diff --git a/inst/ShinyApp/server.R b/inst/ShinyApp/server.R
index 55b4552af9596e852ec5863f4fa739ea056b44e5..d7bca876a42ba9e56ebf96d255c3512c185830e7 100644
--- a/inst/ShinyApp/server.R
+++ b/inst/ShinyApp/server.R
@@ -1,11 +1,24 @@
server <- function(input, output, session){
+ ##--------------------------------------------
+ ## Hide all inputs excepted actionButtons --
+ ##--------------------------------------------
+ output$hide_pop_size <- eventReactive({
+ input$button_pop_size
+ },{
+ if(input$button_pop_size%%2 == 1) TRUE else FALSE
+ }, ignoreInit = TRUE)
+
+ outputOptions(output, "hide_pop_size", suspendWhenHidden = FALSE)
+
+
##--------------------------------------------
## Hide all inputs excepted actionButtons --
##--------------------------------------------
observe({
- shinyjs::hide("fatal_constant")
+
+ #shinyjs::hide("fatal_constant")
shinyjs::hide("fatalities_input_type")
shinyjs::hide("fatalities_mean")
shinyjs::hide("fatalities_se")
@@ -13,16 +26,19 @@ server <- function(input, output, session){
shinyjs::hide("fatalities_run_expert")
shinyjs::hide("farm_number_cumulated")
shinyjs::hide("fatalities_mat_cumulated")
- shinyjs::hide("pop_size_type")
+
+ #shinyjs::hide("pop_size_unit")
shinyjs::hide("pop_size_input_type")
shinyjs::hide("pop_size_mean")
shinyjs::hide("pop_size_se")
shinyjs::hide("pop_size_mat_expert")
shinyjs::hide("pop_size_run_expert")
+
shinyjs::hide("carrying_cap_input_type")
shinyjs::hide("carrying_capacity")
shinyjs::hide("carrying_cap_mat_expert")
shinyjs::hide("carrying_cap_run_expert")
+
shinyjs::hide("pop_growth_input_type")
shinyjs::hide("pop_growth_mean")
shinyjs::hide("pop_growth_se")
@@ -30,17 +46,20 @@ server <- function(input, output, session){
shinyjs::hide("pop_growth_run_expert")
shinyjs::hide("pop_trend")
shinyjs::hide("pop_trend_strength")
+
shinyjs::hide("mat_fill_vr")
+
# Show fatalities part
if(input$button_fatalities%%2 == 1){
- shinyjs::show("fatal_constant")
+ #shinyjs::show("fatal_constant")
# Show inputs for none cumulated impacts scenario
if(input$analysis_choice == "scenario"){
shinyjs::show("fatalities_input_type")
+
if(input$fatalities_input_type == "val"){
shinyjs::show("fatalities_mean")
shinyjs::show("fatalities_se")
@@ -63,7 +82,7 @@ server <- function(input, output, session){
# Show inputs for population size part
if(input$button_pop_size%%2 == 1){
- shinyjs::show("pop_size_type")
+ #shinyjs::show("pop_size_unit")
shinyjs::show("pop_size_input_type")
if(input$pop_size_input_type == "val"){
shinyjs::show("pop_size_mean")
@@ -109,7 +128,7 @@ server <- function(input, output, session){
# Show inputs vital rates part
if(input$button_vital_rates%%2 == 1){
- shinyjs::show("mat_fill_vr")
+ shinyjs::show("mat_fill_vr")
}
}) # en observe show/hide
@@ -144,7 +163,7 @@ server <- function(input, output, session){
pop_size_mean = NULL,
pop_size_se = NULL,
- pop_size_type = NULL,
+ pop_size_unit = NULL,
pop_growth_mean = NULL,
pop_growth_se = NULL,
@@ -168,7 +187,7 @@ server <- function(input, output, session){
pop_size_eli_result = NULL,
pop_growth_eli_result = NULL,
carrying_cap_eli_result = NULL
- )
+ )
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
@@ -187,7 +206,7 @@ server <- function(input, output, session){
param$time_horzion = time_horzion
param$coeff_var_environ = coeff_var_environ
- }) # end observe
+ }) # end observe
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
@@ -220,7 +239,7 @@ server <- function(input, output, session){
observeEvent({
input$fatalities_input_type
},{
- output$fatalities_distri_plot <- renderPlot({ plot_gamma(mu = input$fatalities_mean, se = input$fatalities_se) })
+ output$fatalities_distri_plot <- renderPlot({ plot_gamma(mu = input$fatalities_mean, se = input$fatalities_se) })
})
## Population size ###~~~~~~~~~~~~~~~~~~~~~~~~~~###
@@ -321,7 +340,7 @@ server <- function(input, output, session){
