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#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 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)) %>% sort
species_list <- c(species_list, "Espce gnrique")
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 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, -5, -2, 0, 0.95,
0.2, -3, 0, 1, 0.90,
0.5, -8, -4, -1, 0.90,
0.3, -10, -5, -2, 0.70)
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
titlePanel("eolpop : Impact demographique des collisions aviaires avec les oliennes"),
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
# Head Panel 1 : type of analysis and species
{wellPanel(
p("Choix d'analyse et espce", style="font-size:28px"),
{fluidRow(
# Select type of analysis : cumulated impacted or not
{column(width = 4,
# Choose analysis type (radioButton)
{radioButtons(inputId = "analysis_choice",
label = h4(strong("Type d'analyse"),
bsButton("Q_analysis_choice", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_analysis_choice",
title = "Choix du type d\\'analyse",
content = HTML(
"<b>Impacts non cumuls</b> : pour analyser l\\'impact d\\'<b>un seul parc olien</b>. <br><br> <b>Impact cumuls</b> : pour analyser l\\'impact de <b>plusieurs parcs oliens</b> (attention : il faudra fournir des valeurs de mortalits propres chaque parc)."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
),
choices = c("Impacts non cumuls" = "single_farm", "Impacts cumuls" = "cumulated", "Multiple scnarios" = "multi_scenario")
# Choose species (selectInput)
{selectInput(inputId = "species_choice",
selected = "Aigle de Bonelli", width = '80%',
label = h4(strong("Slectionner une espce"),
bsButton("Q_species_choice", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_species_choice",
title = "Choix de l\\'espce",
content = HTML(
"Ncessaire pour fixer les valeurs de <b>paramtres dmographiques</b> (survie, fcondit). <br> La liste fournie correspond une liste d\\'espces prioritaires. Au besoin, une option \\'espce gnrique\\' est disponible la fin de la liste."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
),
choices = species_list)},
br(),
# Show dispersal distances : mean and d = 5%
h4(strong("Distances de dispersion"),
bsButton("Q_dispersal_info", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_dispersal_info",
title = "Distances de dispersion",
content = HTML(
"(1) <b>Distance moyenne de dispersion</b> de l\\'espce, estime partir des relations allomtriques publies dans l\\'article de Claramunt (2021).<br><br> (2) Distance quivalente un <b>taux de dispersion relatif de 3%, 5% et 10%</b>, sous l\\'hypothse que la distance de dispersion suit une loi exponentielle.<br><br><u>Reference cite</u> : Claramunt, S. (2021). Flight efficiency explains differences in natal dispersal distances in birds. <i>Ecology</i>, e03442."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
),
#br(),
span(textOutput(outputId = "dispersal_mean_info"), style="font-size:16px"),
br(),
span(textOutput(outputId = "dispersal_d03p_info"), style="font-size:16px"),
span(textOutput(outputId = "dispersal_d05p_info"), style="font-size:16px"),
span(textOutput(outputId = "dispersal_d10p_info"), style="font-size:16px"),
# Show vital rate values (tableOutput)
h4(strong("Paramtres dmographiques"),
bsButton("Q_vital_rates_info", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_vital_rates_info",
title = "Paramtres dmographiques",
content = HTML(
"Valeurs de <b>survie et fcondits par classe d\\'ge</b>, pour l\\'espce slectionne. <br><br><b>L\\'ge 0</b> (ex : Juv 0) correspond un individu n dans l\\'anne, n\\'ayant <u>pas encore</u> 1 an rvolu.<br><b>L\\'ge 1</b> correspond un individu ayant 1 an rvolu, donc dans sa 2<sup>e</sup> anne de vie.<br>Etc."
