--- a/small_rna_maps.r	Sun Oct 06 21:11:19 2019 -0400
+++ b/small_rna_maps.r	Sun May 09 17:11:00 2021 +0000
@@ -1,6 +1,9 @@
 ## Setup R error handling to go to stderr
-options( show.error.messages=F,
-         error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+options(show.error.messages = F,
+        error = function() {
+            cat(geterrmessage(), file = stderr()); q("no", 1, F)
+        }
+)
 options(warn = -1)
 library(RColorBrewer)
 library(lattice)
@@ -11,165 +14,164 @@
 
 
 option_list <- list(
-  make_option(c("-i", "--ymin"), type="double", help="set min ylimit. e.g. '-100.0'"),
-  make_option(c("-a", "--ymax"), type="double", help="set max ylimit. e.g. '100.0'"),
-  make_option(c("-f", "--first_dataframe"), type="character", help="path to first dataframe"),
-  make_option(c("-e", "--extra_dataframe"), type="character", help="path to additional dataframe"),
-  make_option(c("-n", "--normalization"), type="character", help="space-separated normalization/size factors"),
-  make_option("--first_plot_method", type = "character", help="How additional data should be plotted"),
-  make_option("--extra_plot_method", type = "character", help="How additional data should be plotted"),
-  make_option("--global", type = "character", help="data should be plotted as global size distribution"),
-  make_option("--output_pdf", type = "character", help="path to the pdf file with plots")
+  make_option(c("-i", "--ymin"), type = "double", help = "set min ylimit. e.g. '-100.0'"),
+  make_option(c("-a", "--ymax"), type = "double", help = "set max ylimit. e.g. '100.0'"),
+  make_option(c("-f", "--first_dataframe"), type = "character", help = "path to first dataframe"),
+  make_option(c("-e", "--extra_dataframe"), type = "character", help = "path to additional dataframe"),
+  make_option(c("-n", "--normalization"), type = "character", help = "space-separated normalization/size factors"),
+  make_option("--first_plot_method", type = "character", help = "How additional data should be plotted"),
+  make_option("--extra_plot_method", type = "character", help = "How additional data should be plotted"),
+  make_option("--global", type = "character", help = "data should be plotted as global size distribution"),
+  make_option("--output_pdf", type = "character", help = "path to the pdf file with plots")
 )
 
 parser <- OptionParser(usage = "%prog [options] file", option_list = option_list)
-args = parse_args(parser)
+args <- parse_args(parser)
 
 # data frames implementation
 
 ## first table
-Table = read.delim(args$first_dataframe, header=T, row.names=NULL)
-colnames(Table)[1] <- "Dataset"
+table <- read.delim(args$first_dataframe, header = T, row.names = NULL)
+colnames(table)[1] <- "Dataset"
 dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility
-Table <- Table[,!(names(Table) %in% dropcol)]
+table <- table[, !(names(table) %in% dropcol)]
 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size") {
-  Table <- within(Table, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
+  table <- within(table, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * - 1))
 }
-n_samples=length(unique(Table$Dataset))
-samples = unique(Table$Dataset)
+n_samples <- length(unique(table$Dataset))
+samples <- unique(table$Dataset)
 if (args$normalization != "") {
-  norm_factors = as.numeric(unlist(strsplit(args$normalization, " ")))
+  norm_factors <- as.numeric(unlist(strsplit(args$normalization, " ")))
 } else {
-  norm_factors = rep(1, n_samples)
+  norm_factors <- rep(1, n_samples)
 }
 if (args$first_plot_method == "Counts" | args$first_plot_method == "Size" | args$first_plot_method == "Coverage") {
-  i = 1
+  i <- 1
   for (sample in samples) {
-    # Warning
-    # Here the column is hard coded as the last column (dangerous)
+    # Warning Here the column is hard coded as the last column (dangerous)
     # because its name changes with the method
-    Table[, length(Table)][Table$Dataset==sample] <- Table[, length(Table)][Table$Dataset==sample]*norm_factors[i]
-    i = i + 1
+    table[, length(table)][table$Dataset == sample] <- table[, length(table)][table$Dataset == sample] * norm_factors[i]
+    i <- i + 1
   }
 }
-genes=unique(Table$Chromosome)
-per_gene_readmap=lapply(genes, function(x) subset(Table, Chromosome==x))
-per_gene_limit=lapply(genes, function(x) c(1, unique(subset(Table, Chromosome==x)$Chrom_length)) )
-n_genes=length(per_gene_readmap)
+genes <- unique(table$Chromosome)
+per_gene_readmap <- lapply(genes, function(x) subset(table, Chromosome == x))
+per_gene_limit <- lapply(genes, function(x) c(1, unique(subset(table, Chromosome == x)$Chrom_length)))
+n_genes <- length(per_gene_readmap)
 
