From 74ff14dbd22fbf5909d2eb7e87eeed04dd44295c Mon Sep 17 00:00:00 2001
From: thierrychambert <thierry.chambert@gmail.com>
Date: Wed, 11 Aug 2021 16:52:13 +0200
Subject: [PATCH] replace ui and server with thierry2 version
---
inst/ShinyApp/server.R | 27 +-
inst/ShinyApp/server_old.R | 605 +++++++++++++++++++++++++++++++++++++
inst/ShinyApp/ui.R | 27 +-
inst/ShinyApp/ui_old.R | 389 ++++++++++++++++++++++++
4 files changed, 1021 insertions(+), 27 deletions(-)
create mode 100644 inst/ShinyApp/server_old.R
create mode 100644 inst/ShinyApp/ui_old.R
diff --git a/inst/ShinyApp/server.R b/inst/ShinyApp/server.R
index f914e7a..fbb0a94 100644
--- a/inst/ShinyApp/server.R
+++ b/inst/ShinyApp/server.R
@@ -545,24 +545,25 @@ server <- function(input, output, session){
## Update matrix cumulated impact
- observeEvent({
- input$farm_number_cumulated
- }, {
-
- park_names <- function(n){
+ observeEvent({input$farm_number_cumulated}, {
+ rows_names <- function(n){
v <- c(paste0("Parc n°", c(1:n)))
return(v)
- }
+ }
- n_row <- input$farm_number_cumulated
+ nrow <- input$farm_number_cumulated
+ number_parks <- rows_names(nrow)
+ # data_fatalities_cumulated <- c(c(input$fatalities_mat_cumulated[,1]),
+ # c(input$fatalities_mat_cumulated[,2]),
+ # c(input$fatalities_mat_cumulated[,3]))
updateMatrixInput(session, inputId = "fatalities_mat_cumulated",
- value = matrix(init_cumul, nrow = n_row, 3, byrow = TRUE,
- dimnames = list(park_names(n_row),
- c("Moyenne",
- "Ecart-type",
+ value = matrix("", nrow = nrow, 3,
+ dimnames = list(number_parks,
+ c("Moyennes des mortalités annuelles",
+ "Ecart-type des mortalités annuelles",
"Année de mise en service du parc"))))
- }) # end observEvent
+ })
# Survivals and Fecundities
@@ -601,5 +602,3 @@ server <- function(input, output, session){
}
# End server
-
-
diff --git a/inst/ShinyApp/server_old.R b/inst/ShinyApp/server_old.R
new file mode 100644
index 0000000..f914e7a
--- /dev/null
+++ b/inst/ShinyApp/server_old.R
@@ -0,0 +1,605 @@
+server <- function(input, output, session){
+
+ # Hide all inputs excepted actionButtons
+
+ observe({
+ shinyjs::hide("fatal_constant")
+ shinyjs::hide("fatalities_input_type")
+ shinyjs::hide("fatalities_mean")
+ shinyjs::hide("fatalities_se")
+ shinyjs::hide("fatalities_mat_expert")
+ shinyjs::hide("fatalities_run_expert")
+ shinyjs::hide("farm_number_cumulated")
+ shinyjs::hide("fatalities_mat_cumulated")
+ shinyjs::hide("pop_size_type")
+ shinyjs::hide("pop_size_input_type")
+ shinyjs::hide("pop_size_mean")
+ shinyjs::hide("pop_size_se")
+ shinyjs::hide("pop_size_mat_expert")
+ shinyjs::hide("pop_size_run_expert")
+ shinyjs::hide("carrying_cap_input_type")
+ shinyjs::hide("carrying_capacity")
+ shinyjs::hide("carrying_cap_mat_expert")
+ shinyjs::hide("carrying_cap_run_expert")
+ shinyjs::hide("lambda_input_type")
+ shinyjs::hide("pop_growth_mean")
+ shinyjs::hide("pop_growth_se")
+ shinyjs::hide("pop_growth_mat_expert")
+ shinyjs::hide("pop_growth_run_expert")
+ shinyjs::hide("pop_trend")
+ shinyjs::hide("pop_trend_strength")
+ shinyjs::hide("fill_type_vr")
+ shinyjs::hide("mat_display_vr")
+ shinyjs::hide("mat_fill_vr")
+
+ # Show fatalities part
+
+ if(input$button_fatalities%%2 == 1){
+ 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 == "Valeurs"){
+ shinyjs::show("fatalities_mean")
+ shinyjs::show("fatalities_se")
+ }
+ if(input$fatalities_input_type == "Elicitation d'expert"){
+ shinyjs::show("fatalities_mat_expert")
+ shinyjs::show("fatalities_run_expert")
+ }
+ }
+
+ # Show inputs for cumulated scenario
+
+ if(input$analysis_choice == "cumulated"){
+ shinyjs::show("farm_number_cumulated")
+ shinyjs::show("fatalities_mat_cumulated")
+ }
+
+ }
+
+ # Show inputs for population size part
