Changing color schemes of figures

Author: LimaRAF

R/06_prep_pop_size_data.R

@@ -107,7 +107,7 @@ high.pop.sizes <- rowsum(high.pop.sizes, spp_names)
 
 ## OBTAINING POPULATION SIZES FOR MISSING YEARS ##
 ## Getting pop. sizes for all possible generation lengths
-gen.lengths <- c(10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 90, 100)
+gen.lengths <- c(7, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 90, 100)
 pos.gen.length <- sort(unique(gen.lengths * rep(c(1:3), each=length(gen.lengths))), decreasing = TRUE)
 miss.years <- 2018 - pos.gen.length
 miss.years1 <- miss.years[miss.years < 1992]

R/11_criterionC.R

@@ -28,7 +28,7 @@ for(x in 1:length(res.means)) {
 }
 
 #### LOADING THREAT HABITAT AND ECOLOGY DATA ####
-## Includes species info on Generation Length and Proportion of matrue individuals
+## Includes species info on Generation Length and Proportion of mature individuals
 hab <- readRDS("data/threat_habitats.rds")
 
 PopData <- merge(decline.models, hab, by.x= "row.names", by.y = "Name_submitted", all.x = TRUE)

R/12_area_of_habitat.R

@@ -37,7 +37,7 @@ hab.class <- toto$NB_LAB[grepl("ForestCover", toto$LegendTreeCoSimp)]
 
 ## Loading aggregated rasters and extracting the habitat loss/quality
 ano1 = 2000
-hab.map <- raster::stack(paste0("data/ESA_Land_Cover_map_",ano1,"_2015_AF_1km.tif"))
+hab.map <- raster::stack(paste0("data/ESA_Land_Cover_map_", ano1, "_2015_AF_1km.tif"))
 names(hab.map) <- paste("ESA",c(ano1,2015), sep=".")
 anos <- 2015 - ano1 
 

R/14_criterionA.R

@@ -286,9 +286,12 @@ colnames(mat) <- gsub(" ", "", colnames(mat))
 rownames(mat) <- gsub(" ", "", rownames(mat))
 
 #Defining the colors of tracks and links
-grid.col = c(CR_25 = "red", EN_25 = "darkorange", VU_25 = "gold", LCorNT_25 = "khaki",
-             CR_opt = "red", EN_opt = "darkorange", VU_opt = "gold", LCorNT_opt = "khaki")
-col_mat = rep(rev(c("red","darkorange","gold","khaki")), each=4)
+# grid.col = c(CR_25 = "red", EN_25 = "darkorange", VU_25 = "gold", LCorNT_25 = "khaki",
+#              CR_opt = "red", EN_opt = "darkorange", VU_opt = "gold", LCorNT_opt = "khaki")
+# col_mat = rep(rev(c("red","darkorange","gold","khaki")), each=4)
+grid.col = c(CR_25 = "#CD0000", EN_25 = "#E69F00", VU_25 = "#FFFF00", LCorNT_25 = "khaki",
+             CR_opt = "#CD0000", EN_opt = "#E69F00", VU_opt = "#FFFF00", LCorNT_opt = "khaki")
+col_mat = rep(rev(c("#CD0000","#E69F00","#FFFF00","khaki")), each=4)
 col_mat[mat >= 15] = adjustcolor(col_mat[mat >= 15], alpha.f = 0.5)
 col_mat[mat < 15] = adjustcolor(col_mat[mat < 15], alpha.f = 0.9)
 #col_mat[mat < 5] = "#00000000"
@@ -348,9 +351,12 @@ colnames(mat) <- gsub(" ", "", colnames(mat))
 rownames(mat) <- gsub(" ", "", rownames(mat))
 
