diff --git a/R/plot_impact.R b/R/plot_impact.R
index ebf20860494ffa8c2be6e1bb8e1acf00550c43b7..f0a2614ce9ce4b8d67cf50b22eea0a6bb223f152 100644
--- a/R/plot_impact.R
+++ b/R/plot_impact.R
@@ -20,8 +20,6 @@
#' @importFrom scales pretty_breaks
#' @import ggplot2
#'
-#' @examples
-#'
#'
plot_impact <- function(N, onset_year = NULL, percent = TRUE, xlab = "Year", ylab = "Relative impact (%)",
Legend = NULL, ...){
@@ -75,7 +73,8 @@ plot_impact <- function(N, onset_year = NULL, percent = TRUE, xlab = "Year", yla
breaks = scales::pretty_breaks(n = 10),
sec.axis = sec_axis(trans = ~.*1, name = "",
breaks = scales::pretty_breaks(n = 10))) +
- scale_x_continuous(expand = c(0,0))
+ scale_x_continuous(expand = expansion(mult = c(0.015, 0)),
+ breaks = scales::pretty_breaks(n = 10))
# Add horizontal dashed lines (for better viz)
p <- p + geom_hline(yintercept = seq(0 , -100, by = -10), size = 0.5, linetype = 3, colour = grey(0.15))
diff --git a/R/plot_traj.R b/R/plot_traj.R
index 96eb7a194afb709e1bd6b1f53fe7fb279a9cbc49..17e53affb9bb9df8bb080f78b1c5dc51a64d8773 100644
--- a/R/plot_traj.R
+++ b/R/plot_traj.R
@@ -6,8 +6,6 @@
#' @param N a 4-D array containing demographic projection outputs
#' @param onset_year a vector containing the years of each wind farm start being active
#' (thus, the year at whihc each fatality value starts kicking in)
-#' @param percent a logical value indicating whether the impact should be displayed in % (y axis).
-#' If FALSE, the impact value displayed is between 0 and -1 (negative impact).
#' @param xlab a character string. Label for the x axis.
#' @param ylab a character string. Label for the y axis.
#' @param Legend a vector of character strings. The legend to show on the side of the plot.
@@ -20,10 +18,9 @@
#' @importFrom scales pretty_breaks
#' @import ggplot2
#'
-#' @examples
#'
#'
-plot_traj <- function(N, onset_year = NULL, percent = TRUE, xlab = "Year", ylab = "Relative impact (%)",
+plot_traj <- function(N, onset_year = NULL, xlab = "Year", ylab = "Relative impact (%)",
Legend = NULL, ...){
# Get metrics and dimensions
@@ -83,7 +80,9 @@ plot_traj <- function(N, onset_year = NULL, percent = TRUE, xlab = "Year", ylab
# Add y-axis on right side, and make pretty x/y axis and limits
p <- p +
scale_y_continuous(expand = expansion(mult = c(0.025, 0.005)),
- breaks = scales::pretty_breaks(n = 10)) +
+ breaks = scales::pretty_breaks(n = 10),
+ sec.axis = sec_axis(trans = ~.*1, name = "",
+ breaks = scales::pretty_breaks(n = 10))) +
scale_x_continuous(expand = expansion(mult = c(0.015, 0)),
breaks = scales::pretty_breaks(n = 10))
diff --git a/inst/ShinyApp/server.R b/inst/ShinyApp/server.R
index 78c55c542f241e8d47b020c788147af4d569e256..e31c64e0dcf8ca12dddea176553d1d172e8bf840 100644
--- a/inst/ShinyApp/server.R
+++ b/inst/ShinyApp/server.R
@@ -1375,8 +1375,15 @@ server <- function(input, output, session){
## Function to plot the impact
plot_out_impact <- function(){
if(is.null(out$run)) {} else {
+
+ n_scen <- dim(out$run$N)[3]
+ Legend <- NULL