## run elicitation analysis
output$carrying_cap_distri_plot <- renderPlot({
plot_expert(param$carrying_cap_eli_result$out, show_se = FALSE)
- })
+ })
} else {
print("missing value")
@@ -395,12 +414,12 @@ server <- function(input, output, session){
print("Erreur: Vous n'avez pas lancer l'analyse 'valeurs experts'")
}
- # Case 2 : Values directly provided (i.e., not from expert elicitation)
+ # Case 2 : Values directly provided (i.e., not from expert elicitation)
} else {
param$pop_size_mean <- input$pop_size_mean
param$pop_size_se <- input$pop_size_se
}
- param$pop_size_type <- input$pop_size_type
+ param$pop_size_unit <- input$pop_size_unit
})
@@ -430,7 +449,7 @@ server <- function(input, output, session){
print("Erreur: Vous n'avez pas lancer l'analyse 'valeurs experts'")
}
- # Case 2 : Trend information
+ # Case 2 : Trend information
} else if(input$pop_growth_input_type == "trend"){
if(input$pop_trend == "Croissance") {
@@ -454,7 +473,7 @@ server <- function(input, output, session){
}
param$pop_growth_se <- 0.03
- # Case 3 : Values directly provided (i.e., not from expert elicitation)
+ # Case 3 : Values directly provided (i.e., not from expert elicitation)
} else {
param$pop_growth_mean <- round(min(1 + param$rMAX_species, input$pop_growth_mean), 2)
param$pop_growth_se <- input$pop_growth_se
@@ -475,7 +494,9 @@ server <- function(input, output, session){
# Observe carrying capacity
- observeEvent({input$run}, {
+ observeEvent({
+ input$run
+ }, {
if(input$carrying_cap_input_type == "eli_exp"){
if(!(is.null(param$carrying_cap_eli_result))){
param$carrying_capacity <- round(param$carrying_cap_eli_result$mean)
@@ -487,7 +508,9 @@ server <- function(input, output, session){
}
})
- observeEvent({input$run}, {
+ observeEvent({
+ input$run
+ }, {
print(param$pop_growth_mean)
print(param$pop_growth_se)
})
@@ -511,7 +534,7 @@ server <- function(input, output, session){
pop_size_mean = param$pop_size_mean,
pop_size_se = param$pop_size_se,
- pop_size_type = param$pop_size_type,
+ pop_size_type = param$pop_size_unit,
pop_growth_mean = param$pop_growth_mean,
pop_growth_se = param$pop_growth_se,
@@ -557,7 +580,7 @@ server <- function(input, output, session){
## Display General information --
##--------------------------------------------
- output$species_name <- renderText({ paste0("Espèce : ", as.character(input$species_choice)) })
+ #output$species_name <- renderText({ paste0("Espèce : ", as.character(input$species_choice)) })
## Fatalities
@@ -590,18 +613,10 @@ server <- function(input, output, session){
})
-
-
-
-
-
-
-
-
## Poplutation size
- output$pop_size_type_info <- renderText({
- if(input$pop_size_type == "Npair"){
+ output$pop_size_unit_info <- renderText({
+ if(input$pop_size_unit == "Npair"){
paste0("Nombre de couple")
} else {
paste0("Effectif total")
@@ -635,48 +650,56 @@ server <- function(input, output, session){
## Carrying capacity
output$carrying_capacity_info <- renderText({
+
+ # N type
+ if(input$pop_size_unit == "Npair"){
+ info1 <- paste0("Nombre de couple : ")
+ } else {
+ info1 <- paste0("Effectif total : ")
+ }
+
+ # value of K
if(input$carrying_cap_input_type == "eli_exp"){
if(!(is.null(param$carrying_cap_eli_result))){
- info <- round(param$carrying_cap_eli_result$mean)
+ info2 <- round(param$carrying_cap_eli_result$mean)
} else {info <- NA}
}
else {
- info <- input$carrying_capacity
+ info2 <- input$carrying_capacity
}
- paste0("Valeur : ", info)
+
+ # paste for printing
+ paste0(info1, info2)
})
## Population growth
-
- output$pop_trend_type_info <- renderText({paste0("Type de Tendance de pop : ", input$pop_growth_input_type)})
-
output$pop_growth_mean_info <- renderText({
if(input$pop_growth_input_type == "eli_exp"){
if(!(is.null(param$pop_growth_eli_result))){
info <- round(param$pop_growth_eli_result$mean, 2)
} else {info <- NA}
} else if(input$pop_growth_input_type == "trend"){
- if(input$pop_trend == "Croissance") {