 #Defining the colors of tracks and links
-grid.col = c(CR_20= "red", EN_20 = "darkorange", VU_20 = "gold", LCorNT_20 = "khaki",
-             CR_opt = "red", EN_opt = "darkorange", VU_opt = "gold", LCorNT_opt = "khaki")
-col_mat = rep(rev(c("red","darkorange","gold","khaki")), each=4)
+# grid.col = c(CR_20= "red", EN_20 = "darkorange", VU_20 = "gold", LCorNT_20 = "khaki",
+#              CR_opt = "red", EN_opt = "darkorange", VU_opt = "gold", LCorNT_opt = "khaki")
+# col_mat = rep(rev(c("red","darkorange","gold","khaki")), each=4)
+grid.col = c(CR_20= "#CD0000", EN_20 = "#E69F00", VU_20 = "#FFFF00", LCorNT_20 = "khaki",
+             CR_opt = "#CD0000", EN_opt = "#E69F00", VU_opt = "#FFFF00", LCorNT_opt = "khaki")
+col_mat = rep(rev(c("#CD0000","#E69F00","#FFFF00","khaki")), each=4)
 col_mat[mat >= 15] = adjustcolor(col_mat[mat >= 15], alpha.f = 0.5)
 col_mat[mat < 15] = adjustcolor(col_mat[mat < 15], alpha.f = 0.9)
 #col_mat[mat < 5] = "#00000000"
@@ -447,7 +453,9 @@ colnames(pct) <- gl.vals
 pct <- round(100 * pct/dim(all.GL)[1], 1)
 
 par(mar=c(3,3.5,0.75, 0.25), mgp=c(1.9,0.25,0),tcl=-0.2,las=1)
-barplot(pct, horiz = FALSE, col = c("red","darkorange","gold","khaki"),
+barplot(pct, horiz = FALSE, 
+        # col = c("red","darkorange","gold","khaki"),
+        col = c("#CD0000","#E69F00","#FFFF00","khaki"),
         legend.text = c("CR", "EN", "VU", "LC or NT"),
         xlab = "Generation length (years)",  
         args.legend = list(ncol=4, x.intersp = 1, y.intersp = 1, yjust = -1,

R/23_figures.R

@@ -4153,8 +4153,10 @@ makeSubColor=function(main,no=3){
 #'Draw a PieDonut plot
 my.PieDonut=function(data,mapping,
                      start=getOption("PieDonut.start",0),
-                     addPieLabel=TRUE,addDonutLabel=TRUE,
-                     showRatioDonut=TRUE,showRatioPie=TRUE,
+                     addPieLabel=TRUE,
+                     addDonutLabel=TRUE,
+                     showRatioDonut=TRUE,
+                     showRatioPie=TRUE,
                      ratioByGroup=TRUE,
                      showRatioThreshold=getOption("PieDonut.showRatioThreshold",0.02),
                      labelposition=getOption("PieDonut.labelposition",2),
@@ -4175,18 +4177,25 @@ my.PieDonut=function(data,mapping,
                      pieLabelSize=4,
                      donutLabelSize=3,
                      titlesize=5,
-                     explodePie=TRUE,explodeDonut=FALSE,
-                     use.label=TRUE,use.labels=TRUE,
+                     explodePie=TRUE,
+                     explodeDonut=FALSE,
+                     use.label=TRUE,
+                     use.labels=TRUE,
                      family=getOption("PieDonut.family",""),
                      labels = NULL,
                      main.colors = NULL,
+                     sub.colors = NULL,
                      correct.legend.x = NULL,
                      correct.legend.y = NULL,
                      tidy.legend.donut = NULL,
                      tidy.legend.pie = NULL,
                      plot.piedonut = TRUE,
                      ratioAccuracy = c("pie" = 0.1, "donut" = 0.1),
-                     return = "both"){
+                     return = "both",
+                     legend.pie = FALSE,
+                     legend.donut = FALSE,
+                     explode.cor.pie = 1,
+                     explode.cor.donut = 1){
 
   # data = pie.df
   # mapping = aes(category, main.criteria)
@@ -4199,8 +4208,8 @@ my.PieDonut=function(data,mapping,
   # family = ""
   # main.colors = cores[-1]
 