+
+ if(input$button_pop_size%%2 == 1){
+ shinyjs::show("pop_size_type")
+ shinyjs::show("pop_size_input_type")
+ if(input$pop_size_input_type == "Valeurs"){
+ shinyjs::show("pop_size_mean")
+ shinyjs::show("pop_size_se")
+ }
+ if(input$pop_size_input_type == "Elicitation d'expert"){
+ shinyjs::show("pop_size_mat_expert")
+ shinyjs::show("pop_size_run_expert")
+ }
+ }
+
+ # Show inputs for carrying capacity part
+
+ if(input$button_carrying_cap%%2 == 1){
+ shinyjs::show("carrying_cap_input_type")
+ if(input$carrying_cap_input_type == "Valeurs"){
+ shinyjs::show("carrying_capacity")
+ }
+ if(input$carrying_cap_input_type == "Elicitation d'expert"){
+ shinyjs::show("carrying_cap_mat_expert")
+ shinyjs::show("carrying_cap_run_expert")
+ }
+ }
+
+ # Show inputs for population trend part
+
+ if(input$button_pop_trend%%2 == 1){
+ shinyjs::show("lambda_input_type")
+ if(input$lambda_input_type == "Taux de croissance"){
+ shinyjs::show("pop_growth_mean")
+ shinyjs::show("pop_growth_se")
+ }
+ if(input$lambda_input_type == "Elicitation d'expert"){
+ shinyjs::show("pop_growth_mat_expert")
+ shinyjs::show("pop_growth_run_expert")
+ }
+ if(input$lambda_input_type == "Tendance locale ou régionale"){
+ shinyjs::show("pop_trend")
+ shinyjs::show("pop_trend_strength")
+ }
+ }
+
+ # Show inputs vital rates part
+
+ if(input$button_vital_rates%%2 == 1){
+ shinyjs::show("fill_type_vr")
+ if(input$fill_type_vr == "Automatique"){
+ shinyjs::show("mat_display_vr")
+ }
+ if(input$fill_type_vr == "Manuelle"){
+ shinyjs::show("mat_fill_vr")
+ }
+ }
+ })
+
+ # Elicitation experts part
+
+ func_eli <- function(mat_expert){
+ t_mat_expert <- t(mat_expert)
+ vals = t_mat_expert[3:5,]
+ Cp = t_mat_expert[6,]
+ weights = t_mat_expert[2,]
+
+ out <- elicitation(vals, Cp, weights)
+ return(list(out = out, mean = out$mean_smooth, SE = sqrt(out$var_smooth)))
+ }
+
+ func_eli_plot <- function(out){
+ plot_elicitation(out)
+ }
+
+ ## Output
+
+ param <- reactiveValues(N1 = NULL,
+ fatalities_mean = NULL,
+ fecundities = NULL,
+ survivals = NULL,
+ s_calibrated = NULL,
+ f_calibrated = NULL,
+ vr_calibrated = NULL,
+ cumulated_impacts = NULL,
+ onset_time = NULL,
+ onset_year = NULL,
+ carrying_capacity = NULL,
+ rMAX_species = rMAX_species,
+ theta = theta,
+ fatalities_eli_result = NULL,
+ pop_size_eli_result = NULL,
+ pop_size_mean = NULL,
+ pop_size_se = NULL,
+ pop_size_type = NULL,
+ pop_growth_eli_result = NULL,
+ pop_growth_mean = NULL,
+ pop_growth_se = NULL,
+ carrying_cap_eli_result = NULL)
+
+ # Elicitation
+
+ ## Fatalities
+
+ observeEvent({input$fatalities_run_expert}, {
+ if(all(is.na(input$fatalities_mat_expert))) {} else {
+ param$fatalities_eli_result <- func_eli(input$fatalities_mat_expert)
+
+ ### Plot fatalities
+ output$fatalities_expert_plot <- renderPlot({func_eli_plot(param$fatalities_eli_result$out)})}
+ })
+
+ ## Population size
+
+ observeEvent({input$pop_size_run_expert}, {
+ if(all(is.na(input$pop_size_mat_expert))) {} else {
+ param$pop_size_eli_result <- func_eli(input$pop_size_mat_expert)
+
+ ### Plot pop size
+ output$pop_size_expert_plot <- renderPlot({func_eli_plot(param$pop_size_eli_result$out)})}
+ })
+
+ ## Population growth
+
+ observeEvent({input$pop_growth_run_expert},{
+ if(all(is.na(input$pop_growth_mat_expert))) {} else {
+ param$pop_growth_eli_result <- func_eli(input$pop_growth_mat_expert)
+
+ ### plot pop growth
+ output$pop_growth_expert_plot <- renderPlot({func_eli_plot(param$pop_growth_eli_result$out)})
+ }
+ })
+
+ ## Carrying capacity
+
+ observeEvent({input$carrying_cap_run_expert},{
+ if(all(is.na(input$carrying_cap_mat_expert))) {} else {
+ param$carrying_cap_eli_result <- func_eli(input$carrying_cap_mat_expert)