 # second table
-if (args$extra_plot_method != '') {
-  ExtraTable=read.delim(args$extra_dataframe, header=T, row.names=NULL)
-  colnames(ExtraTable)[1] <- "Dataset"
-  dropcol <- c("Strandness", "z.score") # not used by this Rscript and is dropped for backward compatibility
-  Table <- Table[,!(names(Table) %in% dropcol)]
-  if (args$extra_plot_method == "Counts" | args$extra_plot_method=='Size') {
-    ExtraTable <- within(ExtraTable, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
+if (args$extra_plot_method != "") {
+  extra_table <- read.delim(args$extra_dataframe, header = T, row.names = NULL)
+  colnames(extra_table)[1] <- "Dataset"
+  dropcol <- c("Strandness", "z.score")
+  table <- table[, !(names(table) %in% dropcol)]
+  if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size") {
+    extra_table <- within(extra_table, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * -1))
   }
   if (args$extra_plot_method == "Counts" | args$extra_plot_method == "Size" | args$extra_plot_method == "Coverage") {
-    i = 1
+    i <- 1
     for (sample in samples) {
-      ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample] <- ExtraTable[, length(ExtraTable)][ExtraTable$Dataset==sample]*norm_factors[i]
-      i = i + 1
+      extra_table[, length(extra_table)][extra_table$Dataset == sample] <- extra_table[, length(extra_table)][extra_table$Dataset == sample] * norm_factors[i]
+      i <- i + 1
     }
   }
-  per_gene_size=lapply(genes, function(x) subset(ExtraTable, Chromosome==x))
+  per_gene_size <- lapply(genes, function(x) subset(extra_table, Chromosome == x))
 }
 