-  (cols=colnames(data))
-  if(use.labels) data=moonBook::addLabelDf(data,mapping)
+  (cols = colnames(data))
+  if (use.labels) data=moonBook::addLabelDf(data,mapping)
 
   count<-NULL
 
@@ -4242,8 +4251,8 @@ my.PieDonut=function(data,mapping,
   df$focus=0
   if(explodePie) df$focus[explode]=explodePos
   df$mid=(df$start1+df$end1)/2
-  df$x=ifelse(df$focus==0,0,df$focus*sin(df$mid))
-  df$y=ifelse(df$focus==0,0,df$focus*cos(df$mid))
+  df$x=ifelse(df$focus==0,0,df$focus*sin(df$mid) * explode.cor.pie)
+  df$y=ifelse(df$focus==0,0,df$focus*cos(df$mid) * explode.cor.pie)
   df$label=df[[pies]]
   df$ratio=df$Freq/sum(df$Freq)
 
@@ -4256,8 +4265,7 @@ my.PieDonut=function(data,mapping,
     #                 as.character(df$label))
     df$label = paste0(df$label, " (", scales::percent(df$ratio, accuracy = ratioAccuracy["pie"]), ")")
     df$label[tidy.legend.pie] = gsub(" \\(", "\n(",df$label[tidy.legend.pie])
-    
-    
+
   }
 
   if(is.null(correct.legend.x)) {
@@ -4292,7 +4300,6 @@ my.PieDonut=function(data,mapping,
     subColor = makeSubColor(mainCol, no = length(unique(data[[donuts]])))
     subColor
 
-    
     data
     if(!is.null(count)){
 
@@ -4380,8 +4387,8 @@ my.PieDonut=function(data,mapping,
         xpos=df$focus[explode[i]]*sin(df$mid[explode[i]])
         ypos=df$focus[explode[i]]*cos(df$mid[explode[i]])
 
-        df3$x[df3$pie==explode[i]]=xpos
-        df3$y[df3$pie==explode[i]]=ypos
+        df3$x[df3$pie==explode[i]] = xpos * explode.cor.donut
+        df3$y[df3$pie==explode[i]] = ypos * explode.cor.donut
       }
     }
     df3$no=1:nrow(df3)
@@ -4442,6 +4449,22 @@ my.PieDonut=function(data,mapping,
     }
     df3
 
+    if (!is.null(sub.colors)) {
+      no.sub <- length(sub.colors)
+      no <- length(unique(data[[donuts]]))
+      
+      if (no.sub == length(subColor)) subColor <- sub.colors
+      
+      if (no.sub != no)
+        stop(cat("Length of subcolors is different than the number of", donuts))
+      
+      if (no.sub == no) {
+        cores <- sub.colors
+        names(cores) <- sort(unique(data[[donuts]]))
+        subColor <- unname(cores[match(df3[[donuts]], names(cores))])
+      }
+    }
+    
     del=which(df3$Freq==0)
     del
     if(length(del)>0) subColor<-subColor[-del]
@@ -4462,15 +4485,16 @@ my.PieDonut=function(data,mapping,
                                              fill = pies),alpha=pieAlpha,color=color, data = df) + 
     transparent()+
     scale_fill_manual(values=mainCol)+
-    xlim(r3*c(-1.2,1.2)) + ylim(r3*c(-1,1)) + guides(fill=FALSE) +
+    xlim(r3*c(-1.2,1.2)) + ylim(r3*c(-1,1)) + 
+    guides(fill = legend.pie) +
     theme_void()
 
   if ((labelposition == 1) & (is.null(donuts))) {
     p1 <- p1 + geom_segment(aes_string(x = "segx", y = "segy",
                                        xend="segxend",yend="segyend"),data=df)+
       geom_text(aes_string(x="segxend",y="segyend",label="label",hjust="hjust",vjust="vjust"),size=pieLabelSize,data=df,family=family)
 
