I run a loop to get a map on each additional setup of my dataset and apply the appropriate palette (and corresponding legend), respectively.

People tend to dislike using for () loops and maximize the vectoriness of their approaches. I do not know how best to vectorize processes using this particular dataset.

In this particular case, I process a relatively large dataset (Atlas distribution types), which is especially complicated because different methodologies are used, and for each type it is necessary to transfer different options, taking into account a certain season, different observations, etc. Species may be present in one season and overlooked in another (they may be a breeder, resident or migrant). Maps should be created for all cases (seasons), empty if they are absent. Additional data (in addition to field work) can be accessed and used. The Legend map must take into account all options, in addition to presenting the variable as a percentage (number) in a user discrete scale.

By launching the cycle that I feel (in my limited experience), I can easily save and control several necessary objects, at the same time entering the stream that I created to create objects of interest, and, finally, creating sets of view distribution maps.

My problem is that I save every ggplot I get in the list () object. Each species in each season will be stored in a list. The problem I encountered is related to scale_fill_manual when used inside a loop .

The behavior is strange as I get cards, but with colors that apply only to the last output ggplot. However, all values ​​are still correctly identified in the legend.

to illustrate:

Packages

if (!require(ggplot2)) install.packages("ggplot2",
    repos = "http://cran.r-project.org"); library(ggplot2)
if (!require(grid)) install.packages("grid",
    repos = "http://cran.r-project.org"); library(grid)
if (!require(RColorBrewer)) install.packages("RColorBrewer",
    repos = "http://cran.r-project.org"); library(RColorBrewer)
if (!require(reshape)) install.packages("reshape",
    repos = "http://cran.r-project.org"); library(reshape)

Simple example first

#Create a list of colors to be used with scale_manual
palette.l <- list()
palette.l[[1]] <- c('red', 'blue', 'green')
palette.l[[2]] <- c('pink', 'blue', 'yellow')
# Store each ggplot in a list object
plot.l <- list()
#Loop it
for(i in 1:2){
  plot.l[[i]] <- qplot(mpg, wt, data = mtcars, colour = factor(cyl)) +
    scale_colour_manual(values = palette.l[[i]])
}

plot.l [1] .l [2].

ArrangeGraph <- function(..., nrow=NULL, ncol=NULL, as.table=FALSE) {
  dots <- list(...)
  n <- length(dots)
  if(is.null(nrow) & is.null(ncol)) { nrow = floor(n/2) ; ncol = ceiling(n/nrow)}
  if(is.null(nrow)) { nrow = ceiling(n/ncol)}
  if(is.null(ncol)) { ncol = ceiling(n/nrow)}
  ## NOTE see n2mfrow in grDevices for possible alternative
  grid.newpage()
  pushViewport(viewport(layout=grid.layout(nrow,ncol)))
  ii.p <- 1
  for(ii.row in seq(1, nrow)) {
    ii.table.row <- ii.row
    if(as.table) {ii.table.row <- nrow - ii.table.row + 1}
    for(ii.col in seq(1, ncol)) {
      ii.table <- ii.p
      if(ii.p > n) break
      print(dots[[ii.table]], vp=VPortLayout(ii.table.row, ii.col))
      ii.p <- ii.p + 1
    }
  }
}

ViewPort

VPortLayout <- function(x, y) viewport(layout.pos.row=x, layout.pos.col=y)

bd.aves.1 <- structure(list(quad = c("K113", "K114", "K114", "K114", "K114",...
due to limited body character number limit, please download entire code from
https://docs.google.com/open?id=0BxSZDr4eTnb9R09iSndzZjBMS28

list.esp.1 <- c("Sylv mela", "Saxi rube","Ocea leuc")#
# download from the above link

txcon.1 <- structure(list(id = c(156L, 359L, 387L), grupo = c("Aves", "Aves",# 
# download from the above link

kSeason.1 <- c("Inverno", "Primavera", "Outono")

grid500.df.1 <- structure(list(id = c("K113", "K113", "K113", "K113", "K113",#... 
# download from the above link

Shoreline

coastline.df.1 <- structure(list(long = c(182554.963670234, 180518, 178865.39,#...
# download from the above link

kFacx1 <- c(9000, -13000, -10000, -12000)

