diff --git a/inst/ShinyApp/ui.R b/inst/ShinyApp/ui.R
index 19f71a344d9b85e3b866d969575a7982547a018b..be05a8777bce858e0ec33e3c343e522e573ff803 100644
--- a/inst/ShinyApp/ui.R
+++ b/inst/ShinyApp/ui.R
@@ -11,24 +11,38 @@ library(eolpop)
source("./inst/ShinyApp/param_fixes.R")
species_data <- read.csv("./inst/ShinyApp/species_list.csv", sep = ",")
-species_list <- unique(as.character(data$NomEspece))
+species_list <- species_data$NomEspece
+data_sf <- read.csv("./survivals_fecundities_species.csv", sep = ";", encoding = "UTF-8")
-# Data elicitation, fatalities for cumulated impacts, vital rates and DD_params
+# 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, 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_eli_trend = c("", 1, 0.60, 0.66, 0.78, 0.80, "", 0.2, 0.75, 0.83, 0.89, 0.90, "", 0.2, 0.56, 0.67, 0.77, 0.90, "", 0.1, 0.76, 0.89, 0.94, 0.70)
+data_fatalities = c(10, 5, 8, 0.05, 0.05, 0.05, 2010, 2015, 2018)
data_vr = c(0.5, 0.7, 0.8, 0.95, 0, 0, 0.05, 0.55)
-rMax = NULL
+# DD parameters
theta = 1
-DD_params = list(rMax = rMax, K = NULL, theta = theta)
-# UI
+# 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_2 : Impact demographique des éoliennes"),
+ titlePanel("eolpop : Impact demographique des éoliennes"),
# Creation of the first page (select species, analysis type choice)
@@ -41,42 +55,48 @@ ui <- fluidPage(
choices = c("Impacts non cumulés" = "scenario", "Impacts cumulés" = "cumulated"))
), # End wellPanel
- # Info
+
+ ##--------------------------------------------
+ ## General information --
+ ##--------------------------------------------
wellPanel(
fluidRow(
- column(width = 6,
+ column(width = 4,
textOutput(outputId = "specie_name"),
- h4("#Partie Mortalités"),
+ h4("Mortalités"),
textOutput(outputId = "fatalities_mean_info"),
textOutput(outputId = "fatalities_se_info"),
- textOutput(outputId = "fatalities_expert_info"),
- h4("#Partie Taille de la population"),
+ h4("Taille de la population"),
+ textOutput(outputId = "pop_size_type_info"),
textOutput(outputId = "pop_size_mean_info"),
- textOutput(outputId = "pop_size_se_info"),
- textOutput(outputId = "pop_size_expert_info")),
+ textOutput(outputId = "pop_size_se_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"),
+ 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_trend_mean_info"),
- textOutput(outputId = "pop_trend_se_info"),
- textOutput(outputId = "pop_trend_expert_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
- # Creation of units (fatalities, pop size, carrying capacity, pop trend and vital rates).
+ # Paramter Inputs (fatalities, pop size, carrying capacity, pop trend and vital rates).
sidebarLayout(
sidebarPanel(
- # First part : Fatalities
+ ##--------------------------------------------
+ ## 1. Fatalities --
+ ##--------------------------------------------
actionButton(inputId = "button_fatalities",
label = "Mortalités"),
@@ -85,134 +105,186 @@ ui <- fluidPage(
choices = c("Taux de mortalités (h) constant" = "h",
"Nombre de mortalités (M) constant" = "M")),
- # Part for none cumulated impacts
-
+ ### 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 = 1),
+ 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 = 1),
+ min = 0, max = Inf, step = 0.1),
+
+ # Matrix for expert elicitation
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
+ actionButton(inputId = "fatalities_run_expert", label = "Analyse"),
+
+ ### Part for cumulated impacts
numericInput(inputId = "farm_number_cumulated",
label = "Nombre de parcs éoliens",
- value = 2, min = 2, max = Inf, step = 1),
+ value = 3, 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"))),
+ value = matrix(data_fatalities, 3, 3,
+ dimnames = list(c(paste0("Parc n°", c(1:3))),
+ 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(),
+ ##--------------------------------------------
+ ## 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 = 100),
+ 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 = data_eli, 4, 6, dimnames = list(c("#1", "#2", "#3", "#4"), c("Nom", "Poids", "Min", "Meilleure Estimation", "Max", "IC (coverage)" )), byrow = TRUE),
+ value = matrix(data = data_eli, 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)),
- # Third part : Carrying capacity
+ actionButton(inputId = "pop_size_run_expert", label = "Analyse"),
+
- br(),
+ ##--------------------------------------------
+ ## 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_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),
+
+ numericInput(inputId = "carrying_capacity",
+ label = "Capacité de charge",
+ value = 1000,
+ min = 0, max = Inf, step = 100),
+
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)" ))),
+ 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)),
- # Fourth part : Pop trend
+ actionButton(inputId = "carrying_cap_run_expert", label = "Analyse"),
+
+ ##--------------------------------------------
+ ## 4. Population Trend --
+ ##--------------------------------------------
- br(),
+ 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),
+ min = 0, max = Inf, step = 0.01),
+
numericInput(inputId = "pop_growth_se",
label = "Ecart-type de la croissance de la population",
- value = 0.03,
- min = 0, max = Inf, step = 1),
+ value = 0,
+ min = 0, max = Inf, step = 0.01),
+
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)" ))),
+ value = matrix(data = data_eli_trend, 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)),
+
+ actionButton(inputId = "pop_growth_run_expert", label = "Analyse"),
+
+ h4("Tendance de la population"),
+
radioButtons(inputId = "pop_trend",
- label = h4("Tendance de la population"),
+ label = NULL,
choices = c("Croissance", "Stable", "Déclin")),
+
radioButtons(inputId = "pop_trend_strength",
label = NULL,
choices = c("Faible", "Moyen", "Fort")),
- # Fifth part : Vital rates
+ # tags$style("#pop_trend_strength {position:fixed; top: 600px; right: 100px;}"),
+
- br(),
+ ##--------------------------------------------
+ ## 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 = 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
@@ -221,6 +293,9 @@ 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),
+ br(),
actionButton(inputId = "run", label = "Lancer l'analyse"),
hr(),
h4("Graphique : Impact relatif de chaque scénario", align = "center"),
@@ -230,28 +305,18 @@ ui <- fluidPage(
plotOutput("graph_traj", width = "100%", height = "550px")),
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"),
),
@@ -278,4 +343,4 @@ ui <- fluidPage(
# End UI
-# shinyApp(ui = ui, server = server)
+shinyApp(ui = ui, server = server)