#"Valeurs de <b>survie et fcondits par classe d\\'ge</b>, pour l\\'espce slectionne. <br><br><b>Juv 0</b> correspond un individu n dans l\\'anne, n\\'ayant <u>pas encore</u> 1 an rvolu.<br><b>Juv 1</b> correspond un individu ayant 1 an rvolu, donc dans sa 2<sup>e</sup> anne de vie.<br>Etc."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
),
), # close fluidRow
# Display the intrinsic lambda(i.e., based solely on the Leslie matrix)
# Output display (intrinsic lambda)
h5(strong("Taux de croissance intrinsque"),
bsButton("Q_lambda0_info", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_lambda0_info",
title = "Taux de croissance intrinsque",
content = HTML(
"Taux de croissance bas seulement sur la matrice de Leslie (survies et fcondits de l\\'espce), <b> avant considration de la tendance de population locale</b>. <br><br>Ce taux de croissance est fourni simplement titre informatif. La valeur qui sera utilise dans les simulations correspond au taux de croissance fourni dans la partie \\'Taux de croissance\\'."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
),
span(textOutput(outputId = "lambda0_info", inline = TRUE), style = "color:black; font-size:16px"),
## Modify vital rates, if needed (actionButton and matrixInput)
{column(width = 4,
tags$style(HTML('#button_vital_rates{background-color:#C2C8D3}')),
actionButton(inputId = "button_vital_rates",
label = tags$span("Modifier les paramtres dmographiques",
style = "font-weight: bold; font-size: 18px;")
),
thierrychambert
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br(" "),
numericInput(inputId = "vr_mat_number_age_classes",
label = "Nombre de classes d'age",
value = 3, min = 2, max = Inf, step = 1),
thierrychambert
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#br(),
matrixInput(inputId = "mat_fill_vr",
label = "",
value = matrix(data = NA, 3, 2,
thierrychambert
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dimnames = list(c("Juv 0", "Sub 1", "Adulte"), c("Survie", "Fcondit"))),
class = "numeric",
rows = list(names = TRUE),
cols = list(names = TRUE)
)
)}, # close column
)}, # End fluidRow
)}, # End wellPanel
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
# Head Panel 2 : Model parameters
{wellPanel(
## Enter parameter values (TITLE)
{p("Saisie des paramtres", style="font-size:28px",
bsButton("Q_param_enter", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_param_enter",
title = "Saisie des paramtres pour l\\'analyse",
content = HTML(
"Cliquer sur les boutons ci-dessous pour saisir les valeurs des quatre paramtres requis pour l\\'analyse : <br>(1) Mortalits annuelles, <br>(2) Taille de la population, <br>(3) Tendance de la population, <br>(4) Capacit de charge."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
{fluidRow(
##~~~~~~~~~~~~~~~~~~~~~~~~~
## 1. Fatalities
##~~~~~~~~~~~~~~~~~~~~~~~~~
{column(width = 3,
tags$style(HTML('#button_fatalities{background-color:#C2C8D3}')),
actionButton(inputId = "button_fatalities", width = '100%',
label = tags$span("Mortalits annuelles", style = "font-weight: bold; font-size: 18px;")
),
bsPopover(id = "button_fatalities",
title = "Mortalits annuelles",
content = HTML(
"Nombre de mortalits totales <b><u>annuelles</u> (cad. sur 12 mois) </b> attendues, pour l\\'espce slectionne, sur chaque parc olien concern (somme des mortalits attendues sur toutes les oliennes d\\'un parc)."
),
placement = "top",
trigger = "hover",
options = list(container='body')
),
### Part for non-cumulated impacts
# Input type
{conditionalPanel("output.hide_fatalities",
br(),
{wellPanel(style = "background:#FFF8DC",
radioButtons(inputId = "fatalities_unit", inline = FALSE,
label = "Unit",
choices = c("Nombre de mortalits" = "M",
"Taux de mortalit (%)" = "h"),
)}, # close wellPanel 1
{wellPanel(style = "background:#F0F8FF",
radioButtons(inputId = "fatalities_input_type",
label = "Type de saisie",
choices = c("Intervalle" = "itvl",
"Valeurs" = "val",
selected = "itvl"),
numericInput(inputId = "fatalities_lower",
label = "Borne infrieure (mortalits annuelles)",
min = 0, max = Inf, step = 0.5),
numericInput(inputId = "fatalities_upper",
label = "Borne suprieure (mortalits annuelles)",
min = 0, max = Inf, step = 0.5),
# Values
numericInput(inputId = "fatalities_mean",
label = "Moyenne (mortalits annuelles)",
min = 0, max = Inf, step = 0.5),
numericInput(inputId = "fatalities_se",
label = "Erreur-type (mortalits annuelles)",
min = 0, max = Inf, step = 0.1),
# Matrix for expert elicitation
numericInput(inputId = "fatalities_number_expert",
label = "Nombre d'experts",
value = 4, min = 1, max = Inf, step = 1),
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 oliens",
value = 3, min = 2, max = Inf, step = 1),
matrixInput(inputId = "fatalities_mat_cumulated",
label = span("Mortalits dans chaque parc",
bsButton("Q_fatalities_mat_cumulated", label = "", icon = icon("question"), size = "extra-small"),
bsPopover(id = "Q_fatalities_mat_cumulated",
title = "Mortalits cumules",
content = HTML(
"1 ligne = 1 parc <br><br>Les parcs doivent tre fournis dans l\\'<b>ordre chronologique</b> de leur mise en service (\\'Anne dbut\\'). <br><br>Pour chaque parc, veuillez indiquer la <u>moyenne</u> et l\\'<u>erreur-type</u> du nombre de mortalits estimes, ainsi que son <u>anne de mise en service</u>."
