diff --git a/inst/ShinyApp/ui.R b/inst/ShinyApp/ui.R
index 3510487f0e480c88df51507af83c400cbaa01938..19f71a344d9b85e3b866d969575a7982547a018b 100644
--- a/inst/ShinyApp/ui.R
+++ b/inst/ShinyApp/ui.R
@@ -10,136 +10,272 @@ library(eolpop)
# source("./inst/ShinyApp/f_output.R")
source("./inst/ShinyApp/param_fixes.R")
-data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
-choicesSpecies <- unique(as.character(data$NomEspece))
+species_data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
+species_list <- unique(as.character(data$NomEspece))
+# Data elicitation, fatalities for cumulated impacts, vital rates and DD_params
+
+data_eli = c("",1, 50, 70, 100, 0.80, "", 0.2, 200, 240, 280, 0.90, "", 0.2, 100, 180, 300, 0.90,"", 0.1, 120, 160, 220, 0.70)
+data_fatalities = c(5, 10, 15, 0.05, 0.05, 0.05, 2010, 2013, 2016)
+data_vr = c(0.5, 0.7, 0.8, 0.95, 0, 0, 0.05, 0.55)
+
+rMax = NULL
+theta = 1
+DD_params = list(rMax = rMax, K = NULL, theta = theta)
+
+# UI
+
ui <- fluidPage(
useShinyjs(),
- titlePanel("EolPop : Impact demographique des eoliennes"),
- fluidRow(column(width = 6,
- selectInput(inputId = "species_choice",
- h4(strong("Selectionner une espece")),
- c(choose = "", choicesSpecies)))),
+ titlePanel("eolpop_2 : 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 ou groupe d'espèces")),
+ 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
+
+ # Info
+
+ wellPanel(
+ fluidRow(
+ column(width = 6,
+ textOutput(outputId = "specie_name"),
+ h4("#Partie Mortalités"),
+ textOutput(outputId = "fatalities_mean_info"),
+ textOutput(outputId = "fatalities_se_info"),
+ textOutput(outputId = "fatalities_expert_info"),
+ h4("#Partie Taille de la population"),
+ textOutput(outputId = "pop_size_mean_info"),
+ textOutput(outputId = "pop_size_se_info"),
+ textOutput(outputId = "pop_size_expert_info")),
+ fluidRow(
+ column(width = 6,
+ h4("#Partie Capacité de charge"),
+ textOutput(outputId = "carrying_cap_mean_info"),
+ textOutput(outputId = "carrying_cap_se_info"),
+ textOutput(outputId = "carrying_cap_expert_info"),
+ h4("#Partie Tendance de la population"),
+ textOutput(outputId = "pop_trend_type_info"),
+ textOutput(outputId = "pop_trend_mean_info"),
+ textOutput(outputId = "pop_trend_se_info"),
+ textOutput(outputId = "pop_trend_expert_info"))
+ )
+ )
+ ), # End wellPanel
+
+
+ # Creation of units (fatalities, pop size, carrying capacity, pop trend and vital rates).
+
sidebarLayout(
sidebarPanel(
- fluidRow(
- column(width = 12,
-
- ## 1. Fatalities
- # h4(strong("Mortalites")),
- fluidRow(
- actionButton(inputId = "Mortality", label = "Mortalites"),
- radioButtons(inputId = "morta_type",
- h4("Type de mortalite"),
- choices = list(
- "Non cumulees (1 seul parc eolien)",
- "Cumulees")),
- textInput(inputId = "nber_park",
- label = NULL,
- placeholder = "--Nombre de parcs--"),
- textInput(inputId = "nber_wind_turbine",
- label = NULL,
- placeholder = "--Nombre d'eoliennes--"),
- radioButtons(inputId = "data",
- h4("Source des donnees"),
- choices = list("Suivi (observations terrains + EolApp)", "Modele predictif (type Band)",
- "Dire d'expert")),
- matrixInput("expert",
- value = matrix("", 4, 6, dimnames = list(c("#1", "#2", "#3", "#4"), c("Nom", "Poids", "Min", "Best", "Max", "IC" ))),
- class = "numeric",
- rows = list(extend = FALSE),
- cols = list(names = TRUE)),
- actionButton(inputId = "run_expert", label = "Analyse Experts"),
-
- numericInput(inputId = "M1", label = h5("Valeur estimee"), min = 0, max = Inf, step = 1, value = 5),
- numericInput(inputId = "M1_se", label = h5("Erreur standard"), min = 0, max = Inf, step = 1, value = 0),
- numericInput(inputId = "M1_ic", label = h5("Intervalle de confiance"), min = Inf, max = Inf, step = 1, value = 0),
- br(),
- radioButtons(inputId = "mort_cons",
- h4("Modelisation"),