- if(input$pop_trend_strength == "Faible") {
- info <- 1.01
- } else if(input$pop_trend_strength == "Moyen"){
- info <- 1.03
- } else {
- info <- 1.06
- }
- } else if(input$pop_trend == "Déclin"){
- if(input$pop_trend_strength == "Faible") {
- info <- 0.99
- } else if(input$pop_trend_strength == "Moyen"){
- info <- 0.97
- } else {
- info <- 0.94
- }
+ if(input$pop_trend == "Croissance") {
+ if(input$pop_trend_strength == "Faible") {
+ info <- 1.01
+ } else if(input$pop_trend_strength == "Moyen"){
+ info <- 1.03
+ } else {
+ info <- 1.06
+ }
+ } else if(input$pop_trend == "Déclin"){
+ if(input$pop_trend_strength == "Faible") {
+ info <- 0.99
+ } else if(input$pop_trend_strength == "Moyen"){
+ info <- 0.97
} else {
- info <- 1.00
+ info <- 0.94
}
+ } else {
+ info <- 1.00
+ }
} else {
- info <- input$pop_growth_mean
+ info <- input$pop_growth_mean
}
paste0("Moyenne : ", info)
})
@@ -697,12 +720,9 @@ server <- function(input, output, session){
## Vital rates
-
output$vital_rates_info <- renderTable({
input$mat_fill_vr
- }, rownames = TRUE)
-
- # End genral informations output
+ }, rownames = TRUE)
@@ -737,7 +757,7 @@ server <- function(input, output, session){
}
-## Update the vital rate matrix when changing species in the list
+ ## Update the vital rate matrix when changing species in the list
observeEvent({input$species_choice}, {
if(input$species_choice == "Espèce générique") {} else {
@@ -768,6 +788,6 @@ server <- function(input, output, session){
}) # end observeEvent species_list
-###################################################################################
+ ###################################################################################
} # End server
diff --git a/inst/ShinyApp/ui.R b/inst/ShinyApp/ui.R
index 6d38db5081d70a06ad7f96400a8b0f53ddd3e261..d0a5a247c3ffbc1c61a1056a673cd89793ecb7d6 100644
--- a/inst/ShinyApp/ui.R
+++ b/inst/ShinyApp/ui.R
@@ -1,353 +1,382 @@
rm(list = ls(all.names = TRUE))
-## Load libraries
-library(shiny)
-library(shinyjs)
-library(shinyMatrix)
-library(tidyverse)
-library(eolpop)
-
-
-## Load species list
-species_data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
-species_list <- unique(as.character(species_data$NomEspece))
-
-## Load survival and fecundities data
-data_sf <- read.csv("./inst/ShinyApp/survivals_fecundities_species.csv", sep = ",")#, encoding = "UTF-8")
-(data_sf)
-
-# Fixed parameters (for now)
-nsim = 10
-coeff_var_environ = 0.10
-time_horzion = 30
-theta = 1 # DD parameter theta
-
-#####################
-### Pre-fill data ###
-#####################
-
-## Data elicitation pre-fill data
-# fatalities
-eli_fatalities <- c(1.0, 2, 5, 8, 0.80,
- 0.2, 0, 3, 6, 0.90,
- 0.2, 2, 4, 10, 0.90,
- 0.1, 1, 3, 7, 0.70)
-
-# population size
-eli_pop_size <- c(1.0, 150, 200, 250, 0.80,
- 0.5, 120, 180, 240, 0.90,
- 0.8, 170, 250, 310, 0.90,
- 0.3, 180, 200, 230, 0.70)
-
-# carrying capacity
-eli_carrying_cap <- c(1.0, 500, 700, 1000, 0.80,
- 0.5, 1000, 1500, 2000, 0.90,
- 0.8, 800, 1200, 1600, 0.90,
- 0.3, 100, 1200, 1500, 0.70)
-
-# population growth rate
-eli_pop_growth <- c(1 , 0.95, 0.98, 1.00, 0.95,
- 0.2, 0.97, 1.00, 1.01, 0.90,
- 0.5, 0.92, 0.96, 0.99, 0.90,
- 0.3, 0.90, 0.95, 0.98, 0.70)
-
-## Other pre-fill data
-# fatalities for several wind farms (cumulated impacts)
-init_cumul <- c(10, 5, 8,
- 0.05, 0.05, 0.05,
- 2010, 2015, 2018)
-
-init_cumul_add <- c(3, 0.05, 2020)
-
-###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Initial stuff
+{
+ ## Load libraries
+ library(shiny)
+ library(shinyjs)
+ library(shinyMatrix)
+ library(tidyverse)
+ library(eolpop)
+
+ ## Load species list
+ species_data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
+ species_list <- unique(as.character(species_data$NomEspece))
+
+ ## Load survival and fecundities data
+ data_sf <- read.csv("./inst/ShinyApp/survivals_fecundities_species.csv", sep = ",")#, encoding = "UTF-8")
