Merge pull request #1 from leibniz-psychology/staging

Staging
leibniz-psychology · Nov 23, 2023 · 5371af0 · 5371af0
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diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
@@ -0,0 +1,21 @@
+# Pull-Request
+
+## References
+
+*Add references/links to any related issues or PRs. These may include:*
+
+- Fixes #`issue-number` (if this fixes an issue ticket)
+- plan.io ticket number
+
+## Description
+
+Short summary of changes (1-2 sentences).
+
+## Instructions for Reviewers
+
+*Are there special features to be aware of when looking at the code changes. Please list:*
+
+something
+
+- ...
+- ...
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -8,6 +8,13 @@ Sonstiges:
 Bugs:
 * updated Dockerfile, removed static ssl files
 
+## Release 1.0.8
+### 11.2023
+Bugs:
+* added new datasets CAMA_TFP_Reduce, CAMA_TFP_Enhance
+* added metadata files for all datasets
+
+
 ## Hotfix Release 1.0.5
 ### 04.2023
 Bugs:

diff --git a/NAMESPACE b/NAMESPACE
@@ -1,25 +1,27 @@
-# Generated by roxygen2: do not edit by hand
-
-importFrom("stats", "as.formula", "pnorm", "predict", "qnorm")
-importFrom("utils", "str")
-
-export(cumulforest)
-export(forest)
-export(funnelPLot)
-export(jsonSummary)
-export(metapower)
-export(netMetaForestplot)
-export(netMetaMatrix)
-export(netMetaModel)
-export(netMetaNetgraph)
-export(netMetaSummary)
-export(netMetagetTRTS)
-export(pcurves)
-export(powersim)
-export(rmaMVModel)
-export(rmaModel)
-export(scatterPlot)
-export(pureForest)
-export(pureCumulForest)
-export(rma)
-export(checkIntegrity)
+# Generated by roxygen2: do not edit by hand
+
+importFrom("stats", "as.formula", "pnorm", "predict", "qnorm")
+importFrom("utils", "str")
+
+export(cumulforest)
+export(forest)
+export(funnelPLot)
+export(jsonSummary)
+export(metapower)
+export(netMetaForestplot)
+export(netMetaMatrix)
+export(netMetaModel)
+export(netMetaNetgraph)
+export(netMetaSummary)
+export(netMetagetTRTS)
+export(pcurves)
+export(powersim)
+export(rmaMVModel)
+export(rmaModel)
+export(scatterPlot)
+export(pureForest)
+export(pureCumulForest)
+export(rma)
+export(checkIntegrity)
+export(checkData)
+export(checkParameter)
diff --git a/R/checkData.R b/R/checkData.R
@@ -1,25 +1,26 @@
-#' @title check data
-#' @description
-#' Helper function to check if the given dataset exists
-#' @param d
-#' A \code{string} representing the dataset name that should be used.
-checkData <- function(d) {
-
-  dat <- tryCatch(
-    {get(d)},
-    error=function(cond) {
-      message(paste("This dataset does not exist:", d))
-      message("Here's the original error message:")
-      message(cond)
-      stop("stopped code because of error")
-    },
-    warning=function(cond) {
-      message(paste("input caused a warning:", d))
-      message("Here's the original warning message:")
-      message(cond)
-      # Choose a return value in case of warning
-      stop("stopped code because of error")
-    }
-  )
-  return(dat)
-}
+#' @title check data
+#' @description
+#' Helper function to check if the given dataset exists
+#' @param d
+#' A \code{string} representing the dataset name that should be used.
+
+checkData <- function(d) {
+
+  dat <- tryCatch(
+    {get(d)},
+    error=function(cond) {
+      message(paste("This dataset does not exist:", d))
+      message("Here's the original error message:")
+      message(cond)
+      stop("stopped code because of error")
+    },
+    warning=function(cond) {
+      message(paste("input caused a warning:", d))
+      message("Here's the original warning message:")
+      message(cond)
+      # Choose a return value in case of warning
+      stop("stopped code because of error")
+    }
+  )
+  return(dat)
+}
diff --git a/R/checkIntegrity.R b/R/checkIntegrity.R
@@ -1,19 +1,46 @@
-#' @title check integrity
-#' @description
-#' Helper function to check if the
-#' @export
-#' @param d
-#' A \code{string} representing the dataset name that should be used.
