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app.R
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library(shiny)
library(shinyjs)
#### Start UI ####
ui <- fluidPage(
useShinyjs(),
titlePanel("Western Gulf of Maine Cod and Haddock Recreational Fisheries Decision Support Tool"),
#### Regulation Selection ####
tabsetPanel(
tabPanel("Cod and Haddock Model Summary",
p("This page summarizes models results for sets of policies that have been run to date. These are
intended as a jumping off point for your own model runs."),
p("Hover over each point to view the detailed statistics for each model run. The first section contains
a table of recreational management measures. The second section contains graphs of mortality. The third
section has graphs of other performance measures, including relative change in Angler Satisfaction, Trips, and Discards."),
shinyjs::useShinyjs(),
shinyjs::extendShinyjs(text = "shinyjs.refresh_page = function() { location.reload(); }", functions = "refresh_page"),
actionButton("updatedat", "Update"),
plotly::plotlyOutput(outputId = "totCatch"),
DT::DTOutput(outputId = "DTout"),
shinyWidgets::awesomeCheckboxGroup(
inputId = "fig",
label = "Supplemental Figures",
choices = c( "Angler Satisfaction","Discards", "Trips"),
inline = TRUE,
status = "danger"),
uiOutput("addCVCod"),
uiOutput("addCVHad"),
uiOutput("addReleaseCod"),
uiOutput("addReleaseHad"),
uiOutput("addTripsCod"),
uiOutput("addTripsHad")),
tabPanel( "Regulation Selection",
strong(div("Use this page to set up the regulations that you would like to simulate. Only click the button ONCE before clicking over to Results tab. We have pre-loaded the status quo regulations.", style = "color:blue")), # Warning for users
#Run Button
actionButton("runmeplease", "Run Me"),
textInput("Run_Name", "Please name this using your initials and the number of the run (ex. AB1)."),
fluidRow(
column(6,
titlePanel("Cod"),
sliderInput(inputId = "CodFH_seas1", label ="For Hire Open Season 1",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value =c(as.Date("2025-09-01","%Y-%m-%d"),as.Date("2025-10-31","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "CodFH_1_bag", label = "Bag Limit",
min = 0, max = 100, value = 1)),
column(5,
sliderInput(inputId = "CodFH_1_len", label = "Min Length",
min = 15, max = 30, value = 23, step = 1))),
sliderInput(inputId = "CodPR_seas1", label ="Private Open Season 1",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value =c(as.Date("2025-09-01","%Y-%m-%d"),as.Date("2025-10-31","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "CodPR_1_bag", label = "Bag Limit",
min = 0, max = 100, value = 1)),
column(5,
sliderInput(inputId = "CodPR_1_len", label = "Min Length",
min = 15, max = 30, value = 23, step = 1))),
actionButton("CODaddSeason", "Add Season"),
shinyjs::hidden( div(ID = "CodSeason2",
sliderInput(inputId = "CodFH_seas2", label ="For Hire Open Season 2",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value=c(as.Date("2026-01-01","%Y-%m-%d"),as.Date("2026-01-01","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "CodFH_2_bag", label ="Bag Limit",
min = 0, max = 20, value = 0)),
column(6,
sliderInput(inputId = "CodFH_2_len", label ="Min Length",
min = 15, max = 25, value = 23, step = 1))),
sliderInput(inputId = "CodPR_seas2", label ="Private Open Season 2",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value=c(as.Date("2026-01-01","%Y-%m-%d"),as.Date("2026-01-01","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "CodPR_2_bag", label ="Bag Limit",
min = 0, max = 20, value = 0)),
column(6,
sliderInput(inputId = "CodPR_2_len", label ="Min Length",
min = 15, max = 25, value = 23, step = 1)))))),
column(6,
titlePanel("Haddock"),
sliderInput(inputId = "HadFH_seas1", label ="For Hire Open Season 1",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value =c(as.Date("2025-05-01","%Y-%m-%d"),as.Date("2026-02-28","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "HadFH_1_bag", label = "Bag Limit",
min = 0, max = 100, value = 15)),
column(5,
sliderInput(inputId = "HadFH_1_len", label = "Min Length",
min = 15, max = 30, value = 18, step = 1))),
sliderInput(inputId = "HadPR_seas1", label ="Private Open Season 1",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value =c(as.Date("2025-05-01","%Y-%m-%d"),as.Date("2026-02-28","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "HadPR_1_bag", label = "Bag Limit",
min = 0, max = 100, value = 15)),
column(5,
sliderInput(inputId = "HadPR_1_len", label = "Min Length",
min = 15, max = 30, value = 18, step = 1))),
sliderInput(inputId = "HadFH_seas2", label ="For Hire Open Season 2",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value =c(as.Date("2026-04-01","%Y-%m-%d"),as.Date("2026-04-30","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "HadFH_2_bag", label = "Bag Limit",