+ (data_sf)
+
+ # Fixed parameters (for now)
+ nsim = 10
+ coeff_var_environ = 0.10
+ time_horzion = 30
+ theta = 1 # DD parameter theta
+}
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Pre-fill data
+{
+ ## Data elicitation pre-fill data
+ # fatalities
+ eli_fatalities <- c(1.0, 2, 5, 8, 0.80,
+ 0.2, 0, 3, 6, 0.90,
+ 0.2, 2, 4, 10, 0.90,
+ 0.1, 1, 3, 7, 0.70)
+
+ # population size
+ eli_pop_size <- c(1.0, 150, 200, 250, 0.80,
+ 0.5, 120, 180, 240, 0.90,
+ 0.8, 170, 250, 310, 0.90,
+ 0.3, 180, 200, 230, 0.70)
+
+ # carrying capacity
+ eli_carrying_cap <- c(1.0, 500, 700, 1000, 0.80,
+ 0.5, 1000, 1500, 2000, 0.90,
+ 0.8, 800, 1200, 1600, 0.90,
+ 0.3, 100, 1200, 1500, 0.70)
+
+ # population growth rate
+ eli_pop_growth <- c(1 , 0.95, 0.98, 1.00, 0.95,
+ 0.2, 0.97, 1.00, 1.01, 0.90,
+ 0.5, 0.92, 0.96, 0.99, 0.90,
+ 0.3, 0.90, 0.95, 0.98, 0.70)
+
+ ## Other pre-fill data
+ # fatalities for several wind farms (cumulated impacts)
+ init_cumul <- c(10, 5, 8,
+ 0.05, 0.05, 0.05,
+ 2010, 2015, 2018)
+
+ init_cumul_add <- c(3, 0.05, 2020)
+}
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
-##--------------------------------------------
-## User Interface --
-##--------------------------------------------
+## User Interface
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
-ui <- fluidPage(
+# fluidPage
+{ui <- fluidPage(
+
useShinyjs(),
titlePanel("eolpop : Impact demographique des éoliennes"),
- # Creation of the first page (select species, analysis type choice)
- wellPanel(
-
- selectInput(inputId = "species_choice", selected = 1,
- h4(strong("Sélectionner une espèce")),
- choices = species_list),
-
- radioButtons(inputId = "analysis_choice",
- h4(strong("Sélectionner un type d'analyse")),
- choices = c("Impacts non cumulés" = "scenario", "Impacts cumulés" = "cumulated"))
-
- ), # End wellPanel
-
-
- ##--------------------------------------------
- ## General information --
- ##--------------------------------------------
-
- wellPanel(
- h2("Valeurs actuelles"),
-
- fluidRow(
- column(width = 4,
- div( textOutput(outputId = "species_name") ,
- style="color:black; font-size:18px; font-weight: bold", align = "left"),
- )
- ),
-
- fluidRow(
- column(width = 4,
-
- br(),
- h3("Mortalités"),
- textOutput(outputId = "fatalities_mean_info"),
- textOutput(outputId = "fatalities_se_info"),
-
- br(),
- h3("Taille de la population"),
- textOutput(outputId = "pop_size_type_info"),
- textOutput(outputId = "pop_size_mean_info"),
- textOutput(outputId = "pop_size_se_info")),
-
- column(width = 4,
-
- br(),
- h3("Capacité de charge"),
- textOutput(outputId = "carrying_capacity_info"),
-
- br(),
- h3("Tendance de la population"),
- textOutput(outputId = "pop_trend_type_info"),
- textOutput(outputId = "pop_growth_mean_info"),
- textOutput(outputId = "pop_growth_se_info")),
-
- column(width = 4,
+ ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+ # Head Panel 1 : type of analysis and species
+ {wellPanel(
+ {fluidRow(
+
+ # Select type of analysis : cumulated impacted or not
+ {column(width = 4,
+ radioButtons(inputId = "analysis_choice",
+ h4(strong("Sélectionner un type d'analyse")),
+ choices = c("Impacts non cumulés" = "scenario", "Impacts cumulés" = "cumulated")),
+
+ selectInput(inputId = "species_choice",
+ selected = 1, width = '80%',
+ label = h4(strong("Sélectionner une espèce")),
+ choices = species_list),
+ )}, # close column
+
+ # Show vital rate values
+ {column(width = 4,
+ fluidRow(
+ h4(strong("Paramètres démographiques")),
+ tableOutput(outputId = "vital_rates_info"),
+ ),
+ )}, # close column
+
+
+ ## Modify vital rates (if needed)
+ {column(width = 4,
+ tags$style(HTML('#button_vital_rates{background-color:#C2C8D3}')),
+ actionButton(inputId = "button_vital_rates",
+ label = tags$span("Modifier les paramètres démographiques",
+ style = "font-weight: bold; font-size: 18px;")