-  } else 
+  } else {
     if ((labelposition == 2) & (is.null(donuts))) {
       p1<-p1+ geom_segment(aes_string(x="segx",y="segy",
                                       xend="segxend",yend="segyend"),data=df[df$ratio<labelpositionThreshold,])+
@@ -4486,6 +4510,7 @@ my.PieDonut=function(data,mapping,
         family = family
       )
     }
+  }
 
   if(showPieName) p1<-p1+annotate("text",x=0,y=0,label=pies,size=titlesize,family=family)
 
@@ -4508,8 +4533,9 @@ my.PieDonut=function(data,mapping,
     }
 
     p3 <- p3 + transparent()+
-      scale_fill_manual(values=subColor)+
-      xlim(r3*c(-1,1))+ylim(r3*c(-1,1))+guides(fill=FALSE)+
+      scale_fill_manual(values=subColor) +
+      xlim(r3*c(-1,1))+ylim(r3*c(-1,1)) + 
+      guides(fill = legend.donut) +
       theme_void()
 
     p3
@@ -4519,7 +4545,7 @@ my.PieDonut=function(data,mapping,
                                       xend="segxend",yend="segyend"),data=df3)+
         geom_text(aes_string(x="segxend",y="segyend",
                              label="label",hjust="hjust",vjust="vjust"),size=donutLabelSize,data=df3,family=family)
-    } else 
+    } else {
       if(labelposition==0){
         p3<-p3+geom_text(aes_string(x="labelx",y="labely",
                                     label="label"),size=donutLabelSize,data=df3,family=family)
@@ -4532,6 +4558,7 @@ my.PieDonut=function(data,mapping,
                                label="label"),size=donutLabelSize,data=df3[df3$ratio1>=labelpositionThreshold,],family=family)
 
       }
+    }
 
     if(!is.null(title)) 
       p3<-p3+annotate("text",x=0,y=r3,label=title,size=titlesize,family=family)

R/99_functions.R

@@ -53,7 +53,7 @@ are:
 
   1. **Definitions**: the basic definitions that will determine which species 
 will be included in the assessments;
-  2. **Species information**: the essential information that must be 
+  2. **Species information**: the information that must be 
 compiled, estimated or imputed to obtain the population metrics;
   3. **Population metrics**: the estimation of the population metrics defined by
 the IUCN as indicators of the threat of a species or taxon;
@@ -71,22 +71,22 @@ and the export of the information necessary for submission to the IUCN Red List.
 
 Note that the Pre-Assessment stage involves the compilation of key information
 from the study region and the group of organism included in the assessment.
-Although it represents the first steps of the workflow, the codes in this
+Although it represents the first step of the workflow, the code in this
 repository assume that this key information was already compiled, estimated, or
 imputed and that it is available in the `data` folder.
 
-Note as well that our Post-assessment stage does not include the submission of
+Note as well that the Post-assessment stage does not include the submission of
 the assessments to the IUCN Red List and their review by the IUCN Red List
 units. This is beyond the scope of the workflow which is producing the
 conservation assessments and the associated files to facilitate the process of submission by the Red List Authority.
 
 <br />
 
 
-##### Details on step 1
+##### Details on step 1 - Definitions
 
-Step 1 will define which species will have global (for all populations of the
-species) or regional assessments (for populations of the species). For instance,
+Step 1 will define which assessment will be global (for all populations of the
+species) or regional (for some populations of the species). For instance,
 THREAT project has a regional scope (i.e. Atlantic Forest), so for species that
 are non-endemic and endemic to the Atlantic Forest the assessments are regional
 and global, respectively. This is why it is important to know beforehand the
@@ -104,7 +104,7 @@ process of reviewing the assessments before submission.
 
 <br />
 
-##### Details on step 2
+##### Details on step 2 - Species information
 
 Species occurrence records are the input data used to obtain the species'
 geographic range and other spatial population metrics that are required to
@@ -128,7 +128,7 @@ specimen duplicates across biological collections.
 