min = 0, max = 100, value = 15)),
column(5,
sliderInput(inputId = "HadFH_2_len", label = "Min Length",
min = 15, max = 30, value = 18, step = 1))),
sliderInput(inputId = "HadPR_seas2", label ="Private Open Season 2",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value =c(as.Date("2026-04-01","%Y-%m-%d"),as.Date("2026-04-30","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "HadPR_2_bag", label = "Bag Limit",
min = 0, max = 100, value = 15)),
column(5,
sliderInput(inputId = "HadPR_2_len", label = "Min Length",
min = 15, max = 30, value = 18, step = 1))),
actionButton("HADaddSeason", "Add Season"),
shinyjs::hidden( div(ID = "HadSeason3",
sliderInput(inputId = "HadFH_seas3", label ="For Hire Open Season 3",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value=c(as.Date("2026-01-01","%Y-%m-%d"),as.Date("2026-01-01","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "HadFH_3_bag", label ="Bag Limit",
min = 0, max = 20, value = 0)),
column(6,
sliderInput(inputId = "HadFH_3_len", label ="Min Length",
min = 15, max = 30, value = 18, step = 1))),
sliderInput(inputId = "HadPR_seas3", label ="Private Open Season 3",
min = as.Date("2025-05-01","%Y-%m-%d"),
max = as.Date("2026-04-30","%Y-%m-%d"),
value=c(as.Date("2026-01-01","%Y-%m-%d"),as.Date("2026-01-01","%Y-%m-%d")),
timeFormat = "%Y-%m-%d", ticks = FALSE),
fluidRow(
column(4,
numericInput(inputId = "HadPR_3_bag", label ="Bag Limit",
min = 0, max = 20, value = 0)),
column(6,
sliderInput(inputId = "HadPR_3_len", label ="Min Length",
min = 15, max = 30, value = 18, step = 1)))))))),
#### Results ####
tabPanel("Results",
conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Calculating...This will take ~50 min.",id="loadmessage")), #Warning for users
downloadButton(outputId = "downloadData", "Download"),
actionButton("bymode", "Results by Mode"),
# Add table outputs
## KB - Make tables DTs - should fix RMD documentation issue
tableOutput(outputId = "regtableout"),
tableOutput(outputId = "catch_tableout"),
#tableOutput(outputId = "catchmode"),
tableOutput(outputId = "welfare_tableout"),
#tableOutput(outputId = "welfaremode"),
tableOutput(outputId = "keep_tableout")),
#tableOutput(outputId = "keepmode")),
#### By Mode ####
# tabPanel("Results - By Mode",
# tableOutput(outputId = "regtableout"),
# tableOutput(outputId = "catchmode"),
# tableOutput(outputId = "welfaremode"),
# tableOutput(outputId = "keepmode"))#,
### Documentation ####
tabPanel("Documentation",
htmlOutput("documentation"))
))
####### Start Server ###################
server <- function(input, output, session){
library(magrittr)
library(ggplot2)
#library(webshot)
observeEvent(input$updatedat,{
print("updating")
shinyjs::js$refresh_page();
})
df2 <- function(){
fnames <- list.files(path=here::here("output/"),pattern = "*.csv",full.names = T)
fnames2<- as.data.frame(fnames) %>%
tidyr::separate(fnames, into = c("a", "b"), sep = "_") %>%
dplyr::mutate(b = ifelse(stringr::str_detect(b, "202501"), "NA", b),
c=c(1:nrow(.)),
run_name = dplyr::case_when(b != "NA" ~ b, TRUE ~ as.character(c))) %>%
dplyr::select(run_name)
# fnames2<- as.data.frame(fnames) %>%
# tidyr::separate(fnames, into = c("a", "b", "c"), sep = "_") %>%
# dplyr::mutate(c = ifelse(stringr::str_detect(c, "202501"), "NA", c),
# d=c(1:nrow(.)),
# run_name = dplyr::case_when(c != "NA" ~ c, TRUE ~ as.character(c))) %>%
# dplyr::select(run_name)
df <- fnames %>%
purrr::map_df(~data.table::fread(.,stringsAsFactors=F,check.names=T,strip.white=T))
df2<- df %>% dplyr::mutate(run_number = as.character(rep(fnames2$run_name, each = 6030)))
return(df2)
}
cod_acl <- function(){
cod_acl = 99
return(cod_acl)
}
had_acl <- function(){
had_acl = 1075
return(had_acl)
}
lb_to_mt <- function(){
lb_to_mt = 0.000454
return(lb_to_mt)
}
Run_Name <- function(){
if(stringr::str_detect(input$Run_Name, "_")){
Run_Name <- gsub("_", "-", input$Run_Name)
}else {
Run_Name <- input$Run_Name
}
print(Run_Name)
return(Run_Name)
}
output$DTout <- DT::renderDT({
SQ_regulations <- read.csv(here::here("data-raw/SQ_regulations.csv")) %>%
dplyr::rename(Category = Var,
SQ = Val)
df3<- df2() %>% dplyr::filter(!Category %in% c("CV", "ntrips", "nchoiceoccasions","cod" , "had")) %>%
dplyr::select(Category, Value, run_number) %>%
dplyr::left_join(SQ_regulations, by = c("Category"))
seas<- df3 %>% dplyr::filter(stringr::str_detect(Category, "Season")) %>%
tidyr::separate(Value, into = c("Value1", "Value2"), sep = " - ") %>%
tidyr::separate(SQ, into = c("SQ1", "SQ2"), sep = " - ") %>%
dplyr::mutate(Value = as.integer(lubridate::ymd(Value2)-lubridate::ymd(Value1)),
SQ = as.integer(lubridate::ymd(SQ2)-lubridate::ymd(SQ1))) %>%
dplyr::mutate(Diff_from_SQ = dplyr::case_when(Value < SQ ~ "Shorter Season", TRUE ~ ""),
Diff_from_SQ = dplyr::case_when(Value > SQ ~ "Longer Season", TRUE ~ Diff_from_SQ),