+
+ ### Plot carrying capacity
+ output$carrying_cap_expert_plot <- renderPlot({func_eli_plot(param$carrying_cap_eli_result$out)})
+ }
+ })
+
+ # Reactive values (cumulated impacts, fatalities mean, fatalities se, onset_time, survivals mean, fecundities mean)
+
+ observeEvent({input$run}, {
+ if(input$analysis_choice == "scenario"){
+ param$cumulated_impacts = FALSE
+ } else {
+ param$cumulated_impacts = TRUE
+ }
+ })
+
+ # Fatalities
+ ## onset time, mean and se
+
+ observeEvent({input$run}, {
+ if(input$analysis_choice == "scenario"){
+ if(input$fatalities_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$fatalities_eli_result))) {
+ param$fatalities_mean <- c(0, round(param$fatalities_eli_result$mean))
+ param$onset_time = NULL
+ param$fatalities_se <- c(0, round(param$fatalities_eli_result$SE))
+ } else {
+ print("#Intégrer un message d'erreur")
+ }
+ } else {
+ param$fatalities_mean <- c(0, input$fatalities_mean)
+ param$onset_time = NULL
+ param$fatalities_se <- c(0, input$fatalities_se)
+ }
+ } else {
+ param$fatalities_mean <- c(0, input$fatalities_mat_cumulated[,1])
+ param$onset_year <- c(min(input$fatalities_mat_cumulated[,3]), input$fatalities_mat_cumulated[,3])
+ param$onset_time <- param$onset_year - min(param$onset_year) + 1
+ param$fatalities_se <- c(0, input$fatalities_mat_cumulated[,2])
+ }
+ })
+
+ # Population size
+ ## Mean, se and type
+
+ observeEvent({input$run},{
+ if(input$pop_size_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$pop_size_eli_result))){
+ param$pop_size_mean <- round(param$pop_size_eli_result$mean)
+ param$pop_size_se <- round(param$pop_size_eli_result$SE)
+ } else {
+ print("#intégrer un message d'erreur")
+ }
+ } 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
+ })
+
+ # Observe pop growth value
+ ## Avoid unrealistic scenarios
+
+ observeEvent({input$run}, {
+ if(input$lambda_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$pop_growth_eli_result))){
+ param$pop_growth_mean <- round(min(1 + param$rMAX_species, round(param$pop_growth_eli_result$mean, 2)), 2)
+ param$pop_growth_se <- round(param$pop_growth_eli_result$SE, 2)
+ } else {
+ print("#intégrer un message d'erreur")
+ }
+ } else if(input$lambda_input_type == "Tendance locale ou régionale"){
+ if(input$pop_trend == "Croissance") {
+ if(input$pop_trend_strength == "Faible") {
+ param$pop_growth_mean <- 1.01
+ } else if(input$pop_trend_strength == "Moyen"){
+ param$pop_growth_mean <- 1.03
+ } else {
+ param$pop_growth_mean <- 1.06
+ }
+ } else if(input$pop_trend == "Déclin"){
+ if(input$pop_trend_strength == "Faible") {
+ param$pop_growth_mean <- 0.99
+ } else if(input$pop_trend_strength == "Moyen"){
+ param$pop_growth_mean <- 0.97
+ } else {
+ param$pop_growth_mean <- 0.94
+ }
+ } else {
+ param$pop_growth_mean <- 1
+ }
+ param$pop_growth_se <- 0.03
+ }
+ else {
+ param$pop_growth_mean <- round(min(1 + param$rMAX_species, input$pop_growth_mean), 2)
+ param$pop_growth_se <- input$pop_growth_se
+ }
+ })
+
+ # Survivals and fecundities
+
+ observeEvent({input$run}, {
+ if(input$fill_type_vr == "Manuelle"){
+ param$survivals <- input$mat_fill_vr[,1]
+ param$fecundities <- input$mat_fill_vr[,2]
+ } else {
+ param$survivals <- survivals
+ param$fecundities <- fecundities
+ }
+ })
+
+ # Survival and fecundity calibration
+
+ observeEvent({
+ input$run
+ # input$species_choice
+ # input$pop_growth_mean
+ },{
+
+ ## Avoid unrealistic scenarios
+ #param$pop_growth_mean <- min(1 + param$rMAX_species, input$pop_growth_mean)
+
+ param$vr_calibrated <- calibrate_params(
+ inits = init_calib(s = param$survivals, f = param$fecundities, lam0 = param$pop_growth_mean),
+ f = param$fecundities, s = param$survivals, lam0 = param$pop_growth_mean
