72.0,68.0,87.0,1,1,2,1,,,1.2,Tomato,2019-07-27 +3,57.0,1,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,1.2,,2019-07-10 +3,48.0,2,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,,,,0.8,Avocado,2019-11-19 +3,,1,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,,1,,1,1.2,Avocado,2019-12-19 +3,18.0,1,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,1.2,Carrot,2019-12-16 +3,25.0,2,2,1,1,4,,3,4,,45.0,,58.0,85.0,86.0,1,2,1,,2,,0.8,Carrot,2019-01-04 +3,40.0,2,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,,0.8,Avocado,2019-11-14 +3,43.0,2,2,1,1,1,5,5,,74.0,68.0,68.0,84.0,70.0,85.0,2,2,,,,,0.8,Pepper,2019-07-02 +3,22.0,2,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,,,,,0.8,Onion,2019-12-13 +4,48.0,1,2,1,1,,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,,,,1.2,Pepper,2019-12-04 +4,26.0,1,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,,1,,1,1.2,Pepper,2019-12-28 +4,37.0,1,2,1,1,1,4,3,,62.0,90.0,78.0,60.0,88.0,87.0,2,,,,,,1.2,Onion,2019-12-26 +4,28.0,1,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,,2,1.2,Tomato,2019-07-25 +4,56.0,1,,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,,1,1.2,Lettuce,2019-07-29 +4,18.0,2,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,0.8,Lettuce,2019-05-26 +4,47.0,2,2,1,1,5,4,3,4,70.0,49.0,67.0,,53.0,85.0,1,,2,,,,0.8,Tomato,2019-09-14 +4,37.0,2,2,,1,5,1,,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,,2,,0.8,Avocado,2019-06-20 +4,22.0,1,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,,,,1.2,Tomato,2019-07-08 +4,24.0,2,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,,,,0.8,Green beans,2019-02-06 +4,54.0,2,2,2,1,4,4,3,,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,,2,,0.8,Lettuce,2019-11-24 +4,34.0,2,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,,1,,2,,0.8,Carrot,2019-03-07 +4,48.0,2,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,,,,,0.8,Green beans,2019-12-11 +4,,,1,1,1,1,,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,,1,0.8,Tomato,2019-07-04 +4,21.0,2,2,,1,4,4,3,3,69.0,54.0,70.0,,84.0,92.0,1,1,1,2,1,,0.8,,2019-11-27 +4,42.0,1,2,2,1,,,3,3,68.0,55.0,67.0,,82.0,84.0,1,1,2,1,,1,1.2,Lettuce,2019-11-16 +4,32.0,2,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,,1,,1,0.8,Tomato,2019-08-18 +4,30.0,2,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,,2,,0.8,Lettuce,2019-01-17 +4,46.0,2,1,2,2,3,5,3,5,64.0,72.0,61.0,,61.0,,1,1,,1,,1,0.8,Avocado,2019-02-25 +4,24.0,2,,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,,,,0.8,Avocado,2019-03-27 +4,53.0,1,1,1,2,2,4,5,4,71.0,73.0,,87.0,83.0,83.0,1,2,2,,,,1.2,Onion,2019-10-06 +4,27.0,1,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,,2,,,,,1.2,Lettuce,2019-09-24 +4,65.0,2,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,,1,,0.8,Pepper,2019-01-14 +4,36.0,1,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,,1,,1,1.2,Carrot,2019-12-16 +4,48.0,,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,,1,2,2,,,,1.2,Avocado,2019-12-23 +4,47.0,1,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,,1.2,Onion,2019-12-19 +4,30.0,2,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,,1,,0.8,Green beans, +4,20.0,2,,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,,2,0.8,Lettuce,2019-12-05 +4,21.0,1,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,,,,,1.2,Green beans,2019-07-31 +4,34.0,1,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,,1,,2,1.2,Onion,2019-09-02 +5,23.0,2,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,,,,0.8,Lettuce,2019-11-06 +5,55.0,2,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,,,,,0.8,Pepper, +5,41.0,2,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,0.8,Onion,2019-06-24 +5,64.0,2,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,,,,,0.8,Lettuce,2019-03-14 +5,27.0,2,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,,2,,0.8,Pepper, +5,44.0,1,2,,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,,1,1.2,Lettuce,2019-10-27 +5,41.0,1,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,,80.0,1,1,1,1,1,2,1.2,Avocado,2019-03-29 +5,54.0,2,2,1,1,4,2,5,4,,68.0,60.0,84.0,54.0,92.0,1,2,2,,,,0.8,Green beans,2019-11-26 +5,59.0,2,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,,2,,0.8,Lettuce,2019-12-04 +5,34.0,1,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,,1,,1,1.2,Tomato,2019-05-05 +5,,2,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,0.8,Onion,2019-02-28 +5,28.0,1,1,2,,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,,1,,1,1.2,Carrot,2019-06-15 +5,18.0,,1,,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,,2,,,,1.2,Carrot,2019-02-12 +5,26.0,1,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,,1.2,Green beans,2019-10-30 +5,62.0,2,1,1,1,4,1,,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,,,,,0.8,Carrot,2019-03-29 +5,64.0,2,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,,,,0.8,Carrot,2019-07-02 +5,22.0,1,1,1,1,4,4,1,3,74.0,,77.0,63.0,68.0,87.0,1,2,2,,,,1.2,Pepper,2019-05-28 +5,46.0,1,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,,,,,1.2,Carrot,2019-10-06 +5,45.0,2,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,0.8,Avocado,2019-12-21 +5,34.0,2,1,2,2,,3,3,5,79.0,51.0,,81.0,78.0,89.0,1,,2,,,,0.8,Onion,2019-12-10 +5,33.0,1,2,2,1,,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,,,,1.2,Carrot,2019-06-03 +5,45.0,1,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,,,,1.2,Avocado,2019-07-14 +5,24.0,2,,2,1,4,5,4,3,86.0,50.0,73.0,,52.0,90.0,1,1,2,1,,2,0.8,,2019-07-02 +5,28.0,1,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,,,,,1.2,Green beans,2019-11-11 +5,19.0,,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,,2,0.8,Lettuce,2019-07-17 +5,60.0,2,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,,2,,0.8,Lettuce,2019-07-17 +5,44.0,1,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,,,,1.2,Lettuce,2019-07-26 +5,59.0,2,2,1,1,5,4,4,,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,,,,0.8,Carrot,2019-02-16 +5,18.0,2,,,1,,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,,2,,,,0.8,Avocado,2019-12-10 +5,50.0,2,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,,1,2,2,,,,0.8,,2019-04-24 +6,61.0,2,2,2,2,4,1,1,3,,57.0,87.0,87.0,65.0,80.0,1,2,1,,1,,0.8,Green beans,2019-09-21 +6,31.0,2,1,2,,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,,,,0.8,Pepper,2019-04-13 +6,57.0,,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,,,,0.8,Tomato,2019-09-27 +6,51.0,1,1,2,1,,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,,,,,1.2,Onion,2019-03-29 +6,64.0,1,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,,1,1,1,1,2,1,1.2,Green beans,2019-04-05 +6,47.0,1,2,,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,,1,1.2,Green beans,2019-10-22 +6,64.0,2,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,,93.0,1,1,1,1,1,1,0.8,Lettuce,2019-10-04 +6,37.0,2,2,1,1,1,4,5,5,63.0,,74.0,92.0,71.0,88.0,1,2,1,,2,,0.8,Pepper,2019-05-29 +6,41.0,2,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,,,,,0.8,Tomato,2019-07-13 +6,53.0,2,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,,,,0.8,Green beans,2019-05-14 +6,20.0,2,1,1,1,4,2,3,4,89.0,85.0,,55.0,60.0,90.0,2,1,,1,,1,0.8,Tomato,2019-03-27 +6,53.0,1,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,,,,,1.2,Lettuce,2019-09-24 +6,61.0,2,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,,87.0,2,1,,1,,1,0.8,Avocado,2019-08-26 +6,21.0,2,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,,,2,2,,,,,0.8,Green beans,2019-03-18 +6,53.0,2,1,2,1,4,2,3,4,79.0,67.0,86.0,,73.0,89.0,,1,,1,,1,0.8,Green beans,2019-09-17 +6,35.0,2,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,,,,0.8,Carrot,2019-12-09 +6,,1,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,,,1.2,Green beans,2019-11-19 +6,,1,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,,,,1.2,Pepper,2019-02-19 +6,40.0,2,1,1,2,,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,,,,,0.8,Carrot, +6,64.0,1,2,1,1,1,1,1,4,62.0,71.0,83.0,,51.0,90.0,1,2,2,,,,1.2,Tomato,2019-08-30 +6,59.0,1,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,,1,1.2,Onion,2019-08-05 +6,48.0,2,2,1,1,3,5,,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,,,,,0.8,Tomato,2019-09-28 +6,51.0,2,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,,1,,1,,0.8,Pepper,2019-04-20 +6,38.0,2,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,,,,,0.8,Onion,2019-07-24 +6,60.0,1,1,1,1,,1,5,4,82.0,56.0,75.0,65.0,53.0,,2,1,,1,,2,1.2,Onion,2019-01-26 +6,29.0,2,1,2,1,,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,,,,,,,0.8,Lettuce,2019-08-06 +6,33.0,1,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,,,1.2,Carrot,2019-06-18 +6,37.0,2,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,,,,,0.8,Lettuce,2019-09-22 +6,37.0,1,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,,,,1.2,Onion,2019-08-12 +6,57.0,2,,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,,1,,0.8,Carrot,2019-05-31 +7,29.0,2,,2,,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,,1,,1,0.8,Avocado,2019-08-31 +7,48.0,2,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,,2,0.8,,2019-10-09 +7,47.0,1,2,1,1,1,1,4,3,82.0,40.0,,84.0,67.0,81.0,1,2,2,,,,1.2,Pepper,2019-01-29 +7,25.0,1,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,,1,,1,1.2,Pepper,2019-06-30 +7,50.0,1,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,,2,2,,,,,1.2,Pepper,2019-11-15 +7,53.0,1,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,,,,,1.2,Onion,2019-01-18 +7,37.0,2,2,2,1,4,1,5,,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,0.8,Lettuce,2019-07-24 +7,43.0,2,2,1,2,,1,,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,,,,0.8,Green beans,2019-02-12 +7,22.0,1,1,,1,1,1,3,4,75.0,83.0,82.0,77.0,,83.0,2,2,,,,,1.2,Lettuce,2019-02-28 +7,47.0,2,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,0.8,Carrot, +7,27.0,1,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,,,,,1.2,Onion,2019-10-30 +7,43.0,1,2,2,1,4,4,1,,70.0,91.0,61.0,55.0,57.0,85.0,2,2,,,,,1.2,Onion,2019-04-26 +7,47.0,1,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,,,,,1.2,Onion,2019-09-25 +7,56.0,2,1,1,2,,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,,1,,1,0.8,Lettuce,2019-02-15 +7,51.0,2,2,,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,,2,,,0.8,Green beans,2019-12-13 +7,35.0,2,1,1,2,4,4,5,3,,61.0,66.0,88.0,58.0,,1,2,2,,,,0.8,Carrot,2019-05-02 +7,54.0,1,2,1,1,4,1,1,5,61.0,85.0,70.0,,50.0,80.0,1,2,2,,,,1.2,Green beans,2019-09-01 +7,42.0,2,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,,,,,0.8,Avocado,2019-05-02 +7,44.0,2,2,2,1,2,,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,,,,0.8,Green beans,2019-07-25 +7,60.0,2,1,2,2,4,1,3,,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,,1,,0.8,Avocado,2019-03-14 +7,41.0,2,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,,,,,0.8,Lettuce,2019-09-29 +7,36.0,2,1,2,,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,,,,0.8,Carrot,2019-03-12 +7,59.0,2,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,,2,,0.8,Onion,2019-10-09 +7,37.0,1,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,,,,2,,,,,1.2,Pepper,2019-11-03 +7,50.0,2,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,,,,0.8,Green beans,2019-09-08 +7,54.0,1,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,,,,,1.2,Lettuce,2019-04-21 +7,54.0,2,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,,,,,0.8,Lettuce,2019-01-05 +7,44.0,1,,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,,1,1.2,Avocado,2019-02-16 +7,27.0,2,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,,,,0.8,Avocado,2019-04-28 +7,50.0,2,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,,,,,0.8,Lettuce,2019-06-18 diff --git a/mocks/dataset-fixtures/veg.csv b/mocks/dataset-fixtures/veg.csv index 9503970d4..996796d6c 100644 --- a/mocks/dataset-fixtures/veg.csv +++ b/mocks/dataset-fixtures/veg.csv @@ -1,211 +1,211 @@ -wave,age,healthy_eater,enjoy_mr_savory,enjoy_mr_spicy,enjoy_mr_sweet,veg_enjoy_ca_healthy,veg_enjoy_ca_tasty,veg_enjoy_ca_filling,veg_enjoy_ca_env,ratings_numa_avocado,ratings_numa_brussel_sprout,ratings_numa_carrot,ratings_numa_daikon,ratings_numa_eggplant,ratings_numa_fennel,funnel_aware_mr_1,funnel_aware_mr_2,funnel_consider_mr_1,funnel_consider_mr_2,funnel_buy_mr_1,funnel_buy_mr_2,weight,last_vegetable,last_vegetable_date,funnel_aware_1,veg_environmental,enjoy_savory_food,rating_avocado,enjoy_spicy_food,veg_tasty,rating_fennel,veg_healthy,enjoy_sweet_food,funnel_consider_2,funnel_buy_1,funnel_aware_2,funnel_consider_1,rating_brussel_sprout,rating_eggplant,resp_id,rating_daikon,rating_carrot,veg_filling,funnel_buy_2 -1,25.0,2,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,No Data,95.0,1,1,-1,1,-1,1,0.8,Carrot,2019-01-04,1,3,1,69.0,2,5,95.0,3,2,1,-1,1,-1,53.0,No Data,1.0,69.0,88.0,1,1 -1,43.0,1,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,1.2,Avocado,2019-11-25,1,2,2,62.0,1,1,88.0,1,1,1,1,1,1,59.0,65.0,2.0,80.0,94.0,1,1 -1,20.0,1,1,2,1,1,1,1,-1,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,1.2,Pepper,2019-04-04,1,-1,1,61.0,2,1,85.0,1,1,1,1,1,1,57.0,71.0,3.0,75.0,87.0,1,1 -1,39.0,1,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,1.2,Onion,2019-03-29,1,3,2,68.0,1,1,90.0,1,1,1,1,1,1,32.0,69.0,No Data,51.0,94.0,3,1 -1,31.0,2,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,No Data,1,1,1,1,1,1,0.8,Green beans,2019-11-16,1,4,1,70.0,2,1,No Data,5,1,1,1,1,1,45.0,67.0,5.0,93.0,86.0,4,1 -1,50.0,2,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,-1,1,-1,0.8,Green beans,No Data,1,3,2,63.0,2,4,83.0,5,1,-1,1,1,1,34.0,78.0,6.0,78.0,63.0,3,-1 -1,33.0,2,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,0.8,Pepper,2019-01-03,1,1,2,69.0,2,3,87.0,2,1,1,1,1,1,31.0,71.0,7.0,66.0,78.0,5,1 -1,29.0,2,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,3,1,0.8,Carrot,2019-09-11,1,4,2,67.0,2,5,83.0,4,2,1,3,1,2,31.0,60.0,8.0,91.0,83.0,3,1 -1,52.0,1,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,1.2,Avocado,2019-02-08,1,2,1,75.0,1,1,92.0,2,1,1,1,1,1,46.0,56.0,9.0,89.0,91.0,3,1 -1,39.0,1,1,1,1,1,4,3,-1,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,1.2,Avocado,2019-12-22,1,-1,1,81.0,1,4,93.0,1,1,1,1,1,1,41.0,81.0,10.0,57.0,80.0,3,1 -1,62.0,1,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,3,1,3,1.2,Onion,2019-05-22,1,1,2,78.0,1,1,92.0,2,1,3,1,2,1,36.0,70.0,11.0,64.0,91.0,3,3 -1,39.0,1,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,1.2,Avocado,2019-04-16,1,2,2,69.0,1,1,86.0,2,1,1,1,1,1,35.0,58.0,12.0,58.0,91.0,1,1 -1,18.0,2,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,No Data,1,1,1,1,1,1,0.8,Avocado,2019-08-25,1,4,1,74.0,1,3,No Data,4,1,1,1,1,1,96.0,84.0,13.0,89.0,82.0,3,1 -1,56.0,2,1,1,1,1,5,5,-1,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,-1,0.8,Avocado,No Data,1,-1,1,70.0,1,5,84.0,1,1,1,1,1,1,94.0,67.0,14.0,78.0,83.0,5,-1 -1,64.0,2,2,1,1,4,4,3,1,64.0,34.0,65.0,No Data,84.0,94.0,1,1,1,1,2,1,0.8,Green beans,2019-08-21,1,1,2,64.0,1,4,94.0,4,1,1,2,1,1,34.0,84.0,15.0,No Data,65.0,3,1 -1,41.0,2,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,-1,1,-1,1,-1,1,0.8,Avocado,2019-03-23,-1,3,2,60.0,2,1,84.0,1,1,1,-1,1,-1,33.0,88.0,16.0,57.0,60.0,5,1 -1,No Data,1,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,No Data,1,1,1,1,-1,1,1.2,Carrot,No Data,1,1,1,66.0,2,4,No Data,1,1,1,-1,1,1,60.0,87.0,17.0,57.0,70.0,3,1 -1,65.0,1,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,1.2,Onion,2019-12-26,1,1,2,67.0,1,1,89.0,1,1,1,1,1,1,42.0,62.0,18.0,51.0,85.0,3,1 -1,34.0,2,1,1,1,4,4,3,1,67.0,No Data,82.0,60.0,65.0,85.0,1,1,1,-1,1,-1,0.8,No Data,2019-07-25,1,1,1,67.0,1,4,85.0,4,1,-1,1,1,1,No Data,65.0,19.0,60.0,82.0,3,-1 -1,64.0,2,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-08-20,1,1,2,64.0,1,1,91.0,4,2,3,3,2,2,45.0,69.0,20.0,88.0,82.0,3,3 -1,26.0,2,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,0.8,Onion,2019-02-15,1,3,2,71.0,1,1,84.0,5,2,1,1,1,1,41.0,60.0,21.0,69.0,83.0,5,1 -1,59.0,2,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-06-26,1,1,2,71.0,1,4,83.0,1,2,1,-1,1,-1,41.0,64.0,22.0,58.0,75.0,5,1 -1,45.0,2,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,0.8,Lettuce,2019-03-26,1,4,2,75.0,2,5,91.0,4,2,1,1,1,1,31.0,86.0,23.0,80.0,74.0,3,1 -1,60.0,1,2,2,1,4,1,3,3,73.0,48.0,No Data,53.0,80.0,84.0,1,2,1,3,2,3,1.2,Carrot,2019-08-02,1,3,2,73.0,2,1,84.0,4,1,3,2,2,1,48.0,80.0,24.0,53.0,No Data,3,3 -1,47.0,1,1,-1,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,1.2,Tomato,2019-12-27,1,3,1,64.0,-1,2,87.0,3,1,1,1,1,1,86.0,83.0,25.0,53.0,68.0,3,1 -1,23.0,1,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,1.2,Tomato,2019-11-03,1,4,1,80.0,2,4,91.0,4,1,1,1,1,1,64.0,62.0,26.0,76.0,70.0,3,1 -1,57.0,2,1,-1,2,4,1,5,-1,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,3,2,3,0.8,Avocado,2019-01-13,1,-1,1,79.0,-1,1,85.0,4,2,3,2,2,1,41.0,84.0,27.0,60.0,81.0,5,3 -1,59.0,1,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,-1,3,-1,3,1.2,Carrot,2019-06-11,1,2,2,62.0,1,1,81.0,4,1,3,-1,2,-1,36.0,84.0,28.0,93.0,63.0,3,3 -1,38.0,1,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,3,1,1.2,Onion,2019-09-08,1,3,1,73.0,2,1,81.0,4,2,1,3,1,2,71.0,86.0,29.0,55.0,86.0,3,1 -1,50.0,2,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,-1,1,-1,1,-1,0.8,Avocado,No Data,1,3,2,79.0,1,3,89.0,4,1,-1,1,-1,1,33.0,89.0,30.0,66.0,83.0,5,-1 -2,35.0,2,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,3,3,3,0.8,Green beans,2019-03-26,1,1,2,64.0,1,4,86.0,2,2,3,3,2,2,43.0,72.0,31.0,67.0,69.0,3,3 -2,29.0,1,1,2,-1,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,3,1,3,1.2,No Data,No Data,1,1,1,74.0,2,1,80.0,2,-1,3,1,2,1,95.0,58.0,32.0,58.0,67.0,4,3 -2,36.0,1,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,3,2,3,1.2,Avocado,2019-04-18,1,3,2,66.0,1,4,80.0,4,1,3,2,2,1,53.0,64.0,33.0,95.0,68.0,3,3 -2,21.0,2,1,1,1,2,4,-1,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,3,2,3,0.8,Pepper,2019-03-04,1,4,1,80.0,1,4,82.0,2,1,3,2,2,1,35.0,75.0,No Data,85.0,61.0,-1,3 -2,60.0,1,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,3,3,3,1.2,Lettuce,2019-01-25,1,4,2,71.0,1,3,87.0,4,1,3,3,2,2,90.0,63.0,35.0,75.0,90.0,3,3 -2,34.0,2,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,0.8,Pepper,2019-05-02,1,2,2,75.0,2,4,84.0,4,2,1,2,1,1,63.0,81.0,No Data,61.0,81.0,3,1 -2,22.0,2,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,No Data,81.0,2,1,3,1,3,1,0.8,Onion,2019-09-25,2,4,1,83.0,2,4,81.0,1,1,1,3,1,3,70.0,No Data,37.0,75.0,94.0,3,1 -2,48.0,2,-1,2,2,5,5,3,3,No Data,62.0,92.0,63.0,78.0,93.0,2,2,3,3,3,3,0.8,Green beans,2019-09-23,2,3,-1,No Data,2,5,93.0,5,2,3,3,2,3,62.0,78.0,38.0,63.0,92.0,3,3 -2,57.0,1,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,3,1,3,1.2,Onion,2019-11-04,1,2,2,66.0,1,1,88.0,1,1,3,1,2,1,85.0,52.0,39.0,82.0,90.0,1,3 -2,28.0,1,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,1.2,Lettuce,2019-10-19,1,4,1,72.0,1,3,86.0,5,1,1,1,1,1,85.0,65.0,40.0,77.0,76.0,3,1 -2,42.0,1,2,1,1,1,1,1,4,68.0,No Data,78.0,64.0,52.0,83.0,1,2,1,3,1,3,1.2,Pepper,2019-10-20,1,4,2,68.0,1,1,83.0,1,1,3,1,2,1,No Data,52.0,41.0,64.0,78.0,1,3 -2,31.0,1,2,2,1,-1,5,3,3,66.0,No Data,76.0,52.0,67.0,89.0,1,2,1,3,2,3,1.2,Carrot,2019-10-28,1,3,2,66.0,2,5,89.0,-1,1,3,2,2,1,No Data,67.0,42.0,52.0,76.0,3,3 -2,45.0,2,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,3,1,3,1,0.8,Green beans,2019-01-07,2,3,2,61.0,2,5,81.0,1,1,1,3,1,3,82.0,68.0,43.0,78.0,62.0,3,1 -2,35.0,2,2,2,1,2,-1,3,3,83.0,70.0,71.0,66.0,83.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-02-15,1,3,2,83.0,2,-1,No Data,2,1,3,3,2,2,70.0,83.0,44.0,66.0,71.0,3,3 -2,45.0,1,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,-1,1.2,Tomato,2019-10-22,1,4,1,65.0,1,1,89.0,1,1,1,2,1,1,68.0,88.0,45.0,70.0,66.0,1,-1 -2,No Data,2,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,No Data,91.0,1,1,1,1,2,1,0.8,Green beans,2019-05-11,1,4,2,76.0,1,4,91.0,4,1,1,2,1,1,63.0,No Data,46.0,85.0,85.0,3,1 -2,44.0,1,-1,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,3,3,3,1.2,Pepper,2019-10-20,1,2,-1,86.0,1,5,88.0,4,1,3,3,2,2,61.0,79.0,47.0,58.0,76.0,3,3 -2,60.0,2,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,0.8,Green beans,2019-04-09,1,3,2,83.0,1,1,87.0,1,1,1,1,1,1,84.0,61.0,48.0,78.0,68.0,5,1 -2,48.0,2,2,2,-1,-1,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,3,1,3,0.8,Carrot,2019-03-09,1,4,2,71.0,2,2,82.0,-1,-1,3,1,2,1,53.0,73.0,49.0,83.0,64.0,3,3 -2,36.0,1,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,3,2,3,1.2,Pepper,2019-02-20,1,2,2,84.0,1,1,85.0,4,1,3,2,2,1,30.0,55.0,No Data,68.0,61.0,3,3 -2,58.0,1,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,3,1,3,1,1.2,Pepper,2019-05-07,2,4,2,70.0,1,2,86.0,4,1,1,3,1,3,33.0,70.0,51.0,67.0,85.0,3,1 -2,No Data,1,1,1,1,-1,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,-1,1.2,Lettuce,2019-08-03,1,4,1,70.0,1,2,84.0,-1,1,1,1,1,1,44.0,83.0,52.0,71.0,82.0,3,-1 -2,54.0,2,1,1,1,5,1,4,3,68.0,38.0,70.0,No Data,67.0,83.0,1,2,1,3,2,3,0.8,Tomato,2019-12-28,1,3,1,68.0,1,1,83.0,5,1,3,2,2,1,38.0,67.0,53.0,No Data,70.0,4,3 -2,41.0,2,1,1,2,4,-1,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,3,3,3,0.8,Onion,2019-05-08,1,4,1,87.0,1,-1,81.0,4,2,3,3,2,2,93.0,87.0,54.0,84.0,81.0,5,3 -2,No Data,2,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,3,2,3,0.8,Green beans,2019-10-19,1,4,2,80.0,2,1,82.0,4,1,3,2,2,1,46.0,68.0,55.0,84.0,64.0,3,3 -2,41.0,2,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,3,1,0.8,Tomato,2019-04-06,1,4,2,72.0,1,2,93.0,3,1,1,3,1,2,31.0,52.0,56.0,72.0,89.0,5,1 -2,53.0,2,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,3,3,3,0.8,Avocado,2019-09-11,1,4,2,63.0,2,1,81.0,5,1,3,3,2,2,46.0,72.0,No Data,72.0,68.0,1,3 -2,No Data,1,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,3,1,3,1.2,Onion,2019-08-03,1,1,2,61.0,1,1,86.0,4,1,3,1,2,1,45.0,71.0,58.0,71.0,85.0,1,3 -2,24.0,2,1,-1,1,4,1,-1,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,3,3,3,0.8,Tomato,2019-10-01,1,4,1,62.0,-1,1,81.0,4,1,3,3,2,2,35.0,66.0,59.0,75.0,63.0,-1,3 -2,60.0,2,2,-1,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,3,2,3,0.8,Pepper,2019-10-12,1,4,2,65.0,-1,5,84.0,2,1,3,2,2,1,34.0,62.0,60.0,92.0,79.0,3,3 -3,60.0,2,1,1,1,3,5,-1,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,3,1,3,0.8,Tomato,2019-04-24,1,4,1,71.0,1,5,83.0,3,1,3,1,2,1,41.0,73.0,61.0,53.0,73.0,-1,3 -3,19.0,2,1,1,1,1,1,4,-1,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,3,-1,3,0.8,Lettuce,2019-08-14,1,-1,1,62.0,1,1,86.0,1,1,3,-1,2,1,54.0,76.0,62.0,65.0,71.0,4,3 -3,65.0,2,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,3,3,0.8,Green beans,2019-05-26,1,4,2,75.0,1,4,90.0,2,1,2,3,1,2,38.0,66.0,63.0,75.0,85.0,3,3 -3,53.0,2,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,3,3,3,3,0.8,No Data,2019-05-09,2,5,2,75.0,2,3,81.0,4,1,3,3,2,3,55.0,80.0,64.0,79.0,94.0,3,3 -3,No Data,1,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,3,1,1.2,Green beans,2019-06-28,1,3,1,65.0,2,3,89.0,4,1,1,3,1,2,55.0,87.0,65.0,75.0,75.0,1,1 -3,49.0,2,-1,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,3,3,3,3,0.8,No Data,2019-08-10,2,4,-1,77.0,1,4,90.0,4,1,3,3,2,3,55.0,89.0,66.0,91.0,84.0,3,3 -3,25.0,2,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,No Data,No Data,1,1,1,1,-1,1,0.8,No Data,2019-07-11,1,1,1,75.0,1,5,No