Value = paste0(Value1, " - ", Value2)) %>%
dplyr::select(Category, Diff_from_SQ, run_number)
bag<- df3 %>% dplyr::filter(stringr::str_detect(Category, "bag")) %>%
dplyr::mutate(Diff_from_SQ = dplyr::case_when(as.numeric(Value) < as.numeric(SQ) ~ "Smaller Bag", TRUE ~ ""),
Diff_from_SQ = dplyr::case_when(as.numeric(Value) > as.numeric(SQ) ~ "Larger Bag", TRUE ~ Diff_from_SQ)) %>%
dplyr::select(Category, Diff_from_SQ, run_number)
size<- df3 %>% dplyr::filter(stringr::str_detect(Category, "size")) %>%
dplyr::mutate(Diff_from_SQ = dplyr::case_when(as.numeric(Value) < as.numeric(SQ) ~ "Smaller Min Length", TRUE ~ ""),
Diff_from_SQ = dplyr::case_when(as.numeric(Value) > as.numeric(SQ) ~ "Larger Min Length", TRUE ~ Diff_from_SQ)) %>%
dplyr::select(Category, Diff_from_SQ, run_number)
df4<- rbind(seas, bag, size) %>%
dplyr::ungroup()
Regs_out <- df3 %>%
dplyr::left_join(df4, by = c("Category", "run_number")) %>%
dplyr::select(!SQ) %>%
dplyr::select(!Opt) %>%
tidyr::separate(Category, into =c("Species", "mode", "Var"), sep = "_") %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = Var, values_from = c(Value, Diff_from_SQ)) %>%
dplyr::mutate(Value_Season = dplyr::case_when(Value_bag == 0 ~"NA", TRUE ~ Value_Season),
Value_size = dplyr::case_when(Value_bag == 0 ~"NA", TRUE ~ Value_size),
Diff_from_SQ_bag = dplyr::case_when(Value_bag == 0 ~"NA", TRUE ~ Diff_from_SQ_bag),
Diff_from_SQ_size = dplyr::case_when(Value_bag == 0 ~"NA", TRUE ~ Diff_from_SQ_size),
Diff_from_SQ_Season = dplyr::case_when(Value_bag == 0 ~"NA", TRUE ~ Diff_from_SQ_Season),
Value_bag = dplyr::case_when(Value_bag == 0 ~"NA", TRUE ~ Value_bag)) %>%
dplyr::ungroup() %>%
dplyr::mutate(Diff_from_SQ = paste0(Diff_from_SQ_bag,Diff_from_SQ_size,Diff_from_SQ_Season)) %>%
dplyr::select(!c(Diff_from_SQ_bag,Diff_from_SQ_size,Diff_from_SQ_Season)) %>%
tidyr::pivot_wider(names_from = Species, values_from = c(Diff_from_SQ, Value_bag, Value_size, Value_Season)) %>%
dplyr::ungroup() %>%
dplyr::mutate(cod_bag = paste0(Value_bag_Cod1, " , ", Value_bag_Cod2),
cod_size = paste0(Value_size_Cod1, " , ", Value_size_Cod2),
cod_season = paste0(Value_Season_Cod1, " , ", Value_Season_Cod2),
had_bag = paste0(Value_bag_Had1, " , ", Value_bag_Had2, " , ", Value_bag_Had3),
had_size = paste0(Value_size_Had1, " , ", Value_size_Had2, " , ", Value_size_Had3),
had_season = paste0(Value_Season_Had1, " , ", Value_Season_Had2, " , ", Value_Season_Had3),
cod_bag = stringr::str_remove(cod_bag, " , NA"),
cod_size = stringr::str_remove(cod_size, " , NA"),
cod_season = stringr::str_remove(cod_season, " , NA"),
had_bag = stringr::str_remove(had_bag, " , NA"),
had_size = stringr::str_remove(had_size, " , NA"),
had_season = stringr::str_remove(had_season, " , NA"),
cod_bag = stringr::str_remove(cod_bag, "NA ,"),
cod_size = stringr::str_remove(cod_size, "NA ,"),
cod_season = stringr::str_remove(cod_season, "NA ,"),
had_bag = stringr::str_remove(had_bag, "NA ,"),
had_size = stringr::str_remove(had_size, "NA ,"),
had_season = stringr::str_remove(had_season, "NA ,"),
Diff_from_SQ_cod = paste0(Diff_from_SQ_Cod1, " , ", Diff_from_SQ_Cod2),
Diff_from_SQ_had = paste0(Diff_from_SQ_Had1, " , ", Diff_from_SQ_Had2, " , ", Diff_from_SQ_Had3),
Diff_from_SQ_cod = stringr::str_remove(Diff_from_SQ_cod, " , NA"),
Diff_from_SQ_cod = stringr::str_remove(Diff_from_SQ_cod, "NANA"),
Diff_from_SQ_cod = stringr::str_remove(Diff_from_SQ_cod, "NA ,"),
Diff_from_SQ_had = stringr::str_remove(Diff_from_SQ_had, "NA ,"),
Diff_from_SQ_had = stringr::str_remove(Diff_from_SQ_had, " , NA"),
Diff_from_SQ_had = stringr::str_remove(Diff_from_SQ_had, " , NANA"),
Diff_from_SQ_had = stringr::str_remove(Diff_from_SQ_had, "NANA")) %>%
dplyr::select(mode, run_number, Diff_from_SQ_cod, Diff_from_SQ_had, cod_bag, cod_size, cod_season, had_bag, had_size, had_season) %>%
dplyr::mutate(cod_season = stringr::str_remove_all(cod_season, "202.-"),
had_season = stringr::str_remove_all(had_season, "202.-")) %>%
dplyr::mutate(mode = dplyr::recode(mode, "FH" = "For Hire",
"PR" = "Private")) %>%
dplyr::select(run_number, mode, cod_bag, cod_size, cod_season, Diff_from_SQ_cod,
had_bag, had_size, had_season, Diff_from_SQ_had) %>%
dplyr::rename(Mode = mode,
`Run Identifier` = run_number,
`Cod Bag Limit` = cod_bag,
`Cod Minimum Size (in)` = cod_size,
`Cod Season(s)` = cod_season,
`Haddock Bag Limit` = had_bag,
`Haddock Minimum Size (in)` = had_size,
`Haddock Season(s)` = had_season,
`Difference from Cod SQ` = Diff_from_SQ_cod,
`Difference from haddock SQ` = Diff_from_SQ_had)
DT::datatable(Regs_out)
})
output$totCatch <- plotly::renderPlotly({
# sq<- read.csv("predictions_sq_no_august.csv") %>%
# dplyr::mutate(run_number = "SQ")
#
# aug<- read.csv("predictions_with_open_august.csv") %>%
# dplyr::mutate(run_number = "aug")
#
# all<-read.csv("predictions_open_all.csv") %>%
# dplyr::mutate(run_number = "all")
#
# dat<- sq %>% rbind(aug, all)
catch_agg<- df2() %>%