 ## functions
-globalbc = function(df, global="", ...) {
+globalbc <- function(df, global = "", ...) {
   if (global == "yes") {
-    bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)),
+    bc <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)),
                    data = df, origin = 0,
-                   horizontal=FALSE,
-                   col=c("darkblue"),
-                   scales=list(y=list(tick.number=4, rot=90, relation="same", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
-                   xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
-                   ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
-                   main=title_first_method[[args$first_plot_method]],
-                   layout = c(2, 6), newpage=T,
-                   as.table=TRUE,
-                   aspect=0.5,
-                   strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"),
+                   horizontal = FALSE,
+                   col = c("darkblue"),
+                   scales = list(y = list(tick.number = 4, rot = 90, relation = "same", cex = 0.5, alternating = T), x = list(rot = 0, cex = 0.6, tck = 0.5, alternating = c(3, 3))),
+                   xlab = list(label = bottom_first_method[[args$first_plot_method]], cex = .85),
+                   ylab = list(label = legend_first_method[[args$first_plot_method]], cex = .85),
+                   main = title_first_method[[args$first_plot_method]],
+                   layout = c(2, 6), newpage = T,
+                   as.table = TRUE,
+                   aspect = 0.5,
+                   strip = strip.custom(par.strip.text = list(cex = 1), which.given = 1, bg = "lightblue"),
                    ...
     )
   } else {
-    bc <- barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset)),
+    bc <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)),
                    data = df, origin = 0,
-                   horizontal=FALSE,
-                   group=Polarity,
-                   stack=TRUE,
-                   col=c('red', 'blue'),
-                   scales=list(y=list(tick.number=4, rot=90, relation="same", cex=0.5, alternating=T), x=list(rot=0, cex=0.6, tck=0.5, alternating=c(3,3))),
-                   xlab=list(label=bottom_first_method[[args$first_plot_method]], cex=.85),
-                   ylab=list(label=legend_first_method[[args$first_plot_method]], cex=.85),
-                   main=title_first_method[[args$first_plot_method]],
-                   layout = c(2, 6), newpage=T,
-                   as.table=TRUE,
-                   aspect=0.5,
-                   strip = strip.custom(par.strip.text = list(cex = 1), which.given=1, bg="lightblue"),
+                   horizontal = FALSE,
+                   group = Polarity,
+                   stack = TRUE,
+                   col = c("red", "blue"),
+                   scales = list(y = list(tick.number = 4, rot = 90, relation = "same", cex = 0.5, alternating = T), x = list(rot = 0, cex = 0.6, tck = 0.5, alternating = c(3, 3))),
+                   xlab = list(label = bottom_first_method[[args$first_plot_method]], cex = .85),
+                   ylab = list(label = legend_first_method[[args$first_plot_method]], cex = .85),
+                   main = title_first_method[[args$first_plot_method]],
+                   layout = c(2, 6), newpage = T,
+                   as.table = TRUE,
+                   aspect = 0.5,
+                   strip = strip.custom(par.strip.text = list(cex = 1), which.given = 1, bg = "lightblue"),
                    ...
     )
   }
   return(bc)
 }
-plot_unit = function(df, method=args$first_plot_method, ...) {
-  if (exists('ymin', where=args)){
-    min=args$ymin
-  }else{
-    min=''
+plot_unit <- function(df, method = args$first_plot_method, ...) {
+  if (exists("ymin", where = args)) {
+    min <- args$ymin
+  } else {
+    min <- ""
   }
-  if ((exists('ymax', where=args))){
-    max=args$ymax
-  }else{
-    max=''
+  if ((exists("ymax", where = args))) {
+    max <- args$ymax
+  } else {
+    max <- ""
   }
-  ylimits=c(min,max)
-  if (method == 'Counts') {
-    p = xyplot(Counts~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
-               data=df,
-               type='h',
-               lwd=1.5,
-               scales= list(relation="free", x=list(rot=0, cex=0.7, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)),
-               xlab=NULL, main=NULL, ylab=NULL, ylim=ylimits,
-               as.table=T,
+  ylimits <- c(min, max)
+  if (method == "Counts") {
+    p <- xyplot(Counts ~ Coordinate | factor(Dataset, levels = unique(Dataset)) + factor(Chromosome, levels = unique(Chromosome)),
+               data = df,
+               type = "h",
+               lwd = 1.5,
+               scales = list(relation = "free", x = list(rot = 0, cex = 0.7, axs = "i", tck = 0.5), y = list(tick.number = 4, rot = 90, cex = 0.7)),
+               xlab = NULL, main = NULL, ylab = NULL, ylim = ylimits,
+               as.table = T,
                origin = 0,
-               horizontal=FALSE,
-               group=Polarity,
-               col=c("red","blue"),
-               par.strip.text = list(cex=0.7),
+               horizontal = FALSE,
+               group = Polarity,
+               col = c("red", "blue"),
+               par.strip.text = list(cex = 0.7),
                ...)
-    p=combineLimits(p)
+    p <- combineLimits(p)
   } else if (method != "Size") {
-    p = xyplot(eval(as.name(method))~Coordinate|factor(Dataset, levels=unique(Dataset))+factor(Chromosome, levels=unique(Chromosome)),
-               data=df,
-               type= ifelse(method=='Coverage', 'l', 'p'),
-               pch=19,
-               cex=0.35,
-               scales= list(relation="free", x=list(rot=0, cex=0.7, axs="i", tck=0.5), y=list(tick.number=4, rot=90, cex=0.7)),
-               xlab=NULL, main=NULL, ylab=NULL, ylim=ylimits,
-               as.table=T,
+    p <- xyplot(eval(as.name(method)) ~ Coordinate | factor(Dataset, levels = unique(Dataset)) + factor(Chromosome, levels = unique(Chromosome)),
+               data = df,
+               type = ifelse(method == "Coverage", "l", "p"),
+               pch = 19,
+               cex = 0.35,
+               scales = list(relation = "free", x = list(rot = 0, cex = 0.7, axs = "i", tck = 0.5), y = list(tick.number = 4, rot = 90, cex = 0.7)),
+               xlab = NULL, main = NULL, ylab = NULL, ylim = ylimits,
+               as.table = T,
                origin = 0,
-               horizontal=FALSE,
-               group=Polarity,
-               col=c("red","blue"),
-               par.strip.text = list(cex=0.7),
+               horizontal = FALSE,
+               group = Polarity,
+               col = c("red", "blue"),
+               par.strip.text = list(cex = 0.7),
                ...)
-    p=combineLimits(p)
+    p <- combineLimits(p)
   } else {
-    p = barchart(Counts~as.factor(Size)|factor(Dataset, levels=unique(Dataset))+Chromosome, data = df, origin = 0,
-                 horizontal=FALSE,
-                 group=Polarity,
-                 stack=TRUE,
-                 col=c('red', 'blue'),
-                 scales=list(y=list(rot=90, relation="free", cex=0.7), x=list(rot=0, cex=0.7, axs="i", tck=c(1,0))),
+    p <- barchart(Counts ~ as.factor(Size) | factor(Dataset, levels = unique(Dataset)) + Chromosome, data = df, origin = 0,
+                 horizontal = FALSE,
+                 group = Polarity,
+                 stack = TRUE,
+                 col = c("red", "blue"),
+                 scales = list(y = list(rot = 90, relation = "free", cex = 0.7), x = list(rot = 0, cex = 0.7, axs = "i", tck = c(1, 0))),
                  xlab = NULL,
                  ylab = NULL,
                  main = NULL,
-                 as.table=TRUE,
-                 par.strip.text = list(cex=0.6),
+                 as.table = TRUE,
+                 par.strip.text = list(cex = 0.6),
                  ...)
-    p=combineLimits(p)
+    p <- combineLimits(p)
   }
   return(p)
 }
@@ -177,88 +179,95 @@
 