Data,1,1,1,-1,1,1,81.0,No Data,67.0,85.0,73.0,3,1 -3,34.0,1,1,1,2,-1,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,3,2,3,1.2,Avocado,2019-07-14,1,4,1,84.0,1,4,84.0,-1,2,3,2,2,1,68.0,86.0,68.0,74.0,81.0,3,3 -3,21.0,1,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,3,1,3,1.2,Onion,2019-12-27,1,4,1,70.0,1,1,93.0,1,1,3,1,2,1,93.0,57.0,69.0,66.0,65.0,3,3 -3,64.0,1,1,1,1,-1,-1,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,3,3,3,1.2,No Data,2019-04-13,1,1,1,66.0,1,-1,93.0,-1,1,3,3,2,2,60.0,56.0,70.0,54.0,73.0,3,3 -3,36.0,2,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,No Data,86.0,2,-1,3,-1,3,-1,0.8,Onion,2019-01-15,2,4,2,82.0,2,5,86.0,4,1,-1,3,-1,3,78.0,No Data,71.0,87.0,65.0,5,-1 -3,35.0,2,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,3,1,3,1,0.8,Tomato,2019-09-02,2,4,2,63.0,1,4,88.0,4,1,1,3,1,3,84.0,90.0,72.0,90.0,65.0,3,1 -3,41.0,1,1,-1,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,3,2,3,1.2,Pepper,2019-02-02,1,3,1,76.0,-1,4,82.0,4,1,3,2,2,1,65.0,67.0,73.0,75.0,93.0,1,3 -3,19.0,2,-1,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,3,3,3,0.8,Green beans,2019-12-31,1,4,-1,64.0,1,3,90.0,4,1,3,3,2,2,45.0,88.0,74.0,89.0,71.0,4,3 -3,63.0,2,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,-1,3,-1,3,-1,0.8,Carrot,2019-12-31,2,1,1,70.0,1,4,90.0,4,1,-1,3,-1,3,40.0,80.0,75.0,75.0,70.0,3,-1 -3,64.0,2,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,0.8,Tomato,2019-02-21,1,2,2,63.0,1,1,81.0,4,1,1,2,1,1,74.0,50.0,76.0,86.0,69.0,3,2 -3,45.0,2,1,1,1,-1,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,3,3,3,0.8,Tomato,2019-04-25,1,3,1,61.0,1,2,91.0,-1,1,3,3,2,2,34.0,79.0,77.0,85.0,62.0,5,3 -3,55.0,1,-1,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,3,1,3,1.2,Lettuce,2019-11-06,1,4,-1,77.0,1,3,91.0,4,1,3,1,2,1,66.0,58.0,78.0,85.0,80.0,4,3 -3,33.0,2,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-05-09,1,4,2,82.0,1,2,92.0,2,1,3,3,2,2,49.0,59.0,79.0,80.0,91.0,5,3 -3,28.0,1,1,-1,1,3,5,3,5,66.0,48.0,No Data,80.0,65.0,92.0,1,2,2,3,3,3,1.2,Onion,2019-07-09,1,5,1,66.0,-1,5,92.0,3,1,3,3,2,2,48.0,65.0,80.0,80.0,No Data,3,3 -3,34.0,2,2,2,1,1,4,-1,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,3,1,3,0.8,Lettuce,2019-04-07,1,3,2,67.0,2,4,84.0,1,1,3,1,2,1,83.0,55.0,No Data,54.0,62.0,-1,3 -3,39.0,1,2,1,1,1,1,3,4,67.0,No Data,86.0,72.0,68.0,87.0,1,1,2,1,3,-1,1.2,Tomato,2019-07-27,1,4,2,67.0,1,1,87.0,1,1,1,3,1,2,No Data,68.0,82.0,72.0,86.0,3,-1 -3,57.0,1,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,1.2,No Data,2019-07-10,1,4,1,62.0,1,1,87.0,2,1,1,2,1,1,86.0,68.0,83.0,77.0,70.0,3,1 -3,48.0,2,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,3,3,3,0.8,Avocado,2019-11-19,1,2,2,78.0,1,4,88.0,4,1,3,3,2,2,50.0,59.0,84.0,52.0,79.0,3,3 -3,No Data,1,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,3,1,3,1,1.2,Avocado,2019-12-19,2,4,1,66.0,1,4,89.0,4,1,1,3,1,3,64.0,69.0,85.0,83.0,89.0,3,1 -3,18.0,1,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,1.2,Carrot,2019-12-16,1,4,1,75.0,2,3,91.0,4,1,1,1,1,1,73.0,84.0,86.0,64.0,82.0,3,1 -3,25.0,2,2,1,1,4,-1,3,4,No Data,45.0,No Data,58.0,85.0,86.0,1,2,1,3,2,3,0.8,Carrot,2019-01-04,1,4,2,No Data,1,-1,86.0,4,1,3,2,2,1,45.0,85.0,87.0,58.0,No Data,3,3 -3,40.0,2,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,3,0.8,Avocado,2019-11-14,1,4,2,83.0,1,5,86.0,4,1,2,1,1,1,74.0,56.0,88.0,80.0,84.0,3,3 -3,43.0,2,2,1,1,1,5,5,-1,74.0,68.0,68.0,84.0,70.0,85.0,2,2,3,3,3,3,0.8,Pepper,2019-07-02,2,-1,2,74.0,1,5,85.0,1,1,3,3,2,3,68.0,70.0,89.0,84.0,68.0,5,3 -3,22.0,2,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,3,3,3,3,0.8,Onion,2019-12-13,2,5,1,78.0,1,5,88.0,4,1,3,3,2,3,62.0,86.0,90.0,68.0,63.0,5,3 -4,48.0,1,2,1,1,-1,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,3,3,3,1.2,Pepper,2019-12-04,1,4,2,75.0,1,2,85.0,-1,1,3,3,2,2,38.0,68.0,91.0,75.0,94.0,3,3 -4,26.0,1,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,3,1,3,1,1.2,Pepper,2019-12-28,2,4,1,84.0,1,5,91.0,4,1,1,3,1,3,60.0,73.0,92.0,86.0,91.0,3,1 -4,37.0,1,2,1,1,1,4,3,-1,62.0,90.0,78.0,60.0,88.0,87.0,2,-1,3,-1,3,-1,1.2,Onion,2019-12-26,2,-1,2,62.0,1,4,87.0,1,1,-1,3,-1,3,90.0,88.0,93.0,60.0,78.0,3,-1 -4,28.0,1,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,-1,2,1.2,Tomato,2019-07-25,1,4,1,74.0,1,1,90.0,1,1,1,-1,1,1,50.0,72.0,94.0,68.0,72.0,3,2 -4,56.0,1,-1,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-07-29,1,1,-1,61.0,1,1,84.0,4,1,1,3,1,2,51.0,55.0,95.0,87.0,75.0,1,1 -4,18.0,2,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,0.8,Lettuce,2019-05-26,1,4,2,65.0,2,4,82.0,5,2,1,1,1,1,80.0,62.0,96.0,64.0,66.0,3,1 -4,47.0,2,2,1,1,5,4,3,4,70.0,49.0,67.0,No Data,53.0,85.0,1,-1,2,-1,3,-1,0.8,Tomato,2019-09-14,1,4,2,70.0,1,4,85.0,5,1,-1,3,-1,2,49.0,53.0,97.0,No Data,67.0,3,-1 -4,37.0,2,2,-1,1,5,1,-1,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,3,2,3,0.8,Avocado,2019-06-20,1,1,2,64.0,-1,1,83.0,5,1,3,2,2,1,53.0,65.0,98.0,59.0,76.0,-1,3 -4,22.0,1,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,-1,-1,-1,1.2,Tomato,2019-07-08,1,4,1,65.0,2,5,90.0,4,1,-1,-1,1,1,38.0,84.0,No Data,53.0,88.0,3,-1 -4,24.0,2,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,3,3,3,0.8,Green beans,2019-02-06,1,5,2,63.0,1,5,87.0,4,2,3,3,2,2,98.0,76.0,100.0,65.0,62.0,3,3 -4,54.0,2,2,2,1,4,4,3,-1,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,3,2,3,0.8,Lettuce,2019-11-24,1,-1,2,69.0,2,4,92.0,4,1,3,2,2,1,81.0,71.0,101.0,53.0,75.0,3,3 -4,34.0,2,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,-1,1,-1,2,-1,0.8,Carrot,2019-03-07,1,3,1,85.0,2,4,87.0,5,1,-1,2,-1,1,57.0,52.0,102.0,63.0,77.0,5,-1 -4,48.0,2,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,3,3,3,3,0.8,Green beans,2019-12-11,2,4,2,62.0,2,5,87.0,4,1,3,3,2,3,84.0,78.0,103.0,54.0,70.0,3,3 -4,No Data,-1,1,1,1,1,-1,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,3,1,0.8,Tomato,2019-07-04,1,4,1,84.0,1,-1,94.0,1,1,1,3,1,2,57.0,52.0,104.0,73.0,71.0,3,1 -4,21.0,2,2,-1,1,4,4,3,3,69.0,54.0,70.0,No Data,84.0,92.0,1,1,1,2,1,3,0.8,No Data,2019-11-27,1,3,2,69.0,-1,4,92.0,4,1,2,1,1,1,54.0,84.0,105.0,No Data,70.0,3,3 -4,42.0,1,2,2,1,-1,-1,3,3,68.0,55.0,67.0,No Data,82.0,84.0,1,1,2,1,3,1,1.2,Lettuce,2019-11-16,1,3,2,68.0,2,-1,84.0,-1,1,1,3,1,2,55.0,82.0,106.0,No Data,67.0,3,1 -4,32.0,2,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,3,1,3,1,0.8,Tomato,2019-08-18,2,4,2,63.0,1,3,94.0,3,1,1,3,1,3,56.0,73.0,107.0,79.0,75.0,4,1 -4,30.0,2,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,3,2,3,0.8,Lettuce,2019-01-17,1,4,2,72.0,1,4,94.0,4,1,3,2,2,1,89.0,86.0,108.0,90.0,78.0,3,3 -4,46.0,2,1,2,2,3,5,3,5,64.0,72.0,61.0,No Data,61.0,No Data,1,1,-1,1,-1,1,0.8,Avocado,2019-02-25,1,5,1,64.0,2,5,No Data,3,2,1,-1,1,-1,72.0,61.0,109.0,No Data,61.0,3,1 -4,24.0,2,-1,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,-1,3,-1,0.8,Avocado,2019-03-27,1,3,-1,89.0,1,4,85.0,4,1,-1,3,1,2,32.0,78.0,110.0,86.0,76.0,5,-1 -4,53.0,1,1,1,2,2,4,5,4,71.0,73.0,No Data,87.0,83.0,83.0,1,2,2,3,3,3,1.2,Onion,2019-10-06,1,4,1,71.0,1,4,83.0,2,2,3,3,2,2,73.0,83.0,111.0,87.0,No Data,5,3 -4,27.0,1,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,-1,2,-1,3,-1,3,1.2,Lettuce,2019-09-24,-1,1,2,76.0,2,2,85.0,2,1,3,-1,2,-1,85.0,60.0,112.0,54.0,62.0,4,3 -4,65.0,2,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,3,1,3,0.8,Pepper,2019-01-14,1,4,2,65.0,1,4,87.0,1,1,3,1,2,1,44.0,74.0,113.0,79.0,65.0,5,3 -4,36.0,1,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,3,1,3,1,1.2,Carrot,2019-12-16,2,3,1,68.0,1,2,88.0,2,1,1,3,1,3,57.0,72.0,114.0,76.0,71.0,3,1 -4,48.0,-1,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,No Data,1,2,2,3,3,3,1.2,Avocado,2019-12-23,1,4,2,63.0,1,2,No Data,1,1,3,3,2,2,59.0,72.0,115.0,80.0,65.0,3,3 -4,47.0,1,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,3,1.2,Onion,2019-12-19,1,4,2,77.0,2,4,90.0,4,1,2,2,1,1,87.0,75.0,116.0,66.0,66.0,3,3 -4,30.0,2,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,3,1,3,0.8,Green beans,No Data,1,3,1,70.0,2,1,81.0,4,1,3,1,2,1,98.0,56.0,117.0,81.0,63.0,3,3 -4,20.0,2,-1,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,3,2,0.8,Lettuce,2019-12-05,1,2,-1,89.0,1,1,81.0,4,1,1,3,1,2,45.0,71.0,118.0,81.0,81.0,3,2 -4,21.0,1,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,3,3,3,3,1.2,Green beans,2019-07-31,2,2,1,88.0,2,4,93.0,1,1,3,3,2,3,74.0,87.0,119.0,59.0,80.0,3,3 -4,34.0,1,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,3,1,3,2,1.2,Onion,2019-09-02,2,5,1,64.0,2,5,91.0,1,1,1,3,1,3,39.0,72.0,120.0,54.0,74.0,3,2 -5,23.0,2,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,3,3,3,0.8,Lettuce,2019-11-06,1,4,2,71.0,2,2,89.0,4,1,3,3,2,2,55.0,72.0,121.0,65.0,76.0,3,3 -5,55.0,2,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,3,3,3,3,0.8,Pepper,No Data,2,4,2,70.0,2,2,86.0,1,1,3,3,2,3,81.0,58.0,122.0,72.0,88.0,3,3 -5,41.0,2,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,0.8,Onion,2019-06-24,1,2,2,70.0,2,1,83.0,2,1,1,1,1,1,87.0,74.0,123.0,72.0,70.0,5,1 -5,64.0,2,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,3,3,3,3,0.8,Lettuce,2019-03-14,2,5,1,64.0,1,1,82.0,4,2,3,3,2,3,78.0,79.0,124.0,94.0,77.0,5,3 -5,27.0,2,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,3,2,3,0.8,Pepper,No Data,1,4,1,74.0,1,4,91.0,1,1,3,2,2,1,48.0,84.0,125.0,87.0,67.0,3,3 -5,44.0,1,2,-1,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,3,1,1.2,Lettuce,2019-10-27,1,4,2,74.0,-1,1,88.0,3,1,1,3,1,2,76.0,63.0,126.0,85.0,94.0,3,1 -5,41.0,1,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,No Data,80.0,1,1,1,1,1,2,1.2,Avocado,2019-03-29,1,3,2,76.0,2,1,80.0,2,1,1,1,1,1,43.0,No Data,127.0,51.0,91.0,3,2 -5,54.0,2,2,1,1,4,2,5,4,No Data,68.0,60.0,84.0,54.0,92.0,1,2,2,3,3,3,0.8,Green beans,2019-11-26,1,4,2,No Data,1,2,92.0,4,1,3,3,2,2,68.0,54.0,128.0,84.0,60.0,5,3 -5,59.0,2,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,3,2,3,0.8,Lettuce,2019-12-04,1,4,1,66.0,1,1,85.0,4,1,3,2,2,1,58.0,64.0,129.0,61.0,63.0,1,3 -5,34.0,1,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,3,1,3,1,1.2,Tomato,2019-05-05,2,1,1,65.0,1,5,93.0,4,1,1,3,1,3,65.0,66.0,130.0,77.0,84.0,3,1 -5,No Data,2,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,0.8,Onion,2019-02-28,1,4,2,75.0,1,5,81.0,4,1,1,2,1,1,54.0,74.0,131.0,67.0,71.0,3,2 -5,28.0,1,1,2,-1,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,3,1,3,1,1.2,Carrot,2019-06-15,2,5,1,79.0,2,1,86.0,1,-1,1,3,1,3,83.0,65.0,No Data,58.0,81.0,4,1 -5,18.0,-1,1,-1,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,-1,2,-1,3,-1,1.2,Carrot,2019-02-12,1,3,1,80.0,-1,1,82.0,4,2,-1,3,-1,2,36.0,86.0,133.0,80.0,61.0,5,-1 -5,26.0,1,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,3,1.2,Green beans,2019-10-30,1,4,1,76.0,1,4,91.0,4,1,2,2,1,1,60.0,52.0,134.0,71.0,92.0,5,3 -5,62.0,2,1,1,1,4,1,-1,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,3,3,3,3,0.8,Carrot,2019-03-29,2,4,1,80.0,1,1,80.0,4,1,3,3,2,3,86.0,88.0,135.0,79.0,68.0,-1,3 -5,64.0,2,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-07-02,1,4,2,81.0,1,1,91.0,5,2,3,3,2,2,84.0,64.0,136.0,64.0,68.0,5,3 -5,22.0,1,1,1,1,4,4,1,3,74.0,No Data,77.0,63.0,68.0,87.0,1,2,2,3,3,3,1.2,Pepper,2019-05-28,1,3,1,74.0,1,4,87.0,4,1,3,3,2,2,No Data,68.0,137.0,63.0,77.0,1,3 -5,46.0,1,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,3,3,3,3,1.2,Carrot,2019-10-06,2,4,1,83.0,1,3,88.0,2,1,3,3,2,3,57.0,74.0,138.0,56.0,94.0,3,3 -5,45.0,2,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,0.8,Avocado,2019-12-21,1,4,2,88.0,2,1,80.0,4,1,1,1,1,1,76.0,64.0,139.0,57.0,92.0,3,2 -5,34.0,2,1,2,2,-1,3,3,5,79.0,51.0,No Data,81.0,78.0,89.0,1,-1,2,-1,3,-1,0.8,Onion,2019-12-10,1,5,1,79.0,2,3,89.0,-1,2,-1,3,-1,2,51.0,78.0,140.0,81.0,No