+ ),
+
+ br(),
+ matrixInput(inputId = "mat_fill_vr",
+ label = "",
+ value = matrix(data = NA, 3, 2,
+ dimnames = list(c("Juv 1", "Juv 2", "Adulte"), c("Survie", "Fécondité"))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)
+ )
+
+ )}, # close column
+
+ )}, # End fluidRow
+ )}, # End wellPanel
+ ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- br(),
- h3("Paramètres démographiques"),
- tableOutput(outputId = "vital_rates_info"))
- ) # # End wellPanel
- ), # End fluidRow
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+ # Head Panel 2 : Model parameters
+ {wellPanel(
+
+ #h2("Saisie des paramètres"),
+ p("Saisie des paramètres", style="font-size:28px"),
+
+ {fluidRow(
+
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ ## 1. Fatalities
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ {column(width = 3,
+
+ tags$style(HTML('#button_fatalities{background-color:#C2C8D3}')),
+ actionButton(inputId = "button_fatalities",
+ label = tags$span("Mortalités annuelles", style = "font-weight: bold; font-size: 18px;")
+ ),
+
+ br(""),
+
+ ### Part for non-cumulated impacts
+ # Input type
+ radioButtons(inputId = "fatalities_input_type",
+ label = "Type de saisie",
+ choices = c("Valeurs" = "val", "Elicitation d'expert" = "eli_exp")),
+
+ # Values
+ numericInput(inputId = "fatalities_mean",
+ label = "Moyenne des mortalités annuelles",
+ value = 5,
+ min = 0, max = Inf, step = 0.5),
+ numericInput(inputId = "fatalities_se",
+ label = "Erreur-type des mortalités annuelles",
+ value = 0.05,
+ min = 0, max = Inf, step = 0.1),
+
+ # Matrix for expert elicitation
+ matrixInput(inputId = "fatalities_mat_expert",
+ value = matrix(data = eli_fatalities, nrow = 4, ncol = 5,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "fatalities_run_expert", label = "Utiliser valeurs experts"),
+
+ ### Part for cumulated impacts
+
+ numericInput(inputId = "farm_number_cumulated",
+ label = "Nombre de parcs éoliens",
+ value = 3, min = 2, max = Inf, step = 1),
+
+ matrixInput(inputId = "fatalities_mat_cumulated",
+ value = matrix(init_cumul, 3, 3,
+ dimnames = list(c(paste0("Parc n°", c(1:3))),
+ c("Moyenne",
+ "Erreur-type",
+ "Année de mise en service du parc"))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ )}, # end column "mortalité"
+ ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- ##--------------------------------------------
- ## Paramter Inputs --
- ##--------------------------------------------
- sidebarLayout(
- sidebarPanel(
-
- h2("Modifier les paramètres"),
-
- br(" "),
- ##--------------------------------------------
- ## 1. Fatalities --
- ##--------------------------------------------
- tags$style(HTML('#button_fatalities{background-color:#C2C8D3}')),
- actionButton(inputId = "button_fatalities",
- label = tags$span("Mortalités annuelles", style = "font-weight: bold; font-size: 18px;")
- ),
-
- radioButtons(inputId = "fatal_constant",
- label = h4("Modélisation"),
- choices = c("Taux de mortalités (h) constant" = "h",
- "Nombre de mortalités (M) constant" = "M")),
-
- ### Part for non-cumulated impacts
- # Input type
- radioButtons(inputId = "fatalities_input_type",
- label = h4("Type de saisie"),
- choices = c("Valeurs" = "val", "Elicitation d'expert" = "eli_exp")),
-
- # Values
- numericInput(inputId = "fatalities_mean",
- label = "Moyenne des mortalités annuelles",
- value = 5,
- min = 0, max = Inf, step = 0.5),
- numericInput(inputId = "fatalities_se",
- label = "Erreur-type des mortalités annuelles",
- value = 0.05,
- min = 0, max = Inf, step = 0.1),
-
- # Matrix for expert elicitation
- matrixInput(inputId = "fatalities_mat_expert",
- value = matrix(data = eli_fatalities, nrow = 4, ncol = 5,
- dimnames = list(c("#1", "#2", "#3", "#4"),
- c("Poids", "Min", "Best", "Max", "% IC" )),
- byrow = TRUE),
- class = "numeric",
- rows = list(names = TRUE),
- cols = list(names = TRUE)),
-