+ )
+ param$s_calibrated <- head(param$vr_calibrated, length(param$survivals))
+ param$f_calibrated <- tail(param$vr_calibrated, length(param$fecundities))
+ })
+
+
+ # Observe carrying capacity
+ observeEvent({input$run}, {
+ if(input$carrying_cap_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$carrying_cap_eli_result))){
+ param$carrying_capacity <- round(param$carrying_cap_eli_result$mean)
+ } else {
+ print("#intégrer un message d'erreur")
+ }
+ } else {
+ param$carrying_capacity <- input$carrying_capacity
+ }
+ })
+
+ observeEvent({input$run}, {
+ print(param$pop_growth_mean)
+ print(param$pop_growth_se)
+ })
+
+ # End of reactive
+
+ # Simulations
+
+ observeEvent({
+ input$run
+ }, {
+
+ withProgress(message = 'Simulation progress', value = 0, {
+
+ param$N1 <- run_simul_shiny(nsim = input$nsim,
+ cumuated_impacts = param$cumulated_impacts,
+
+ fatalities_mean = param$fatalities_mean,
+ fatalities_se = param$fatalities_se,
+ onset_time = param$onset_time,
+
+ pop_size_mean = param$pop_size_mean,
+ pop_size_se = param$pop_size_se,
+ pop_size_type = param$pop_size_type,
+
+ pop_growth_mean = param$pop_growth_mean,
+ pop_growth_se = param$pop_growth_se,
+
+ survivals = param$s_calibrated,
+ fecundities = param$f_calibrated,
+
+ carrying_capacity = param$carrying_capacity,
+ theta = param$theta,
+ rMAX_species = param$rMAX_species,
+
+ model_demo = NULL,
+ time_horzion = time_horzion,
+ coeff_var_environ = coeff_var_environ,
+ fatal_constant = input$fatal_constant)
+ }) # Close withProgress
+ }) # Close observEvent
+
+
+ # Plot Impacts
+
+ plot_out_impact <- function(){
+ if(is.null(param$N1)) {} else {plot_impact(N = param$N1$N, xlab = "year", ylab = "pop size")}
+ }
+
+ output$graph_impact <- renderPlot({
+ plot_out_impact()
+ })
+
+ # Plot trajectories
+
+ plot_out_traj <- function(){
+ if(is.null(param$N1)) {} else {plot_traj(N = param$N1$N, xlab = "year", ylab = "pop size")}
+ }
+
+ output$graph_traj <- renderPlot({
+ plot_out_traj()
+ })
+ # End simulations
+
+ # General informations output
+
+ ## Fatalities
+
+ output$fatalities_mean_info <- renderText({
+ if(input$fatalities_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$fatalities_eli_result))){
+ info <- round(param$fatalities_eli_result$mean)
+ } else {info <- NA}
+ }
+ else {
+ info <- input$fatalities_mean
+ }
+ paste0("Moyenne des mortalités : ", info)
+ })
+
+ output$fatalities_se_info <- renderText({
+ if(input$fatalities_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$fatalities_eli_result))){
+ info <- round(param$fatalities_eli_result$SE)
+ } else {info <- NA}
+ }
+ else {
+ info <- input$fatalities_se
+ }
+ paste0("Ecart-type des mortalités : ", info)
+ })
+
+ ## Poplutation size
+
+ output$pop_size_type_info <- renderText({
+ if(input$pop_size_type == "Npair"){
+ paste0("Type de taille de pop : ", "Nombre de couple")
+ } else {
+ paste0("Type de taille de pop : ", "Effectif total")
+ }
+ })
+
+ output$pop_size_mean_info <- renderText({
+ if(input$pop_size_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$pop_size_eli_result))){
+ info <- round(param$pop_size_eli_result$mean)
+ } else {info <- NA}
+ }
+ else {
+ info <- input$pop_size_mean
+ }
+ paste0("Moyenne de la taille de la population : ", info)
+ })
+
+ output$pop_size_se_info <- renderText({
+ if(input$pop_size_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$pop_size_eli_result))){
+ info <- round(param$pop_size_eli_result$SE)
+ } else {info <- NA}
+ }
+ else {
+ info <- input$pop_size_se
+ }
+ paste0("Ecart-type de la taille de la population : ", info)
+ })
+
+ ## Carrying capacity
+
+ output$carrying_capacity_info <- renderText({