- choices = list("Taux de mortalite (h) constant" = "h",
- "Nombre de mortalites (M) constant" = "M"),
- selected = "h"),
- ),
-
- br(),
- ## 2. Population size
- # h4(strong("Taille de la population")),
- fluidRow(
- actionButton(inputId = "pop_size", label = "Taille de la population"),
- radioButtons(inputId = "N_type",
- h4("Unite"),
- choices = list("Nombre de couple" = "Npair",
- "Effectif total" = "Ntotal"),
- selected = "Npair"),
- matrixInput("expert_2",
- value = matrix("", 4, 3, dimnames = list(c("juv 1", "juv 2", "juv 3", "Adulte"), c("Moy", "LCI", "UCI" ))),
- rows = list(names = TRUE),
- cols = list(names = TRUE)),
- numericInput(inputId = "N00_mu", label = h5("Valeur estimee"), min = 0, max = Inf, step = 50, value = 200),
- numericInput(inputId = "N00_se", label = h5("Erreur standard"), min = 0, max = Inf, step = 1, value = 0),
- numericInput(inputId = "IC_2", label = h5("Intervalle de confiance"), min = Inf, max = Inf, step = 1, value = 0),
- ),
-
-
- br(),
- ## 3. Population trend
- # h4(strong("Tendance de la population")),
- fluidRow(
- actionButton(inputId = "pop_trend", label = "Tendance de la population"),
- radioButtons(inputId = "lambda_type", h4("Type"),
- choices = list("Taux de croissance", "Tendance locale ou regionale",
- "Tendance nationale")),
-
- numericInput(inputId = "lam0_mu", label = h5("Valeur estimee"), min = 0, max = Inf, step = 0.05, value = 0.95),
- numericInput(inputId = "lam0_se", label = h5("Erreur standard"), min = 0, max = Inf, step = 1, value = 0),
- numericInput(inputId = "IC_3", label = h5("Intervalle de confiance"), min = Inf, max = Inf, step = 1, value = 0),
- radioButtons("trend", h4("Tendance"), choices = list("Croissance", "Stable", "Declin")),
- radioButtons(inputId = "trend_2", label = NULL, choices = list("Fort", "Moyen", "Faible")),
- ),
-
-
- br(),
- ## 4. Vital rates
- # h4(strong("Parametres demographiques")),
- fluidRow(
- actionButton(inputId = "params_demog", label = "Parametres demographiques"),
- radioButtons(inputId = "auto", label = "Saisie", choices = list("Automatique", "Manuelle")),
- matrixInput("mat_params_demog",
- value = matrix("", 4, 3, dimnames = list(c("juv 1", "juv 2", "juv 3", "Adulte"), c("Moy", "LCI", "UCI" ))),
- rows = list(names = TRUE),
- cols = list(names = TRUE)
- )
- ),
- )
- )
- ),
-
-
- ### MAIN PANEL
+ # First part : 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 none cumulated impacts
+
+ radioButtons(inputId = "fatalities_input_type",
+ label = h4("Source des données"),
+ choices = c("Valeurs", "Elicitation d'expert")),
+ numericInput(inputId = "fatalities_mean",
+ label = "Moyenne des mortalités annuelles",
+ value = 5,
+ min = 0, max = Inf, step = 1),
+ numericInput(inputId = "fatalities_se",
+ label = "Ecart-type des mortalités annuelles",
+ value = 0.05,
+ min = 0, max = Inf, step = 1),
+ matrixInput(inputId = "fatalities_mat_expert",
+ value = matrix(data = data_eli, 4, 6, dimnames = list(c("#1", "#2", "#3", "#4"), c("Nom", "Poids", "Min", "Meilleure Estimation", "Max", "IC (coverage)" )), byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ # Part for cumulated impacts
+
+ numericInput(inputId = "farm_number_cumulated",
+ label = "Nombre de parcs éoliens",
+ value = 2, min = 2, max = Inf, step = 1),
+ matrixInput(inputId = "fatalities_mat_cumulated",
+ value = matrix(data = data_fatalities, 3, 3, dimnames = list(c("#1", "#2", "#3"), c("Moyennes des mortalités annuelles",
+ "Ecart-type des mortalités annuelles",
+ "Année de mise en service du parc"))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ # Second part : Pop 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 = 100),
+ 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 = data_eli, 4, 6, dimnames = list(c("#1", "#2", "#3", "#4"), c("Nom", "Poids", "Min", "Meilleure Estimation", "Max", "IC (coverage)" )), byrow = TRUE),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ # Third part : 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_cap_mean",
