Update R code after running 'lintr::lint()' which detects style, synt…

…ax and semantic issues

R/PCAdataplot.R

@@ -1,83 +1,69 @@
-# Plot of trend repartition per group of items, 
-# (e.g. from biological annotation), 
-# and optionally per molecular level 
+# Plot of trend repartition per group of items,
+# (e.g. from biological annotation),
+# and optionally per molecular level
 # (or per another additional grouping level )
-PCAdataplot <- function(omicdata, batch, label)
-{
-  if (!(inherits(omicdata, "microarraydata") | 
-        inherits(omicdata, "RNAseqdata") |
+PCAdataplot <- function(omicdata, batch, label) {
+  if (!(inherits(omicdata, "microarraydata") ||
+        inherits(omicdata, "RNAseqdata") ||
         inherits(omicdata, "continuousomicdata")))
     stop("Use only with 'microarraydata', 'RNAseqdata' or 'continuousomicdata' 
     objects, respectively created with functions 'microarraydata()', 'RNAseqdata()' 
     or 'continuousomicdata()'.")
-
+  
   dosef <- as.factor(omicdata$dose)
-  if (!missing(batch)) 
-  {
+  if (!missing(batch)) {
     if (length(batch) != length(dosef))
       stop("Argument batch must be a factor of length the number of samples in
            your omic data set.")
-    datacondition <- data.frame(batch = as.factor(batch), 
-                                dose = dosef)
-
-   } else
-   {
-     datacondition <- data.frame(dose = dosef)
-
-   }
+    datacondition <- data.frame(batch = as.factor(batch), dose = dosef)
+  } else {
+    datacondition <- data.frame(dose = dosef)
+  }
 
-  pseudologdata <- as.data.frame(omicdata$data) # data after log 2 scale 
-  if (missing(label))
-  {
+  pseudologdata <- as.data.frame(omicdata$data) # data after log 2 scale
+  if (missing(label)) {
     label <- FALSE
-  } 
-  if (length(label) > 1) 
-  {
+  }
+  if (length(label) > 1) {
     add.label <- TRUE
-    if (length(label) != length(dosef))
-    {
+    if (length(label) != length(dosef)) {
       stop("Argument label must be TRUE, FALSE of a character vector 
        giving the name the samples, so of length 
             the number of samples in your omic data set.")
-    } else
-    {
+    } else {
       colnames(pseudologdata) <- as.character(label)
-
     }
-  } else
-  {
-    if (label) {add.label <- TRUE} else {add.label <- FALSE}
+  } else {
+    if (label) {
+      add.label <- TRUE
+    } else {
+      add.label <- FALSE
+    }
   }
 
   # colnames(pseudologdata) <- 1:ncol(pseudologdata)
   # prcomp{stats}
   # autoplot du package ggfortify qui est un ajout de ggplot2
   # https://cran.r-project.org/web/packages/ggfortify/vignettes/plot_pca.html
   pca.info <- stats::prcomp(t(pseudologdata), scale. = FALSE)
-  if (add.label)
-  {
-    pca.plot <- autoplot(pca.info, 
-                         data = datacondition, 
+  if (add.label) {
+    pca.plot <- autoplot(pca.info,
+                         data = datacondition,
                          colour = "dose",
                          shape = FALSE,
                          label = TRUE)
-
-  } else
-  {
-    if (missing(batch))
-    {
-      pca.plot <- autoplot(pca.info, 
-                           data = datacondition, 
+  } else {
+    if (missing(batch)) {
+      pca.plot <- autoplot(pca.info,
+                           data = datacondition,
                            colour = "dose")
 
-    } else
-    {
-      pca.plot <- autoplot(pca.info, 
-                           data = datacondition, 
-                           shape = "batch", 
+    } else {
+      pca.plot <- autoplot(pca.info,
+                           data = datacondition,
+                           shape = "batch",
                            colour = "dose")
     }
-
   }
   return(pca.plot)
 }

R/RNAseqdata.R

@@ -1,16 +1,13 @@
 ### import, check normalize and transform RNAseq data
 
-RNAseqdata <- function(file, backgrounddose, check = TRUE, 
-                       transfo.method, 
-                       transfo.blind = TRUE, round.counts = FALSE)
-{
-  if (is.data.frame(file))
-  {
+RNAseqdata <- function(file, backgrounddose, check = TRUE,
+                       transfo.method,
+                       transfo.blind = TRUE, round.counts = FALSE) {
+
+  if (is.data.frame(file)) {
     d <- file
-  } else
-  {
-    if (check)
-    {
+  } else {
+    if (check) {
       # check argument file
       if (!is.character(file))
         stop("The argument file must be a character string.")
@@ -20,109 +17,93 @@ RNAseqdata <- function(file, backgrounddose, check = TRUE,
         stop("The argument file must be a character string ending by .txt.")
     }
     d <- utils::read.table(file, header = FALSE)
-    colnames(d) <- c("item", paste0("S", 1:(ncol(d)-1)))
-
+    colnames(d) <- c("item", paste0("S", 1:(ncol(d) - 1)))
   }
+
   nrowd <- nrow(d)
   ncold <- ncol(d)
-  data <- as.matrix(d[2:nrowd, 2:ncold]) 
+  data <- as.matrix(d[2:nrowd, 2:ncold])
   nrowdata <- nrowd - 1
   ncoldata <- ncold - 1
 
-  if(!all(stats::complete.cases(data)))
+  if (!all(stats::complete.cases(data)))
     stop("RNAseqdata() should not be used with data including NA values.")
 