+ if(input$carrying_cap_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$carrying_cap_eli_result))){
+ info <- round(param$carrying_cap_eli_result$mean)
+ } else {info <- NA}
+ }
+ else {
+ info <- input$carrying_capacity
+ }
+ paste0("Capacité de charge du milieu : ", info)
+ })
+
+ ## Population growth
+
+ output$pop_trend_type_info <- renderText({paste0("Type de Tendance de pop : ", input$lambda_input_type)})
+
+ output$pop_growth_mean_info <- renderText({
+ if(input$lambda_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$pop_growth_eli_result))){
+ info <- round(param$pop_growth_eli_result$mean, 2)
+ } else {info <- NA}
+ } else if(input$lambda_input_type == "Tendance locale ou régionale"){
+ 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
+ }
+ } else {
+ info <- 1.00
+ }
+ } else {
+ info <- input$pop_growth_mean
+ }
+ paste0("Moyenne de la croissance de la population : ", info)
+ })
+
+ output$pop_growth_se_info <- renderText({
+ if(input$lambda_input_type == "Elicitation d'expert"){
+ if(!(is.null(param$pop_growth_eli_result))){
+ info <- round(param$pop_growth_eli_result$SE, 2)
+ } else {info <- NA}
+ } else if (input$lambda_input_type == "Tendance locale ou régionale") {
+ info <- 0.03
+ }
+ else {
+ info <- input$pop_growth_se
+ }
+ paste0("Ecart-type de la croissance de la population : ", info)
+ })
+
+ ## Vital rates
+
+ output$vital_rates_info <- renderTable({
+ if(input$fill_type_vr == "Automatique"){
+ input$mat_display_vr
+ } else {
+ input$mat_fill_vr
+ }
+ })
+ # End genral informations output
+
+ ## Update matrix cumulated impact
+
+ observeEvent({
+ input$farm_number_cumulated
+ }, {
+
+ park_names <- function(n){
+ v <- c(paste0("Parc n°", c(1:n)))
+ return(v)
+ }
+
+ n_row <- input$farm_number_cumulated
+
+ updateMatrixInput(session, inputId = "fatalities_mat_cumulated",
+ value = matrix(init_cumul, nrow = n_row, 3, byrow = TRUE,
+ dimnames = list(park_names(n_row),
+ c("Moyenne",
+ "Ecart-type",
+ "Année de mise en service du parc"))))
+ }) # end observEvent
+
+ # Survivals and Fecundities
+
+ create.matrice <- function(species){
+ tab_test <- data_sf %>%
+ filter(species == data_sf$Nom_espece) %>%
+ select(classes_age, survie, fecondite)
+ return(tab_test)
+ }
+
+ observeEvent({input$species_list}, {
+ if(input$species_list == "Espèce") {} else {
+ tab_species <- create.matrice(input$species_list)
+
+ if(all(is.na(tab_species))) {
+ updateMatrixInput(session, inputId = "mat_fill_vr",
+ value = matrix(data = "",
+ nrow = 4,
+ ncol = 2,
+ dimnames = list(c("Juv 1", "Juv 2", "Juv 3", "Adulte"), c("Survie", "Fécondité"))))
+
+ } else {
+ number_age_class <- nrow(tab_species)
+ ages <- tab_species$classes_age
+ survivals <- tab_species$survie
+ fecundities <- tab_species$fecondite
+
+ updateMatrixInput(session, inputId = "mat_fill_vr",
+ value = matrix(data = c(survivals, fecundities),
+ nrow = number_age_class,
+ ncol = 2,
+ dimnames = list(ages, c("Survie", "Fécondité"))))
+ }
+ }
+ })
+}
+# End server
+
+
+
diff --git a/inst/ShinyApp/ui.R b/inst/ShinyApp/ui.R
index 47624bc..c04acc4 100644
--- a/inst/ShinyApp/ui.R
+++ b/inst/ShinyApp/ui.R
@@ -8,9 +8,6 @@ library(tidyverse)
library(eolpop)
-# source("./inst/ShinyApp/f_output.R")
-# source("./inst/ShinyApp/param_fixes.R")
-
## Load species list
species_data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
species_list <- unique(as.character(species_data$NomEspece))
@@ -27,6 +24,10 @@ time_horzion = 30
survivals <- c(0.5, 0.7, 0.8, 0.95)
fecundities <- c(0, 0, 0.05, 0.55)
+#####################
+### Pre-fill data ###
+#####################
+
## Data elicitation pre-fill data
# fatalities
eli_fatalities <- c("A", 1.0, 2, 5, 8, 0.80,
@@ -48,27 +49,28 @@ eli_carrying_cap <- c("A", 1.0, 500, 700, 1000, 0.80,