- actionButton(inputId = "fatalities_run_expert", label = "Utiliser valeurs experts"),
-
- ### Part for cumulated impacts
-
- numericInput(inputId = "farm_number_cumulated",
- label = "Nombre de parcs éoliens",
- value = 3, min = 2, max = Inf, step = 1),
-
- matrixInput(inputId = "fatalities_mat_cumulated",
- value = matrix(init_cumul, 3, 3,
- dimnames = list(c(paste0("Parc n°", c(1:3))),
- c("Moyenne",
- "Erreur-type",
- "Année de mise en service du parc"))),
- class = "numeric",
- rows = list(names = TRUE),
- cols = list(names = TRUE)),
-
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ ## 2. Population Size
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ {column(width = 3,
+
+ tags$style(HTML('#button_pop_size{background-color:#C2C8D3}')),
+ actionButton(inputId = "button_pop_size",
+ label = tags$span("Taille de la population", style = "font-weight: bold; font-size: 18px;")
+ ),
+
+ br(""),
+
+ conditionalPanel("output.hide_pop_size",
+ wellPanel(style = "background:#FFF8DC",
+ radioButtons(inputId = "pop_size_unit", inline = TRUE,
+ label = "Unité",
+ choices = c("Nombre de couple" = "Npair", "Effectif total" = "Ntotal"),
+ selected = "Ntotal"),
+ ),
+ ),
+
+ radioButtons(inputId = "pop_size_input_type",
+ label = "Type de saisie",
+ choices = c("Valeurs" = "val", "Elicitation d'expert" = "eli_exp")),
+
+ numericInput(inputId = "pop_size_mean",
+ label = "Moyenne de la taille de la population",
+ value = 200,
+ min = 0, max = Inf, step = 50),
+
+ numericInput(inputId = "pop_size_se",
+ label = "Erreur-type de la taille de la population",
+ value = 25,
+ min = 0, max = Inf, step = 1),
+
+ matrixInput(inputId = "pop_size_mat_expert",
+ value = matrix(data = eli_pop_size, nrow = 4, ncol = 5,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "pop_size_run_expert", label = "Utiliser valeurs experts"),
+
+ )}, # end column "mortalité"
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- hr(),
- ##--------------------------------------------
- ## 2. Population Size --
- ##--------------------------------------------
- tags$style(HTML('#button_pop_size{background-color:#C2C8D3}')),
- actionButton(inputId = "button_pop_size",
- label = tags$span("Taille de la population", style = "font-weight: bold; font-size: 18px;")
- ),
-
- radioButtons(inputId = "pop_size_type",
- label = h4("Unité"),
- choices = c("Nombre de couple" = "Npair", "Effectif total" = "Ntotal"),
- selected = "Ntotal"),
-
- radioButtons(inputId = "pop_size_input_type",
- label = h4("Type de saisie"),
- choices = c("Valeurs" = "val", "Elicitation d'expert" = "eli_exp")),
-
- numericInput(inputId = "pop_size_mean",
- label = "Moyenne de la taille de la population",
- value = 200,
- min = 0, max = Inf, step = 50),
-
- numericInput(inputId = "pop_size_se",
- label = "Erreur-type de la taille de la population",
- value = 25,
- min = 0, max = Inf, step = 1),
-
- matrixInput(inputId = "pop_size_mat_expert",
- value = matrix(data = eli_pop_size, nrow = 4, ncol = 5,
- dimnames = list(c("#1", "#2", "#3", "#4"),
- c("Poids", "Min", "Best", "Max", "% IC" )),
- byrow = TRUE),
- class = "numeric",
- rows = list(names = TRUE),
- cols = list(names = TRUE)),
-
- actionButton(inputId = "pop_size_run_expert", label = "Utiliser valeurs experts"),
+
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ ## 3. Population Growth
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ {column(width = 3,
+
+ tags$style(HTML('#button_pop_trend{background-color:#C2C8D3}')),
+ actionButton(inputId = "button_pop_trend",
+ label = tags$span("Tendance de la population", style = "font-weight: bold; font-size: 18px;")
+ ),
+
+ br(""),
+ radioButtons(inputId = "pop_growth_input_type",
+ label = "Type de saisie",
+ choices = c("Taux de croissance" = "val",
+ "Elicitation d'expert" = "eli_exp",
+ "Tendance locale ou régionale" = "trend")),
+
+ numericInput(inputId = "pop_growth_mean",