R

for(i in listsp.1) { # LOOP 1 - Species
  # Set up objects 
  sist.i <- list() # Sistematic observations
  nsist.i <- list() # Non-Sistematic observations
  breaks.nind.1 <- list() # Breaks on abundances
  ## Grid and merged dataframe
  spij.1 <- list() # Stores a dataframe for sp i at season j
  ## Palette build
  classes.1 <- list()
  cllevels.1 <- list()
  palette.nind.1 <- list() # Color palette
  ## Maps
  grid500ij.1 <- list() # Grid for species i at season j
  map.dist.ij.1 <- NULL
  for(j in 1:length(kSeason.1)) { # LOOP 2 - Seasons
    # j assume each season: Inverno, Primavera, Outono
    # Sistematic occurences ===================================================
    sist.i.tmp <- nrow(subset(bd.aves.1, esp == i & cod_tipo %in% sistematica &
      periodo == kSeason.1[j]))
    if (sist.i.tmp!= 0) { # There is sistematic entries, Then:
      sist.i[[j]]<- ddply(subset(bd.aves.1,
                                 esp == i & cod_tipo %in% sistematica & 
                                   periodo == kSeason.1[j]),
                          .(periodo, quad), summarise, nind = sum(n_ind),
                          codnid = max(cod_nidi))
    } else { # No Sistematic entries, Then: 
      sist.i[[j]] <- data.frame('quad' = NA, 'periodo' = NA, 'nind' = NA, 
                                'codnid' = NA, stringsAsFactors = F)
    }
    # Additional Entries (RS1) e other non-sistematic entries  (biblio) =======
    nsist.tmp.i = nrow(subset(bd.aves.1, esp == i & !cod_tipo %in% sistematica &
      periodo == kSeason.1[j]))
    if (nsist.tmp.i != 0) { # RS1 and biblio entries, Then:
      nsist.i[[j]] <- subset(bd.aves.1,
                             esp == i & !cod_tipo %in% sistematica &
                               periodo == kSeason.1[j] & 
                               !quad %in% if (nrow(sist.i[[j]]) != 0) {
                                            subset(sist.i[[j]],
                                                   select = quad)$quad
                                          } else NA,
                             select = c(quad, periodo, cod_tipo, cod_nidi)
                             )
      names(nsist.i[[j]])[4] <- 'codnid'
    } else { # No RS1 and biblio entries, Then:      
        nsist.i[[j]] = data.frame('quad' = NA, 'periodo' = NA, 'cod_tipo' = NA,
                                'codnid' = NA, stringsAsFactors = F)
      }
    # Quantile breaks =========================================================
    if (!is.na(sist.i[[j]]$nind[1])) {
      breaks.nind.1[[j]] <- c(0,
                            unique(
                              ceiling(
                                quantile(unique(
                                  subset(sist.i[[j]], is.na(nind) == F)$nind), 
                                         q = seq(0, 1, by = 0.25)))))
    } else {
        breaks.nind.1[[j]] <- 0
      }
    # =========================================================================
    # Build Species dataframe and merge to grid
    # =========================================================================
    if (!is.na(sist.i[[j]]$nind[1])) { # There are Sistematic entries, Then:
      spij.1[[j]] <- merge(unique(subset(grid500df.1, select = id)),
                         sist.i[[j]],
                         by.x = 'id', by.y = 'quad', all.x = T)
      # Adjust abundances when equals to NA ===================================
      spij.1[[j]]$nind[is.na(spij.1[[j]]$nind) == T] <- 0
      # Break abundances to create discrete variable ==========================
      spij.1[[j]]$cln <- if (length(breaks.nind.1[[j]]) > 2) {
        cut(spij.1[[j]]$nind, breaks = breaks.nind.1[[j]], 
            include.lowest = T, right = F)
        } else {
            cut2(spij.1[[j]]$nind, g = 2)
          }
      # Variable Abundance ====================================================
      classes.1[[j]] = nlevels(spij.1[[j]]$cln)
      cllevels.1[[j]] = levels(spij.1[[j]]$cln)
      # Color Palette for abundances - isolated Zero class (color #FFFFFF) ====
      if (length(breaks.nind.1[[j]]) > 2) {
        palette.nind.1[[paste(kSeason.1[j])]] = c("#FFFFFF", brewer.pal(length(
          cllevels.1[[j]]) - 1, "YlOrRd"))
