plots improvements

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))

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))
 

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 = "\nAnne", ylab = "Impact relatif (%)\n")
+                  xlab = "\nAnne", 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$N, xlab = "Anne", ylab = "Taille de population (toutes classes d'ges)")}
+
+      n_scen <- dim(out$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 = "\nAnne", ylab = "Taille de population\n", Legend = Legend)}
   }
 
   output$title_traj_plot <- renderText({

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{
-
-
-}

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{
-
-
-}

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)