+ if(out$analysis_choice == "single_farm") Legend <- c("Sans parc", "Avec parc")
+ if(out$analysis_choice == "cumulated") Legend <- c("Sans parc", "+ Parc 1", paste("... + Parc", (3:n_scen)-1))
+ if(out$analysis_choice == "multi_scenario") Legend <- paste("Scenario", (1:n_scen)-1)
+
plot_impact(N = out$run$N, onset_year = param$onset_year, percent = TRUE,
- xlab = "\nAnn�e", ylab = "Impact relatif (%)\n")
+ xlab = "\nAnn�e", ylab = "Impact relatif (%)\n", Legend = Legend)
}
}
@@ -1397,7 +1404,15 @@ server <- function(input, output, session){
plot_out_traj <- function(){
if(is.null(out$run)) {
} else {
- plot_traj(N = out$run$N, xlab = "Ann�e", ylab = "Taille de population (toutes classes d'�ges)")}
+
+ n_scen <- dim(out$run$N)[3]
+ Legend <- NULL
+ if(out$analysis_choice == "single_farm") Legend <- c("Sans parc", "Avec parc")
+ if(out$analysis_choice == "cumulated") Legend <- c("Sans parc", "+ Parc 1", paste("... + Parc", (3:n_scen)-1))
+ if(out$analysis_choice == "multi_scenario") Legend <- paste("Scenario", (1:n_scen)-1)
+
+ plot_traj(N = out$run$N, onset_year = param$onset_year,
+ xlab = "\nAnn�e", ylab = "Taille de population\n", Legend = Legend)}
}
output$title_traj_plot <- renderText({
diff --git a/man/plot_impact.Rd b/man/plot_impact.Rd
index 3018f936e941888b0d06863baf8c42440496b7f7..285883ccca52723aefbdea00a9b99294183f69b9 100644
--- a/man/plot_impact.Rd
+++ b/man/plot_impact.Rd
@@ -37,7 +37,3 @@ a plot of the relative impact of each scenario.
\description{
Plot the relative impact for each scenario
}
-\examples{
-
-
-}
diff --git a/man/plot_traj.Rd b/man/plot_traj.Rd
index d094bbf49f13c9e5414dcc77610fef6e4100cc50..e872f32dce84b36971f7e34b0d656e3f9438a562 100644
--- a/man/plot_traj.Rd
+++ b/man/plot_traj.Rd
@@ -7,7 +7,6 @@
plot_traj(
N,
onset_year = NULL,
- percent = TRUE,
xlab = "Year",
ylab = "Relative impact (\%)",
Legend = NULL,
@@ -20,9 +19,6 @@ plot_traj(
\item{onset_year}{a vector containing the years of each wind farm start being active
(thus, the year at whihc each fatality value starts kicking in)}
-\item{percent}{a logical value indicating whether the impact should be displayed in \% (y axis).
-If FALSE, the impact value displayed is between 0 and -1 (negative impact).}
-
\item{xlab}{a character string. Label for the x axis.}
\item{ylab}{a character string. Label for the y axis.}
@@ -37,7 +33,3 @@ a plot of the relative impact of each scenario.
\description{
Plot demographic trajectories
}
-\examples{
-
-
-}
diff --git a/run_analysis.R b/run_analysis.R
index 696c3ab87bbaba870541a60b2b6a3e33d4dc4faf..afad34c5569f64d4dc9160d277d1b204762ca4f5 100644
--- a/run_analysis.R
+++ b/run_analysis.R
@@ -7,7 +7,7 @@ library(magrittr)
library(eolpop)
## Inputs
-nsim = 100
+nsim = 10
pop_size_mean = 50
pop_size_se = 0
@@ -118,6 +118,9 @@ colSums(N) %>% apply(., c(1,2), mean)
out = list()
out$run = run0
+
+dim(out$run$N)
+
get_metrics(N = out$run$N)$scenario$impact[time_horzion, ,-1] %>% round(.,2)
res = get_metrics(N = out$run$N, cumulated_impacts = cumulated_impacts)