# population growth rate
eli_pop_growth <- c("A", 1 , 0.95, 0.98, 1.00, 0.95,
- "B", 0.2, 0.97, 1.00, 1.01, 0.90,
- "C", 0.5, 0.92, 0.96, 0.99, 0.90,
- "D", 0.3, 0.90, 0.95, 0.98, 0.70)
+ "B", 0.2, 0.97, 1.00, 1.01, 0.90,
+ "C", 0.5, 0.92, 0.96, 0.99, 0.90,
+ "D", 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)
+ 0.05, 0.05, 0.05,
+ 2010, 2015, 2018)
+
init_cumul_add <- c(3, 0.05, 2020)
# vital rates
-init_vr = c(survivals, fecundities)
+data_vr = c(survivals, fecundities)
# DD parameters
theta = 1
# Define theoretical rMAX for the species
-rMAX_species <- rMAX_spp(surv = tail(survivals, 1), afr = min(which(fecundities != 0)))
+rMAX_species <- rMAX_spp(surv = tail(survivals,1), afr = min(which(fecundities != 0)))
rMAX_species
@@ -83,7 +85,7 @@ ui <- fluidPage(
wellPanel(
selectInput(inputId = "species_list",
- h4(strong("Sélection d'une espèce")),
+ h4(strong("Sélection d'une espèce ou groupe d'espèces")),
choices = species_list),
radioButtons(inputId = "analysis_choice",
h4(strong("Sélectionner un type d'analyse")),
@@ -318,7 +320,7 @@ ui <- fluidPage(
cols = list(names = TRUE)),
matrixInput(inputId = "mat_fill_vr",
- value = matrix(data = init_vr, 4, 2, dimnames = list(c("Juv 1", "Juv 2", "Juv 3", "Adulte"), c("Survie", "Fécondité"))),
+ value = matrix(data = data_vr, 4, 2, dimnames = list(c("Juv 1", "Juv 2", "Juv 3", "Adulte"), c("Survie", "Fécondité"))),
class = "numeric",
rows = list(names = TRUE),
cols = list(names = TRUE))
@@ -386,4 +388,3 @@ ui <- fluidPage(
) # FluidPage
# End UI
-
diff --git a/inst/ShinyApp/ui_old.R b/inst/ShinyApp/ui_old.R
new file mode 100644
index 0000000..47624bc
--- /dev/null
+++ b/inst/ShinyApp/ui_old.R
@@ -0,0 +1,389 @@
+rm(list = ls(all.names = TRUE))
+
+## Load libraries
+library(shiny)
+library(shinyjs)
+library(shinyMatrix)
+library(tidyverse)
+library(eolpop)
+
+
+# source("./inst/ShinyApp/f_output.R")
+# source("./inst/ShinyApp/param_fixes.R")
+
+## Load species list
+species_data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
+species_list <- unique(as.character(species_data$NomEspece))
+# species_list <- species_data$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
+survivals <- c(0.5, 0.7, 0.8, 0.95)
+fecundities <- c(0, 0, 0.05, 0.55)
+
+## Data elicitation pre-fill data
+# fatalities
+eli_fatalities <- c("A", 1.0, 2, 5, 8, 0.80,
+ "B", 0.2, 0, 3, 6, 0.90,
+ "C", 0.2, 2, 4, 10, 0.90,
+ "D", 0.1, 1, 3, 7, 0.70)
+
+# population size
+eli_pop_size <- c("A", 1.0, 150, 200, 250, 0.80,
+ "B", 0.5, 120, 180, 240, 0.90,
+ "C", 0.8, 170, 250, 310, 0.90,
+ "D", 0.3, 180, 200, 230, 0.70)
+
+# carrying capacity
+eli_carrying_cap <- c("A", 1.0, 500, 700, 1000, 0.80,
+ "B", 0.5, 1000, 1500, 2000, 0.90,
+ "C", 0.8, 800, 1200, 1600, 0.90,
+ "D", 0.3, 100, 1200, 1500, 0.70)
+
+# population growth rate
+eli_pop_growth <- c("A", 1 , 0.95, 0.98, 1.00, 0.95,
+ "B", 0.2, 0.97, 1.00, 1.01, 0.90,
+ "C", 0.5, 0.92, 0.96, 0.99, 0.90,
+ "D", 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)
+
+
+
+# vital rates
+init_vr = c(survivals, fecundities)
+
+# DD parameters
+theta = 1
+
+# Define theoretical rMAX for the species
+rMAX_species <- rMAX_spp(surv = tail(survivals, 1), afr = min(which(fecundities != 0)))
+rMAX_species
+
+
+##--------------------------------------------
+## User Interface --
+##--------------------------------------------
+ui <- fluidPage(
+ useShinyjs(),
+ titlePanel("eolpop : Impact demographique des éoliennes"),
+
+ # Creation of the first page (select species, analysis type choice)
+
+ wellPanel(
+ selectInput(inputId = "species_list",
+ h4(strong("Sélection d'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(
+ fluidRow(
+ column(width = 4,
+ textOutput(outputId = "specie_name"),