#dat %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, Category,draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::mutate(under_acl = dplyr::case_when(Category == "cod" & Value <= cod_acl() ~ 1, TRUE ~ 0),
under_acl = dplyr::case_when(Category == "had" & Value <= had_acl() ~ 1, TRUE ~ under_acl)) %>%
dplyr::group_by(run_number, Category) %>%
dplyr::summarise(under_acl = sum(under_acl),
Value = round(median(Value),0)) %>%
tidyr::pivot_wider(names_from = Category, values_from = c(Value, under_acl))
catch_agg2<- catch_agg %>%
dplyr::mutate(under_acl_cod2 = dplyr::case_when(under_acl_cod < 50 ~ "Less than 50%", TRUE ~ ""),
under_acl_cod2 = dplyr::case_when(under_acl_cod >= 50 & under_acl_cod < 60 ~ "50-59%", TRUE ~ under_acl_cod2),
under_acl_cod2 = dplyr::case_when(under_acl_cod >= 60 & under_acl_cod < 70~ "60-69%", TRUE ~ under_acl_cod2),
under_acl_cod2 = dplyr::case_when(under_acl_cod >= 70 & under_acl_cod < 80 ~ "70-79%", TRUE ~ under_acl_cod2),
under_acl_cod2 = dplyr::case_when(under_acl_cod >= 80 & under_acl_cod < 90 ~ "80-89%", TRUE ~ under_acl_cod2),
under_acl_cod2 = dplyr::case_when(under_acl_cod >= 90 & under_acl_cod <=100 ~ "90-100%", TRUE ~ under_acl_cod2)) %>%
dplyr::mutate(under_acl_had2 = dplyr::case_when(under_acl_had < 50 ~ "Less than 50%", TRUE ~ ""),
under_acl_had2 = dplyr::case_when(under_acl_had >= 50 & under_acl_had < 60 ~ "50-59%", TRUE ~ under_acl_had2),
under_acl_had2 = dplyr::case_when(under_acl_had >= 60 & under_acl_had < 70~ "60-69%", TRUE ~ under_acl_had2),
under_acl_had2 = dplyr::case_when(under_acl_had >= 70 & under_acl_had < 80 ~ "70-79%", TRUE ~ under_acl_had2),
under_acl_had2 = dplyr::case_when(under_acl_had >= 80 & under_acl_had < 90 ~ "80-89%", TRUE ~ under_acl_had2),
under_acl_had2 = dplyr::case_when(under_acl_had >= 90 & under_acl_had <=100 ~ "90-100%", TRUE ~ under_acl_had2)) %>%
dplyr::rename(`Cod Mortality`=Value_cod) %>%
dplyr::rename(`Haddock Mortality`=Value_had) %>%
dplyr::ungroup()
# catch_agg <- data.frame(run_number = c("SQ","what", "test"),
# Cod_Mortality = c(43, 60, 130),
# Haddock_Mortality = c(810, 955, 1099),
# under_acl_cod2 = c("90-100%","60-69%", "Less than 50%"),
# under_acl_had2 = c("90-100%","60-69%", "Less than 50%")) %>%
# dplyr::rename(`Cod Mortality`=Cod_Mortality, `Haddock Mortality`=Haddock_Mortality)
# my_palette <- c("red3","red3","red3","red3","red3","#C5E8B7",
# "#ABE098", "#83D475","green4","darkgreen")
p<- catch_agg2 %>%
#dplyr::mutate(under_acl_cod = as.numeric(under_acl_cod)) %>%
ggplot2::ggplot(ggplot2::aes(x = `Cod Mortality`, y = `Haddock Mortality`))+
ggplot2::geom_point(ggplot2::aes(colour = under_acl_cod2, size = under_acl_had2)) +
ggplot2::scale_color_manual(values = c("50-59%" = "#A9DFBF", "60-69%" = "#7DCEA0",
"70-79%" = "#52BE80","80-89%" = "#27AE60",
"90-100%" = "#1B5E20", "Less than 50%" = "red3"))+
ggplot2::scale_size_manual(values = c("50-59%" = 1, "60-69%" = 1,
"70-79%" = 1,"80-89%" = 1,
"90-100%" = 1, "Less than 50%" = 1))+
ggplot2::labs(colour="% of simulations under cod ACL",
size="% of simulations under haddock ACL")+
# ggplot2::scale_colour_stepsn(limits = c(0,100), n.breaks = 10,
# colors = c("red3","red3","red3","red3","red3","#C5E8B7",
# "#ABE098", "#83D475","green4","darkgreen"),
# name = "% Under Cod ACL")+
ggplot2::geom_text(ggplot2::aes(label = run_number), check_overlap = TRUE)+
ggplot2::geom_vline( xintercept =cod_acl(), linetype="dashed")+
ggplot2::geom_hline( yintercept =had_acl(), color="grey45")+
ggplot2::annotate(geom="text", x=cod_acl(), label="Cod ACL", y=1200) +
ggplot2::annotate(geom="text", y=had_acl(), label="Had ACL", x=80) +
ggplot2::guides(size = "none")+
ggplot2::ggtitle("Cod and Haddock Mortality")+
ggplot2::ylab("Median Recreational Haddock Mortality (mt)")+
ggplot2::xlab("Median Recreational Cod Mortality (mt)")
fig<- plotly::ggplotly(p) %>% #,
#tooltip = c("x", "y", "colour")) %>%
plotly::style(textposition = "top center")
fig
})
output$addCVCod <- renderUI({
if(any("Angler Satisfaction" == input$fig)){
plotly::renderPlotly({
SQ<-read.csv(here::here("data-raw/sq_predictions_cm.csv")) %>%
dplyr::filter(Category == "CV") %>%
dplyr::group_by(draw_out, Category) %>%
dplyr::summarise(Value_SQ = sum(Value))
welfare <- df2() %>%
dplyr::filter(Category == c("CV")) %>%
dplyr::group_by(run_number, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::left_join(SQ) %>%
dplyr::mutate(Value_diff = Value_SQ-Value) %>%
dplyr::summarise(median_cv = median(Value_diff))
catch<- df2() %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number, option, Category) %>%
dplyr::summarise(Value =round(median(Value),0)) %>%
tidyr::pivot_wider(names_from = Category, values_from = Value) %>%