-checkIntegrity <- function() {
-  datasetList<-data(package='psychOpenCama')$results[,"Item"]
-  tablesThatShouldExist<-c("r_year","r_peer","r_author")
-
-  for (datasetName in datasetList) {
-    dat<-checkData(datasetName)
-    print(datasetName)
-    checkParameter(dat,tablesThatShouldExist)
-
-  }
-
-}
-
+#' @title check integrity
+#' @description
+#' Helper function to check if the
+#' @export
+#' @param d
+#' A \code{string} representing the dataset name that should be used.
+# Load the required library
+library(jsonlite)
+
+
+
+checkIntegrity <- function() {
+  datasetList<-data(package='psychOpenCama')$results[,"Item"]
+
+  tablesThatShouldExist<-c("r_year","r_peer","r_author")
+  metadata_folder <- "metadata"
+
+
+  for (datasetName in datasetList) {
+    dat<-psychOpenCama::checkData(datasetName)
+    print(datasetName)
+    checkParameter(dat,tablesThatShouldExist)
+
+
+    jsonFileName <- paste0("Datensatz_", datasetName,".json")
+    #print(jsonFileName);
+
+    # Define the path to the JSON file within your package
+    json_file <- system.file("metadata", jsonFileName, package = "psychOpenCama")
+
+    rel_path_from_root <- file.path("metadata", jsonFileName)
+
+    if (file.exists(rel_path_from_root)) {
+      # Read and parse the JSON file
+      json_data <- jsonlite::fromJSON(rel_path_from_root)
+
+      # Print the JSON data (for demonstration purposes)
+      #print(json_data)
+    } else {
+      #cat("JSON file not found in the 'metadata' subfolder.\n")
+    }
+  }
+
+
+}
+
diff --git a/data/CAMA_TFP.RData b/data/CAMA_TFP.RData
diff --git a/data/CAMA_TFP_Enhance.RData b/data/CAMA_TFP_Enhance.RData
diff --git a/data/CAMA_TFP_Reduce.RData b/data/CAMA_TFP_Reduce.RData
diff --git a/metadata/Datensatz_CAMA_Abstractr.json b/metadata/Datensatz_CAMA_Abstractr.json
@@ -0,0 +1,140 @@
+{
+	"datensatz" : {
+		"name" : "Abstract rule learning",
+		"data_name":"CAMA_Abstractr",
+		"psychArchivesHandle": "http://dx.doi.org/10.23668/psycharchives.4740",
+		"domain": "Cognitive development",
+		"metaanalysis_type": "standard",
+		"moderators" : [{"type":"cat","value":"t_semantics","label": "Semantics"},{"type": "cat","value":"t_modality","label": "Modality" },{"type": "num","value":"s_meanage","label": "Mean Age" }],
+		"effect_strength" : {
+			"label" : "SMD",
+			"variable_labels":[
+				{
+					"name":"hedges_g",
+					"data_label_yi":"o_g_calc",
+					"varianz_label_vi": "o_g_var_calc"
+				},
+				{
+					"name":"cohens_d",
+					"data_label_yi":"o_d_calc",
+					"varianz_label_vi": "o_d_var_calc"
+				}
+			]
+		},
+		"analysis_levels":["outcome_ID","sample_ID","report_ID"]
+	},
+	"dokumentation" : {
+		"title": "Rabagliati, H,Ferguson, B,Lew‐Williams, C.The profile of abstract rule learning in infancy: Meta‐analytic and experimental evidence. Dev Sci.2019;22:e12704.",
+		"doi": "https://doi.org/10.1111/desc.12704",
+		"url":"",
+		"dataProvider": "http://metalab.stanford.edu/: Bergmann, C., Tsuji, S., Piccinini, P.E., Lewis, M.L., Braginsky, M., Frank, M.C., & Cristia, A. (2018). Promoting replicability in developmental research through meta-analyses: Insights from language acquisition research. Child Development, 89, 1996-2009 . DOI: 10.1111/cdev.13079 [Repository]",
+		"license": "CC BY 4.0",
+		"licenseUrl": "https://creativecommons.org/licenses/by-sa/4.0/",
+		"researchQuestion": "Can infants learn abstract repition rules from different types of stimuli?",