+ h4("Mortalités"),
+ textOutput(outputId = "fatalities_mean_info"),
+ textOutput(outputId = "fatalities_se_info"),
+ h4("Taille de la population"),
+ textOutput(outputId = "pop_size_type_info"),
+ textOutput(outputId = "pop_size_mean_info"),
+ textOutput(outputId = "pop_size_se_info")),
+ fluidRow(
+ column(width = 4,
+ h4("Capacité de charge"),
+ textOutput(outputId = "carrying_capacity_info"),
+ h4("Tendance de la population"),
+ textOutput(outputId = "pop_trend_type_info"),
+ textOutput(outputId = "pop_growth_mean_info"),
+ textOutput(outputId = "pop_growth_se_info")),
+ fluidRow(
+ column(width = 4,
+ h4("Paramètres démographiques"),
+ tableOutput(outputId = "vital_rates_info"))
+ )
+ )
+ )
+ ), # End wellPanel
+
+
+ # Paramter Inputs (fatalities, pop size, carrying capacity, pop trend and vital rates).
+
+ sidebarLayout(
+ sidebarPanel(
+
+ ##--------------------------------------------
+ ## 1. Fatalities --
+ ##--------------------------------------------
+
+ actionButton(inputId = "button_fatalities",
+ label = "Mortalités"),
+ 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("Source des données"),
+ choices = c("Valeurs", "Elicitation d'expert")),
+
+ # Values
+ numericInput(inputId = "fatalities_mean",
+ label = "Moyenne des mortalités annuelles",
+ value = 5,
+ min = 0, max = Inf, step = 0.5),
+ numericInput(inputId = "fatalities_se",
+ label = "Ecart-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, 4, 6,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Nom", "Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "fatalities_run_expert", label = "Analyse"),
+
+ ### 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",
+ "Ecart-type",
+ "Année de mise en service du parc"))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+
+ ##--------------------------------------------
+ ## 2. Population Size --
+ ##--------------------------------------------
+
+ br(" "),
+ actionButton(inputId = "button_pop_size",
+ label = "Taille de la population"),
+
+ radioButtons(inputId = "pop_size_type",
+ label = h4("Unité"),
+ choices = c("Nombre de couple" = "Npair", "Effectif total" = "Ntotal")),
+
+ radioButtons(inputId = "pop_size_input_type",
+ label = h4("Type de saisie"),
+ choices = c("Valeurs", "Elicitation d'expert")),
+
+ 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 = "Ecart-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, 4, 6,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Nom", "Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "pop_size_run_expert", label = "Analyse"),
+
+
+ ##--------------------------------------------
+ ## 3. Carrying capacity --
+ ##--------------------------------------------
+
+ br(" "),
+ actionButton(inputId = "button_carrying_cap",
+ label = "Capacité de charge"),
+
+ radioButtons(inputId = "carrying_cap_input_type",
+ label = h4("Type d'unité"),
+ choices = c("Valeurs", "Elicitation d'expert")),
+
+ numericInput(inputId = "carrying_capacity",
+ label = "Capacité de charge",
+ value = 1000,
+ min = 0, max = Inf, step = 100),
+
+ matrixInput(inputId = "carrying_cap_mat_expert",
+ value = matrix(data = eli_carrying_cap, 4, 6,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Nom", "Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "carrying_cap_run_expert", label = "Analyse"),
+
+ ##--------------------------------------------
+ ## 4. Population Trend --
+ ##--------------------------------------------
+
+ br(" "),
+ actionButton(inputId = "button_pop_trend",
+ label = "Tendance de la population"),
+
+ radioButtons(inputId = "lambda_input_type",
+ label = h4("Type de tendance"),
+ choices = c("Taux de croissance", "Elicitation d'expert", "Tendance locale ou régionale")),