dplyr::left_join(welfare) %>%
dplyr::rename(`Angler Satisfaction($)` = median_cv)
p1<- catch %>% ggplot2::ggplot(ggplot2::aes(x = `Angler Satisfaction($)`, y = cod))+
ggplot2::geom_point() +
ggplot2::geom_hline( yintercept =cod_acl())+
ggplot2::geom_text(ggplot2::aes(label=run_number), check_overlap = TRUE)+
ggplot2::geom_text(ggplot2::aes(y=cod_acl(), label="Cod ACL", x=1000000)) +
ggplot2::xlab("Relative Change in Angler Satisfaction ($)")+
ggplot2::ylab("Total Recreational Cod Mortality (mt)")+
ggplot2::labs(title = "Cod Mortality (mt) compared to Angler Satisfaction (Compared to status-quo regulations, how much better- or worse-off are anglers, in dollars?)",
subtitle = "testing")+
ggplot2::theme(legend.position = "none")
fig1<- plotly::ggplotly(p1) %>%
# graphics::layout(title = list(text = paste0('Cod Mortality (mt) compared to Angler Satisfaction',
# '<br>',
# '<sup>',
# 'More descirptuon of CV','</sup>'))) %>%
plotly::style(textposition = "top center")
fig1
})
}
})
output$addCVHad <- renderUI({
if(any("Angler Satisfaction" == input$fig)){
plotly::renderPlotly({
SQ<-read.csv(here::here("data-raw/sq_predictions_cm.csv")) %>%
dplyr::filter(Category == "CV") %>%
dplyr::group_by(draw_out, Category) %>%
dplyr::summarise(Value_SQ = sum(Value))
welfare <- df2() %>%
dplyr::filter(Category == c("CV")) %>%
dplyr::group_by(run_number, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::left_join(SQ) %>%
dplyr::mutate(Value_diff = Value_SQ-Value) %>%
dplyr::summarise(median_cv = median(Value_diff))
catch<- df2() %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number, option, Category) %>%
dplyr::summarise(Value =round(median(Value),0)) %>%
tidyr::pivot_wider(names_from = Category, values_from = Value) %>%
dplyr::left_join(welfare) %>%
dplyr::rename(`Angler Satisfaction($)` = median_cv)
p2<- catch %>% ggplot2::ggplot(ggplot2::aes(x = `Angler Satisfaction($)`, y = had))+
ggplot2::geom_point() +
ggplot2::geom_hline( yintercept =had_acl())+
ggplot2::geom_text(ggplot2::aes(label=run_number), check_overlap = TRUE)+
ggplot2::xlab("Relative Change in Angler Satisfaction ($)")+
ggplot2::ylab("Total Recreational Haddock Mortality (mt)")+
ggplot2::geom_text(ggplot2::aes(x=had_acl(), label="Had ACL", y=1075)) +
ggplot2::labs(title = "Haddock Mortality (mt) compared to Angler Satisfaction (Compared to status-quo regulations, how much better- or worse-off are anglers, in dollars?)",
subtitle = "testing")+
ggplot2::theme(legend.position = "none")
fig2<- plotly::ggplotly(p2) %>%
# graphics::layout(title = list(text = paste0('Haddock Mortality (mt) compared to Angler Satisfaction',
# '<br>',
# '<sup>',
# 'More descirptuon of CV','</sup>'))) %>%
plotly::style(textposition = "top center")
fig2
})
}
})
output$addReleaseCod <- renderUI({
if(any("Discards" == input$fig)){
plotly::renderPlotly({
release <- df2() %>%
dplyr::filter(catch_disposition %in% c("Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number,option, Category) %>%
dplyr::summarise(Discmortality = round(median(Value),0))
catch<- df2() %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number, option, Category) %>%
dplyr::summarise(Value =round(median(Value),0)) %>%
dplyr::left_join(release) %>%
tidyr::pivot_wider(names_from = Category, values_from = c(Value, Discmortality))%>%
dplyr::rename(`Cod Mortality`=Value_cod) %>%
dplyr::rename(`Haddock Mortality`=Value_had)%>%
dplyr::rename(`Cod Discard Mortality`=Discmortality_cod) %>%
dplyr::rename(`Haddock Discard Mortality`=Discmortality_had)
p3<- catch %>% ggplot2::ggplot(ggplot2::aes(x = `Cod Discard Mortality`, y = `Cod Mortality`))+
ggplot2::geom_point() +
ggplot2::geom_hline( yintercept =cod_acl())+
ggplot2::geom_text(ggplot2::aes(label=run_number), check_overlap = TRUE)+
ggplot2::geom_text(ggplot2::aes(y=cod_acl(), label="Cod ACL", x=240)) +
ggplot2::xlab("Cod Discard Mortality (mt)")+
ggplot2::ylab("Total Recreational Cod Mortality (mt)")+
ggplot2::labs(title = "Total Cod Mortality (mt) compared to Discard Mortality(mt)",
subtitle = "testing")+
ggplot2::theme(legend.position = "none")
fig3<- plotly::ggplotly(p3)%>%
# graphics::layout(title = list(text = paste0('Cod Mortality (mt) compared to Cod Releases (mt)'))) %>%
plotly::style(textposition = "top center")
fig3
})
}
})
output$addReleaseHad <- renderUI({
if(any("Discards" == input$fig)){
plotly::renderPlotly({
release <- df2() %>%
dplyr::filter(catch_disposition %in% c("Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number,option, Category) %>%
dplyr::summarise(Discmortality = round(median(Value),0))
catch<- df2() %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number, option, Category) %>%
dplyr::summarise(Value = round(median(Value),0)) %>%
dplyr::left_join(release) %>%
tidyr::pivot_wider(names_from = Category, values_from = c(Value, Discmortality))%>%