),
placement = "right",
trigger = "click",
options = list(container='body')
)
),
value = matrix(c(2, 0.5, 2010,
5, 0.5, 2015,
3, 0.5, 2018),
nrow = 3, ncol = 3, byrow = TRUE,
dimnames = list(c(paste0("Parc num.", c(1:3))),
c("Moyenne",
"Erreur-type",
"Anne de mise en service du parc"))),
class = "numeric",
rows = list(names = TRUE),
cols = list(names = TRUE)),
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### Part for "scenarios option"
selectizeInput(inputId = "fatalities_vec_scenario",
label = HTML(
"Saisir chaque valeur de mortalit<br>
(sparer par un espace)"
),
choices = NULL,
multiple = TRUE,
options = list(
create = TRUE,
delimiter = ' ',
create = I("function(input, callback){
return {
value: input,
text: input
};
}")
)
),
)}, # close wellPanel
)}, # close conditional panel
)}, # end column "fatalities"
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##~~~~~~~~~~~~~~~~~~~~~~~~~
## 2. Population Size
##~~~~~~~~~~~~~~~~~~~~~~~~~
{column(width = 3,
tags$style(HTML('#button_pop_size{background-color:#C2C8D3}')),
actionButton(inputId = "button_pop_size", width = '100%',
label = tags$span("Taille de la population", style = "font-weight: bold; font-size: 18px;")
),
bsPopover(id = "button_pop_size",
title = "Taille de la population",
content = HTML(
"Effectif de la population cible pour l\\'analyse d\\'impact. <br> Il peut s\\'agir soit du <b>nombre de couples</b>, soit de l\\'<b>effectif total</b> de la population (cad. toutes classes d\\'ge incluses)."
),
placement = "top",
trigger = "hover",
options = list(container='body')
),
{conditionalPanel("output.hide_pop_size",
br(),
{wellPanel(style = "background:#FFF8DC",
radioButtons(inputId = "pop_size_unit", inline = TRUE,
label = "Unit",
choices = c("Nombre de couples" = "Npair", "Effectif total" = "Ntotal"),
)}, # close wellPanel 1
{wellPanel(style = "background:#F0F8FF",
radioButtons(inputId = "pop_size_input_type",
label = "Type de saisie",
choices = c("Intervalle" = "itvl",
"Valeurs" = "val",
"Elicitation d'expert" = "eli_exp")),
# Interval
numericInput(inputId = "pop_size_lower",
label = "Borne infrieure (taille population)",
value = 750,
min = 0, max = Inf, step = 10),
numericInput(inputId = "pop_size_upper",
label = "Borne suprieure (taille population)",
value = 850,
min = 0, max = Inf, step = 10),
# Values
numericInput(inputId = "pop_size_mean",
label = "Moyenne de la taille de la population",
value = 800,
min = 0, max = Inf, step = 50),
numericInput(inputId = "pop_size_se",
label = "Erreur-type de la taille de la population",
min = 0, max = Inf, step = 1),
numericInput(inputId = "pop_size_number_expert",
label = "Nombre d'experts",
value = 4, min = 1, 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"),
)}, # close wellPanel 2
# Display matrix for stable age distribution
h5(strong("Effectifs par classe d'ge")),
)}, # close conditional panel
)}, # end column "pop size"
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##~~~~~~~~~~~~~~~~~~~~~~~~~
## 3. Population Growth
##~~~~~~~~~~~~~~~~~~~~~~~~~
{column(width = 3,
tags$style(HTML('#button_pop_growth{background-color:#C2C8D3}')),
actionButton(inputId = "button_pop_growth", width = '100%',
label = tags$span("Taux de croissance", style = "font-weight: bold; font-size: 18px;")
bsPopover(id = "button_pop_growth",
title = "Taux de croissance",
"Taux d\\'accroissement annuel de la population <b>en %</b> : valeur positive pour une population en croissance; valeur <b>ngative</b> pour une population en <b>dclin</b> (ex : -4 pour un dclin de 4% par an) ; 0 pour une population stable.<br><br>A dfaut, on pourra juste cocher la <b>tendance globale</b> (dclin, stabilit ou croissance) et l\\'intensit de cette tendance (faible, moyenne, forte)."
