From 8f2867b28bcbc2fbd0c9aea248eecbb583d9bd0e Mon Sep 17 00:00:00 2001
From: thierrychambert <thierry.chambert@gmail.com>
Date: Thu, 12 Aug 2021 13:49:39 +0200
Subject: [PATCH] =?UTF-8?q?Cleaning=20shiny=20Esp=C3=A8ce=20g=C3=A9n=C3=A9?=
=?UTF-8?q?rique=20moved=20at=20end=20of=20list=20This=20version=20work=20?=
=?UTF-8?q?well.?=
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---
inst/ShinyApp/server.R | 38 +++++++++++++++++-----------------
inst/ShinyApp/species_list.csv | 2 +-
inst/ShinyApp/ui.R | 11 ++--------
run_analysis.R | 6 ++++++
4 files changed, 28 insertions(+), 29 deletions(-)
diff --git a/inst/ShinyApp/server.R b/inst/ShinyApp/server.R
index 6c07612..b583e13 100644
--- a/inst/ShinyApp/server.R
+++ b/inst/ShinyApp/server.R
@@ -1,7 +1,9 @@
server <- function(input, output, session){
- # Hide all inputs excepted actionButtons
+ ##--------------------------------------------
+ ## Hide all inputs excepted actionButtons --
+ ##--------------------------------------------
observe({
shinyjs::hide("fatal_constant")
shinyjs::hide("fatalities_input_type")
@@ -113,10 +115,10 @@ server <- function(input, output, session){
}) # en observe show/hide
-
-
- # Elicitation experts part
-
+ ##--------------------------------------------
+ ## Functions --
+ ##--------------------------------------------
+ # Function to extract value from elicitation matrix and run the elication analysis
func_eli <- function(mat_expert){
t_mat_expert <- t(mat_expert)
vals = t_mat_expert[3:5,]
@@ -127,12 +129,16 @@ server <- function(input, output, session){
return(list(out = out, mean = out$mean_smooth, SE = sqrt(out$var_smooth)))
}
+ # Function to plot output from elicitation analysis
func_eli_plot <- function(out){
plot_elicitation(out)
}
- ## Output
+
+ ##--------------------------------------------
+ ## Reactive values --
+ ##--------------------------------------------
param <- reactiveValues(N1 = NULL,
fatalities_mean = NULL,
fecundities = NULL,
@@ -144,7 +150,7 @@ server <- function(input, output, session){
onset_time = NULL,
onset_year = NULL,
carrying_capacity = NULL,
- rMAX_species = rMAX_species,
+ rMAX_species = NULL,
theta = theta,
fatalities_eli_result = NULL,
pop_size_eli_result = NULL,
@@ -156,7 +162,9 @@ server <- function(input, output, session){
pop_growth_se = NULL,
carrying_cap_eli_result = NULL)
- # Elicitation
+
+
+
## Fatalities
@@ -236,9 +244,8 @@ server <- function(input, output, session){
}
})
- # Population size
+ # Observe pop size value
## Mean, se and type
-
observeEvent({input$run},{
if(input$pop_size_input_type == "Elicitation d'expert"){
if(!(is.null(param$pop_size_eli_result))){
@@ -255,8 +262,6 @@ server <- function(input, output, session){
})
# Observe pop growth value
- ## Avoid unrealistic scenarios
-
observeEvent({input$run}, {
if(input$lambda_input_type == "Elicitation d'expert"){
if(!(is.null(param$pop_growth_eli_result))){
@@ -298,19 +303,14 @@ server <- function(input, output, session){
observeEvent({input$run}, {
param$survivals <- input$mat_fill_vr[,1]
param$fecundities <- input$mat_fill_vr[,2]
+ param$rMAX_species <- rMAX_spp(surv = tail(param$survivals,1), afr = min(which(param$fecundities != 0)))
}) # end observeEvent
# 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$pop_growth_mean
@@ -349,7 +349,7 @@ server <- function(input, output, session){
withProgress(message = 'Simulation progress', value = 0, {
param$N1 <- run_simul_shiny(nsim = input$nsim,
- cumuated_impacts = param$cumulated_impacts,
+ cumulated_impacts = param$cumulated_impacts,
fatalities_mean = param$fatalities_mean,
fatalities_se = param$fatalities_se,
diff --git a/inst/ShinyApp/species_list.csv b/inst/ShinyApp/species_list.csv
index 62ef529..e24e302 100644
--- a/inst/ShinyApp/species_list.csv
+++ b/inst/ShinyApp/species_list.csv
@@ -1,5 +1,4 @@
NomEspece,OrdreFamille
-Espèce générique,NA
Aigle de Bonelli,Accipitriformes
Aigle royal,Accipitriformes
Autour des palombes,Accipitriformes
@@ -56,3 +55,4 @@ Cigogne blanche,Pelecaniformes
Cigogne noire,Pelecaniformes
Grand Duc d'Europe,Strigiformes
Hibou des marais,Strigiformes
+Espèce générique,NA
diff --git a/inst/ShinyApp/ui.R b/inst/ShinyApp/ui.R
index f0bfac0..69395b7 100644
--- a/inst/ShinyApp/ui.R
+++ b/inst/ShinyApp/ui.R
@@ -20,8 +20,6 @@ data_sf <- read.csv("./inst/ShinyApp/survivals_fecundities_species.csv", sep = "
nsim = 10
coeff_var_environ = 0.10
time_horzion = 30
-init_survivals <- c(0.5, 0.5, 0.5)
-init_fecundities <- c(0, 0.8, 1.2)
#####################
### Pre-fill data ###
@@ -61,16 +59,11 @@ init_cumul <- c(10, 5, 8,
init_cumul_add <- c(3, 0.05, 2020)
-
-# vital rates
-init_vr = c(init_survivals, init_fecundities)
-
# DD parameters
theta = 1
# Define theoretical rMAX for the species
-rMAX_species <- rMAX_spp(surv = tail(init_survivals,1), afr = min(which(init_fecundities != 0)))
-rMAX_species
+#rMAX_species <- rMAX_spp(surv = tail(init_survivals,1), afr = min(which(init_fecundities != 0)))
##--------------------------------------------
@@ -327,7 +320,7 @@ ui <- fluidPage(
label = "Paramètres démographiques"),
matrixInput(inputId = "mat_fill_vr",
- value = matrix(data = init_vr, 3, 2,
+ value = matrix(data = NA, 3, 2,
dimnames = list(c("Juv 1", "Juv 2", "Adulte"), c("Survie", "Fécondité"))),
class = "numeric",
rows = list(names = TRUE),
diff --git a/run_analysis.R b/run_analysis.R
index e24bb75..9a581d0 100644
--- a/run_analysis.R
+++ b/run_analysis.R
@@ -64,6 +64,12 @@ lambda( build_Leslie(s = s_calibrated, f = f_calibrated) )
+pop_size_mean = 200
+pop_growth_mean = 1.1
+fatal_constant = "M"
+pop_size_type = "Ntotal"
+carrying_capacity = 500
+
##==============================================================================
## Analyses (simulations) ==
--
GitLab