 <br />
 
-##### Details on step 3
+##### Details on step 3 - Population metrics
 
 Different population metrics are necessary for applying the IUCN criteria A, B,
 C and D (criterion E is currently not considered in this workflow). A new
@@ -141,8 +141,8 @@ a specific point in time (`ConR::pop.decline.fit()`), based on linear,
 exponential, quadratic, logistic and other trends of population decline 
 through time. This is important because the time interval of population decline
 needed for the assessments of criterion A or C (10 years or one-, two- or
-three-generation lengths - see IUCN 2019) often do not correspond exactly to the
-observed population sizes for most species.
+three-generation lengths - see IUCN 2019) often does not correspond exactly to
+the observed population sizes for most species.
 
 For criterion B, the `ConR` package provides functions for calculating the
 Extent of Occurrence (EOO - `ConR::EOO.computing()`), Area of Occupancy (AOO -
@@ -158,7 +158,7 @@ conditions necessary for assessing criterion B.
 
 <br />
 
-##### Details on step 4
+##### Details on step 4 - IUCN Red List Criteria assessment
 
 This step corresponds to the assignment of one of the IUCN Red List Categories
 (i.e. EX, EW, CR, EN, VU, NT, LC, DD) by comparing the population metrics
@@ -170,9 +170,9 @@ of the `ConR` R package (Dauby et al. 2017, Dauby & Lima 2022 - see functions
 `ConR::criterion_D()`). A specific function was also developed to tell apart NT
 from LC categories (see function `ConR::near.threatened()`).
 
-In the case of multiple IUCN criteria, there is also the need to generate a
-consensus IUCN Red List Category among the results of each IUCN Criteria (see
-function `ConR:::cat_mult_criteria()`).
+In the case of multiple IUCN Red List Criteria, there is also the need to
+generate a consensus IUCN Red List Category among the results of each IUCN
+Criteria (see function `ConR:::cat_mult_criteria()`).
 
 This step of the workflow also includes the assignment of tags, such as the
 'Possibly Extinct' tag for Critically Endangered species or tags for species
@@ -193,7 +193,7 @@ include uncertainties in the assessments.
 
 <br />
 
-##### Details on step 5
+##### Details on step 5 - Summary and export
 
 Many different summaries and analyses can be done to present the results of the
 assessments. Besides a general description of the results (e.g. proportion of
@@ -256,11 +256,11 @@ species defined based on their potential maximum height as an adult and on their
 ecological groups. The choice of which values were assigned to each group was
 based on the empirical evidence currently available in the literature.
 
-Still related to criteria A and C, it is expected that harvest trends (e.g.
-timber species) and changes in habitat quality will have an impact on the change
-in population sizes. For long-lived organisms, this information will probably be
-missing for most species. But it is advised to take them into account for
-calibrating the final results of the assessments.
+Still related to the IUCN criteria A and C, it is expected that harvest trends
+(e.g. timber species) and changes in habitat quality will have an impact on the
+change in population sizes. For long-lived organisms, this information will
+probably be missing for most species. But it is advised to take them into
+account for calibrating the final results of the assessments.
 
 For the assessment of criteria B, the Average Dispersal Distance (DD) is
 mandatory information for estimating the number of subpopulations and their
@@ -367,11 +367,11 @@ Renato A. F. de Lima & Gilles Dauby
 
 Please cite this repository as: 
 
-> **{{ Renato A. F. de Lima, Gilles Dauby, André L. de Gasper, Eduardo P.
+> **Renato A. F. de Lima, Gilles Dauby, André L. de Gasper, Eduardo P.
 Fernandez, Alexander C. Vibrans, Alexandre A. de Oliveira, Paulo I. Prado,
 Vinícius C. Souza, Marinez F. de Siqueira & Hans ter Steege. (2023). The
 Atlantic Forest trees: a flora on the verge of extinction. Manuscript under
-review }}**
+review**
 
 ### Funding