placement = "top",
trigger = "hover",
options = list(container='body')
),
{conditionalPanel("output.hide_pop_growth",
br(),
{wellPanel(style = "background:#F0F8FF",
radioButtons(inputId = "pop_growth_input_type",
label = "Type de saisie",
choices = c("Intervalle" = "itvl",
"Elicitation d'expert" = "eli_exp",
"Tendance population" = "trend")),
# Interval
numericInput(inputId = "pop_growth_lower",
label = HTML("Borne infrieure<br>(taux d'accroissement en %)"),
thierrychambert
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min = -100, max = Inf, step = 1),
numericInput(inputId = "pop_growth_upper",
label = HTML("Borne suprieure<br>(taux d'accroissement en %)"),
thierrychambert
committed
min = -100, max = Inf, step = 1),
numericInput(inputId = "pop_growth_mean",
label = "Moyenne (taux d'accroissement en %)",
thierrychambert
committed
min = -100, max = Inf, step = 1),
numericInput(inputId = "pop_growth_se",
label = "Erreur-type (aussi en %)",
min = 0, max = Inf, step = 0.5),
numericInput(inputId = "pop_growth_number_expert",
label = "Nombre d'experts",
value = 4, min = 1, max = Inf, step = 1),
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"),
## Input trend: radio buttons
{fluidRow(
# Trend
column(6,
radioButtons(inputId = "pop_trend",
label = "Tendance",
choices = c("En croissance" = "growth",
"Stable" = "stable",
),
# Strength of trend
column(6,
radioButtons(inputId = "pop_trend_strength",
choices = c("Faible" = "weak",
"Moyenne" = "average",
"Forte" = "strong")),
actionButton(inputId = "button_calibrate_vr", label = "Calibrer survies et fcondits"),
)}, # close wellPanel
)}, # close conditional panel
)}, # end column "pop growth"
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
##~~~~~~~~~~~~~~~~~~~~~~~~~
## 4. Carrying capacity
##~~~~~~~~~~~~~~~~~~~~~~~~~
{column(width = 3,
tags$style(HTML('#button_carrying_cap{background-color:#C2C8D3}')),
actionButton(inputId = "button_carrying_cap", width = '100%',
label = tags$span("Capacit de charge", style = "font-weight: bold; font-size: 18px;")
),
bsPopover(id = "button_carrying_cap",
"La capacit de charge (K) correspond la <b>taille maximale que peut atteindre la population</b> dans son environnement et les limites spatiales considres. <br><br><u>Note:</u> Ce chiffre sera exprim dans la <b>mme unit</b> que la taille de population (cad. nombre de couples ou effectif total). <br>Il n\\'a pas besoin d\\'tre trs prcis ; il doit simplement fournir un ordre de grandeur de la taille limite au-del de laquelle la population ne peut plus crotre (environnement local satur)."