 ## function parameters
 
-#par.settings.firstplot = list(layout.heights=list(top.padding=11, bottom.padding = -14))
-#par.settings.secondplot=list(layout.heights=list(top.padding=11, bottom.padding = -15), strip.background=list(col=c("lavender","deepskyblue")))
-par.settings.firstplot = list(layout.heights=list(top.padding=-2, bottom.padding=-2),strip.background=list(col=c("lightblue","lightgreen")))
-par.settings.secondplot=list(layout.heights=list(top.padding=-1, bottom.padding=-1),strip.background=list(col=c("lightblue","lightgreen")))
-title_first_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
-title_extra_method = list(Counts="Read Counts", Coverage="Coverage depths", Median="Median sizes", Mean="Mean sizes", Size="Size Distributions")
-legend_first_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count")
-legend_extra_method =list(Counts="Read count", Coverage="Coverage depth", Median="Median size", Mean="Mean size", Size="Read count")
-bottom_first_method =list(Counts="Coordinates (nucleotides)",Coverage="Coordinates (nucleotides)", Median="Coordinates (nucleotides)", Mean="Coordinates (nucleotides)", Size="Sizes of reads")
-bottom_extra_method =list(Counts="Coordinates (nucleotides)",Coverage="Coordinates (nucleotides)", Median="Coordinates (nucleotides)", Mean="Coordinates (nucleotides)", Size="Sizes of reads")
+par_settings_firstplot <- list(layout.heights = list(top.padding = -2, bottom.padding = -2), strip.background = list(col = c("lightblue", "lightgreen")))
+par_settings_secondplot <- list(layout.heights = list(top.padding = -1, bottom.padding = -1), strip.background = list(col = c("lightblue", "lightgreen")))
+title_first_method <- list(Counts = "Read Counts", Coverage = "Coverage depths", Median = "Median sizes", Mean = "Mean sizes", Size = "Size Distributions")
+title_extra_method <- list(Counts = "Read Counts", Coverage = "Coverage depths", Median = "Median sizes", Mean = "Mean sizes", Size = "Size Distributions")
+legend_first_method <- list(Counts = "Read count", Coverage = "Coverage depth", Median = "Median size", Mean = "Mean size", Size = "Read count")
+legend_extra_method <- list(Counts = "Read count", Coverage = "Coverage depth", Median = "Median size", Mean = "Mean size", Size = "Read count")
+bottom_first_method <- list(Counts = "Coordinates (nucleotides)", Coverage = "Coordinates (nucleotides)", Median = "Coordinates (nucleotides)", Mean = "Coordinates (nucleotides)", Size = "Sizes of reads")
+bottom_extra_method <- list(Counts = "Coordinates (nucleotides)", Coverage = "Coordinates (nucleotides)", Median = "Coordinates (nucleotides)", Mean = "Coordinates (nucleotides)", Size = "Sizes of reads")
 
