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Commit 45d27977 authored by thierrychambert's avatar thierrychambert
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Thies version of Shiny works (thierry branch) !

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inst/ShinyApp/server.R
+ 5
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View file @ 45d27977
......@@ -553,15 +553,14 @@ server <- function(input, output, session){
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]))
init_cumul_new <- rep(init_cumul_add, nrow)
updateMatrixInput(session, inputId = "fatalities_mat_cumulated",
value = matrix("", nrow = nrow, 3,
value = matrix(init_cumul_new, nrow = nrow, 3, byrow = TRUE,
dimnames = list(number_parks,
c("Moyennes des mortalits annuelles",
"Ecart-type des mortalits annuelles",
c("Moyenne",
"Ecart-type",
"Anne de mise en service du parc"))))
})
......
inst/ShinyApp/server_old.R deleted 100644 → 0
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− 605
View file @ af5e404f
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$lambda_input_type == "Tendance locale ou rgionale"){
shinyjs::show("pop_trend")
shinyjs::show("pop_trend_strength")
}
}
# Show inputs vital rates part
if(input$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$SE))
} else {
print("#Intgrer un message d'erreur")
}
} else {
param$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$SE)
} else {
print("#intgrer un message d'erreur")
}
} else {
param$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$SE, 2)
} else {
print("#intgrer un message d'erreur")
}
} else if(input$lambda_input_type == "Tendance locale ou rgionale"){
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$pop_trend == "Dclin"){
if(input$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("#intgrer 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$fatalities_mean
}
paste0("Moyenne des mortalits : ", info)
})
output$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$fatalities_se
}
paste0("Ecart-type des mortalits : ", info)
})
## Poplutation size
output$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$carrying_capacity
}
paste0("Capacit de charge du milieu : ", info)
})
## Population growth
output$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$lambda_input_type == "Tendance locale ou rgionale"){
if(input$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$pop_trend == "Dclin"){
if(input$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 rgionale") {
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$farm_number_cumulated
updateMatrixInput(session, inputId = "fatalities_mat_cumulated",
value = matrix(init_cumul, nrow = n_row, 3, byrow = TRUE,
dimnames = list(park_names(n_row),
c("Moyenne",
"Ecart-type",
"Anne de mise en service du parc"))))
}) # end observEvent
# Survivals and Fecundities
create.matrice <- function(species){
tab_test <- data_sf %>%
filter(species == data_sf$Nom_espece) %>%
select(classes_age, survie, fecondite)
return(tab_test)
}
observeEvent({input$species_list}, {
if(input$species_list == "Espce") {} else {
tab_species <- create.matrice(input$species_list)
if(all(is.na(tab_species))) {
updateMatrixInput(session, inputId = "mat_fill_vr",
value = matrix(data = "",
nrow = 4,
ncol = 2,
dimnames = list(c("Juv 1", "Juv 2", "Juv 3", "Adulte"), c("Survie", "Fcondit"))))
} else {
number_age_class <- nrow(tab_species)
ages <- tab_species$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", "Fcondit"))))
}
}
})
}
# End server
inst/ShinyApp/ui_old.R deleted 100644 → 0
+ 0
− 389
View file @ af5e404f
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 oliennes"),
# Creation of the first page (select species, analysis type choice)
wellPanel(
selectInput(inputId = "species_list",
h4(strong("Slection d'une espce")),
choices = species_list),
radioButtons(inputId = "analysis_choice",
h4(strong("Slectionner un type d'analyse")),
choices = c("Impacts non cumuls" = "scenario", "Impacts cumuls" = "cumulated"))
), # End wellPanel
##--------------------------------------------
## General information --
##--------------------------------------------
wellPanel(
fluidRow(
column(width = 4,
textOutput(outputId = "specie_name"),
h4("Mortalits"),
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("Capacit 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("Paramtres dmographiques"),
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 = "Mortalits"),
radioButtons(inputId = "fatal_constant",
label = h4("Modlisation"),
choices = c("Taux de mortalits (h) constant" = "h",
"Nombre de mortalits (M) constant" = "M")),
### Part for non-cumulated impacts
# Input type
radioButtons(inputId = "fatalities_input_type",
label = h4("Source des donnes"),
choices = c("Valeurs", "Elicitation d'expert")),
# Values
numericInput(inputId = "fatalities_mean",
label = "Moyenne des mortalits annuelles",
value = 5,
min = 0, max = Inf, step = 0.5),
numericInput(inputId = "fatalities_se",
label = "Ecart-type des mortalits 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 oliens",
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",
"Anne 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("Unit"),
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 = "Capacit de charge"),
radioButtons(inputId = "carrying_cap_input_type",
label = h4("Type d'unit"),
choices = c("Valeurs", "Elicitation d'expert")),
numericInput(inputId = "carrying_capacity",
label = "Capacit 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 rgionale")),
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", "Dclin")),
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 = "Paramtres dmographiques"),
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", "Fcondit"))),
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", "Fcondit"))),
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 scnario", align = "center"),
plotOutput("graph_impact", width = "100%", height = "550px"),
hr(),
h4("Graphique : Trajectoire dmographique", align = "center"),
plotOutput("graph_traj", width = "100%", height = "550px")),
tabPanel(title = "Distribution paramtres",
br(),
hr(),
h4("#Graphe licitation d'expert pour les mortalits", align = "center"),
plotOutput(outputId = "fatalities_expert_plot"),
hr(),
h4("#Graphe licitation d'expert pour la taille de la population", align = "center"),
plotOutput(outputId = "pop_size_expert_plot"),
hr(),
h4("#Graphe licitation d'expert pour la capacit de charge", align = "center"),
plotOutput(outputId = "carrying_cap_expert_plot"),
hr(),
h4("#Graphe licitation 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'espce"),
choices = c("Sdentaire", "Non-sdentaire nicheur", "Non-sdentaire hivernant", "Migrateur de passage")),
numericInput(inputId = "wind_turbines",
h4("Nombre d'oliennes"),
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'oliennes"),
value = 1, min = 0, max = Inf, step = 1)
) # End tabPanel
) # End tabSetPanel
) # End mainPanel
) # sidebarLayout
) # FluidPage
# End UI
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Documentation | Mentions légales | CGU | Accessibilité : non conforme