),
placement = "top",
trigger = "hover",
options = list(container='body')
),
{conditionalPanel("output.hide_carrying_cap",
br(),
{wellPanel(style = "background:#FFF8DC",
span(textOutput(outputId = "carrying_cap_unit_info"), style="font-size:16px"),
)}, # close wellPanel 1
{wellPanel(style = "background:#F0F8FF",
radioButtons(inputId = "carrying_cap_input_type",
label = "Type de saisie",
choices = c("Intervalle" = "itvl",
"Valeur" = "val",
"Elicitation d'expert" = "eli_exp",
# Interval
numericInput(inputId = "carrying_capacity_lower",
label = "Borne infrieure (capacit de charge)",
value = 850,
min = 0, max = Inf, step = 100),
numericInput(inputId = "carrying_capacity_upper",
label = "Borne suprieure (capacit de charge)",
value = 1250,
min = 0, max = Inf, step = 100),
# Values
numericInput(inputId = "carrying_capacity_mean",
label = "Moyenne de la capacit de charge",
min = 0, max = Inf, step = 100),
numericInput(inputId = "carrying_capacity_se",
label = "Erreur-type de la capacit de charge",
value = 100,
min = 0, max = Inf, step = 50),
# Expert Elicitation Matrix
numericInput(inputId = "carrying_cap_number_expert",
label = "Nombre d'experts",
value = 4, min = 1, max = Inf, step = 1),
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 = "carrying_cap_run_expert", label = "Utiliser valeurs experts"),
)}, # close wellPanel 2
)}, # close conditional panel
)}, # end column "carrying capacity"
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
)}, # # End wellPanel
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
###~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###
{sidebarLayout(
p("Valeurs slectionnes", style="font-size:24px",
bsButton("Q_selected_values", label = "", icon = icon("question"), size = "extra-small"),
bsTooltip(id = "Q_selected_values",
title = "Rappel des valeurs de paramtres actuellement slectionnes.",
placement = "right",
trigger = "click",
options = list(container='body')
)
),
# Mortalites annuelles
span("Mortalits annuelles", style="font-size:18px; font-weight: bold"),
shiny::tags$i(textOutput(outputId = "fatalities_unit_info"), style="font-size:15px"),
span(textOutput(outputId = "fatalities_mean_info"), style="font-size:15px"),
span(textOutput(outputId = "fatalities_se_info"), style="font-size:15px"),
span("Taille de la population", style="font-size:18px; font-weight: bold"),
shiny::tags$i(textOutput(outputId = "pop_size_unit_info"), style="font-size:15px"),
span(textOutput(outputId = "pop_size_mean_info"), style="font-size:15px"),
span(textOutput(outputId = "pop_size_se_info"), style="font-size:15px"),
span(HTML("Taux de croissance (λ)"), style="font-size:18px; font-weight: bold"),
span(textOutput(outputId = "pop_growth_mean_info"), style="font-size:15px"),
span(textOutput(outputId = "pop_growth_se_info"), style="font-size:15px"),
# Capacite de charge
span("Capacit de charge", style="font-size:18px; font-weight: bold"),
shiny::tags$i(textOutput(outputId = "carrying_capacity_unit_info"), style="font-size:15px"),
span(textOutput(outputId = "carrying_capacity_mean_info"), style="font-size:15px"),
span(textOutput(outputId = "carrying_capacity_se_info"), style="font-size:15px"),
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
## Parameter distribution
{tabPanel(title = "Distribution paramtres",
span(textOutput(outputId = "title_distri_plot"), style="font-size:24px; font-weight:bold"),
plotOutput(outputId = "distri_plot"),
## Population Impact : simulations
{tabPanel(title = "Impact population",
label = "Nombre d'annes",
value = 30, min = 5, max = Inf, step = 10),
br(),
numericInput(inputId = "nsim",
label = "Nombre de simulations",
value = 10, min = 0, max = Inf, step = 10),
actionButton(inputId = "run", label = "Lancer l'analyse"),
br(" "),
span(textOutput("run_time"), align = "left", style = "font-weight: normal; font-size: 16px;"),
#hr(),
#br(),
#actionButton(inputId = "show_results", label = "Rsultats"),
#p("Rsultats", style="font-size:28px"),
## Report
{tabPanel(title = "Rapport",
br(" "),
textAreaInput("intro_report", label = "Contexte de l'tude", value = "Analyse ralise dans le cadre de ...", width = "1000px"),