+ label = "Moyenne de la croissance de la population",
+ value = 1.1,
+ min = 0, max = Inf, step = 0.01),
+
+ numericInput(inputId = "pop_growth_se",
+ label = "Erreur-type de la croissance de la population",
+ value = 0.01,
+ min = 0, max = Inf, step = 0.01),
+
+ matrixInput(inputId = "pop_growth_mat_expert",
+ value = matrix(data = eli_pop_growth, nrow = 4, ncol = 5,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "pop_growth_run_expert", label = "Utiliser valeurs experts"),
+
+ radioButtons(inputId = "pop_trend",
+ label = NULL,
+ choices = c("Croissance", "Stable", "Déclin")),
+
+ radioButtons(inputId = "pop_trend_strength",
+ label = NULL,
+ choices = c("Faible", "Moyen", "Fort")),
+ )}, # end column "mortalité"
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- hr(),
- ##--------------------------------------------
- ## 3. Population Growth --
- ##--------------------------------------------
- tags$style(HTML('#button_pop_trend{background-color:#C2C8D3}')),
- actionButton(inputId = "button_pop_trend",
- label = tags$span("Tendance de la population", style = "font-weight: bold; font-size: 18px;")
- ),
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ ## 4. Carrying capacity
+ ##~~~~~~~~~~~~~~~~~~~~~~~~~
+ {column(width = 3,
- radioButtons(inputId = "pop_growth_input_type",
- label = h4("Type de saisie"),
- choices = c("Taux de croissance" = "val",
- "Elicitation d'expert" = "eli_exp",
- "Tendance locale ou régionale" = "trend")),
+ tags$style(HTML('#button_carrying_cap{background-color:#C2C8D3}')),
+ actionButton(inputId = "button_carrying_cap",
+ label = tags$span("Capacité de charge", style = "font-weight: bold; font-size: 18px;")
+ ),
- numericInput(inputId = "pop_growth_mean",
- label = "Moyenne de la croissance de la population",
- value = 1.1,
- min = 0, max = Inf, step = 0.01),
+ br(""),
+ radioButtons(inputId = "carrying_cap_input_type",
+ label = "Type de saisie",
+ choices = c("Valeurs" = "val", "Elicitation d'expert" = "eli_exp")),
- numericInput(inputId = "pop_growth_se",
- label = "Erreur-type de la croissance de la population",
- value = 0.01,
- min = 0, max = Inf, step = 0.01),
+ numericInput(inputId = "carrying_capacity",
+ label = "Capacité de charge",
+ value = 500,
+ min = 0, max = Inf, step = 100),
- matrixInput(inputId = "pop_growth_mat_expert",
- value = matrix(data = eli_pop_growth, nrow = 4, ncol = 5,
- dimnames = list(c("#1", "#2", "#3", "#4"),
- c("Poids", "Min", "Best", "Max", "% IC" )),
- byrow = TRUE),
- class = "numeric",
- rows = list(names = TRUE),
- cols = list(names = TRUE)),
+ matrixInput(inputId = "carrying_cap_mat_expert",
+ value = matrix(data = eli_carrying_cap, nrow = 4, ncol = 5,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
- actionButton(inputId = "pop_growth_run_expert", label = "Utiliser valeurs experts"),
+ actionButton(inputId = "carrying_cap_run_expert", label = "Utiliser valeurs experts"),
- radioButtons(inputId = "pop_trend",
- label = NULL,
- choices = c("Croissance", "Stable", "Déclin")),
+ )}, # end column "mortalité"
+ ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- radioButtons(inputId = "pop_trend_strength",
- label = NULL,
- choices = c("Faible", "Moyen", "Fort")),
+ )}, # # End fluidRow
- ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+ )}, # # End wellPanel
+ ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
- hr(),
- ##--------------------------------------------
- ## 4. Carrying capacity --
- ##--------------------------------------------
- tags$style(HTML('#button_carrying_cap{background-color:#C2C8D3}')),
- actionButton(inputId = "button_carrying_cap",
- label = tags$span("Capacité de charge", style = "font-weight: bold; font-size: 18px;")
- ),
- radioButtons(inputId = "carrying_cap_input_type",
- label = h4("Type de saisie"),
- choices = c("Valeurs" = "val", "Elicitation d'expert" = "eli_exp")),
+ ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+ ## Side Panel : Parameter information
+ {sidebarLayout(
+ {sidebarPanel(
- numericInput(inputId = "carrying_capacity",
- label = "Capacité de charge",
- value = 500,
- min = 0, max = Inf, step = 100),
+ p("Valeurs saisies", style="font-size:28px"),
- matrixInput(inputId = "carrying_cap_mat_expert",
- value = matrix(data = eli_carrying_cap, nrow = 4, ncol = 5,
- dimnames = list(c("#1", "#2", "#3", "#4"),
- c("Poids", "Min", "Best", "Max", "% IC" )),
- byrow = TRUE),
- class = "numeric",
- rows = list(names = TRUE),
- cols = list(names = TRUE)),
+ # Mortalités annuelles
+ {wellPanel(style = "background:#DCDCDC",
+ p("Mortalités annuelles", style="font-size:20px; font-weight: bold"),
+ span(textOutput(outputId = "fatalities_mean_info"), style="font-size:16px"),
+ span(textOutput(outputId = "fatalities_se_info"), style="font-size:16px"),
+ )},
- actionButton(inputId = "carrying_cap_run_expert", label = "Utiliser valeurs experts"),
+ # Taille de population
+ {wellPanel(style = "background:#DCDCDC",
+ p("Taille de la population", style="font-size:20px; font-weight: bold"),
+ shiny::tags$u(textOutput(outputId = "pop_size_unit_info"), style="font-size:16px"),
+ p(""),
+ span(textOutput(outputId = "pop_size_mean_info"), style="font-size:16px"),
+ span(textOutput(outputId = "pop_size_se_info"), style="font-size:16px"),
- ###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
+ )},
+ # Tendance de la population
+ {wellPanel(style = "background:#DCDCDC",
+ p("Tendance de la population", style="font-size:20px; font-weight: bold"),
+ span(textOutput(outputId = "pop_growth_mean_info"), style="font-size:16px"),
+ span(textOutput(outputId = "pop_growth_se_info"), style="font-size:16px"),
+ )},
- hr(),
- ##--------------------------------------------
- ## 5. Vital rates --
- ##--------------------------------------------
- tags$style(HTML('#button_vital_rates{background-color:#C2C8D3}')),
- actionButton(inputId = "button_vital_rates",
- label = tags$span("Paramètres démographiques", style = "font-weight: bold; font-size: 18px;")
- ),
+ # Capacité de charge
+ {wellPanel(style = "background:#DCDCDC",
+ p("Capacité de charge", style="font-size:20px; font-weight: bold"),
+ span(textOutput(outputId = "carrying_capacity_info"), style="font-size:16px"),
+ )},
- br(),
- matrixInput(inputId = "mat_fill_vr",
- label = "",
- value = matrix(data = NA, 3, 2,
- dimnames = list(c("Juv 1", "Juv 2", "Adulte"), c("Survie", "Fécondité"))),
- class = "numeric",
- rows = list(names = TRUE),
- cols = list(names = TRUE))
- ), # End sidebarPanel
+ )}, # End sidebarPanel
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -356,7 +385,7 @@ ui <- fluidPage(
# Creation of outputs parts
- mainPanel(
+ {mainPanel(
tabsetPanel(
tabPanel(title = "Distribution paramètres",
br(),
@@ -384,11 +413,22 @@ ui <- fluidPage(
tabPanel(title = "Impact population",
- strong(span(textOutput("message"), style="color:blue; font-size:24px", align = "center")),
- br(),
+
numericInput(inputId = "nsim", label = "Nombre de simulations",
value = 50, min = 0, max = Inf, step = 10),
+
+ ###### A DEPLACER #############
+ radioButtons(inputId = "fatal_constant",
+ label = h4("Modélisation"),
+ choices = c("Taux de mortalités (h) constant" = "h",
+ "Nombre de mortalités (M) constant" = "M")),
+ ###############################
+
+ br(),
+
+ strong(span(textOutput("message"), style="color:blue; font-size:24px", align = "center")),
br(),
+
actionButton(inputId = "run", label = "Lancer l'analyse"),
hr(),
h4("Graphique : Impact relatif de chaque scénario", align = "center"),
@@ -417,9 +457,11 @@ ui <- fluidPage(
) # End tabPanel
) # End tabSetPanel
- ) # End mainPanel
+ )} # End mainPanel
+
+ )} # sidebarLayout
+
+)} # FluidPage
- ) # sidebarLayout
-) # FluidPage
+# End UI #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
-# End UI