dplyr::rename(`Cod Mortality`=Value_cod) %>%
dplyr::rename(`Haddock Mortality`=Value_had)%>%
dplyr::rename(`Cod Discard Mortality`=Discmortality_cod) %>%
dplyr::rename(`Haddock Discard Mortality`=Discmortality_had)
p4<- catch %>% ggplot2::ggplot(ggplot2::aes(x = `Haddock Discard Mortality` , y = `Haddock Mortality`))+
ggplot2::geom_point() +
ggplot2::geom_hline( yintercept = had_acl())+
ggplot2::geom_text(ggplot2::aes(label=run_number), check_overlap = TRUE)+
ggplot2::geom_text(ggplot2::aes(y=had_acl(), label="Had ACL", x=600)) +
ggplot2::xlab("Haddock Discard Mortality (mt)")+
ggplot2::ylab("Total Recreational Haddock Mortality (mt)")+
ggplot2::labs(title = "Total Haddock Mortality (mt) compared to Discard Mortality (mt)",
subtitle = "testing")+
ggplot2::theme(legend.position = "none")
fig4<- plotly::ggplotly(p4)%>%
# graphics::layout(title = list(text = paste0('Haddock Mortality (mt) compared to Haddock Releases (mt)'))) %>%
plotly::style(textposition = "top center")
fig4
})
}
})
output$addTripsCod <- renderUI({
if(any("Trips" == input$fig)){
plotly::renderPlotly({
trips <- df2() %>%
dplyr::filter(Category %in% c("ntrips")) %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::group_by(run_number,option, Category) %>%
dplyr::summarise(Trips = round(median(Value),0))
catch<- df2() %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number, option, Category) %>%
dplyr::summarise(Value = round(median(Value),0)) %>%
tidyr::pivot_wider(names_from = Category, values_from = Value) %>%
dplyr::left_join(trips) %>%
dplyr::select(!Category)%>%
dplyr::rename(`Cod Mortality`=cod) %>%
dplyr::rename(`Haddock Mortality`=had)
p5<- catch %>% ggplot2::ggplot(ggplot2::aes(x = Trips, y = `Cod Mortality`))+
ggplot2::geom_point() +
ggplot2::geom_hline( yintercept = cod_acl())+
ggplot2::geom_text(ggplot2::aes(label=run_number), check_overlap = TRUE)+
ggplot2::geom_text(ggplot2::aes(y=cod_acl(), label="Cod ACL", x=167000), angle=90) +
ggplot2::xlab("Number of Trips")+
ggplot2::ylab("Total Recreational Cod Mortality (mt)")+
ggplot2::labs(title = "Cod Mortality (mt) compared to Total Number of Trips",
subtitle = "testing")+
ggplot2::theme(legend.position = "none")
fig5<- plotly::ggplotly(p5)%>%
#graphics::layout(title = list(text = paste0('Cod Mortality (mt) compared to Total Number of Trips'))) %>%
plotly::style(textposition = "top center")
fig5
})
}
})
output$addTripsHad <- renderUI({
if(any("Trips" == input$fig)){
plotly::renderPlotly({
trips <- df2() %>%
dplyr::filter(Category %in% c("ntrips")) %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::group_by(run_number,option, Category) %>%
dplyr::summarise(Trips = round(median(Value),0))
catch<- df2() %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(run_number, option, Category, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::mutate(Value = Value * lb_to_mt()) %>%
dplyr::group_by(run_number, option, Category) %>%
dplyr::summarise(Value = round(median(Value),0)) %>%
tidyr::pivot_wider(names_from = Category, values_from = Value) %>%
dplyr::left_join(trips) %>%
dplyr::select(!Category)%>%
dplyr::rename(`Cod Mortality`=cod) %>%
dplyr::rename(`Haddock Mortality`=had)
p6<- catch %>% ggplot2::ggplot(ggplot2::aes(x =Trips , y = `Haddock Mortality`))+
ggplot2::geom_point() +
ggplot2::geom_hline( yintercept = had_acl())+
ggplot2::geom_text(ggplot2::aes(label=run_number), check_overlap = TRUE)+
ggplot2::geom_text(ggplot2::aes(y=had_acl(), label="Had ACL", x=167000)) +
ggplot2::xlab("Number of Trips")+
ggplot2::ylab("Total Recreational Haddock Mortality (mt)")+
ggplot2::labs(title = "Haddock Mortality (mt) compared to Total Number of Trips",
subtitle = "testing")+
ggplot2::theme(legend.position = "none")
fig6<- plotly::ggplotly(p6)%>%
#layout(title = list(text = paste0('Haddock Mortality (mt) compared to Total Number of Trips'))) %>%
plotly::style(textposition = "top center")
fig6
})
}
})
#### Toggle extra seasons on UI ####
# Allows for extra seasons to show and hide based on click
shinyjs::onclick("CODaddSeason",
shinyjs::toggle(id = "CodSeason2", anim = TRUE))
shinyjs::onclick("HADaddSeason",
shinyjs::toggle(id = "HadSeason3", anim = TRUE))
pred <- eventReactive(input$runmeplease,{
print("STarting this process")
source(here::here(paste0("model_run.R")), local = TRUE)
return(predictions_out10)
print("predicitions out")
})
predictions <- reactive({
#test<- read.csv(here::here("output/output_help_20250106_133859.csv"))
predictions_out <- read.csv(here::here("data-raw/SQ_predictions_cm.csv")) %>%
#dplyr::mutate(option = c("SQ")) %>%
#dplyr::select(!X) %>%
#rbind(predictions_out10) %>%
rbind(pred()) %>%
dplyr::mutate(Value = dplyr::case_when(number_weight == "Weight" ~ as.numeric(Value)/2205, TRUE ~ as.numeric(Value)))
return(predictions_out)
})
#### Regulations ####
regs_agg <- reactive({