br(" "),
textAreaInput("def_pop_text", label = "Dlimitation de la population",
value = "Veuillez dcrire ici l'approche et les informations utilises pour dlimiter la population concerne par cette tude d'impact dmographique",
width = "1000px"),
br(" "),
downloadButton("report", "Generate report")
)} # End tabPanel
) # End tabSetPanel
)} # End mainPanel
###############################################################################################################################
hr(),
## Results : text
{sidebarLayout(
## Side Panel
{conditionalPanel("output.hide_show_CI",
{sidebarPanel(
##
## Choose CI
sliderInput("show_CI", label = "Intervalle de confiance (%)", min = 0, max = 100, value = 95, step = 1),
hr(),
)}, # End sidebarPanel
)}, # close conditional panel
#######################################################
{mainPanel(
## Outputs
{conditionalPanel("output.hide_results",
##
# Text : Global impact
{column(
width = 7,
span(textOutput("title_impact_result"), align = "left", style = "font-weight: bold; font-size: 18px;"),
strong(span(tableOutput("impact_table"), style="color:blue; font-size:18px", align = "left")),
)},
{column(
width = 5,
span(textOutput("title_PrExt_result"), align = "left", style = "font-weight: bold; font-size: 18px;"),
strong(span(tableOutput("PrExt_table"), style="color:orange; font-size:18px", align = "left")),
)},
##
)}, # close conditional panel
)}, # End mainPanel
###############################################################################################################################
hr(),
## Results : Graphs
{sidebarLayout(
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## Side Panel
{conditionalPanel("output.hide_graph_choice",
{sidebarPanel(
##
## Choose which graph to show
radioButtons("choose_graph", label = h5(strong("Graphique (choix)")),
choices = c(
"Impact final : densit de probabilit" = "show_PDF",
"Impact final : probabilit cumule" = "show_ECDF",
"Impact relatif au cours du temps" = "show_impact_time",
"Projections dmographiques" = "show_demog_proj"
)),
br(),
## Choose "scenario" to show
radioButtons("show_scenario", label = h5(strong("Choix du scnario")),
choices = c("all")),
br(),
hr(),
br(),
## Choose "quantile" : level of under-estimation risk (1 - QT)
conditionalPanel("output.hide_risk_A",
wellPanel(style = "background:#F0F8FF",
sliderInput("risk_A", label = "Risque (%) de sous-estimation de l'impact", min = 0, max = 100, value = 5, step = 0.5),
)
), # close conditional panel
##
)}, # End sidebarPanel
)}, # close conditional panel
#######################################################
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{mainPanel(
## Graph : Probability Density (PDF)
{conditionalPanel("output.hide_graph_PDF",
tags$h4(textOutput("title_PDF_plot"), align = "center"),
plotOutput("PDF_plot", width = "100%", height = "550px"),
)}, # close conditional panel
## Graph : Cumulative Distibution (ECDF)
{conditionalPanel("output.hide_graph_ECDF",
tags$h4(textOutput("title_ECDF_plot"), align = "center"),
plotOutput("ECDF_plot", width = "100%", height = "550px"),
)}, # close conditional panel
## Graph : Relative Impact over time
{conditionalPanel("output.hide_graph_impact_time",
tags$h4(textOutput("title_impact_plot"), align = "center"),
plotOutput("impact_plot", width = "100%", height = "550px"),
)}, # close conditional panel
## Graph : Population trajectory (pop size over time)
{conditionalPanel("output.hide_graph_demog_proj",
tags$h4(textOutput("title_traj_plot"), align = "center"),
br(),
wellPanel(
span(textOutput("warning_traj_plot"), align = "left", style = "font-size: 14px;"),
),
br(" "),
radioButtons(inputId = "age_class_show",
label = "Classes d'ge inclure sur le graphe",
choices = c("Tous ges sauf juvniles" = "NotJuv0",
"Tous ges, y compris juvniles" = "all",
"Nombre de couples" = "pairs"),
inline = TRUE
),
plotOutput("traj_plot", width = "100%", height = "550px")
##
)}, # close conditional panel
# End UI #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~###