+ label = "Moyenne de la capacité de charge",
+ value = 500,
+ min = 0, max = Inf, step = 1),
+ numericInput(inputId = "carrying_cap_se",
+ label = "Ecart-type de la capacité de charge",
+ value = 1,
+ min = 0, max = Inf, step = 1),
+ matrixInput(inputId = "carrying_cap_mat_expert",
+ value = matrix("", 4, 6, dimnames = list(c("#1", "#2", "#3", "#4"), c("Nom", "Poids", "Min", "Meilleure Estimation", "Max", "IC (coverage)" ))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+
+ # Fourth part : Pop 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 = 1),
+ numericInput(inputId = "pop_growth_se",
+ label = "Ecart-type de la croissance de la population",
+ value = 0.03,
+ min = 0, max = Inf, step = 1),
+ matrixInput(inputId = "pop_growth_mat_expert",
+ value = matrix("", 4, 6, dimnames = list(c("#1", "#2", "#3", "#4"), c("Nom", "Poids", "Min", "Meilleure Estimation", "Max", "IC (coverage)" ))),
+ class = "numeric",
+ rows = list(names = TRUE),
+ cols = list(names = TRUE)),
+ radioButtons(inputId = "pop_trend",
+ label = h4("Tendance de la population"),
+ choices = c("Croissance", "Stable", "Déclin")),
+ radioButtons(inputId = "pop_trend_strength",
+ label = NULL,
+ choices = c("Faible", "Moyen", "Fort")),
+
+ # Fifth part : Vital rates
+
+ br(),
+ actionButton(inputId = "button_vital_rates",
+ label = "Paramètres démographiques"),
+ radioButtons(inputId = "fill_type_vr",
+ label = "Type de saisie",
+ choices = c("Automatique", "Manuelle")),
+ 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 = 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))
+ ), # End sidebarPanel
+
+ # End of units
+
+ # Creation of outputs parts
+
mainPanel(
tabsetPanel(
tabPanel(title = "Impact population",
strong(span(textOutput("message"), style="color:blue; font-size:24px", align = "center")),
+ br(),
actionButton(inputId = "run", label = "Lancer l'analyse"),
hr(),
- h4("Graphique : trajectoire demographique", align = "center"),
- plotOutput("graph", width = "100%", height = "550px"),
- plotOutput("graph_eli", width = "100%", height = "550px"),
- ), # tabPanel
+ 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 parametres",
+ tabPanel(title = "Distribution paramètres",
+ br(),
+ actionButton(inputId = "run_expert", label = "Analyse"),
+ br(),
+ hr(),
+ h4("#Graphe élicitation d'expert pour les mortalités", align = "center"),
+ textOutput(outputId = "fatalities_expert_mean"),
+ textOutput(outputId = "fatalities_expert_sqrt_var"),
+ plotOutput(outputId = "fatalities_expert_plot"),
+ hr(),
+ h4("#Graphe élicitation d'expert pour la taille de la population", align = "center"),
+ textOutput(outputId = "pop_size_expert_mean"),
+ textOutput(outputId = "pop_size_expert_sqrt_var"),
+ plotOutput(outputId = "pop_size_expert_plot"),
+ hr(),
+ h4("#Graphe élicitation d'expert pour la capacité de charge", align = "center"),
+ textOutput(outputId = "carrying_cap_expert_mean"),
+ textOutput(outputId = "carrying_cap_expert_sqrt_var"),
+ plotOutput(outputId = "carrying_cap_expert_plot"),
+ hr(),
+ h4("#Graphe élicitation d'expert pour la tendance de la population", align = "center"),
+ textOutput(outputId = "pop_growth_expert_mean"),
+ textOutput(outputId = "pop_growth_expert_sqrt_var"),
+ plotOutput(outputId = "pop_growth_expert_plot"),
+ ),
- textOutput(outputId = "Mean"),
- textOutput(outputId = "sqsrt_var"),
- plotOutput(outputId = "plot")
- ) # tabPanel
+ 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)
- ) # tabsetPanel
- ) # mainPanel
+ ) # End tabPanel
+ ) # End tabSetPanel
+ ) # End mainPanel
) # sidebarLayout
-) # fluidPage
+) # FluidPage
+
+# End UI
# shinyApp(ui = ui, server = server)