 ## Plotting Functions
 
 double_plot <- function(...) {
-  page_height = 15
-  rows_per_page = 10
-  graph_heights=c(40,30,40,30,40,30,40,30,40,30,10)
-  page_width=8.2677 * n_samples / 2
-  pdf(file=args$output_pdf, paper="special", height=page_height, width=page_width)
-  for (i in seq(1,n_genes,rows_per_page/2)) {
-    start=i
-    end=i+rows_per_page/2-1
-    if (end>n_genes) {end=n_genes}
-    if (end-start+1 < 5) {graph_heights=c(rep(c(40,30),end-start+1),10,rep(c(40,30),5-(end-start+1)))}
-    first_plot.list = lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings=par.settings.secondplot), strip.left=strip.custom(par.strip.text = list(cex=0.5)))))
-    second_plot.list = lapply(per_gene_size[start:end], function(x) update(useOuterStrips(plot_unit(x, method=args$extra_plot_method, par.settings=par.settings.firstplot), strip.left=strip.custom(par.strip.text = list(cex=0.5)), strip=FALSE)))
-    plot.list=rbind(first_plot.list, second_plot.list)
-    args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(graph_heights, rep("mm", 11)),
-                                 top=textGrob(paste(title_first_method[[args$first_plot_method]], "and", title_extra_method[[args$extra_plot_method]]), gp=gpar(cex=1), vjust=0, just="top"),
-                                 left=textGrob(paste(legend_first_method[[args$first_plot_method]], "/", legend_extra_method[[args$extra_plot_method]]), gp=gpar(cex=1), vjust=0, hjust=0, x=1, y=(-0.38/4)*(end-start-(3.28/0.38)), rot=90),
-                                 sub=textGrob(paste(bottom_first_method[[args$first_plot_method]], "/", bottom_extra_method[[args$extra_plot_method]]), gp=gpar(cex=1), just="bottom", vjust=2)
+  page_height <- 15
+  rows_per_page <- 10
+  graph_heights <- c(40, 30, 40, 30, 40, 30, 40, 30, 40, 30, 10)
+  page_width <- 8.2677 * n_samples / 2
+  pdf(file = args$output_pdf, paper = "special", height = page_height, width = page_width)
+  for (i in seq(1, n_genes, rows_per_page / 2)) {
+    start <- i
+    end <- i + rows_per_page / 2 - 1
+    if (end > n_genes) {
+      end <- n_genes
+    }
+    if (end - start + 1 < 5) {
+      graph_heights <- c(rep(c(40, 30), end - start + 1), 10, rep(c(40, 30), 5 - (end - start + 1)))
+    }
+    first_plot_list <- lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings = par_settings_secondplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)))))
+    second_plot_list <- lapply(per_gene_size[start:end], function(x) update(useOuterStrips(plot_unit(x, method = args$extra_plot_method, par.settings = par_settings_firstplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)), strip = FALSE)))
+    plot.list <- rbind(first_plot_list, second_plot_list)
+    args_list <- c(plot.list, list(nrow = rows_per_page + 1, ncol = 1, heights = unit(graph_heights, rep("mm", 11)),
+                                 top = textGrob(paste(title_first_method[[args$first_plot_method]], "and", title_extra_method[[args$extra_plot_method]]), gp = gpar(cex = 1), vjust = 0, just = "top"),
+                                 left = textGrob(paste(legend_first_method[[args$first_plot_method]], "/", legend_extra_method[[args$extra_plot_method]]), gp = gpar(cex = 1), vjust = 0, hjust = 0, x = 1, y = (-0.38 / 4) * (end - start - (3.28 / 0.38)), rot = 90),
+                                 sub = textGrob(paste(bottom_first_method[[args$first_plot_method]], "/", bottom_extra_method[[args$extra_plot_method]]), gp = gpar(cex = 1), just = "bottom", vjust = 2)
     )
     )
     do.call(grid.arrange, args_list)
   }
-  devname=dev.off()
+  devname <- dev.off()
 }
 