Data,3,-1 -5,33.0,1,2,2,1,-1,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,3,3,3,1.2,Carrot,2019-06-03,1,4,2,64.0,2,2,82.0,-1,1,3,3,2,2,63.0,54.0,141.0,55.0,90.0,3,3 -5,45.0,1,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,3,3,3,1.2,Avocado,2019-07-14,1,1,1,65.0,2,4,85.0,5,1,3,3,2,2,58.0,69.0,No Data,61.0,77.0,3,3 -5,24.0,2,-1,2,1,4,5,4,3,86.0,50.0,73.0,No Data,52.0,90.0,1,1,2,1,3,2,0.8,No Data,2019-07-02,1,3,-1,86.0,2,5,90.0,4,1,1,3,1,2,50.0,52.0,143.0,No Data,73.0,4,2 -5,28.0,1,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,3,3,3,3,1.2,Green beans,2019-11-11,2,2,1,70.0,1,3,92.0,4,1,3,3,2,3,39.0,79.0,144.0,65.0,66.0,4,3 -5,19.0,-1,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,3,2,0.8,Lettuce,2019-07-17,1,4,1,63.0,1,4,88.0,2,2,1,3,1,2,93.0,58.0,145.0,91.0,70.0,5,2 -5,60.0,2,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,3,2,3,0.8,Lettuce,2019-07-17,1,3,2,77.0,1,2,83.0,5,1,3,2,2,1,74.0,81.0,146.0,73.0,86.0,5,3 -5,44.0,1,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,3,3,3,1.2,Lettuce,2019-07-26,1,4,2,87.0,1,3,95.0,1,1,3,3,2,2,90.0,75.0,147.0,78.0,66.0,3,3 -5,59.0,2,2,1,1,5,4,4,-1,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,3,3,3,0.8,Carrot,2019-02-16,1,-1,2,69.0,1,4,86.0,5,1,3,3,2,2,71.0,64.0,148.0,91.0,86.0,4,3 -5,18.0,2,-1,-1,1,-1,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,-1,2,-1,3,-1,0.8,Avocado,2019-12-10,1,4,-1,61.0,-1,4,86.0,-1,1,-1,3,-1,2,86.0,61.0,149.0,58.0,65.0,3,-1 -5,50.0,2,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,No Data,1,2,2,3,3,3,0.8,No Data,2019-04-24,1,2,1,71.0,1,3,No Data,4,1,3,3,2,2,94.0,55.0,150.0,76.0,63.0,3,3 -6,61.0,2,2,2,2,4,1,1,3,No Data,57.0,87.0,87.0,65.0,80.0,1,2,1,3,1,3,0.8,Green beans,2019-09-21,1,3,2,No Data,2,1,80.0,4,2,3,1,2,1,57.0,65.0,151.0,87.0,87.0,1,3 -6,31.0,2,1,2,-1,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,3,3,3,0.8,Pepper,2019-04-13,1,4,1,86.0,2,1,85.0,2,-1,3,3,2,2,92.0,59.0,152.0,89.0,71.0,5,3 -6,57.0,-1,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,3,-1,3,0.8,Tomato,2019-09-27,1,4,2,75.0,1,3,81.0,4,1,3,-1,2,1,77.0,66.0,153.0,82.0,75.0,5,3 -6,51.0,1,1,2,1,-1,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-03-29,2,4,1,75.0,2,4,92.0,-1,1,3,3,2,3,99.0,63.0,154.0,58.0,67.0,3,3 -6,64.0,1,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,No Data,1,1,1,1,2,1,1.2,Green beans,2019-04-05,1,4,2,67.0,1,1,No Data,4,1,1,2,1,1,93.0,70.0,155.0,68.0,84.0,3,1 -6,47.0,1,2,-1,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,3,1,1.2,Green beans,2019-10-22,1,4,2,84.0,-1,1,83.0,3,1,1,3,1,2,63.0,53.0,156.0,59.0,85.0,3,1 -6,64.0,2,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,No Data,93.0,1,1,1,1,1,1,0.8,Lettuce,2019-10-04,1,4,1,77.0,1,1,93.0,3,1,1,1,1,1,99.0,No Data,157.0,54.0,78.0,5,1 -6,37.0,2,2,1,1,1,4,5,5,63.0,No Data,74.0,92.0,71.0,88.0,1,2,1,3,2,3,0.8,Pepper,2019-05-29,1,5,2,63.0,1,4,88.0,1,1,3,2,2,1,No Data,71.0,158.0,92.0,74.0,5,3 -6,41.0,2,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,3,3,3,3,0.8,Tomato,2019-07-13,2,2,2,83.0,1,4,93.0,4,1,3,3,2,3,60.0,63.0,159.0,91.0,88.0,5,3 -6,53.0,2,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,3,3,3,0.8,Green beans,2019-05-14,1,4,2,61.0,1,3,81.0,4,1,3,3,2,2,42.0,61.0,160.0,80.0,63.0,3,3 -6,20.0,2,1,1,1,4,2,3,4,89.0,85.0,No Data,55.0,60.0,90.0,2,1,3,1,3,1,0.8,Tomato,2019-03-27,2,4,1,89.0,1,2,90.0,4,1,1,3,1,3,85.0,60.0,161.0,55.0,No Data,3,1 -6,53.0,1,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-09-24,2,4,1,65.0,1,1,83.0,4,1,3,3,2,3,72.0,73.0,162.0,88.0,64.0,3,3 -6,61.0,2,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,No Data,87.0,2,1,3,1,3,1,0.8,Avocado,2019-08-26,2,2,2,88.0,1,1,87.0,2,1,1,3,1,3,40.0,No Data,163.0,52.0,83.0,5,1 -6,21.0,2,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,No Data,No Data,2,2,3,3,3,3,0.8,Green beans,2019-03-18,2,3,1,82.0,1,5,No Data,1,1,3,3,2,3,97.0,No Data,164.0,81.0,85.0,5,3 -6,53.0,2,1,2,1,4,2,3,4,79.0,67.0,86.0,No Data,73.0,89.0,-1,1,-1,1,-1,1,0.8,Green beans,2019-09-17,-1,4,1,79.0,2,2,89.0,4,1,1,-1,1,-1,67.0,73.0,165.0,No Data,86.0,3,1 -6,35.0,2,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,3,3,3,0.8,Carrot,2019-12-09,1,4,1,63.0,1,1,91.0,4,2,3,3,2,2,66.0,58.0,166.0,62.0,60.0,5,3 -6,No Data,1,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,3,-1,1.2,Green beans,2019-11-19,1,4,2,60.0,1,2,83.0,1,1,1,3,1,2,88.0,60.0,167.0,69.0,73.0,1,-1 -6,No Data,1,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,3,3,3,1.2,Pepper,2019-02-19,1,4,1,61.0,1,1,83.0,1,1,3,3,2,2,54.0,53.0,168.0,63.0,73.0,5,3 -6,40.0,2,1,1,2,-1,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,3,3,3,3,0.8,Carrot,No Data,2,4,1,62.0,1,4,86.0,-1,2,3,3,2,3,89.0,83.0,No Data,85.0,72.0,3,3 -6,64.0,1,2,1,1,1,1,1,4,62.0,71.0,83.0,No Data,51.0,90.0,1,2,2,3,3,3,1.2,Tomato,2019-08-30,1,4,2,62.0,1,1,90.0,1,1,3,3,2,2,71.0,51.0,170.0,No Data,83.0,1,3 -6,59.0,1,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,3,1,1.2,Onion,2019-08-05,1,4,2,76.0,1,1,82.0,1,1,1,3,1,2,93.0,59.0,171.0,53.0,87.0,3,1 -6,48.0,2,2,1,1,3,5,-1,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,3,3,3,3,0.8,Tomato,2019-09-28,2,5,2,71.0,1,5,88.0,3,1,3,3,2,3,74.0,55.0,172.0,68.0,69.0,-1,3 -6,51.0,2,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,-1,1,-1,1,-1,0.8,Pepper,2019-04-20,1,1,2,69.0,1,1,88.0,4,1,-1,1,-1,1,99.0,55.0,No Data,54.0,78.0,3,-1 -6,38.0,2,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,3,3,3,3,0.8,Onion,2019-07-24,2,3,1,79.0,2,4,91.0,5,1,3,3,2,3,79.0,76.0,174.0,88.0,92.0,3,3 -6,60.0,1,1,1,1,-1,1,5,4,82.0,56.0,75.0,65.0,53.0,No Data,2,1,3,1,3,2,1.2,Onion,2019-01-26,2,4,1,82.0,1,1,No Data,-1,1,1,3,1,3,56.0,53.0,175.0,65.0,75.0,5,2 -6,29.0,2,1,2,1,-1,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,-1,-1,-1,-1,-1,-1,0.8,Lettuce,2019-08-06,-1,3,1,66.0,2,4,84.0,-1,1,-1,-1,-1,-1,43.0,52.0,176.0,93.0,74.0,3,-1 -6,33.0,1,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,-1,3,1.2,Carrot,2019-06-18,1,5,2,69.0,2,4,94.0,4,1,2,-1,1,1,46.0,70.0,177.0,51.0,60.0,3,3 -6,37.0,2,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-22,2,3,2,86.0,2,3,93.0,5,1,3,3,2,3,76.0,69.0,178.0,61.0,77.0,3,3 -6,37.0,1,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,3,3,3,1.2,Onion,2019-08-12,1,4,2,80.0,1,1,85.0,4,1,3,3,2,2,62.0,63.0,179.0,93.0,67.0,3,3 -6,57.0,2,-1,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,3,1,3,0.8,Carrot,2019-05-31,1,4,-1,74.0,2,4,90.0,4,2,3,1,2,1,46.0,73.0,180.0,83.0,63.0,3,3 -7,29.0,2,-1,2,-1,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,3,1,3,1,0.8,Avocado,2019-08-31,2,4,-1,88.0,2,5,90.0,3,-1,1,3,1,3,93.0,61.0,181.0,79.0,89.0,3,1 -7,48.0,2,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,3,2,0.8,No Data,2019-10-09,1,4,2,64.0,2,4,80.0,2,1,1,3,1,2,86.0,59.0,182.0,67.0,64.0,5,2 -7,47.0,1,2,1,1,1,1,4,3,82.0,40.0,No Data,84.0,67.0,81.0,1,2,2,3,3,3,1.2,Pepper,2019-01-29,1,3,2,82.0,1,1,81.0,1,1,3,3,2,2,40.0,67.0,183.0,84.0,No Data,4,3 -7,25.0,1,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,3,1,3,1,1.2,Pepper,2019-06-30,2,3,1,78.0,1,3,93.0,4,1,1,3,1,3,45.0,75.0,184.0,90.0,83.0,3,1 -7,50.0,1,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,No Data,2,2,3,3,3,3,1.2,Pepper,2019-11-15,2,4,1,90.0,1,1,No Data,1,1,3,3,2,3,97.0,60.0,185.0,80.0,64.0,3,3 -7,53.0,1,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,3,3,3,3,1.2,Onion,2019-01-18,2,4,2,76.0,1,1,81.0,4,1,3,3,2,3,69.0,51.0,186.0,76.0,84.0,3,3 -7,37.0,2,2,2,1,4,1,5,-1,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,0.8,Lettuce,2019-07-24,1,-1,2,89.0,2,1,83.0,4,1,1,2,1,1,91.0,51.0,187.0,50.0,89.0,5,1 -7,43.0,2,2,1,2,-1,1,-1,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,3,3,3,0.8,Green beans,2019-02-12,1,4,2,65.0,1,1,85.0,-1,2,3,3,2,2,88.0,54.0,No Data,75.0,61.0,-1,3 -7,22.0,1,1,-1,1,1,1,3,4,75.0,83.0,82.0,77.0,No Data,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-02-28,2,4,1,75.0,-1,1,83.0,1,1,3,3,2,3,83.0,No Data,189.0,77.0,82.0,3,3 -7,47.0,2,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,0.8,Carrot,No Data,1,3,2,63.0,1,5,94.0,1,1,1,2,1,1,71.0,86.0,190.0,64.0,62.0,3,1 -7,27.0,1,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,3,3,3,3,1.2,Onion,2019-10-30,2,4,2,64.0,1,5,92.0,4,1,3,3,2,3,42.0,53.0,191.0,86.0,87.0,3,3 -7,43.0,1,2,2,1,4,4,1,-1,70.0,91.0,61.0,55.0,57.0,85.0,2,2,3,3,3,3,1.2,Onion,2019-04-26,2,-1,2,70.0,2,4,85.0,4,1,3,3,2,3,91.0,57.0,192.0,55.0,61.0,1,3 -7,47.0,1,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,3,3,3,3,1.2,Onion,2019-09-25,2,3,2,74.0,1,1,86.0,4,1,3,3,2,3,33.0,52.0,193.0,82.0,67.0,4,3 -7,56.0,2,1,1,2,-1,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,-1,1,-1,1,0.8,Lettuce,2019-02-15,1,4,1,63.0,1,1,84.0,-1,2,1,-1,1,-1,33.0,52.0,194.0,86.0,66.0,3,1 -7,51.0,2,2,-1,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,3,2,3,3,0.8,Green beans,2019-12-13,2,5,2,83.0,-1,1,84.0,4,1,2,3,1,3,55.0,50.0,195.0,88.0,75.0,3,3 -7,35.0,2,1,1,2,4,4,5,3,No Data,61.0,66.0,88.0,58.0,No Data,1,2,2,3,3,3,0.8,Carrot,2019-05-02,1,3,1,No Data,1,4,No Data,4,2,3,3,2,2,61.0,58.0,196.0,88.0,66.0,5,3 -7,54.0,1,2,1,1,4,1,1,5,61.0,85.0,70.0,No Data,50.0,80.0,1,2,2,3,3,3,1.2,Green beans,2019-09-01,1,5,2,61.0,1,1,80.0,4,1,3,3,2,2,85.0,50.0,197.0,No Data,70.0,1,3 -7,42.0,2,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,-1,3,-1,3,0.8,Avocado,2019-05-02,1,4,2,83.0,1,2,86.0,4,2,3,-1,2,-1,46.0,51.0,198.0,82.0,79.0,5,3 -7,44.0,2,2,2,1,2,-1,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,3,3,3,0.8,Green beans,2019-07-25,1,4,2,73.0,2,-1,83.0,2,1,3,3,2,2,87.0,58.0,199.0,52.0,73.0,3,3 -7,60.0,2,1,2,2,4,1,3,-1,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,3,1,3,0.8,Avocado,2019-03-14,1,-1,1,63.0,2,1,83.0,4,2,3,1,2,1,31.0,57.0,200.0,63.0,71.0,3,3 -7,41.0,2,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,3,3,3,3,0.8,Lettuce,2019-09-29,2,4,1,88.0,1,1,83.0,2,1,3,3,2,3,56.0,56.0,201.0,80.0,76.0,3,3 -7,36.0,2,1,2,-1,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,3,3,3,0.8,Carrot,2019-03-12,1,4,1,83.0,2,4,92.0,4,-1,3,3,2,2,57.0,62.0,202.0,80.0,77.0,3,3 -7,59.0,2,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,3,2,3,0.8,Onion,2019-10-09,1,3,2,76.0,1,1,85.0,2,2,3,2,2,1,68.0,56.0,203.0,81.0,67.0,5,3 -7,37.0,1,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,No Data,No Data,-1,2,-1,3,-1,3,1.2,Pepper,2019-11-03,-1,4,1,81.0,1,3,No Data,2,1,3,-1,2,-1,48.0,No Data,204.0,84.0,86.0,4,3 -7,50.0,2,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,3,3,3,0.8,Green beans,2019-09-08,1,3,2,77.0,1,5,91.0,3,1,3,3,2,2,88.0,54.0,205.0,67.0,75.0,5,3 -7,54.0,1,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,3,3,3,3,1.2,Lettuce,2019-04-21,2,3,2,66.0,1,4,83.0,1,1,3,3,2,3,93.0,64.0,206.0,69.0,92.0,3,3 -7,54.0,2,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,3,3,3,3,0.8,Lettuce,2019-01-05,2,5,2,74.0,1,4,85.0,2,1,3,3,2,3,46.0,71.0,207.0,88.0,79.0,5,3 -7,44.0,1,-1,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,3,1,1.2,Avocado,2019-02-16,1,4,-1,72.0,2,2,81.0,1,1,1,3,1,2,42.0,50.0,208.0,69.0,80.0,3,1 -7,27.0,2,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,3,3,3,0.8,Avocado,2019-04-28,1,3,1,65.0,2,5,86.0,2,2,3,3,2,2,42.0,65.0,209.0,91.0,78.0,5,3 -7,50.0,2,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,3,3,3,3,0.8,Lettuce,2019-06-18,2,4,2,70.0,2,5,90.0,5,1,3,3,2,3,87.0,72.0,210.0,64.0,72.0,4,3 +wave,age,healthy_eater,enjoy_mr[enjoy_mr_savory],enjoy_mr[enjoy_mr_spicy],enjoy_mr[enjoy_mr_sweet],veg_enjoy_ca[veg_enjoy_ca_healthy],veg_enjoy_ca[veg_enjoy_ca_tasty],veg_enjoy_ca[veg_enjoy_ca_filling],veg_enjoy_ca[veg_enjoy_ca_env],ratings_numa[ratings_numa_avocado],ratings_numa[ratings_numa_brussel_sprout],ratings_numa[ratings_numa_carrot],ratings_numa[ratings_numa_daikon],ratings_numa[ratings_numa_eggplant],ratings_numa[ratings_numa_fennel],funnel_aware_mr[funnel_aware_mr_1],funnel_aware_mr[funnel_aware_mr_2],funnel_consider_mr[funnel_consider_mr_1],funnel_consider_mr[funnel_consider_mr_2],funnel_consider_mr[funnel_buy_mr_1],funnel_consider_mr[funnel_buy_mr_2],weight,last_vegetable,last_vegetable_date,rating_daikon,funnel_aware_1,funnel_consider_1,funnel_buy_2,veg_environmental,funnel_aware_2,funnel_consider_2,enjoy_savory_food,resp_id,veg_tasty,rating_fennel,rating_carrot,enjoy_sweet_food,veg_filling,rating_brussel_sprout,rating_eggplant,funnel_buy_1,enjoy_spicy_food,rating_avocado,veg_healthy +1,25.0,2,1,2,2,3,5,1,3,69.0,53.0,88.0,69.0,,95.0,1,1,,1,,1,0.8,Carrot,2019-01-04,3,88.0,5,,,95.0,2,53.0,1,1,69.0,1,1,,1,1.0,2,69.0,3,1 +1,43.0,1,2,1,1,1,1,1,2,62.0,59.0,94.0,80.0,65.0,88.0,1,1,1,1,1,1,1.2,Avocado,2019-11-25,2,94.0,1,65.0,1,88.0,1,59.0,1,1,62.0,1,2,1,1,2.0,1,80.0,1,1 +1,20.0,1,1,2,1,1,1,1,,61.0,57.0,87.0,75.0,71.0,85.0,1,1,1,1,1,1,1.2,Pepper,2019-04-04,,87.0,1,71.0,1,85.0,2,57.0,1,1,61.0,1,1,1,1,3.0,1,75.0,1,1 +1,39.0,1,2,1,1,1,1,3,3,68.0,32.0,94.0,51.0,69.0,90.0,1,1,1,1,1,1,1.2,Onion,2019-03-29,3,94.0,1,69.0,1,90.0,1,32.0,1,1,68.0,1,2,1,3,,1,51.0,1,1 +1,31.0,2,1,2,1,5,1,4,4,70.0,45.0,86.0,93.0,67.0,,1,1,1,1,1,1,0.8,Green beans,2019-11-16,4,86.0,1,67.0,1,,2,45.0,1,1,70.0,1,1,1,4,5.0,1,93.0,5,1 +1,50.0,2,2,2,1,5,4,3,3,63.0,34.0,63.0,78.0,78.0,83.0,1,1,1,,1,,0.8,Green beans,,3,63.0,4,78.0,1,83.0,2,34.0,1,,63.0,1,2,1,3,6.0,1,78.0,5, +1,33.0,2,2,2,1,2,3,5,1,69.0,31.0,78.0,66.0,71.0,87.0,1,1,1,1,1,1,0.8,Pepper,2019-01-03,1,78.0,3,71.0,1,87.0,2,31.0,1,1,69.0,1,2,1,5,7.0,1,66.0,2,1 +1,29.0,2,2,2,2,4,5,3,4,67.0,31.0,83.0,91.0,60.0,83.0,1,1,2,1,,1,0.8,Carrot,2019-09-11,4,83.0,5,60.0,,83.0,2,31.0,1,1,67.0,1,2,2,3,8.0,2,91.0,4,1 +1,52.0,1,1,1,1,2,1,3,2,75.0,46.0,91.0,89.0,56.0,92.0,1,1,1,1,1,1,1.2,Avocado,2019-02-08,2,91.0,1,56.0,1,92.0,1,46.0,1,1,75.0,1,1,1,3,9.0,1,89.0,2,1 +1,39.0,1,1,1,1,1,4,3,,81.0,41.0,80.0,57.0,81.0,93.0,1,1,1,1,1,1,1.2,Avocado,2019-12-22,,80.0,4,81.0,1,93.0,1,41.0,1,1,81.0,1,1,1,3,10.0,1,57.0,1,1 +1,62.0,1,2,1,1,2,1,3,1,78.0,36.0,91.0,64.0,70.0,92.0,1,2,1,,1,,1.2,Onion,2019-05-22,1,91.0,1,70.0,1,92.0,1,36.0,1,,78.0,2,2,1,3,11.0,1,64.0,2, +1,39.0,1,2,1,1,2,1,1,2,69.0,35.0,91.0,58.0,58.0,86.0,1,1,1,1,1,1,1.2,Avocado,2019-04-16,2,91.0,1,58.0,1,86.0,1,35.0,1,1,69.0,1,2,1,1,12.0,1,58.0,2,1 +1,18.0,2,1,1,1,4,3,3,4,74.0,96.0,82.0,89.0,84.0,,1,1,1,1,1,1,0.8,Avocado,2019-08-25,4,82.0,3,84.0,1,,1,96.0,1,1,74.0,1,1,1,3,13.0,1,89.0,4,1 +1,56.0,2,1,1,1,1,5,5,,70.0,94.0,83.0,78.0,67.0,84.0,1,1,1,1,1,,0.8,Avocado,,,83.0,5,67.0,1,84.0,1,94.0,1,1,70.0,1,1,1,5,14.0,1,78.0,1, +1,64.0,2,2,1,1,4,4,3,1,64.0,34.0,65.0,,84.0,94.0,1,1,1,1,2,1,0.8,Green beans,2019-08-21,1,65.0,4,84.0,2,94.0,1,34.0,1,1,64.0,1,2,1,3,15.0,1,,4,1 +1,41.0,2,2,2,1,1,1,5,3,60.0,33.0,60.0,57.0,88.0,84.0,,1,,1,,1,0.8,Avocado,2019-03-23,3,60.0,1,88.0,,84.0,2,33.0,,1,60.0,1,2,,5,16.0,1,57.0,1,1 +1,,1,1,2,1,1,4,3,1,66.0,60.0,70.0,57.0,87.0,,1,1,1,1,,1,1.2,Carrot,,1,70.0,4,87.0,,,2,60.0,1,1,66.0,1,1,1,3,17.0,1,57.0,1,1 +1,65.0,1,2,1,1,1,1,3,1,67.0,42.0,85.0,51.0,62.0,89.0,1,1,1,1,1,1,1.2,Onion,2019-12-26,1,85.0,1,62.0,1,89.0,1,42.0,1,1,67.0,1,2,1,3,18.0,1,51.0,1,1 +1,34.0,2,1,1,1,4,4,3,1,67.0,,82.0,60.0,65.0,85.0,1,1,1,,1,,0.8,,2019-07-25,1,82.0,4,65.0,1,85.0,1,,1,,67.0,1,1,1,3,19.0,1,60.0,4, +1,64.0,2,2,1,2,4,1,3,1,64.0,45.0,82.0,88.0,69.0,91.0,1,2,2,,,,0.8,Green beans,2019-08-20,1,82.0,1,69.0,,91.0,1,45.0,1,,64.0,2,2,2,3,20.0,2,88.0,4, +1,26.0,2,2,1,2,5,1,5,3,71.0,41.0,83.0,69.0,60.0,84.0,1,1,1,1,1,1,0.8,Onion,2019-02-15,3,83.0,1,60.0,1,84.0,1,41.0,1,1,71.0,1,2,1,5,21.0,2,69.0,5,1 +1,59.0,2,2,1,2,1,4,5,1,71.0,41.0,75.0,58.0,64.0,83.0,1,1,,1,,1,0.8,Lettuce,2019-06-26,1,75.0,4,64.0,,83.0,1,41.0,1,1,71.0,1,2,,5,22.0,2,58.0,1,1 +1,45.0,2,2,2,2,4,5,3,4,75.0,31.0,74.0,80.0,86.0,91.0,1,1,1,1,1,1,0.8,Lettuce,2019-03-26,4,74.0,5,86.0,1,91.0,2,31.0,1,1,75.0,1,2,1,3,23.0,2,80.0,4,1 +1,60.0,1,2,2,1,4,1,3,3,73.0,48.0,,53.0,80.0,84.0,1,2,1,,2,,1.2,Carrot,2019-08-02,3,,1,80.0,2,84.0,2,48.0,1,,73.0,2,2,1,3,24.0,1,53.0,4, +1,47.0,1,1,,1,3,2,3,3,64.0,86.0,68.0,53.0,83.0,87.0,1,1,1,1,1,1,1.2,Tomato,2019-12-27,3,68.0,2,83.0,1,87.0,,86.0,1,1,64.0,1,1,1,3,25.0,1,53.0,3,1 +1,23.0,1,1,2,1,4,4,3,4,80.0,64.0,70.0,76.0,62.0,91.0,1,1,1,1,1,1,1.2,Tomato,2019-11-03,4,70.0,4,62.0,1,91.0,2,64.0,1,1,80.0,1,1,1,3,26.0,1,76.0,4,1 +1,57.0,2,1,,2,4,1,5,,79.0,41.0,81.0,60.0,84.0,85.0,1,2,1,,2,,0.8,Avocado,2019-01-13,,81.0,1,84.0,2,85.0,,41.0,1,,79.0,2,1,1,5,27.0,2,60.0,4, +1,59.0,1,2,1,1,4,1,3,2,62.0,36.0,63.0,93.0,84.0,81.0,1,2,,,,,1.2,Carrot,2019-06-11,2,63.0,1,84.0,,81.0,1,36.0,1,,62.0,2,2,,3,28.0,1,93.0,4, +1,38.0,1,1,2,2,4,1,3,3,73.0,71.0,86.0,55.0,86.0,81.0,1,1,2,1,,1,1.2,Onion,2019-09-08,3,86.0,1,86.0,,81.0,2,71.0,1,1,73.0,1,1,2,3,29.0,2,55.0,4,1 +1,50.0,2,2,1,1,4,3,5,3,79.0,33.0,83.0,66.0,89.0,89.0,1,,1,,1,,0.8,Avocado,,3,83.0,3,89.0,1,89.0,1,33.0,1,,79.0,,2,1,5,30.0,1,66.0,4, +2,35.0,2,2,1,2,2,4,3,1,64.0,43.0,69.0,67.0,72.0,86.0,1,2,2,,,,0.8,Green beans,2019-03-26,1,69.0,4,72.0,,86.0,1,43.0,1,,64.0,2,2,2,3,31.0,2,67.0,2, +2,29.0,1,1,2,,2,1,4,1,74.0,95.0,67.0,58.0,58.0,80.0,1,2,1,,1,,1.2,,,1,67.0,1,58.0,1,80.0,2,95.0,1,,74.0,2,1,1,4,32.0,,58.0,2, +2,36.0,1,2,1,1,4,4,3,3,66.0,53.0,68.0,95.0,64.0,80.0,1,2,1,,2,,1.2,Avocado,2019-04-18,3,68.0,4,64.0,2,80.0,1,53.0,1,,66.0,2,2,1,3,33.0,1,95.0,4, +2,21.0,2,1,1,1,2,4,,4,80.0,35.0,61.0,85.0,75.0,82.0,1,2,1,,2,,0.8,Pepper,2019-03-04,4,61.0,4,75.0,2,82.0,1,35.0,1,,80.0,2,1,1,,,1,85.0,2, +2,60.0,1,2,1,1,4,3,3,4,71.0,90.0,90.0,75.0,63.0,87.0,1,2,2,,,,1.2,Lettuce,2019-01-25,4,90.0,3,63.0,,87.0,1,90.0,1,,71.0,2,2,2,3,35.0,1,75.0,4, +2,34.0,2,2,2,2,4,4,3,2,75.0,63.0,81.0,61.0,81.0,84.0,1,1,1,1,2,1,0.8,Pepper,2019-05-02,2,81.0,4,81.0,2,84.0,2,63.0,1,1,75.0,1,2,1,3,,2,61.0,4,1 +2,22.0,2,1,2,1,1,4,3,4,83.0,70.0,94.0,75.0,,81.0,2,1,,1,,1,0.8,Onion,2019-09-25,4,94.0,4,,,81.0,2,70.0,2,1,83.0,1,1,,3,37.0,1,75.0,1,1 +2,48.0,2,,2,2,5,5,3,3,,62.0,92.0,63.0,78.0,93.0,2,2,,,,,0.8,Green beans,2019-09-23,3,92.0,5,78.0,,93.0,2,62.0,2,,,2,,,3,38.0,2,63.0,5, +2,57.0,1,2,1,1,1,1,1,2,66.0,85.0,90.0,82.0,52.0,88.0,1,2,1,,1,,1.2,Onion,2019-11-04,2,90.0,1,52.0,1,88.0,1,85.0,1,,66.0,2,2,1,1,39.0,1,82.0,1, +2,28.0,1,1,1,1,5,3,3,4,72.0,85.0,76.0,77.0,65.0,86.0,1,1,1,1,1,1,1.2,Lettuce,2019-10-19,4,76.0,3,65.0,1,86.0,1,85.0,1,1,72.0,1,1,1,3,40.0,1,77.0,5,1 +2,42.0,1,2,1,1,1,1,1,4,68.0,,78.0,64.0,52.0,83.0,1,2,1,,1,,1.2,Pepper,2019-10-20,4,78.0,1,52.0,1,83.0,1,,1,,68.0,2,2,1,1,41.0,1,64.0,1, +2,31.0,1,2,2,1,,5,3,3,66.0,,76.0,52.0,67.0,89.0,1,2,1,,2,,1.2,Carrot,2019-10-28,3,76.0,5,67.0,2,89.0,2,,1,,66.0,2,2,1,3,42.0,1,52.0,, +2,45.0,2,2,2,1,1,5,3,3,61.0,82.0,62.0,78.0,68.0,81.0,2,1,,1,,1,0.8,Green beans,2019-01-07,3,62.0,5,68.0,,81.0,2,82.0,2,1,61.0,1,2,,3,43.0,1,78.0,1,1 +2,35.0,2,2,2,1,2,,3,3,83.0,70.0,71.0,66.0,83.0,,1,2,2,,,,0.8,Carrot,2019-02-15,3,71.0,,83.0,,,2,70.0,1,,83.0,2,2,2,3,44.0,1,66.0,2, +2,45.0,1,1,1,1,1,1,1,4,65.0,68.0,66.0,70.0,88.0,89.0,1,1,1,1,2,,1.2,Tomato,2019-10-22,4,66.0,1,88.0,2,89.0,1,68.0,1,1,65.0,1,1,1,1,45.0,1,70.0,1, +2,,2,2,1,1,4,4,3,4,76.0,63.0,85.0,85.0,,91.0,1,1,1,1,2,1,0.8,Green beans,2019-05-11,4,85.0,4,,2,91.0,1,63.0,1,1,76.0,1,2,1,3,46.0,1,85.0,4,1 +2,44.0,1,,1,1,4,5,3,2,86.0,61.0,76.0,58.0,79.0,88.0,1,2,2,,,,1.2,Pepper,2019-10-20,2,76.0,5,79.0,,88.0,1,61.0,1,,86.0,2,,2,3,47.0,1,58.0,4, +2,60.0,2,2,1,1,1,1,5,3,83.0,84.0,68.0,78.0,61.0,87.0,1,1,1,1,1,1,0.8,Green beans,2019-04-09,3,68.0,1,61.0,1,87.0,1,84.0,1,1,83.0,1,2,1,5,48.0,1,78.0,1,1 +2,48.0,2,2,2,,,2,3,4,71.0,53.0,64.0,83.0,73.0,82.0,1,2,1,,1,,0.8,Carrot,2019-03-09,4,64.0,2,73.0,1,82.0,2,53.0,1,,71.0,2,2,1,3,49.0,,83.0,, +2,36.0,1,2,1,1,4,1,3,2,84.0,30.0,61.0,68.0,55.0,85.0,1,2,1,,2,,1.2,Pepper,2019-02-20,2,61.0,1,55.0,2,85.0,1,30.0,1,,84.0,2,2,1,3,,1,68.0,4, +2,58.0,1,2,1,1,4,2,3,4,70.0,33.0,85.0,67.0,70.0,86.0,2,1,,1,,1,1.2,Pepper,2019-05-07,4,85.0,2,70.0,,86.0,1,33.0,2,1,70.0,1,2,,3,51.0,1,67.0,4,1 +2,,1,1,1,1,,2,3,4,70.0,44.0,82.0,71.0,83.0,84.0,1,1,1,1,1,,1.2,Lettuce,2019-08-03,4,82.0,2,83.0,1,84.0,1,44.0,1,1,70.0,1,1,1,3,52.0,1,71.0,, +2,54.0,2,1,1,1,5,1,4,3,68.0,38.0,70.0,,67.0,83.0,1,2,1,,2,,0.8,Tomato,2019-12-28,3,70.0,1,67.0,2,83.0,1,38.0,1,,68.0,2,1,1,4,53.0,1,,5, +2,41.0,2,1,1,2,4,,5,4,87.0,93.0,81.0,84.0,87.0,81.0,1,2,2,,,,0.8,Onion,2019-05-08,4,81.0,,87.0,,81.0,1,93.0,1,,87.0,2,1,2,5,54.0,2,84.0,4, +2,,2,2,2,1,4,1,3,4,80.0,46.0,64.0,84.0,68.0,82.0,1,2,1,,2,,0.8,Green beans,2019-10-19,4,64.0,1,68.0,2,82.0,2,46.0,1,,80.0,2,2,1,3,55.0,1,84.0,4, +2,41.0,2,2,1,1,3,2,5,4,72.0,31.0,89.0,72.0,52.0,93.0,1,1,2,1,,1,0.8,Tomato,2019-04-06,4,89.0,2,52.0,,93.0,1,31.0,1,1,72.0,1,2,2,5,56.0,1,72.0,3,1 +2,53.0,2,2,2,1,5,1,1,4,63.0,46.0,68.0,72.0,72.0,81.0,1,2,2,,,,0.8,Avocado,2019-09-11,4,68.0,1,72.0,,81.0,2,46.0,1,,63.0,2,2,2,1,,1,72.0,5, +2,,1,2,1,1,4,1,1,1,61.0,45.0,85.0,71.0,71.0,86.0,1,2,1,,1,,1.2,Onion,2019-08-03,1,85.0,1,71.0,1,86.0,1,45.0,1,,61.0,2,2,1,1,58.0,1,71.0,4, +2,24.0,2,1,,1,4,1,,4,62.0,35.0,63.0,75.0,66.0,81.0,1,2,2,,,,0.8,Tomato,2019-10-01,4,63.0,1,66.0,,81.0,,35.0,1,,62.0,2,1,2,,59.0,1,75.0,4, +2,60.0,2,2,,1,2,5,3,4,65.0,34.0,79.0,92.0,62.0,84.0,1,2,1,,2,,0.8,Pepper,2019-10-12,4,79.0,5,62.0,2,84.0,,34.0,1,,65.0,2,2,1,3,60.0,1,92.0,2, +3,60.0,2,1,1,1,3,5,,4,71.0,41.0,73.0,53.0,73.0,83.0,1,2,1,,1,,0.8,Tomato,2019-04-24,4,73.0,5,73.0,1,83.0,1,41.0,1,,71.0,2,1,1,,61.0,1,53.0,3, +3,19.0,2,1,1,1,1,1,4,,62.0,54.0,71.0,65.0,76.0,86.0,1,2,1,,,,0.8,Lettuce,2019-08-14,,71.0,1,76.0,,86.0,1,54.0,1,,62.0,2,1,1,4,62.0,1,65.0,1, +3,65.0,2,2,1,1,2,4,3,4,75.0,38.0,85.0,75.0,66.0,90.0,1,1,2,2,,,0.8,Green beans,2019-05-26,4,85.0,4,66.0,,90.0,1,38.0,1,2,75.0,1,2,2,3,63.0,1,75.0,2, +3,53.0,2,2,2,1,4,3,3,5,75.0,55.0,94.0,79.0,80.0,81.0,2,2,,,,,0.8,,2019-05-09,5,94.0,3,80.0,,81.0,2,55.0,2,,75.0,2,2,,3,64.0,1,79.0,4, +3,,1,1,2,1,4,3,1,3,65.0,55.0,75.0,75.0,87.0,89.0,1,1,2,1,,1,1.2,Green beans,2019-06-28,3,75.0,3,87.0,,89.0,2,55.0,1,1,65.0,1,1,2,1,65.0,1,75.0,4,1 +3,49.0,2,,1,1,4,4,3,4,77.0,55.0,84.0,91.0,89.0,90.0,2,2,,,,,0.8,,2019-08-10,4,84.0,4,89.0,,90.0,1,55.0,2,,77.0,2,,,3,66.0,1,91.0,4, +3,25.0,2,1,1,1,1,5,3,1,75.0,81.0,73.0,85.0,,,1,1,1,1,,1,0.8,,2019-07-11,1,73.0,5,,,,1,81.0,1,1,75.0,1,1,1,3,67.0,1,85.0,1,1 +3,34.0,1,1,1,2,,4,3,4,84.0,68.0,81.0,74.0,86.0,84.0,1,2,1,,2,,1.2,Avocado,2019-07-14,4,81.0,4,86.0,2,84.0,1,68.0,1,,84.0,2,1,1,3,68.0,2,74.0,, +3,21.0,1,1,1,1,1,1,3,4,70.0,93.0,65.0,66.0,57.0,93.0,1,2,1,,1,,1.2,Onion,2019-12-27,4,65.0,1,57.0,1,93.0,1,93.0,1,,70.0,2,1,1,3,69.0,1,66.0,1, +3,64.0,1,1,1,1,,,3,1,66.0,60.0,73.0,54.0,56.0,93.0,1,2,2,,,,1.2,,2019-04-13,1,73.0,,56.0,,93.0,1,60.0,1,,66.0,2,1,2,3,70.0,1,54.0,, +3,36.0,2,2,2,1,4,5,5,4,82.0,78.0,65.0,87.0,,86.0,2,,,,,,0.8,Onion,2019-01-15,4,65.0,5,,,86.0,2,78.0,2,,82.0,,2,,5,71.0,1,87.0,4, +3,35.0,2,2,1,1,4,4,3,4,63.0,84.0,65.0,90.0,90.0,88.0,2,1,,1,,1,0.8,Tomato,2019-09-02,4,65.0,4,90.0,,88.0,1,84.0,2,1,63.0,1,2,,3,72.0,1,90.0,4,1 +3,41.0,1,1,,1,4,4,1,3,76.0,65.0,93.0,75.0,67.0,82.0,1,2,1,,2,,1.2,Pepper,2019-02-02,3,93.0,4,67.0,2,82.0,,65.0,1,,76.0,2,1,1,1,73.0,1,75.0,4, +3,19.0,2,,1,1,4,3,4,4,64.0,45.0,71.0,89.0,88.0,90.0,1,2,2,,,,0.8,Green beans,2019-12-31,4,71.0,3,88.0,,90.0,1,45.0,1,,64.0,2,,2,4,74.0,1,89.0,4, +3,63.0,2,1,1,1,4,4,3,1,70.0,40.0,70.0,75.0,80.0,90.0,2,,,,,,0.8,Carrot,2019-12-31,1,70.0,4,80.0,,90.0,1,40.0,2,,70.0,,1,,3,75.0,1,75.0,4, +3,64.0,2,2,1,1,4,1,3,2,63.0,74.0,69.0,86.0,50.0,81.0,1,1,1,1,2,2,0.8,Tomato,2019-02-21,2,69.0,1,50.0,2,81.0,1,74.0,1,1,63.0,1,2,1,3,76.0,1,86.0,4,2 +3,45.0,2,1,1,1,,2,5,3,61.0,34.0,62.0,85.0,79.0,91.0,1,2,2,,,,0.8,Tomato,2019-04-25,3,62.0,2,79.0,,91.0,1,34.0,1,,61.0,2,1,2,5,77.0,1,85.0,, +3,55.0,1,,1,1,4,3,4,4,77.0,66.0,80.0,85.0,58.0,91.0,1,2,1,,1,,1.2,Lettuce,2019-11-06,4,80.0,3,58.0,1,91.0,1,66.0,1,,77.0,2,,1,4,78.0,1,85.0,4, +3,33.0,2,2,1,1,2,2,5,4,82.0,49.0,91.0,80.0,59.0,92.0,1,2,2,,,,0.8,Green beans,2019-05-09,4,91.0,2,59.0,,92.0,1,49.0,1,,82.0,2,2,2,5,79.0,1,80.0,2, +3,28.0,1,1,,1,3,5,3,5,66.0,48.0,,80.0,65.0,92.0,1,2,2,,,,1.2,Onion,2019-07-09,5,,5,65.0,,92.0,,48.0,1,,66.0,2,1,2,3,80.0,1,80.0,3, +3,34.0,2,2,2,1,1,4,,3,67.0,83.0,62.0,54.0,55.0,84.0,1,2,1,,1,,0.8,Lettuce,2019-04-07,3,62.0,4,55.0,1,84.0,2,83.0,1,,67.0,2,2,1,,,1,54.0,1, +3,39.0,1,2,1,1,1,1,3,4,67.0,,86.0,72.0,68.0,87.0,1,1,2,1,,,1.2,Tomato,2019-07-27,4,86.0,1,68.0,,87.0,1,,1,1,67.0,1,2,2,3,82.0,1,72.0,1, +3,57.0,1,1,1,1,2,1,3,4,62.0,86.0,70.0,77.0,68.0,87.0,1,1,1,1,2,1,1.2,,2019-07-10,4,70.0,1,68.0,2,87.0,1,86.0,1,1,62.0,1,1,1,3,83.0,1,77.0,2,1 +3,48.0,2,2,1,1,4,4,3,2,78.0,50.0,79.0,52.0,59.0,88.0,1,2,2,,,,0.8,Avocado,2019-11-19,2,79.0,4,59.0,,88.0,1,50.0,1,,78.0,2,2,2,3,84.0,1,52.0,4, +3,,1,1,1,1,4,4,3,4,66.0,64.0,89.0,83.0,69.0,89.0,2,1,,1,,1,1.2,Avocado,2019-12-19,4,89.0,4,69.0,,89.0,1,64.0,2,1,66.0,1,1,,3,85.0,1,83.0,4,1 +3,18.0,1,1,2,1,4,3,3,4,75.0,73.0,82.0,64.0,84.0,91.0,1,1,1,1,1,1,1.2,Carrot,2019-12-16,4,82.0,3,84.0,1,91.0,2,73.0,1,1,75.0,1,1,1,3,86.0,1,64.0,4,1 +3,25.0,2,2,1,1,4,,3,4,,45.0,,58.0,85.0,86.0,1,2,1,,2,,0.8,Carrot,2019-01-04,4,,,85.0,2,86.0,1,45.0,1,,,2,2,1,3,87.0,1,58.0,4, +3,40.0,2,2,1,1,4,5,3,4,83.0,74.0,84.0,80.0,56.0,86.0,1,1,1,2,1,,0.8,Avocado,2019-11-14,4,84.0,5,56.0,1,86.0,1,74.0,1,2,83.0,1,2,1,3,88.0,1,80.0,4, +3,43.0,2,2,1,1,1,5,5,,74.0,68.0,68.0,84.0,70.0,85.0,2,2,,,,,0.8,Pepper,2019-07-02,,68.0,5,70.0,,85.0,1,68.0,2,,74.0,2,2,,5,89.0,1,84.0,1, +3,22.0,2,1,1,1,4,5,5,5,78.0,62.0,63.0,68.0,86.0,88.0,2,2,,,,,0.8,Onion,2019-12-13,5,63.0,5,86.0,,88.0,1,62.0,2,,78.0,2,1,,5,90.0,1,68.0,4, +4,48.0,1,2,1,1,,2,3,4,75.0,38.0,94.0,75.0,68.0,85.0,1,2,2,,,,1.2,Pepper,2019-12-04,4,94.0,2,68.0,,85.0,1,38.0,1,,75.0,2,2,2,3,91.0,1,75.0,, +4,26.0,1,1,1,1,4,5,3,4,84.0,60.0,91.0,86.0,73.0,91.0,2,1,,1,,1,1.2,Pepper,2019-12-28,4,91.0,5,73.0,,91.0,1,60.0,2,1,84.0,1,1,,3,92.0,1,86.0,4,1 +4,37.0,1,2,1,1,1,4,3,,62.0,90.0,78.0,60.0,88.0,87.0,2,,,,,,1.2,Onion,2019-12-26,,78.0,4,88.0,,87.0,1,90.0,2,,62.0,,2,,3,93.0,1,60.0,1, +4,28.0,1,1,1,1,1,1,3,4,74.0,50.0,72.0,68.0,72.0,90.0,1,1,1,1,,2,1.2,Tomato,2019-07-25,4,72.0,1,72.0,,90.0,1,50.0,1,1,74.0,1,1,1,3,94.0,1,68.0,1,2 +4,56.0,1,,1,1,4,1,1,1,61.0,51.0,75.0,87.0,55.0,84.0,1,1,2,1,,1,1.2,Lettuce,2019-07-29,1,75.0,1,55.0,,84.0,1,51.0,1,1,61.0,1,,2,1,95.0,1,87.0,4,1 +4,18.0,2,2,2,2,5,4,3,4,65.0,80.0,66.0,64.0,62.0,82.0,1,1,1,1,1,1,0.8,Lettuce,2019-05-26,4,66.0,4,62.0,1,82.0,2,80.0,1,1,65.0,1,2,1,3,96.0,2,64.0,5,1 +4,47.0,2,2,1,1,5,4,3,4,70.0,49.0,67.0,,53.0,85.0,1,,2,,,,0.8,Tomato,2019-09-14,4,67.0,4,53.0,,85.0,1,49.0,1,,70.0,,2,2,3,97.0,1,,5, +4,37.0,2,2,,1,5,1,,1,64.0,53.0,76.0,59.0,65.0,83.0,1,2,1,,2,,0.8,Avocado,2019-06-20,1,76.0,1,65.0,2,83.0,,53.0,1,,64.0,2,2,1,,98.0,1,59.0,5, +4,22.0,1,1,2,1,4,5,3,4,65.0,38.0,88.0,53.0,84.0,90.0,1,1,1,,,,1.2,Tomato,2019-07-08,4,88.0,5,84.0,,90.0,2,38.0,1,,65.0,1,1,1,3,,1,53.0,4, +4,24.0,2,2,1,2,4,5,3,5,63.0,98.0,62.0,65.0,76.0,87.0,1,2,2,,,,0.8,Green beans,2019-02-06,5,62.0,5,76.0,,87.0,1,98.0,1,,63.0,2,2,2,3,100.0,2,65.0,4, +4,54.0,2,2,2,1,4,4,3,,69.0,81.0,75.0,53.0,71.0,92.0,1,2,1,,2,,0.8,Lettuce,2019-11-24,,75.0,4,71.0,2,92.0,2,81.0,1,,69.0,2,2,1,3,101.0,1,53.0,4, +4,34.0,2,1,2,1,5,4,5,3,85.0,57.0,77.0,63.0,52.0,87.0,1,,1,,2,,0.8,Carrot,2019-03-07,3,77.0,4,52.0,2,87.0,2,57.0,1,,85.0,,1,1,5,102.0,1,63.0,5, +4,48.0,2,2,2,1,4,5,3,4,62.0,84.0,70.0,54.0,78.0,87.0,2,2,,,,,0.8,Green beans,2019-12-11,4,70.0,5,78.0,,87.0,2,84.0,2,,62.0,2,2,,3,103.0,1,54.0,4, +4,,,1,1,1,1,,3,4,84.0,57.0,71.0,73.0,52.0,94.0,1,1,2,1,,1,0.8,Tomato,2019-07-04,4,71.0,,52.0,,94.0,1,57.0,1,1,84.0,1,1,2,3,104.0,1,73.0,1,1 +4,21.0,2,2,,1,4,4,3,3,69.0,54.0,70.0,,84.0,92.0,1,1,1,2,1,,0.8,,2019-11-27,3,70.0,4,84.0,1,92.0,,54.0,1,2,69.0,1,2,1,3,105.0,1,,4, +4,42.0,1,2,2,1,,,3,3,68.0,55.0,67.0,,82.0,84.0,1,1,2,1,,1,1.2,Lettuce,2019-11-16,3,67.0,,82.0,,84.0,2,55.0,1,1,68.0,1,2,2,3,106.0,1,,,1 +4,32.0,2,2,1,1,3,3,4,4,63.0,56.0,75.0,79.0,73.0,94.0,2,1,,1,,1,0.8,Tomato,2019-08-18,4,75.0,3,73.0,,94.0,1,56.0,2,1,63.0,1,2,,4,107.0,1,79.0,3,1 +4,30.0,2,2,1,1,4,4,3,4,72.0,89.0,78.0,90.0,86.0,94.0,1,2,1,,2,,0.8,Lettuce,2019-01-17,4,78.0,4,86.0,2,94.0,1,89.0,1,,72.0,2,2,1,3,108.0,1,90.0,4, +4,46.0,2,1,2,2,3,5,3,5,64.0,72.0,61.0,,61.0,,1,1,,1,,1,0.8,Avocado,2019-02-25,5,61.0,5,61.0,,,2,72.0,1,1,64.0,1,1,,3,109.0,2,,3,1 +4,24.0,2,,1,1,4,4,5,3,89.0,32.0,76.0,86.0,78.0,85.0,1,1,2,,,,0.8,Avocado,2019-03-27,3,76.0,4,78.0,,85.0,1,32.0,1,,89.0,1,,2,5,110.0,1,86.0,4, +4,53.0,1,1,1,2,2,4,5,4,71.0,73.0,,87.0,83.0,83.0,1,2,2,,,,1.2,Onion,2019-10-06,4,,4,83.0,,83.0,1,73.0,1,,71.0,2,1,2,5,111.0,2,87.0,2, +4,27.0,1,2,2,1,2,2,4,1,76.0,85.0,62.0,54.0,60.0,85.0,,2,,,,,1.2,Lettuce,2019-09-24,1,62.0,2,60.0,,85.0,2,85.0,,,76.0,2,2,,4,112.0,1,54.0,2, +4,65.0,2,2,1,1,1,4,5,4,65.0,44.0,65.0,79.0,74.0,87.0,1,2,1,,1,,0.8,Pepper,2019-01-14,4,65.0,4,74.0,1,87.0,1,44.0,1,,65.0,2,2,1,5,113.0,1,79.0,1, +4,36.0,1,1,1,1,2,2,3,3,68.0,57.0,71.0,76.0,72.0,88.0,2,1,,1,,1,1.2,Carrot,2019-12-16,3,71.0,2,72.0,,88.0,1,57.0,2,1,68.0,1,1,,3,114.0,1,76.0,2,1 +4,48.0,,2,1,1,1,2,3,4,63.0,59.0,65.0,80.0,72.0,,1,2,2,,,,1.2,Avocado,2019-12-23,4,65.0,2,72.0,,,1,59.0,1,,63.0,2,2,2,3,115.0,1,80.0,1, +4,47.0,1,2,2,1,4,4,3,4,77.0,87.0,66.0,66.0,75.0,90.0,1,1,1,2,2,,1.2,Onion,2019-12-19,4,66.0,4,75.0,2,90.0,2,87.0,1,2,77.0,1,2,1,3,116.0,1,66.0,4, +4,30.0,2,1,2,1,4,1,3,3,70.0,98.0,63.0,81.0,56.0,81.0,1,2,1,,1,,0.8,Green beans,,3,63.0,1,56.0,1,81.0,2,98.0,1,,70.0,2,1,1,3,117.0,1,81.0,4, +4,20.0,2,,1,1,4,1,3,2,89.0,45.0,81.0,81.0,71.0,81.0,1,1,2,1,,2,0.8,Lettuce,2019-12-05,2,81.0,1,71.0,,81.0,1,45.0,1,1,89.0,1,,2,3,118.0,1,81.0,4,2 +4,21.0,1,1,2,1,1,4,3,2,88.0,74.0,80.0,59.0,87.0,93.0,2,2,,,,,1.2,Green beans,2019-07-31,2,80.0,4,87.0,,93.0,2,74.0,2,,88.0,2,1,,3,119.0,1,59.0,1, +4,34.0,1,1,2,1,1,5,3,5,64.0,39.0,74.0,54.0,72.0,91.0,2,1,,1,,2,1.2,Onion,2019-09-02,5,74.0,5,72.0,,91.0,2,39.0,2,1,64.0,1,1,,3,120.0,1,54.0,1,2 +5,23.0,2,2,2,1,4,2,3,4,71.0,55.0,76.0,65.0,72.0,89.0,1,2,2,,,,0.8,Lettuce,2019-11-06,4,76.0,2,72.0,,89.0,2,55.0,1,,71.0,2,2,2,3,121.0,1,65.0,4, +5,55.0,2,2,2,1,1,2,3,4,70.0,81.0,88.0,72.0,58.0,86.0,2,2,,,,,0.8,Pepper,,4,88.0,2,58.0,,86.0,2,81.0,2,,70.0,2,2,,3,122.0,1,72.0,1, +5,41.0,2,2,2,1,2,1,5,2,70.0,87.0,70.0,72.0,74.0,83.0,1,1,1,1,1,1,0.8,Onion,2019-06-24,2,70.0,1,74.0,1,83.0,2,87.0,1,1,70.0,1,2,1,5,123.0,1,72.0,2,1 +5,64.0,2,1,1,2,4,1,5,5,64.0,78.0,77.0,94.0,79.0,82.0,2,2,,,,,0.8,Lettuce,2019-03-14,5,77.0,1,79.0,,82.0,1,78.0,2,,64.0,2,1,,5,124.0,2,94.0,4, +5,27.0,2,1,1,1,1,4,3,4,74.0,48.0,67.0,87.0,84.0,91.0,1,2,1,,2,,0.8,Pepper,,4,67.0,4,84.0,2,91.0,1,48.0,1,,74.0,2,1,1,3,125.0,1,87.0,1, +5,44.0,1,2,,1,3,1,3,4,74.0,76.0,94.0,85.0,63.0,88.0,1,1,2,1,,1,1.2,Lettuce,2019-10-27,4,94.0,1,63.0,,88.0,,76.0,1,1,74.0,1,2,2,3,126.0,1,85.0,3,1 +5,41.0,1,2,2,1,2,1,3,3,76.0,43.0,91.0,51.0,,80.0,1,1,1,1,1,2,1.2,Avocado,2019-03-29,3,91.0,1,,1,80.0,2,43.0,1,1,76.0,1,2,1,3,127.0,1,51.0,2,2 +5,54.0,2,2,1,1,4,2,5,4,,68.0,60.0,84.0,54.0,92.0,1,2,2,,,,0.8,Green beans,2019-11-26,4,60.0,2,54.0,,92.0,1,68.0,1,,,2,2,2,5,128.0,1,84.0,4, +5,59.0,2,1,1,1,4,1,1,4,66.0,58.0,63.0,61.0,64.0,85.0,1,2,1,,2,,0.8,Lettuce,2019-12-04,4,63.0,1,64.0,2,85.0,1,58.0,1,,66.0,2,1,1,1,129.0,1,61.0,4, +5,34.0,1,1,1,1,4,5,3,1,65.0,65.0,84.0,77.0,66.0,93.0,2,1,,1,,1,1.2,Tomato,2019-05-05,1,84.0,5,66.0,,93.0,1,65.0,2,1,65.0,1,1,,3,130.0,1,77.0,4,1 +5,,2,2,1,1,4,5,3,4,75.0,54.0,71.0,67.0,74.0,81.0,1,1,1,1,2,2,0.8,Onion,2019-02-28,4,71.0,5,74.0,2,81.0,1,54.0,1,1,75.0,1,2,1,3,131.0,1,67.0,4,2 +5,28.0,1,1,2,,1,1,4,5,79.0,83.0,81.0,58.0,65.0,86.0,2,1,,1,,1,1.2,Carrot,2019-06-15,5,81.0,1,65.0,,86.0,2,83.0,2,1,79.0,1,1,,4,,,58.0,1,1 +5,18.0,,1,,2,4,1,5,3,80.0,36.0,61.0,80.0,86.0,82.0,1,,2,,,,1.2,Carrot,2019-02-12,3,61.0,1,86.0,,82.0,,36.0,1,,80.0,,1,2,5,133.0,2,80.0,4, +5,26.0,1,1,1,1,4,4,5,4,76.0,60.0,92.0,71.0,52.0,91.0,1,1,1,2,2,,1.2,Green beans,2019-10-30,4,92.0,4,52.0,2,91.0,1,60.0,1,2,76.0,1,1,1,5,134.0,1,71.0,4, +5,62.0,2,1,1,1,4,1,,4,80.0,86.0,68.0,79.0,88.0,80.0,2,2,,,,,0.8,Carrot,2019-03-29,4,68.0,1,88.0,,80.0,1,86.0,2,,80.0,2,1,,,135.0,1,79.0,4, +5,64.0,2,2,1,2,5,1,5,4,81.0,84.0,68.0,64.0,64.0,91.0,1,2,2,,,,0.8,Carrot,2019-07-02,4,68.0,1,64.0,,91.0,1,84.0,1,,81.0,2,2,2,5,136.0,2,64.0,5, +5,22.0,1,1,1,1,4,4,1,3,74.0,,77.0,63.0,68.0,87.0,1,2,2,,,,1.2,Pepper,2019-05-28,3,77.0,4,68.0,,87.0,1,,1,,74.0,2,1,2,1,137.0,1,63.0,4, +5,46.0,1,1,1,1,2,3,3,4,83.0,57.0,94.0,56.0,74.0,88.0,2,2,,,,,1.2,Carrot,2019-10-06,4,94.0,3,74.0,,88.0,1,57.0,2,,83.0,2,1,,3,138.0,1,56.0,2, +5,45.0,2,2,2,1,4,1,3,4,88.0,76.0,92.0,57.0,64.0,80.0,1,1,1,1,1,2,0.8,Avocado,2019-12-21,4,92.0,1,64.0,1,80.0,2,76.0,1,1,88.0,1,2,1,3,139.0,1,57.0,4,2 +5,34.0,2,1,2,2,,3,3,5,79.0,51.0,,81.0,78.0,89.0,1,,2,,,,0.8,Onion,2019-12-10,5,,3,78.0,,89.0,2,51.0,1,,79.0,,1,2,3,140.0,2,81.0,, +5,33.0,1,2,2,1,,2,3,4,64.0,63.0,90.0,55.0,54.0,82.0,1,2,2,,,,1.2,Carrot,2019-06-03,4,90.0,2,54.0,,82.0,2,63.0,1,,64.0,2,2,2,3,141.0,1,55.0,, +5,45.0,1,1,2,1,5,4,3,1,65.0,58.0,77.0,61.0,69.0,85.0,1,2,2,,,,1.2,Avocado,2019-07-14,1,77.0,4,69.0,,85.0,2,58.0,1,,65.0,2,1,2,3,,1,61.0,5, +5,24.0,2,,2,1,4,5,4,3,86.0,50.0,73.0,,52.0,90.0,1,1,2,1,,2,0.8,,2019-07-02,3,73.0,5,52.0,,90.0,2,50.0,1,1,86.0,1,,2,4,143.0,1,,4,2 +5,28.0,1,1,1,1,4,3,4,2,70.0,39.0,66.0,65.0,79.0,92.0,2,2,,,,,1.2,Green beans,2019-11-11,2,66.0,3,79.0,,92.0,1,39.0,2,,70.0,2,1,,4,144.0,1,65.0,4, +5,19.0,,1,1,2,2,4,5,4,63.0,93.0,70.0,91.0,58.0,88.0,1,1,2,1,,2,0.8,Lettuce,2019-07-17,4,70.0,4,58.0,,88.0,1,93.0,1,1,63.0,1,1,2,5,145.0,2,91.0,2,2 +5,60.0,2,2,1,1,5,2,5,3,77.0,74.0,86.0,73.0,81.0,83.0,1,2,1,,2,,0.8,Lettuce,2019-07-17,3,86.0,2,81.0,2,83.0,1,74.0,1,,77.0,2,2,1,5,146.0,1,73.0,5, +5,44.0,1,2,1,1,1,3,3,4,87.0,90.0,66.0,78.0,75.0,95.0,1,2,2,,,,1.2,Lettuce,2019-07-26,4,66.0,3,75.0,,95.0,1,90.0,1,,87.0,2,2,2,3,147.0,1,78.0,1, +5,59.0,2,2,1,1,5,4,4,,69.0,71.0,86.0,91.0,64.0,86.0,1,2,2,,,,0.8,Carrot,2019-02-16,,86.0,4,64.0,,86.0,1,71.0,1,,69.0,2,2,2,4,148.0,1,91.0,5, +5,18.0,2,,,1,,4,3,4,61.0,86.0,65.0,58.0,61.0,86.0,1,,2,,,,0.8,Avocado,2019-12-10,4,65.0,4,61.0,,86.0,,86.0,1,,61.0,,,2,3,149.0,1,58.0,, +5,50.0,2,1,1,1,4,3,3,2,71.0,94.0,63.0,76.0,55.0,,1,2,2,,,,0.8,,2019-04-24,2,63.0,3,55.0,,,1,94.0,1,,71.0,2,1,2,3,150.0,1,76.0,4, +6,61.0,2,2,2,2,4,1,1,3,,57.0,87.0,87.0,65.0,80.0,1,2,1,,1,,0.8,Green beans,2019-09-21,3,87.0,1,65.0,1,80.0,2,57.0,1,,,2,2,1,1,151.0,2,87.0,4, +6,31.0,2,1,2,,2,1,5,4,86.0,92.0,71.0,89.0,59.0,85.0,1,2,2,,,,0.8,Pepper,2019-04-13,4,71.0,1,59.0,,85.0,2,92.0,1,,86.0,2,1,2,5,152.0,,89.0,2, +6,57.0,,2,1,1,4,3,5,4,75.0,77.0,75.0,82.0,66.0,81.0,1,2,1,,,,0.8,Tomato,2019-09-27,4,75.0,3,66.0,,81.0,1,77.0,1,,75.0,2,2,1,5,153.0,1,82.0,4, +6,51.0,1,1,2,1,,4,3,4,75.0,99.0,67.0,58.0,63.0,92.0,2,2,,,,,1.2,Onion,2019-03-29,4,67.0,4,63.0,,92.0,2,99.0,2,,75.0,2,1,,3,154.0,1,58.0,, +6,64.0,1,2,1,1,4,1,3,4,67.0,93.0,84.0,68.0,70.0,,1,1,1,1,2,1,1.2,Green beans,2019-04-05,4,84.0,1,70.0,2,,1,93.0,1,1,67.0,1,2,1,3,155.0,1,68.0,4,1 +6,47.0,1,2,,1,3,1,3,4,84.0,63.0,85.0,59.0,53.0,83.0,1,1,2,1,,1,1.2,Green beans,2019-10-22,4,85.0,1,53.0,,83.0,,63.0,1,1,84.0,1,2,2,3,156.0,1,59.0,3,1 +6,64.0,2,1,1,1,3,1,5,4,77.0,99.0,78.0,54.0,,93.0,1,1,1,1,1,1,0.8,Lettuce,2019-10-04,4,78.0,1,,1,93.0,1,99.0,1,1,77.0,1,1,1,5,157.0,1,54.0,3,1 +6,37.0,2,2,1,1,1,4,5,5,63.0,,74.0,92.0,71.0,88.0,1,2,1,,2,,0.8,Pepper,2019-05-29,5,74.0,4,71.0,2,88.0,1,,1,,63.0,2,2,1,5,158.0,1,92.0,1, +6,41.0,2,2,1,1,4,4,5,2,83.0,60.0,88.0,91.0,63.0,93.0,2,2,,,,,0.8,Tomato,2019-07-13,2,88.0,4,63.0,,93.0,1,60.0,2,,83.0,2,2,,5,159.0,1,91.0,4, +6,53.0,2,2,1,1,4,3,3,4,61.0,42.0,63.0,80.0,61.0,81.0,1,2,2,,,,0.8,Green beans,2019-05-14,4,63.0,3,61.0,,81.0,1,42.0,1,,61.0,2,2,2,3,160.0,1,80.0,4, +6,20.0,2,1,1,1,4,2,3,4,89.0,85.0,,55.0,60.0,90.0,2,1,,1,,1,0.8,Tomato,2019-03-27,4,,2,60.0,,90.0,1,85.0,2,1,89.0,1,1,,3,161.0,1,55.0,4,1 +6,53.0,1,1,1,1,4,1,3,4,65.0,72.0,64.0,88.0,73.0,83.0,2,2,,,,,1.2,Lettuce,2019-09-24,4,64.0,1,73.0,,83.0,1,72.0,2,,65.0,2,1,,3,162.0,1,88.0,4, +6,61.0,2,2,1,1,2,1,5,2,88.0,40.0,83.0,52.0,,87.0,2,1,,1,,1,0.8,Avocado,2019-08-26,2,83.0,1,,,87.0,1,40.0,2,1,88.0,1,2,,5,163.0,1,52.0,2,1 +6,21.0,2,1,1,1,1,5,5,3,82.0,97.0,85.0,81.0,,,2,2,,,,,0.8,Green beans,2019-03-18,3,85.0,5,,,,1,97.0,2,,82.0,2,1,,5,164.0,1,81.0,1, +6,53.0,2,1,2,1,4,2,3,4,79.0,67.0,86.0,,73.0,89.0,,1,,1,,1,0.8,Green beans,2019-09-17,4,86.0,2,73.0,,89.0,2,67.0,,1,79.0,1,1,,3,165.0,1,,4,1 +6,35.0,2,1,1,2,4,1,5,4,63.0,66.0,60.0,62.0,58.0,91.0,1,2,2,,,,0.8,Carrot,2019-12-09,4,60.0,1,58.0,,91.0,1,66.0,1,,63.0,2,1,2,5,166.0,2,62.0,4, +6,,1,2,1,1,1,2,1,4,60.0,88.0,73.0,69.0,60.0,83.0,1,1,2,1,,,1.2,Green beans,2019-11-19,4,73.0,2,60.0,,83.0,1,88.0,1,1,60.0,1,2,2,1,167.0,1,69.0,1, +6,,1,1,1,1,1,1,5,4,61.0,54.0,73.0,63.0,53.0,83.0,1,2,2,,,,1.2,Pepper,2019-02-19,4,73.0,1,53.0,,83.0,1,54.0,1,,61.0,2,1,2,5,168.0,1,63.0,1, +6,40.0,2,1,1,2,,4,3,4,62.0,89.0,72.0,85.0,83.0,86.0,2,2,,,,,0.8,Carrot,,4,72.0,4,83.0,,86.0,1,89.0,2,,62.0,2,1,,3,,2,85.0,, +6,64.0,1,2,1,1,1,1,1,4,62.0,71.0,83.0,,51.0,90.0,1,2,2,,,,1.2,Tomato,2019-08-30,4,83.0,1,51.0,,90.0,1,71.0,1,,62.0,2,2,2,1,170.0,1,,1, +6,59.0,1,2,1,1,1,1,3,4,76.0,93.0,87.0,53.0,59.0,82.0,1,1,2,1,,1,1.2,Onion,2019-08-05,4,87.0,1,59.0,,82.0,1,93.0,1,1,76.0,1,2,2,3,171.0,1,53.0,1,1 +6,48.0,2,2,1,1,3,5,,5,71.0,74.0,69.0,68.0,55.0,88.0,2,2,,,,,0.8,Tomato,2019-09-28,5,69.0,5,55.0,,88.0,1,74.0,2,,71.0,2,2,,,172.0,1,68.0,3, +6,51.0,2,2,1,1,4,1,3,1,69.0,99.0,78.0,54.0,55.0,88.0,1,,1,,1,,0.8,Pepper,2019-04-20,1,78.0,1,55.0,1,88.0,1,99.0,1,,69.0,,2,1,3,,1,54.0,4, +6,38.0,2,1,2,1,5,4,3,3,79.0,79.0,92.0,88.0,76.0,91.0,2,2,,,,,0.8,Onion,2019-07-24,3,92.0,4,76.0,,91.0,2,79.0,2,,79.0,2,1,,3,174.0,1,88.0,5, +6,60.0,1,1,1,1,,1,5,4,82.0,56.0,75.0,65.0,53.0,,2,1,,1,,2,1.2,Onion,2019-01-26,4,75.0,1,53.0,,,1,56.0,2,1,82.0,1,1,,5,175.0,1,65.0,,2 +6,29.0,2,1,2,1,,4,3,3,66.0,43.0,74.0,93.0,52.0,84.0,,,,,,,0.8,Lettuce,2019-08-06,3,74.0,4,52.0,,84.0,2,43.0,,,66.0,,1,,3,176.0,1,93.0,, +6,33.0,1,2,2,1,4,4,3,5,69.0,46.0,60.0,51.0,70.0,94.0,1,1,1,2,,,1.2,Carrot,2019-06-18,5,60.0,4,70.0,,94.0,2,46.0,1,2,69.0,1,2,1,3,177.0,1,51.0,4, +6,37.0,2,2,2,1,5,3,3,3,86.0,76.0,77.0,61.0,69.0,93.0,2,2,,,,,0.8,Lettuce,2019-09-22,3,77.0,3,69.0,,93.0,2,76.0,2,,86.0,2,2,,3,178.0,1,61.0,5, +6,37.0,1,2,1,1,4,1,3,4,80.0,62.0,67.0,93.0,63.0,85.0,1,2,2,,,,1.2,Onion,2019-08-12,4,67.0,1,63.0,,85.0,1,62.0,1,,80.0,2,2,2,3,179.0,1,93.0,4, +6,57.0,2,,2,2,4,4,3,4,74.0,46.0,63.0,83.0,73.0,90.0,1,2,1,,1,,0.8,Carrot,2019-05-31,4,63.0,4,73.0,1,90.0,2,46.0,1,,74.0,2,,1,3,180.0,2,83.0,4, +7,29.0,2,,2,,3,5,3,4,88.0,93.0,89.0,79.0,61.0,90.0,2,1,,1,,1,0.8,Avocado,2019-08-31,4,89.0,5,61.0,,90.0,2,93.0,2,1,88.0,1,,,3,181.0,,79.0,3,1 +7,48.0,2,2,2,1,2,4,5,4,64.0,86.0,64.0,67.0,59.0,80.0,1,1,2,1,,2,0.8,,2019-10-09,4,64.0,4,59.0,,80.0,2,86.0,1,1,64.0,1,2,2,5,182.0,1,67.0,2,2 +7,47.0,1,2,1,1,1,1,4,3,82.0,40.0,,84.0,67.0,81.0,1,2,2,,,,1.2,Pepper,2019-01-29,3,,1,67.0,,81.0,1,40.0,1,,82.0,2,2,2,4,183.0,1,84.0,1, +7,25.0,1,1,1,1,4,3,3,3,78.0,45.0,83.0,90.0,75.0,93.0,2,1,,1,,1,1.2,Pepper,2019-06-30,3,83.0,3,75.0,,93.0,1,45.0,2,1,78.0,1,1,,3,184.0,1,90.0,4,1 +7,50.0,1,1,1,1,1,1,3,4,90.0,97.0,64.0,80.0,60.0,,2,2,,,,,1.2,Pepper,2019-11-15,4,64.0,1,60.0,,,1,97.0,2,,90.0,2,1,,3,185.0,1,80.0,1, +7,53.0,1,2,1,1,4,1,3,4,76.0,69.0,84.0,76.0,51.0,81.0,2,2,,,,,1.2,Onion,2019-01-18,4,84.0,1,51.0,,81.0,1,69.0,2,,76.0,2,2,,3,186.0,1,76.0,4, +7,37.0,2,2,2,1,4,1,5,,89.0,91.0,89.0,50.0,51.0,83.0,1,1,1,1,2,1,0.8,Lettuce,2019-07-24,,89.0,1,51.0,2,83.0,2,91.0,1,1,89.0,1,2,1,5,187.0,1,50.0,4,1 +7,43.0,2,2,1,2,,1,,4,65.0,88.0,61.0,75.0,54.0,85.0,1,2,2,,,,0.8,Green beans,2019-02-12,4,61.0,1,54.0,,85.0,1,88.0,1,,65.0,2,2,2,,,2,75.0,, +7,22.0,1,1,,1,1,1,3,4,75.0,83.0,82.0,77.0,,83.0,2,2,,,,,1.2,Lettuce,2019-02-28,4,82.0,1,,,83.0,,83.0,2,,75.0,2,1,,3,189.0,1,77.0,1, +7,47.0,2,2,1,1,1,5,3,3,63.0,71.0,62.0,64.0,86.0,94.0,1,1,1,1,2,1,0.8,Carrot,,3,62.0,5,86.0,2,94.0,1,71.0,1,1,63.0,1,2,1,3,190.0,1,64.0,1,1 +7,27.0,1,2,1,1,4,5,3,4,64.0,42.0,87.0,86.0,53.0,92.0,2,2,,,,,1.2,Onion,2019-10-30,4,87.0,5,53.0,,92.0,1,42.0,2,,64.0,2,2,,3,191.0,1,86.0,4, +7,43.0,1,2,2,1,4,4,1,,70.0,91.0,61.0,55.0,57.0,85.0,2,2,,,,,1.2,Onion,2019-04-26,,61.0,4,57.0,,85.0,2,91.0,2,,70.0,2,2,,1,192.0,1,55.0,4, +7,47.0,1,2,1,1,4,1,4,3,74.0,33.0,67.0,82.0,52.0,86.0,2,2,,,,,1.2,Onion,2019-09-25,3,67.0,1,52.0,,86.0,1,33.0,2,,74.0,2,2,,4,193.0,1,82.0,4, +7,56.0,2,1,1,2,,1,3,4,63.0,33.0,66.0,86.0,52.0,84.0,1,1,,1,,1,0.8,Lettuce,2019-02-15,4,66.0,1,52.0,,84.0,1,33.0,1,1,63.0,1,1,,3,194.0,2,86.0,,1 +7,51.0,2,2,,1,4,1,3,5,83.0,55.0,75.0,88.0,50.0,84.0,2,1,,2,,,0.8,Green beans,2019-12-13,5,75.0,1,50.0,,84.0,,55.0,2,2,83.0,1,2,,3,195.0,1,88.0,4, +7,35.0,2,1,1,2,4,4,5,3,,61.0,66.0,88.0,58.0,,1,2,2,,,,0.8,Carrot,2019-05-02,3,66.0,4,58.0,,,1,61.0,1,,,2,1,2,5,196.0,2,88.0,4, +7,54.0,1,2,1,1,4,1,1,5,61.0,85.0,70.0,,50.0,80.0,1,2,2,,,,1.2,Green beans,2019-09-01,5,70.0,1,50.0,,80.0,1,85.0,1,,61.0,2,2,2,1,197.0,1,,4, +7,42.0,2,2,1,2,4,2,5,4,83.0,46.0,79.0,82.0,51.0,86.0,1,2,,,,,0.8,Avocado,2019-05-02,4,79.0,2,51.0,,86.0,1,46.0,1,,83.0,2,2,,5,198.0,2,82.0,4, +7,44.0,2,2,2,1,2,,3,4,73.0,87.0,73.0,52.0,58.0,83.0,1,2,2,,,,0.8,Green beans,2019-07-25,4,73.0,,58.0,,83.0,2,87.0,1,,73.0,2,2,2,3,199.0,1,52.0,2, +7,60.0,2,1,2,2,4,1,3,,63.0,31.0,71.0,63.0,57.0,83.0,1,2,1,,1,,0.8,Avocado,2019-03-14,,71.0,1,57.0,1,83.0,2,31.0,1,,63.0,2,1,1,3,200.0,2,63.0,4, +7,41.0,2,1,1,1,2,1,3,4,88.0,56.0,76.0,80.0,56.0,83.0,2,2,,,,,0.8,Lettuce,2019-09-29,4,76.0,1,56.0,,83.0,1,56.0,2,,88.0,2,1,,3,201.0,1,80.0,2, +7,36.0,2,1,2,,4,4,3,4,83.0,57.0,77.0,80.0,62.0,92.0,1,2,2,,,,0.8,Carrot,2019-03-12,4,77.0,4,62.0,,92.0,2,57.0,1,,83.0,2,1,2,3,202.0,,80.0,4, +7,59.0,2,2,1,2,2,1,5,3,76.0,68.0,67.0,81.0,56.0,85.0,1,2,1,,2,,0.8,Onion,2019-10-09,3,67.0,1,56.0,2,85.0,1,68.0,1,,76.0,2,2,1,5,203.0,2,81.0,2, +7,37.0,1,1,1,1,2,3,4,4,81.0,48.0,86.0,84.0,,,,2,,,,,1.2,Pepper,2019-11-03,4,86.0,3,,,,1,48.0,,,81.0,2,1,,4,204.0,1,84.0,2, +7,50.0,2,2,1,1,3,5,5,3,77.0,88.0,75.0,67.0,54.0,91.0,1,2,2,,,,0.8,Green beans,2019-09-08,3,75.0,5,54.0,,91.0,1,88.0,1,,77.0,2,2,2,5,205.0,1,67.0,3, +7,54.0,1,2,1,1,1,4,3,3,66.0,93.0,92.0,69.0,64.0,83.0,2,2,,,,,1.2,Lettuce,2019-04-21,3,92.0,4,64.0,,83.0,1,93.0,2,,66.0,2,2,,3,206.0,1,69.0,1, +7,54.0,2,2,1,1,2,4,5,5,74.0,46.0,79.0,88.0,71.0,85.0,2,2,,,,,0.8,Lettuce,2019-01-05,5,79.0,4,71.0,,85.0,1,46.0,2,,74.0,2,2,,5,207.0,1,88.0,2, +7,44.0,1,,2,1,1,2,3,4,72.0,42.0,80.0,69.0,50.0,81.0,1,1,2,1,,1,1.2,Avocado,2019-02-16,4,80.0,2,50.0,,81.0,2,42.0,1,1,72.0,1,,2,3,208.0,1,69.0,1,1 +7,27.0,2,1,2,2,2,5,5,3,65.0,42.0,78.0,91.0,65.0,86.0,1,2,2,,,,0.8,Avocado,2019-04-28,3,78.0,5,65.0,,86.0,2,42.0,1,,65.0,2,1,2,5,209.0,2,91.0,2, +7,50.0,2,2,2,1,5,5,4,4,70.0,87.0,72.0,64.0,72.0,90.0,2,2,,,,,0.8,Lettuce,2019-06-18,4,72.0,5,72.0,,90.0,2,87.0,2,,70.0,2,2,,4,210.0,1,64.0,5, diff --git a/mocks/dataset-fixtures/veg_df.rds b/mocks/dataset-fixtures/veg_df.rds index 8f50f7bfeabbf869962ef2f0ab8b03cffabbbfdc..348b4e6a53f08bf966f8f64746949fd23cc54828 100644 GIT binary patch literal 4688 zcma)+`9Bkm`ZP$3Z=%DolIeb)>#nF?>YQjXYM3B7ZbE3;{l z@pdJZ+s4LdXw0z--}mG57kpoj*W>a0<@IWbD^5bl9@vn?LDb)T* zz~c@R0a=AEY96@|i{6x#y8Gvd#*?};hzm-c{;LK*(AR>RW9Wf_{dR8tL!W|Dl#I z(Lyu?uuO4&!^B||kFKy+*-dg=%JsOE>{<{@qOj@>`_7C+O9r!=L8BKh1wyN|nr?~C zUxv&oxPIw3`E#Kx5%YMv)|S9}g3FLti>%+mj4)+&<;-d!e0AhqR6|O4q9Gm&3rKGE zHq!{c05a>)nAgCak$rgAdIS>w!MzqZqdvF_C~{c)vE04xnrL>{sSEhYTq;T$mmw7f z6n%lLGX(0(-+<%{t^6y{5g9bzG?QKgjInS?iK}X-`zETIL&6dapB7}uh%?*98&n0) zFr!w{^sDQAuH~)zq!L-7a-|1Tbz@=n9v7<)6Yu*}D{JBK-mG@u!Kq?!XPCP>@GiJ3 zSx^AZO~ja+jb(x}z=Bjm3oK6vh#3=FhMQC16_To5rOXnZZzTLQLTdwVolSs7sin_S zEckP&e6H>VKH$^-t()oQ%S@W8-Cpn+ 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zaUZ|R2K`>UdwO%DGfdipG}RLyu>R6!+R66Q0lu}W8VzRC%IK(-eKP^7CV!#U*wJ}m=fM7ol7@8yqAc})w3*kYFrE5La zt-wI@;K|Jsix*1n1AhNQ*I=CDV)G^ZK#M}G%tAx`M~hy>_;H_8-C||>lk39ck;~(S zksn+<4s3ea&Th)jR@fFcxot@YBltJ%H8y8k6MM$_HBAJUteRuoC7%Xf5wMV+Po2g`YnBB=w!z4ak#5=4cv%sTwmLP;M_y_#^(4!CHLexBmTX)P@zJF3KcpJ zs^nY}DpaUYp$kNnoJ&H53Kc4JfvA#mNvKevLWM36RdOx~6)IGy&;_DO&LyEjg$fnA zKvd~->A_#Wk3N4q_~*{}bAe*sMP2)*-5*+CR^Jfc(7svOTbBQ{_?6YW>b`01D=Vk0 zzpCO9@=u7*tZ@wKH!a>(^{nd0S?Lq9ch>k{7N2QxEGsvJL)raR{WC32m-R@YaV=f_Mv2IFK2JUd@;l6^eTKis54uw*FG3tNcno z5+$7EENs)u`kFx z<=t55O$pO==H%1?<*Tse)3s`ybQ@h!O3-7K?_kU89XzH3nd!gt#^TBUxEKw|cQHbR z3Kc4JVQBti{Nj@{HF>X_G-=YNZ%Xn>nzWfUCHW*x+RU1ge3B+@W=%;xNs~6SrX-)F zNt;v7~syqj8!h#gdcrC5_Yb>$o%A9cJt$;`r}gOWRwop1$dxKOr{iW?bj& z*M2Wm=Ih6!A78Jl@w>-IYa=jTDYQwB@gPZ*v~hWTKTW#X{^?GdWW|#yAEmvY`=>hx zgPmb=+#f*k<@4dgPRlZSlBQ7n)8n+gGnr{6J6edI`^1>bl3{jg-L=>0c00Ycp1Wbp zrM+Y986;W0YPi!oI!JaB Date: Mon, 5 Aug 2024 14:09:21 -0500 Subject: [PATCH 07/12] [188037693]: tests for new as.data.frame behavior --- tests/testthat/test-as-data-frame.R | 110 ++++++++++++++++++++++++++-- 1 file changed, 103 insertions(+), 7 deletions(-) diff --git a/tests/testthat/test-as-data-frame.R b/tests/testthat/test-as-data-frame.R index 092796061..379d5751c 100644 --- a/tests/testthat/test-as-data-frame.R +++ b/tests/testthat/test-as-data-frame.R @@ -4,6 +4,7 @@ context("Getting values to make local R objects") with_mock_crunch({ ds <- cachedLoadDataset("Vegetables example") + ds_dup <- loadDataset(paste0(envOrOption("crunch.api"), "datasets/dup")) test_that("setup", { expect_identical(nrow(ds), 210L) expect_identical(ncol(ds), 12L) @@ -98,12 +99,67 @@ with_mock_crunch({ expect_POST( as.data.frame(ds, force = TRUE, include.hidden = FALSE), "https://app.crunch.io/api/datasets/veg/export/csv/", - '{"filter":null,"options":{"use_category_ids":true}}' + '{"filter":null,"options":{"header_field":"qualified_alias",', + '"missing_values":"","use_category_ids":true}}' ) }) + test_that("csvColInfo works in simple case with no duplicate aliases", { + col_info <- csvColInfo( + ds[, c("wave", "age", "enjoy_mr", "last_vegetable", "last_vegetable_date")] + ) + + expect_s4_class(attr(col_info, "meta"), "VariableCatalog") + attr(col_info, "meta") <- NULL + + expected <- data.frame( + stringsAsFactors = FALSE, + orig_alias = c("wave","age", + "last_vegetable","last_vegetable_date","enjoy_mr_savory", + "enjoy_mr_spicy","enjoy_mr_sweet"), + parent_alias = c(NA, NA, NA, NA, "enjoy_mr", "enjoy_mr", "enjoy_mr"), + qualified_alias = c("wave","age", + "last_vegetable","last_vegetable_date", + "enjoy_mr[enjoy_mr_savory]","enjoy_mr[enjoy_mr_spicy]", + "enjoy_mr[enjoy_mr_sweet]"), + cond_qualified_alias = c("wave","age", + "last_vegetable","last_vegetable_date","enjoy_mr_savory", + "enjoy_mr_spicy","enjoy_mr_sweet"), + var_type = c("categorical", + "numeric","text","datetime","categorical", + "categorical","categorical") + ) + + expect_equivalent(col_info, expected) + }) + + test_that("csvColInfo works when there are duplicate aliases", { + col_info <- csvColInfo(ds_dup) + + expect_s4_class(attr(col_info, "meta"), "VariableCatalog") + attr(col_info, "meta") <- NULL + + expected <- data.frame( + stringsAsFactors = FALSE, + orig_alias = c("x1","x2","y1", + "y2","z","x1","x2_derived","y1","z"), + parent_alias = c(NA, NA, NA, NA, NA, "x", "x", "y", "y"), + qualified_alias = c("x1","x2","y1", + "y2","z","x[x1]","x[x2_derived]","y[y1]","y[z]"), + cond_qualified_alias = c("x1","x2","y1", + "y2","z","x[x1]","x2_derived","y[y1]","y[z]"), + var_type = c("numeric","numeric", + "categorical","categorical","categorical", + "numeric","numeric","categorical","categorical") + ) + expect_equivalent(col_info, expected) + }) + test_that("csvToDataFrame produces the correct data frame", { - csv_df <- read.csv(datasetFixturePath("veg.csv"), stringsAsFactors = FALSE) + csv_df <- read.csv( + datasetFixturePath("veg.csv"), + stringsAsFactors = FALSE, check.names = FALSE#, na.strings = "" + ) expected <- readRDS(datasetFixturePath("veg_df.rds")) vars <- c( "wave", "age", "healthy_eater", "enjoy_mr", "veg_enjoy_ca", "ratings_numa", @@ -112,20 +168,60 @@ with_mock_crunch({ cdf <- as.data.frame(ds[, vars]) # test local CDF variables cdf$newvar <- expected$newvar <- c(1:209, NA) - - expect_equal(csvToDataFrame(csv_df, cdf), expected) + col_info <- csvColInfo(ds[, vars]) + actual <- csvToDataFrame(csv_df, cdf, col_info) + expect_equal(actual, expected) }) test_that("csvToDataFrame respects include.hidden", { # mock the include.hidden=FALSE by removing variables from csv_df - csv_df <- read.csv(datasetFixturePath("veg-no-hidden.csv"), stringsAsFactors = FALSE) + col_info <- csvColInfo(ds) + csv_df <- read.csv(datasetFixturePath("veg-no-hidden.csv"), stringsAsFactors = FALSE, check.names = FALSE) expected <- readRDS(datasetFixturePath("veg_hidden_df.rds")) cdf <- as.data.frame(ds, include.hidden = FALSE) - actual <- csvToDataFrame(csv_df, cdf) - actual <- actual[, !grepl("^funnel", names(actual))] # funnel vars added after test added + actual <- csvToDataFrame(csv_df, cdf, col_info) expect_equal(actual, expected) }) + test_that("csvToDataFrame handles duplicate aliases", { + csv_df <- read.csv(datasetFixturePath("dup.csv"), stringsAsFactors = FALSE, check.names = FALSE) + col_info <- csvColInfo(ds_dup) + + expected_flat_df <- data.frame( + v1 = 1:3, + v2 = 2:4, + v3 = factor(letters[1:3], levels = letters[1:5]), + v4 = factor(letters[2:4], levels = letters[1:5]), + v5 = factor(letters[11:13], levels = letters[11:15]), + v6 = 1:3, + v7 = 2:4, + v8 = factor(letters[1:3], levels = letters[1:5]), + v9 = factor(letters[2:4], levels = letters[1:5]) + ) + + df_cond_qualified <- csvToDataFrame(csv_df, ds_dup, col_info) + cond_qualified_names <- c("x1", "x2", "y1", "y2", "z", "x[x1]", "x2_derived", "y[y1]", "y[z]") + expect_equal(names(df_cond_qualified), cond_qualified_names) + expect_equivalent( + setNames(df_cond_qualified, paste0("v", seq_len(ncol(df_cond_qualified)))), + expected_flat_df + ) + + df_qualified <- csvToDataFrame(csv_df, ds_dup, col_info, "qualified_alias") + qualified_names <- c("x1", "x2", "y1", "y2", "z", "x[x1]", "x[x2_derived]", "y[y1]", "y[z]") + expect_equal(names(df_qualified), qualified_names) + expect_equivalent( + setNames(df_qualified, paste0("v", seq_len(ncol(df_qualified)))), + expected_flat_df + ) + + expected_packed_df <- setNames(expected_flat_df[, 1:5], c("x1", "x2", "y1", "y2", "z")) + expected_packed_df$v6 <- setNames(expected_flat_df[, 6:7], c("x1", "x2_derived")) + expected_packed_df$v7 <- setNames(expected_flat_df[, 8:9], c("y1", "z")) + df_packed <- csvToDataFrame(csv_df, ds_dup, col_info, "packed") + expect_equivalent(df_packed, expected_packed_df) + }) + test_that("as.data.frame when a variable has an apostrophe in its alias", { t2 <- forceVariableCatalog(ds) From 9a6801789b2c610ce7fde8f2cac12550ef50381e Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 5 Aug 2024 14:09:31 -0500 Subject: [PATCH 08/12] [188037693]: fix warning that snuck into tests --- tests/testthat/test-new-dataset.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test-new-dataset.R b/tests/testthat/test-new-dataset.R index edcff7c9d..05a3be492 100644 --- a/tests/testthat/test-new-dataset.R +++ b/tests/testthat/test-new-dataset.R @@ -115,7 +115,7 @@ with_mock_crunch({ }) test_that("newDataset with a .json schema posts to datasets", { path_json <- system.file("example-datasets", "pets.json", package = "crunch") - content_json <- readLines(path_json) + content_json <- jsonlite::minify(readLines(path_json)) expect_POST( newDataset(x = "helper.R", schema = path_json), "https://app.crunch.io/api/datasets/", From 6bfc939441729b60d2d164d495dbdfeb9850206b Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 5 Aug 2024 14:19:10 -0500 Subject: [PATCH 09/12] [188037693]: add integeration test for dup subvar aliases --- tests/testthat/test-as-data-frame.R | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/tests/testthat/test-as-data-frame.R b/tests/testthat/test-as-data-frame.R index 379d5751c..fea5aca2b 100644 --- a/tests/testthat/test-as-data-frame.R +++ b/tests/testthat/test-as-data-frame.R @@ -395,13 +395,22 @@ with_test_authentication({ test_that("Multiple response variables in as.data.frame(force=TRUE)", { skip_on_local_backend("Vagrant host doesn't serve files correctly") mrds <- mrdf.setup(newDataset(mrdf, name = "test-mrdfmr"), selections = "1.0") + mrds$MR2 <- deriveArray( + list( + VarDef(ds$MR$mr_1, name = "dup mr_1", alias = "mr_1"), + VarDef(ds$MR$mr_2, name = "dup v4", alias = "v4") + ), + name = "MR 2" + ) mrds_df <- as.data.frame(mrds, force = TRUE) expect_equal(ncol(mrds_df), 4) - expect_equal(names(mrds_df), c("mr_1", "mr_2", "mr_3", "v4")) - expect_equal(mrds_df$mr_1, as.vector(mrds$MR$mr_1)) + expect_equal(names(mrds_df), c("MR[mr_1]", "mr_2", "mr_3", "v4", "MR2[mr_1]", "MR2[v4]")) + expect_equal(mrds_df[["MR[mr_1]"]], as.vector(mrds$MR$mr_1)) expect_equal(mrds_df$mr_2, as.vector(mrds$MR$mr_2)) expect_equal(mrds_df$mr_3, as.vector(mrds$MR$mr_3)) expect_equal(mrds_df$v4, as.vector(mrds$v4)) + expect_equal(mrds_df[["MR2[mr_1]"]], as.vector(mrds$MR2$mr_1)) + expect_equal(mrds_df[["MR2[v4]"]], as.vector(mrds$MR2$v4)) }) v2 <- ds$v2 From 31ef3f2cf76aabf4888831afe4c7fe302759fafe Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Tue, 6 Aug 2024 09:52:08 -0500 Subject: [PATCH 10/12] [188037693]: fixes for integration tests --- R/as-data-frame.R | 2 +- tests/testthat/test-as-data-frame.R | 27 ++++++++++++++------------- 2 files changed, 15 insertions(+), 14 deletions(-) diff --git a/R/as-data-frame.R b/R/as-data-frame.R index c3bc7042a..6075371d9 100644 --- a/R/as-data-frame.R +++ b/R/as-data-frame.R @@ -162,7 +162,7 @@ csvColInfo <- function(ds, verbose = TRUE) { alias_info <- paste0(out$orig_alias[msg_rows], " -> ", out$cond_qualified_alias[msg_rows]) message( "Some column names are qualified because there were duplicate aliases ", - "in dataset: ", paste0(alias_info, collapse = ", ") + "in dataset:\n", paste0(alias_info, collapse = ", ") ) } } diff --git a/tests/testthat/test-as-data-frame.R b/tests/testthat/test-as-data-frame.R index fea5aca2b..289d9eb2a 100644 --- a/tests/testthat/test-as-data-frame.R +++ b/tests/testthat/test-as-data-frame.R @@ -379,16 +379,10 @@ with_test_authentication({ df <- as.data.frame(ds, force = TRUE, include.hidden = FALSE) expect_equal(names(df), c("v1", "v2", "v3", "v4", "v5", "vee six !")) - expect_warning( - df <- as.data.frame(ds, force = TRUE, include.hidden = TRUE), - "Variable hidden_var is hidden" - ) + df <- as.data.frame(ds, force = TRUE, include.hidden = TRUE) expect_equal(names(df), c("v1", "v2", "v3", "v4", "v5", "vee six !", "hidden_var")) - expect_warning( - df <- as.data.frame(ds[, c("v1", "hidden_var")], force = TRUE), - "Variable hidden_var is hidden" - ) + df <- as.data.frame(ds[, c("v1", "hidden_var")], force = TRUE) expect_equal(names(df), c("v1", "hidden_var")) }) @@ -397,13 +391,20 @@ with_test_authentication({ mrds <- mrdf.setup(newDataset(mrdf, name = "test-mrdfmr"), selections = "1.0") mrds$MR2 <- deriveArray( list( - VarDef(ds$MR$mr_1, name = "dup mr_1", alias = "mr_1"), - VarDef(ds$MR$mr_2, name = "dup v4", alias = "v4") + VarDef(mrds$MR$mr_1, name = "dup mr_1", alias = "mr_1"), + VarDef(mrds$MR$mr_2, name = "dup v4", alias = "v4") ), - name = "MR 2" + name = "MR 2", numeric = FALSE ) - mrds_df <- as.data.frame(mrds, force = TRUE) - expect_equal(ncol(mrds_df), 4) + expect_message( + mrds_df <- as.data.frame(mrds, force = TRUE), + paste0( + "Some column names are qualified because there were duplicate aliases ", + "in dataset:\nmr_1 -> MR[mr_1], mr_1 -> MR2[mr_1], v4 -> MR2[v4]" + ) + ) + + expect_equal(ncol(mrds_df), 6) expect_equal(names(mrds_df), c("MR[mr_1]", "mr_2", "mr_3", "v4", "MR2[mr_1]", "MR2[v4]")) expect_equal(mrds_df[["MR[mr_1]"]], as.vector(mrds$MR$mr_1)) expect_equal(mrds_df$mr_2, as.vector(mrds$MR$mr_2)) From c26ad0e6af4c47cabb815a9b7d606625f46ac2e8 Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Tue, 6 Aug 2024 11:35:12 -0500 Subject: [PATCH 11/12] [188037693]: ugh --- tests/testthat/test-as-data-frame.R | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tests/testthat/test-as-data-frame.R b/tests/testthat/test-as-data-frame.R index 289d9eb2a..56b78c5b3 100644 --- a/tests/testthat/test-as-data-frame.R +++ b/tests/testthat/test-as-data-frame.R @@ -401,7 +401,8 @@ with_test_authentication({ paste0( "Some column names are qualified because there were duplicate aliases ", "in dataset:\nmr_1 -> MR[mr_1], mr_1 -> MR2[mr_1], v4 -> MR2[v4]" - ) + ), + fixed = TRUE ) expect_equal(ncol(mrds_df), 6) From 0e7b92f7c8a7b12ec98e0af695ad9c717224d989 Mon Sep 17 00:00:00 2001 From: Greg Freedman Ellis Date: Mon, 12 Aug 2024 08:28:16 -0500 Subject: [PATCH 12/12] [188037693]: update docs per review --- R/as-data-frame.R | 6 +++--- man/dataset-to-R.Rd | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/R/as-data-frame.R b/R/as-data-frame.R index 6075371d9..d3f6bf2b8 100644 --- a/R/as-data-frame.R +++ b/R/as-data-frame.R @@ -29,9 +29,9 @@ #' levels matching the Crunch Variable's categories (the default), or, if #' `categorical.mode` is specified as "id" or "numeric", a numeric vector of #' category ids or numeric values, respectively -#' * Array variables (Categorical Array, Multiple Response) are decomposed into -#' their constituent categorical subvariables. An array with three subvariables, -#' for example, will result in three columns in the `data.frame`. +#' * Array variables (Categorical Array, Multiple Response) can be decomposed into +#' their constituent categorical subvariables or put in 'packed' data.frame columns, +#' see the `array_strategy` argument. #' #' Column names in the `data.frame` are the variable/subvariable aliases. #' diff --git a/man/dataset-to-R.Rd b/man/dataset-to-R.Rd index b6ea130d9..5e45774e1 100644 --- a/man/dataset-to-R.Rd +++ b/man/dataset-to-R.Rd @@ -94,9 +94,9 @@ values into their R equivalents using the following rules: levels matching the Crunch Variable's categories (the default), or, if \code{categorical.mode} is specified as "id" or "numeric", a numeric vector of category ids or numeric values, respectively -\item Array variables (Categorical Array, Multiple Response) are decomposed into -their constituent categorical subvariables. An array with three subvariables, -for example, will result in three columns in the \code{data.frame}. +\item Array variables (Categorical Array, Multiple Response) can be decomposed into +their constituent categorical subvariables or put in 'packed' data.frame columns, +see the \code{array_strategy} argument. } Column names in the \code{data.frame} are the variable/subvariable aliases.