Run_Name = if(Run_Name() != ""){
Run_Name = Run_Name()
}else {
Run_Name = "alt"
}
print("start regs")
SQ_regulations <- read.csv(here::here("data-raw/SQ_regulations.csv"))
#Regs <- read.csv(here::here("data-raw/SQ_regulations.csv")) %>%
# mutate(Opt = c("alt"))
Regs<- data.frame(Opt = c(Run_Name),
Var = c("Cod1_FH_bag", "Cod1_FH_size", "Cod1_FH_Season",
"Cod1_PR_bag", "Cod1_PR_size", "Cod1_PR_Season",
"Had1_FH_bag", "Had1_FH_size", "Had1_FH_Season",
"Had1_PR_bag", "Had1_PR_size", "Had1_PR_Season",
"Had2_FH_bag", "Had2_FH_size", "Had2_FH_Season",
"Had2_PR_bag", "Had2_PR_size", "Had2_PR_Season",
"Cod2_FH_bag", "Cod2_FH_size", "Cod2_FH_Season",
"Cod2_PR_bag", "Cod2_PR_size", "Cod2_PR_Season",
"Had3_FH_bag", "Had3_FH_size", "Had3_FH_Season",
"Had3_PR_bag", "Had3_PR_size", "Had3_PR_Season"),
Val = c(input$CodFH_1_bag, input$CodFH_1_len, paste0(input$CodFH_seas1[1], " - ", input$CodFH_seas1[2]),
input$CodPR_1_bag, input$CodPR_1_len, paste0(input$CodPR_seas1[1], " - ", input$CodPR_seas1[2]),
input$HadFH_1_bag, input$HadFH_1_len, paste0(input$HadFH_seas1[1], " - ", input$HadFH_seas1[2]),
input$HadPR_1_bag, input$HadPR_1_len, paste0(input$HadPR_seas1[1], " - ", input$HadPR_seas1[2]),
input$HadFH_2_bag, input$HadFH_2_len, paste0(input$HadFH_seas2[1], " - ", input$HadFH_seas2[2]),
input$HadPR_2_bag, input$HadPR_2_len, paste0(input$HadPR_seas2[1], " - ", input$HadPR_seas2[2]),
input$CodFH_2_bag, input$CodFH_2_len, paste0(input$CodFH_seas2[1], " - ", input$CodFH_seas2[2]),
input$CodPR_2_bag, input$CodPR_2_len, paste0(input$CodPR_seas2[1], " - ", input$CodPR_seas2[2]),
input$HadFH_3_bag, input$HadFH_3_len, paste0(input$HadFH_seas3[1], " - ", input$HadFH_seas3[2]),
input$HadPR_3_bag, input$HadPR_3_len, paste0(input$HadPR_seas3[1], " - ", input$HadPR_seas3[2])))
Regs1<- Regs %>% rbind(SQ_regulations)
Regs_out <- Regs1 %>%
#SQ_regulations %>%
tidyr::separate(Var, into =c("Species", "mode", "Var"), sep = "_") %>%
tidyr::pivot_wider(names_from = Var, values_from = Val) %>%
dplyr::mutate(Season = dplyr::case_when(bag == 0 ~"NA", TRUE ~ Season),
size = dplyr::case_when(bag == 0 ~"NA", TRUE ~ size),
bag = dplyr::case_when(bag == 0 ~"NA", TRUE ~ bag)) %>%
#dplyr::filter(!bag == 0) %>%
tidyr::pivot_wider(names_from = Species, values_from = c(bag, size, Season)) %>%
dplyr::mutate(cod_bag = paste0(bag_Cod1, " , ", bag_Cod2),
cod_size = paste0(size_Cod1, " , ", size_Cod2),
cod_season = paste0(Season_Cod1, " , ", Season_Cod2),
had_bag = paste0(bag_Had1, " , ", bag_Had2, " , ", bag_Had3),
had_size = paste0(size_Had1, " , ", size_Had2, " , ", size_Had3),
had_season = paste0(Season_Had1, " , ", Season_Had2, " , ", Season_Had3),
cod_bag = stringr::str_remove(cod_bag, " , NA"),
cod_size = stringr::str_remove(cod_size, " , NA"),
cod_season = stringr::str_remove(cod_season, " , NA"),
had_bag = stringr::str_remove(had_bag, " , NA"),
had_size = stringr::str_remove(had_size, " , NA"),
had_season = stringr::str_remove(had_season, " , NA"),
cod_bag = stringr::str_remove(cod_bag, "NA ,"),
cod_size = stringr::str_remove(cod_size, "NA ,"),
cod_season = stringr::str_remove(cod_season, "NA ,"),
had_bag = stringr::str_remove(had_bag, "NA ,"),
had_size = stringr::str_remove(had_size, "NA ,"),
had_season = stringr::str_remove(had_season, "NA ,")) %>%
dplyr::select(Opt, mode, cod_bag, cod_size, cod_season, had_bag, had_size, had_season) %>%
dplyr::mutate(cod_season = stringr::str_remove_all(cod_season, "202.-"),
had_season = stringr::str_remove_all(had_season, "202.-")) %>%
dplyr::mutate(mode = dplyr::recode(mode, "FH" = "For Hire",
"PR" = "Private")) %>%
dplyr::rename(Mode = mode,
`Option` = Opt,
`Cod Bag Limit` = cod_bag,
`Cod Minimum Size (in)` = cod_size,
`Cod Season(s)` = cod_season,
`Haddock Bag Limit` = had_bag,
`Haddock Minimum Size (in)` = had_size,
`Haddock Season(s)` = had_season)
print("regs out")
return(Regs_out)
})
##### Catch ###########
which_catch_out<- reactiveVal(TRUE)
catch_agg <- reactive({
catch_agg<- predictions() %>%
#dat %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(option, Category, draw_out) %>%
dplyr::summarise(Value = sum(Value)) %>%
dplyr::mutate(under_acl = dplyr::case_when(Category == "cod" & Value <= cod_acl() ~ 1, TRUE ~ 0),
under_acl = dplyr::case_when(Category == "had" & Value <= had_acl() ~ 1, TRUE ~ under_acl)) %>%
dplyr::group_by(option, Category) %>%
dplyr::summarise(under_acl = sum(under_acl),
Value = median(Value)) %>%
tidyr::pivot_wider(names_from = c(option), values_from = c(Value, under_acl)) %>%
dplyr::mutate(Category = dplyr::recode(Category, "cod" = "Cod",
"had" = "Haddock")) %>%
dplyr::select(Category, Value_SQ, under_acl_SQ, Value_alt, under_acl_alt) %>%
dplyr::rename(Species = Category, `SQ Total Mortality (mt)` = Value_SQ, `SQ % Under ACL (Out of 100 runs)` = under_acl_SQ,