      } else {
          palette.nind.1[[paste(kSeason.1[j])]] = c(
            "#FFFFFF",  brewer.pal(3, "YlOrRd"))[1:classes.1[[j]]]
        }
        names(palette.nind.1[[paste(kSeason.1[j])]])[1 : length(
          palette.nind.1[[paste(kSeason.1[j])]])] <- cllevels.1[[j]]
      # Add RS1 and bilbio values to palette ==================================
      palette.nind.1[[paste(kSeason.1[j])]][length(
        palette.nind.1[[paste(kSeason.1[j])]]) + 1] <- '#CCC5AF'
      names(palette.nind.1[[paste(kSeason.1[j])]])[length(
        palette.nind.1[[paste(kSeason.1[j])]])] <- 'Suplementar'
      palette.nind.1[[paste(kSeason.1[j])]][length(
        palette.nind.1[[paste(kSeason.1[j])]]) + 1] <- '#ADCCD7'
      names(palette.nind.1[[paste(kSeason.1[j])]])[length(
        palette.nind.1[[paste(kSeason.1[j])]])] <- 'Bibliografia'
      # Merge species i dataframe to grid map =================================
      grid500ij.1[[j]] <- subset(grid500df.1, select = c(id, long, lat, order))
      grid500ij.1[[j]]$cln = merge(grid500ij.1[[j]],
                                 spij.1[[j]],
                                 by.x = 'id', by.y = 'id', all.x = T)$cln
      # Adjust factor levels of cln variable - Non-Sistematic data ============
      levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln), 'Suplementar',
                                      'Bibliografia')
      if (!is.na(nsist.i[[j]]$quad[1])) {
        grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
          nsist.i[[j]], cod_tipo == 'RS1', select = quad)$quad] <- 'Suplementar'
        grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
          nsist.i[[j]], cod_tipo == 'biblio', select = quad)$quad] <- 'Bibliografia'
      }
    } else { # No Sistematic entries, Then:
        if (!is.na(nsist.i[[j]]$quad[1])) { # RS1 or Biblio entries, Then:
          grid500ij.1[[j]] <- grid500df
          grid500ij.1[[j]]$cln <- '0'
          grid500ij.1[[j]]$cln <- factor(grid500ij.1[[j]]$cln)
          levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln),
                                          'Suplementar', 'Bibliografia')
          grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
            nsist.i[[j]], cod_tipo == 'RS1', 
            select = quad)$quad] <- 'Suplementar'
          grid500ij.1[[j]]$cln[grid500ij.1[[j]]$id %in% subset(
            nsist.i[[j]],cod_tipo == 'biblio', 
            select = quad)$quad] <- 'Bibliografia'
        } else { # No entries, Then:
            grid500ij.1[[j]] <- grid500df
            grid500ij.1[[j]]$cln <- '0' 
            grid500ij.1[[j]]$cln <- factor(grid500ij.1[[j]]$cln)
            levels(grid500ij.1[[j]]$cln) <- c(levels(grid500ij.1[[j]]$cln),
                                            'Suplementar', 'Bibliografia')      
          }
      } # End of Species dataframe build
    # Distribution Map for  species i at season j =============================    
    if (!is.na(sist.i[[j]]$nind[1])) { # There is sistematic entries, Then:
      map.dist.ij.1[[paste(kSeason.1[j])]] <- ggplot(grid500ij.1[[j]],
                                                  aes(x = long, y = lat)) +
        geom_polygon(aes(group = id, fill = cln), colour = 'grey80') +
        coord_equal() +
        scale_x_continuous(limits = c(100000, 180000)) +
        scale_y_continuous(limits = c(-4000, 50000)) +
        scale_fill_manual(
          name = paste("LEGEND",
                       '\nSeason: ', kSeason.1[j],
                       '\n% of Occupied Cells : ',
                         sprintf("%.1f%%", (length(unique(
                           grid500ij.1[[j]]$id[grid500ij.1[[j]]$cln != levels(
                           grid500ij.1[[j]]$cln)[1]]))/12)*100), # percent 
                        sep = ""
                       ),
          # Set Limits
          limits = names(palette.nind.1[[j]])[2:length(names(palette.nind.1[[j]]))],
          values = palette.nind.1[[j]][2:length(names(palette.nind.1[[j]]))],
          drop = F) +
          opts(
            panel.background = theme_rect(),
            panel.grid.major = theme_blank(),
            panel.grid.minor = theme_blank(),
            axis.ticks = theme_blank(),
