Cleaned and commented

plotting/map.R

@@ -23,7 +23,7 @@
 #
 # plotting/map.R
 #
-#
+# Deals with the creation of a map for presenting the trend analysis of hydrological variables
 
 
 ## 1. MAP PANEL
@@ -333,118 +333,145 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
             df_data_code = df_data[df_data$code == code,]
             # Extracts the trend corresponding to the code
             df_trend_code = df_trend[df_trend$code == code,]
-            
+
+            # Gets the associated time info
             Start = Start_code[Code_code == code][[1]][idPer]
             End = End_code[Code_code == code][[1]][idPer]
-            
+
+            # Extracts the corresponding data for the period
             df_data_code_per =
                 df_data_code[df_data_code$Date >= Start 
                              & df_data_code$Date <= End,]
-            
+            # Same for trend
             df_trend_code_per = 
                 df_trend_code[df_trend_code$period_start == Start 
                               & df_trend_code$period_end == End,]
-            
+
+            # Computes the number of trend analysis selected
             Ntrend = nrow(df_trend_code_per)
+            # If there is more than one trend on the same period
             if (Ntrend > 1) {
+                # Takes only the first because they are similar
                 df_trend_code_per = df_trend_code_per[1,]
             }
-            
+
+            # Computes the mean of the data on the period
             dataMean = mean(df_data_code_per$Qm3s, na.rm=TRUE)
+            # Normalises the trend value by the mean of the data
             trendMean = df_trend_code_per$trend / dataMean
 
+            # Computes the color associated to the mean trend
             color_res = get_color(trendMean, 
                                   minTrendMean[idPer, i],
                                   maxTrendMean[idPer, i],
                                   palette_name='perso',
                                   reverse=TRUE,
                                   ncolor=256)
-
+            # Computes the colorbar info 
             palette_res = get_palette(minTrendMean[idPer, i],
                                       maxTrendMean[idPer, i],
                                       palette_name='perso',
                                       reverse=TRUE,
                                       ncolor=256,
                                       nbTick=nbTick)
-            
+
+            # If it is significative
             if (df_trend_code_per$p <= p_threshold){
+                # The computed color is stored
                 filltmp = color_res
-
+                # If the mean tend is positive
                 if (trendMean >= 0) {
+                    # Uses a triangle up for the shape of the marker
                     shapetmp = 24
+                # If negative
                 } else {
+                    # Uses a triangle down for the shape of the marker
                     shapetmp = 25
                 }
-                
-            } else {                
+            # If it is not significative
+            } else {
+                # The fill color is grey
                 filltmp = 'grey97'
+                # The marker is a circle
                 shapetmp = 21
             }
 
+            # Extracts the localisation of the current station
             lontmp =
                 df_meta[df_meta$code == code,]$L93X_m_BH            
             lattmp =
                 df_meta[df_meta$code == code,]$L93Y_m_BH 
 
+            # Stores all the parameters
             lon = c(lon, lontmp)
             lat = c(lat, lattmp)
             fill = c(fill, filltmp)
             shape = c(shape, shapetmp)
             trend = c(trend, trendMean)
+            # If the trend analysis is significative a TRUE is stored
             p_threshold_Ok = c(p_threshold_Ok,
                                df_trend_code_per$p <= p_threshold)
-
         }
-        
+        # Creates a tibble to stores all the data to plot
         plot_map = tibble(lon=lon, lat=lat, fill=fill,
                           shape=shape, code=Code)
 
         # If there is no specified station code to highlight (mini map)
         if (is.null(codeLight)) {
             map = map +
+                # Plots the trend point
                 geom_point(data=plot_map,
                            aes(x=lon, y=lat),
                            shape=shape, size=5, stroke=1,
                            color='grey50', fill=fill)
         # If there is a specified station code
         } else {
+            # Extract data of all stations not to highlight
             plot_map_codeNo = plot_map[plot_map$code != codeLight,]
+            # Extract data of the station to highlight
             plot_map_code = plot_map[plot_map$code == codeLight,]
-            
+
+            # Plots only the localisation
             map = map +
-                
+                # For all stations not to highlight
                 geom_point(data=plot_map_codeNo,
                            aes(x=lon, y=lat),
                            shape=21, size=0.5, stroke=0.5,
                            color='grey70', fill='grey70') +
-                
+                # For the station to highlight
                 geom_point(data=plot_map_code,
                            aes(x=lon, y=lat),
                            shape=21, size=1.5, stroke=0.5,
                            color='grey40', fill='grey40')
         }
-                
+        
+        # Extracts the position of the tick of the colorbar
         posTick = palette_res$posTick
+        # Extracts the label of the tick of the colorbar
         labTick = palette_res$labTick
+        # Extracts the color corresponding to the tick of the colorbar
         colTick = palette_res$colTick
-        
-        nbTickmod = length(posTick)
 
-        valNorm = nbTickmod * 10
+        # Spreading of the colorbar
+        valNorm = nbTick * 10
+        # Normalisation of the position of ticks
         ytick = posTick / max(posTick) * valNorm
-        
+        # Formatting of label in pourcent
         labTick = as.character(round(labTick*100, 2))
-       
-        xtick = rep(0, times=nbTickmod)
 