`Alternative Total Mortality (mt)` = Value_alt, `Atlernative % Under ACL (Out of 100 runs)` = under_acl_alt)
return(catch_agg)
})
catch_by_mode <- reactive({
print("start catch mode")
catch_by_mode<- predictions() %>%
#dat %>% #test %>%
dplyr::filter(catch_disposition %in% c("keep", "Discmortality"),
number_weight == "Weight") %>%
dplyr::group_by(option, Category, draw_out, mode) %>%
dplyr::summarise(Value = sum(Value)) %>%
dplyr::mutate(under_acl = dplyr::case_when(Category == "cod" & Value <= 99000 ~ 1, TRUE ~ 0),
under_acl = dplyr::case_when(Category == "had" & Value <= 1075000 ~ 1, TRUE ~ under_acl)) %>%
dplyr::group_by(option, Category, mode) %>%
dplyr::summarise(under_acl = sum(under_acl),
Value = median(Value)) %>%
tidyr::pivot_wider(names_from = c(option), values_from = c(Value, under_acl)) %>%
dplyr::mutate(Category = dplyr::recode(Category, "cod" = "Cod",
"had" = "Haddock"),
mode = dplyr::recode(mode, "fh" = "For Hire",
"pr" = "Private")) %>%
dplyr::select(Category, Value_SQ, Value_alt, mode) %>%
dplyr::rename(Species = Category, `SQ Total Mortality (mt)` = Value_SQ,
`Alternative Total Mortality (mt)` = Value_alt, `Mode` = mode)
return(catch_by_mode)
})
#### keep release discards ####
which_keep_out<- reactiveVal(TRUE)
keep_agg <- reactive({
# sq<- read.csv(here::here("data-raw/sq_predictions_cm.csv"))%>%
# dplyr::mutate(Value = dplyr::case_when(number_weight == "Weight" ~ as.numeric(Value)/2205, TRUE ~ as.numeric(Value)))
# out<- read.csv(here::here("output/output_alt1_20250113_102706.csv"))%>%
# dplyr::mutate(Value = dplyr::case_when(number_weight == "Weight" ~ as.numeric(Value)/2205, TRUE ~ as.numeric(Value)))
# dat<- rbind(sq, out)
keep_agg<- predictions() %>%
#dat %>% #redictions_out %>%
dplyr::filter(catch_disposition %in% c("keep", "release", "Discmortality")) %>%
dplyr::group_by(option, Category, catch_disposition, number_weight, draw_out) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::group_by(option, Category, catch_disposition, number_weight) %>%
tidyr::pivot_wider(names_from = c(option, number_weight), values_from = Value) %>%
dplyr::mutate(perc_diff_num = ((alt_Number-SQ_Number)/SQ_Number) * 100,
perc_diff_wt = ((alt_Weight-SQ_Weight)/SQ_Weight) * 100) %>%
dplyr::filter(!perc_diff_num == "NA",
!perc_diff_wt == "NA") %>%
dplyr::summarise(SQ_Number = median(SQ_Number), SQ_Weight = median(SQ_Weight),
alt_Number = median(alt_Number), alt_Weight = median(alt_Weight),
perc_diff_num = median(perc_diff_num), perc_diff_wt = median(perc_diff_wt)) %>%
dplyr::select(!c(SQ_Number, SQ_Weight)) %>%
dplyr::mutate(Category = dplyr::recode(Category, "cod" = "Cod",
"had" = "Haddock"),
catch_disposition = dplyr::recode(catch_disposition, "keep" = "Harvest",
"Discmortality" = "Dead Discards",
"release" = "Discards")) %>%
dplyr::select(Category, catch_disposition, alt_Number, perc_diff_num, alt_Weight, perc_diff_wt) %>%
dplyr::rename(Species = Category, Variable = catch_disposition,
`Total number of fish` = alt_Number, `% difference from SQ for number of fish` = perc_diff_num,
`Total Weight (mt)` = alt_Weight, `% difference from SQ for weight of fish` = perc_diff_wt)
return(keep_agg)
})
keep_by_mode <- reactive({
keep_by_mode<- predictions() %>%
#dat %>% #predictions_out %>%
dplyr::filter(catch_disposition %in% c("keep", "release", "Discmortality")) %>%
dplyr::group_by(option, Category, catch_disposition, number_weight, draw_out, mode) %>%
dplyr::summarise(Value = sum(as.numeric(Value))) %>%
dplyr::group_by(option, Category, catch_disposition, number_weight, mode) %>%
dplyr::summarise(Value = median(Value)) %>%
tidyr::pivot_wider(names_from = c(option, number_weight), values_from = Value) %>%
dplyr::mutate(perc_diff_num = ((alt_Number-SQ_Number)/SQ_Number) * 100,
perc_diff_wt = ((alt_Weight-SQ_Weight)/SQ_Weight) * 100) %>%
dplyr::group_by(Category, catch_disposition, mode) %>%
dplyr::filter(!perc_diff_num == "NA",
!perc_diff_wt == "NA") %>%
dplyr::summarise(SQ_Number = median(SQ_Number), SQ_Weight = median(SQ_Weight),
alt_Number = median(alt_Number), alt_Weight = median(alt_Weight),
perc_diff_num = median(perc_diff_num), perc_diff_wt = median(perc_diff_wt)) %>%
dplyr::select(!c(SQ_Number, SQ_Weight)) %>%
dplyr::mutate(Category = dplyr::recode(Category, "cod" = "Cod",
"had" = "Haddock"),
catch_disposition = dplyr::recode(catch_disposition, "keep" = "Harvest",
"Discmortality" = "Dead Discards",
"release" = "Discards"),
mode = dplyr::recode(mode, "fh" = "For Hire",
"pr" = "Private")) %>%
dplyr::select(Category, catch_disposition, mode, alt_Number, perc_diff_num, alt_Weight, perc_diff_wt) %>%
dplyr::rename(Species = Category, Variable = catch_disposition,
`Total Number of fish` = alt_Number, `% difference in number of fish` = perc_diff_num,
`Total Weight (mt)` = alt_Weight, `% difference in weight of fish` = perc_diff_wt, `Mode` = mode)