+
+ numericInput(inputId = "pop_growth_mean",
+ label = "Moyenne de la croissance de la population",
+ value = 1,
+ min = 0, max = Inf, step = 0.01),
+
+ numericInput(inputId = "pop_growth_se",
+ label = "Ecart-type de la croissance de la population",
+ value = 0,
+ min = 0, max = Inf, step = 0.01),
+
+ matrixInput(inputId = "pop_growth_mat_expert",
+ value = matrix(data = eli_pop_growth, 4, 6,
+ dimnames = list(c("#1", "#2", "#3", "#4"),
+ c("Nom", "Poids", "Min", "Best", "Max", "% IC" )),
+ byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ actionButton(inputId = "pop_growth_run_expert", label = "Analyse"),
+
+ h4("Tendance de la population"),
+
+ radioButtons(inputId = "pop_trend",
+ label = NULL,
+ choices = c("Croissance", "Stable", "Déclin")),
+
+ radioButtons(inputId = "pop_trend_strength",
+ label = NULL,
+ choices = c("Faible", "Moyen", "Fort")),
+
+ # tags$style("#pop_trend_strength {position:fixed; top: 600px; right: 100px;}"),
+
+
+ ##--------------------------------------------
+ ## 5. Vital rates --
+ ##--------------------------------------------
+
+ br(" "),
+ actionButton(inputId = "button_vital_rates",
+ label = "Paramètres démographiques"),
+
+ radioButtons(inputId = "fill_type_vr",
+ label = "Type de saisie",
+ choices = c("Automatique", "Manuelle")),
+
+ # tableOutput(outputId = "mat_display_vr"),
+
+ matrixInput(inputId = "mat_display_vr",
+ value = matrix("", 4, 2, dimnames = list(c("Juv 1", "Juv 2", "Juv 3", "Adulte"), c("Survie", "Fécondité"))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ matrixInput(inputId = "mat_fill_vr",
+ value = matrix(data = init_vr, 4, 2, dimnames = list(c("Juv 1", "Juv 2", "Juv 3", "Adulte"), c("Survie", "Fécondité"))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE))
+
+ ), # End sidebarPanel
+
+
+ #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+
+
+ # Creation of outputs parts
+
+ mainPanel(
+ tabsetPanel(
+ 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),
+ br(),
+ actionButton(inputId = "run", label = "Lancer l'analyse"),
+ hr(),
+ h4("Graphique : Impact relatif de chaque scénario", align = "center"),
+ plotOutput("graph_impact", width = "100%", height = "550px"),
+ hr(),
+ h4("Graphique : Trajectoire démographique", align = "center"),
+ plotOutput("graph_traj", width = "100%", height = "550px")),
+
+ tabPanel(title = "Distribution paramètres",
+ br(),
+ hr(),
+ h4("#Graphe élicitation d'expert pour les mortalités", align = "center"),
+ plotOutput(outputId = "fatalities_expert_plot"),
+ hr(),
+ h4("#Graphe élicitation d'expert pour la taille de la population", align = "center"),
+ plotOutput(outputId = "pop_size_expert_plot"),
+ hr(),
+ h4("#Graphe élicitation d'expert pour la capacité de charge", align = "center"),
+ plotOutput(outputId = "carrying_cap_expert_plot"),
+ hr(),
+ h4("#Graphe élicitation d'expert pour la tendance de la population", align = "center"),
+ plotOutput(outputId = "pop_growth_expert_plot"),
+ ),
+
+ tabPanel(title = "Rapport",
+ br(),
+ radioButtons(inputId = "lifestyle",
+ h4("Mode de vie de l'espèce"),
+ choices = c("Sédentaire", "Non-sédentaire nicheur", "Non-sédentaire hivernant", "Migrateur de passage")),
+ numericInput(inputId = "wind_turbines",
+ h4("Nombre d'éoliennes"),
+ value = 5, min = 0, max = Inf, step = 1),
+ numericInput(inputId = "farm_number",
+ h4("Nombre de parcs"),
+ value = 1, min = 0, max = Inf, step = 1),
+ numericInput(inputId = "wind_turbines_2",
+ h4("Nombre d'éoliennes"),
+ value = 1, min = 0, max = Inf, step = 1)
+
+ ) # End tabPanel
+ ) # End tabSetPanel
+ ) # End mainPanel
+ ) # sidebarLayout
+) # FluidPage
+
+# End UI
+
--
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