+        # X position of ticks all similar
+        xtick = rep(0, times=nbTick)
+
+        # Creates a tibble to store all parameters of colorbar
         plot_palette = tibble(xtick=xtick, ytick=ytick,
                               colTick=colTick, labTick=labTick)
-        
+
+        # New plot with void theme
         title = ggplot() + theme_void() +
-            
+            # Plots separation line
             geom_line(aes(x=c(-0.3, 3.3), y=c(0.05, 0.05)),
                       size=0.6, color="#00A3A8") +
-            
+            # Writes title
             geom_shadowtext(data=tibble(x=-0.3, y=0.2,
                                         label=type),
                             aes(x=x, y=y, label=label),
@@ -452,39 +479,40 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
                             color="#00A3A8",
                             bg.colour="white",
                             hjust=0, vjust=0, size=10) +
-            
+            # X axis
             scale_x_continuous(limits=c(-1, 1 + 3),
                                expand=c(0, 0)) +
-            
+            # Y axis
             scale_y_continuous(limits=c(0, 10),
                                expand=c(0, 0)) +
-            
+            # Margin
             theme(plot.margin=margin(t=5, r=5, b=0, l=0, unit="mm"))
         
-        
+        # New plot with void theme
         pal = ggplot() + theme_void() +
-            
+            # Plots the point of the colorbar
             geom_point(data=plot_palette,
                        aes(x=xtick, y=ytick),
                        shape=21, size=5, stroke=1,
                        color='white', fill=colTick)
 
         pal = pal +
-            
+            # Name of the colorbar
             annotate('text',
                      x=-0.3, y= valNorm + 23,
                      label="Tendance",
                      hjust=0, vjust=0.5,
                      size=6, color='grey40') +
-            
+            # Unit legend of the colorbar
             annotate('text',
                      x=-0.2, y= valNorm + 13,
                      label=bquote(bold("% par an")),
                      hjust=0, vjust=0.5,
                      size=4, color='grey40')
-
-        for (j in 1:nbTickmod) {
+        # For all the ticks
+        for (j in 1:nbTick) {
             pal = pal +
+                # Adds the value
                 annotate('text', x=xtick[j]+0.3,
                          y=ytick[j],
                          label=bquote(bold(.(labTick[j]))),
@@ -493,11 +521,11 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
         }
 
         pal = pal +
-            
+            # Up triangle in the marker legend
             geom_point(aes(x=0, y=-20),
                        shape=24, size=4, stroke=1,
                        color='grey50', fill='grey97') +
-            
+            # Up triangle text legend
             annotate('text',
                      x=0.3, y=-20,
                      label=bquote(bold("Hausse significative à 10%")),
@@ -505,11 +533,11 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
                      size=3, color='grey40')
 
         pal = pal +
-            
+            # Circle in the marker legend
             geom_point(aes(x=0, y=-29),
                        shape=21, size=4, stroke=1,
                        color='grey50', fill='grey97') +
-            
+            # Circle text legend
             annotate('text',
                      x=0.3, y=-29,
                      label=bquote(bold("Non significatif à 10%")),
@@ -517,45 +545,58 @@ map_panel = function (list_df2plot, df_meta, df_shapefile, idPer=1, outdirTmp=''
                      size=3, color='grey40')
 
         pal = pal +
-            
+            # Down triangle in the marker legend
             geom_point(aes(x=0, y=-40),
                        shape=25, size=4, stroke=1,
                        color='grey50', fill='grey97') +
-            
+            # Down triangle text legend
             annotate('text',
                      x=0.3, y=-40,
                      label=bquote(bold("Baisse significative à 10%")),
                      hjust=0, vjust=0.5,
                      size=3, color='grey40')
-        
+
+        # Normalises all the trend values for each station
+        # according to the colorbar
         yTrend = (trend - minTrendMean[idPer, i]) /
             (maxTrendMean[idPer, i] - minTrendMean[idPer, i]) * valNorm
-
+        # Takes only the significative ones
         yTrend = yTrend[p_threshold_Ok]
 
         ## Random distribution ##
         # xTrend = rnorm(length(yTrend), mean=1.75, sd=0.1)
 
-        ## Histogram distribution ##     
+        ## Histogram distribution ##
+        # Computes the histogram of the trend
         res_hist = hist(yTrend, breaks=ytick, plot=FALSE)
+        # Extracts the number of counts per cells
         counts = res_hist$counts
+        # Extracts limits of cells 
         breaks = res_hist$breaks
+        # Extracts middle of cells 
         mids = res_hist$mids
 
+        # Blank vectors to store position of points of
+        # the distribution to plot
         xTrend = c()
         yTrend = c()
+        # Start X position of the distribution
         start_hist = 1.25
+        # X separation bewteen point
         hist_sep = 0.15
-        
+        # For all cells of the histogram
         for (ii in 1:length(mids)) {
-
+            # If the count in the current cell is not zero
             if (counts[ii] != 0) {
+                # Stores the X positions of points of the distribution
+                # for the current cell
                 xTrend = c(xTrend,
                            seq(start_hist,
                                start_hist+(counts[ii]-1)*hist_sep,
                                by=hist_sep))
             }
-            
+            # Stores the Y position which is the middle of the
+            # current cell the number of times it has been counted
             yTrend = c(yTrend, rep(mids[ii], times=counts[ii]))
         }