            title = txcon.1$especie[txcon.1$esp == i],
            plot.title = theme_text(size = 10, face = 'italic'),
            axis.text.x = theme_blank(),
            axis.text.y = theme_blank(),
            axis.title.x = theme_blank(),
            axis.title.y = theme_blank(),
            legend.title = theme_text(hjust = 0,size = 10.5),
            legend.text = theme_text(hjust = -0.2, size = 10.5)
          ) +
          # Shoreline
          geom_path(inherit.aes = F, aes(x = long, y = lat),
                    data = coastline.df.1, colour = "#997744") +
          # Add localities
          geom_point(inherit.aes = F, aes(x = x, y = y),  colour = 'grey20',
                     data = localidades, size = 2) +
          # Add labels
          geom_text(inherit.aes = F, aes(x = x, y = y, label = c('Burgau',
                                                                 'Sagres')),
                    colour = "black",
                    data = data.frame(x = c(142817 + kFacx1[1], 127337 + kFacx1[4]),
                                      y = c(11886, 3962), size = 3))
    } else { # NO sistematic entries,then:
        map.dist.ij.1[[paste(kSeason.1[j])]] <- ggplot(grid500ij.1[[j]],
                                                    aes(x = long, y = lat)) +
          geom_polygon(aes.inherit = F, aes(group = id, fill = cln),
                       colour = 'grey80') +
          #scale_color_manual(values = kCorLimiteGrid) +
          coord_equal() +
          scale_x_continuous(limits = c(100000, 40000)) +
          scale_y_continuous(limits = c(-4000, 180000)) +
          scale_fill_manual(
            name = paste('LEGENDA',
                         '\nSeason: ', kSeason.1[j],
                         '\n% of Occupied Cells :',
                         sprintf("%.1f%%", (length(unique(
                           grid500ij.1[[j]]$id[grid500ij.1[[j]]$cln != levels(
                           grid500ij.1[[j]]$cln)[1]]))/12 * 100)), # percent 
                         sep = ''),
            limits = names(kPaletaNsis)[2:length(names(kPaletaNsis))],
            values = kPaletaNsis[2:length(names(kPaletaNsis))],
            drop = F) +
            opts(
              panel.background = theme_rect(),
              panel.grid.major = theme_blank(),
              panel.grid.minor = theme_blank(),
              title = txcon.1$especie[txcon.1$esp == i],
              plot.title = theme_text(size = 10, face = 'italic'),
              axis.ticks = theme_blank(),
              axis.text.x = theme_blank(),
              axis.text.y = theme_blank(),
              axis.title.x = theme_blank(),
              axis.title.y = theme_blank(),
              legend.title = theme_text(hjust = 0,size = 10.5),
              legend.text = theme_text(hjust = -0.2, size = 10.5)
            ) +
            # Add Shoreline
            geom_path(inherit.aes = F, data = coastline.df.1,
                      aes(x = long, y = lat),
                      colour = "#997744") +
            # Add Localities
            geom_point(inherit.aes = F, aes(x = x, y = y),
                       colour = 'grey20',
                       data = localidades, size = 2) +
            # Add labels
            geom_text(inherit.aes = F, aes(x = x, y = y,
                                           label = c('Burgau', 'Sagres')),
                      colour = "black",
                      data = data.frame(x = c(142817 + kFacx1[1],
                                              127337 + kFacx1[4],),
                                        y = c(11886, 3962)),
                      size = 3)
      } # End of Distribution map building for esp i and j seasons
  } # Fim do LOOP 2: j Estacoes
  # Print Maps
  png(file = paste('panel_species',i,'.png', sep = ''), res = 96, 
      width = 800, height = 800)
  ArrangeGraph(map.dist.ij.1[[paste(kSeason.1[3])]],
               map.dist.ij.1[[paste(kSeason.1[2])]],
               map.dist.ij.1[[paste(kSeason.1[1])]],
               ncol = 2, nrow = 2)
  dev.off()
  graphics.off()
} # End of LOOP 1

map.dist.ij.1 [[paste (kSeason.1 [3])]] , , j-.

R-

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