 
 single_plot <- function(...) {
-  width = 8.2677 * n_samples / 2
-  rows_per_page=8
-  graph_heights=c(rep(40,8),10)
-  pdf(file=args$output_pdf, paper="special", height=15, width=width)
-  for (i in seq(1,n_genes,rows_per_page)) {
-    start=i
-    end=i+rows_per_page-1
-    if (end>n_genes) {end=n_genes}
-    if (end-start+1 < 8) {graph_heights=c(rep(c(40),end-start+1),10,rep(c(40),8-(end-start+1)))}
-    first_plot.list = lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings=par.settings.firstplot),strip.left=strip.custom(par.strip.text = list(cex=0.5)))))
-    plot.list=rbind(first_plot.list)
-    args_list=c(plot.list, list( nrow=rows_per_page+1, ncol=1, heights=unit(graph_heights, rep("mm", 9)),
-                                 top=textGrob(title_first_method[[args$first_plot_method]], gp=gpar(cex=1), vjust=0, just="top"),
-                                 left=textGrob(legend_first_method[[args$first_plot_method]], gp=gpar(cex=1), vjust=0, hjust=0, x=1, y=(-0.41/7)*(end-start-(6.23/0.41)), rot=90),
-                                 sub=textGrob(bottom_first_method[[args$first_plot_method]], gp=gpar(cex=1), just="bottom", vjust=2)
+  width <- 8.2677 * n_samples / 2
+  rows_per_page <- 8
+  graph_heights <- c(rep(40, 8), 10)
+  pdf(file = args$output_pdf, paper = "special", height = 15, width = width)
+  for (i in seq(1, n_genes, rows_per_page)) {
+    start <- i
+    end <- i + rows_per_page - 1
+    if (end > n_genes) {
+      end <- n_genes
+      }
+    if (end - start + 1 < 8) {
+      graph_heights <- c(rep(c(40), end - start + 1), 10, rep(c(40), 8 - (end - start + 1)))
+    }
+    first_plot_list <- lapply(per_gene_readmap[start:end], function(x) update(useOuterStrips(plot_unit(x, par.settings = par_settings_firstplot), strip.left = strip.custom(par.strip.text = list(cex = 0.5)))))
+    plot.list <- rbind(first_plot_list)
+    args_list <- c(plot.list, list(nrow = rows_per_page + 1, ncol = 1, heights = unit(graph_heights, rep("mm", 9)),
+                                 top = textGrob(title_first_method[[args$first_plot_method]], gp = gpar(cex = 1), vjust = 0, just = "top"),
+                                 left = textGrob(legend_first_method[[args$first_plot_method]], gp = gpar(cex = 1), vjust = 0, hjust = 0, x = 1, y = (-0.41 / 7) * (end - start - (6.23 / 0.41)), rot = 90),
+                                 sub = textGrob(bottom_first_method[[args$first_plot_method]], gp = gpar(cex = 1), just = "bottom", vjust = 2)
     )
     )
     do.call(grid.arrange, args_list)
   }
-  devname=dev.off()
+  devname <- dev.off()
 }
 
 # main
 
-if (args$extra_plot_method != '') { double_plot() }
-if (args$extra_plot_method == '' & !exists('global', where=args)) {
+if (args$extra_plot_method != "") {
+  double_plot()
+}
+if (args$extra_plot_method == "" & !exists("global", where = args)) {
   single_plot()
 }
-if (exists('global', where=args)) {
-  pdf(file=args$output, paper="special", height=11.69)
-  Table <- within(Table, Counts[Polarity=="R"] <- abs(Counts[Polarity=="R"])) # retropedalage
+if (exists("global", where = args)) {
+  pdf(file = args$output, paper = "special", height = 11.69)
+  table <- within(table, Counts[Polarity == "R"] <- abs(Counts[Polarity == "R"]))
   library(reshape2)
-  ml = melt(Table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size"))
+  ml <- melt(table, id.vars = c("Dataset", "Chromosome", "Polarity", "Size"))
   if (args$global == "nomerge") {
-    castml = dcast(ml, Dataset+Polarity+Size ~ variable, function(x) sum(x))
-    castml <- within(castml, Counts[Polarity=="R"] <- (Counts[Polarity=="R"]*-1))
-    bc = globalbc(castml, global="no")
+    castml <- dcast(ml, Dataset + Polarity + Size ~ variable, function(x) sum(x))
+    castml <- within(castml, Counts[Polarity == "R"] <- (Counts[Polarity == "R"] * -1))
+    bc <- globalbc(castml, global = "no")
   } else {
-    castml = dcast(ml, Dataset+Size ~ variable, function(x) sum(x))
-    bc = globalbc(castml, global="yes")
+    castml <- dcast(ml, Dataset + Size ~ variable, function(x) sum(x))
+    bc <- globalbc(castml, global = "yes")
   }
   plot(bc)
-  devname=dev.off()
+  devname <- dev.off()
 }
-