+		"literatureSelection": "Inclusion: (1) Participants were typically developing infants under 24 months of age, (2) participants were exposed to strings of three stimuli generated from a single repetition pattern (e.g. AAB, ABB), and (3) participants were tested on their behavioural response to new stringsgenerated from either the familiarized pattern (Familiar pattern trials)or a novel pattern (Novel pattern trials). Exclusion: (1) reports that used different paradigms or asked somewhat different questions, including neuroimaging studies, relational-match-to-sample studies, (3) studies that did not use repetition patterns, and (4) studies in which infants were exposed to two patterns",
+		"effectSize": "Hedge’s g, Cohen’s d",
+		"hierarchicalStructure": "Outcome – Sample - Report"
+	},
+	"literatureintraege": [
+		{
+			"shortCite": "Dawson & Gerken (2009)",
+			"doi": "https://doi.org/10.1016/j.cognition.2009.02.010",
+			"peerReviewed": true
+		},
+		{
+			"shortCite": "Saffran et al (2007)",
+			"doi": "https://psycnet.apa.org/doi/10.1016/j.cognition.2006.11.004",
+			"peerReviewed": true
+		},
+		{
+			"shortCite": "Thiessen (2012)\n",
+			"doi": "https://psycnet.apa.org/doi/10.1080/15475441.2011.583610\n",
+			"peerReviewed": true
+		},
+		{
+			"shortCite": "Bulf et al (2015)\n",
+			"doi": "https://doi.org/10.3389/fpsyg.2015.01595\n",
+			"peerReviewed": true
+		},
+		{
+			"shortCite": "Frank et al (2009)\n",
+			"doi": "https://psycnet.apa.org/doi/10.1111/j.1467-7687.2008.00794.x\n",
+			"peerReviewed": true
+		},
+		{
+			"shortCite": "Ferguson & Lew-Williams (2016)\n",
+			"doi": "https://doi.org/10.1038/srep25434\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Tsui et al (2016)\n",
+			"doi": "https://doi.org/10.1111/desc.12319\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Rabagliati et al (2012)\n",
+			"doi": "https://doi.org/10.1371/journal.pone.0040517\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Marcus et al (2007)\n",
+			"doi": "https://psycnet.apa.org/doi/10.1111/j.1467-9280.2007.01910.x\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Johnson et al (2009)\n",
+			"doi": "https://doi.org/10.1080/15250000802569611\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Marcus et al (1999)\n",
+			"doi": "https://doi.org/10.1126/science.283.5398.77\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Gerken et al (2015)\n",
+			"doi": "https://doi.org/10.1111/desc.12183\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Gerken (2006)\n",
+			"doi": "https://psycnet.apa.org/doi/10.1016/j.cognition.2005.03.003\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Gerken (2010)\n",
+			"doi": "https://psycnet.apa.org/doi/10.1016/j.cognition.2010.01.006\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Pons & Toro (2010)\n",
+			"doi": "https://psycnet.apa.org/doi/10.1016/j.cognition.2010.05.013\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Ferguson & Waxman (2016)\n",
+			"doi": "https://groups.psych.northwestern.edu/waxman/documents/FergusonWaxman2015CSS.pdf\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Gervain & Werker (2013)\n",
+			"doi": "https://doi.org/10.1017/s0305000912000256\n",
+			"peerReviewed": true
+		},
+{
+			"shortCite": "Bahlmann & Levelt (2016)\n",
+			"doi": "",
+			"peerReviewed": false
+		},
+{
+			"shortCite": "Leeuven & Levelt (2016)\n",
+			"doi": "",
+			"peerReviewed": false
+		},
+{
+			"shortCite": "Bulf et al (2017)\n",
+			"doi": "https://doi.org/10.1038/s41598-017-02466-w\n",
+			"peerReviewed": true
+		}
+	]
+}