-  if (round.counts)
-  {
+  if (round.counts) {
     data <- round(data)
-  } else
-  {
+  } else {
     subdata4check <- as.numeric(data[1:min(nrowdata, 10), ])
     subdata4checkT <- trunc(subdata4check)
     if (!identical(subdata4check, subdata4checkT))
       stop("Your data contain non integer values. Make sure that your RNAseq data are imported in raw counts.
-      If your counts come from Kallisto or Salmon put the argument round.counts of RNAseqdata at TRUE to round them.\n") 
+      If your counts come from Kallisto or Salmon put the argument round.counts of RNAseqdata at TRUE to round them.\n")
   }
 
-  if (check)
-  {
+  if (check) {
     if (nrowdata < 100)
       warning(strwrap(prefix = "\n", initial = "\n",
                       "Your dataset contains less than 100 lines. Are you sure you really
       work on RNAseq data ? This function should
       not be used with another type of data."))
 
     # check that doses and responses are numeric
-    if (!is.numeric(as.matrix(d[,2:ncold])))
+    if (!is.numeric(as.matrix(d[, 2:ncold])))
       stop("All the columns except the first one must be numeric with the numeric dose in the firt line 
       and the read counts (integer values corresponding to raw counts) of each item in the other lines.")
   }
 
   # Normalization and count data transformation using DESeq2
-  if (missing(transfo.method))
-  {
-    if (ncoldata > 30) {transfo.method <- "vst"} else {transfo.method <- "rlog"}
-  } else
-  {
+  if (missing(transfo.method)) {
+    if (ncoldata > 30) {
+      transfo.method <- "vst"
+    } else {
+      transfo.method <- "rlog"
+    }
+  } else {
     transfo.method <- match.arg(transfo.method, c("rlog", "vst"))
   }
-  if(transfo.method == "rlog")
+  if (transfo.method == "rlog")
     cat(strwrap(paste0("Just wait, the transformation using regularized logarithm (rlog) may take a few minutes.\n")), fill = TRUE)
 
   raw.counts <- data
   (dose <- as.vector(unlist(d[1, 2:ncold])))
 
-  if(!transfo.blind)
-  {
+  if (!transfo.blind) {
     coldata <- data.frame(fdose = fdose)
     rownames(coldata) <- colnames(data)
     dds <- DESeqDataSetFromMatrix(
       countData = raw.counts,
       colData = coldata,
       design = ~ fdose)
   }
-
-  if (transfo.method == "rlog")
-  {
-    if (transfo.blind) 
-    {
+
+  if (transfo.method == "rlog") {
+    if (transfo.blind) {
       data <- rlog(raw.counts)
-    } else
-    {
-        data <- assay(rlog(dds, blind = FALSE))  
+    } else {
+      data <- assay(rlog(dds, blind = FALSE))
     }
-  } else # transfo.method == "vst"
-  {
+  } else { # transfo.method == "vst"
     nsub <- 1000 # parameter of vst to speed computation
-    if (transfo.blind) 
-    {
-      if (nrowdata < nsub)
-      {
+    if (transfo.blind) {
+      if (nrowdata < nsub) {
         data <- varianceStabilizingTransformation(raw.counts)
-      } else
-      {
+      } else {
         tryvst <- try(vst(raw.counts, nsub = nsub), silent = TRUE)
-        if (!inherits(tryvst, "try-error"))
-        {
+        if (!inherits(tryvst, "try-error")) {
           data <- tryvst
-        } else
-        {
+        } else {
           data <- varianceStabilizingTransformation(raw.counts)
         }
       }
-    } else # VST is not blind to the experimental design
-    {
-      if (nrowdata < nsub)
-      {
+    } else { # VST is not blind to the experimental design
+      if (nrowdata < nsub) {
         data <- assay(varianceStabilizingTransformation(dds, blind = FALSE))
-      } else
-      {
+      } else {
         tryvst <- try(vst(dds, nsub = nsub, blind = FALSE), silent = TRUE)
-        if (!inherits(tryvst, "try-error"))
-        {
+        if (!inherits(tryvst, "try-error")) {
           data <- assay(tryvst)
-        } else
-        {
+        } else {
           data <- assay(varianceStabilizingTransformation(dds, blind = FALSE))
         }
       }
@@ -133,8 +114,7 @@ RNAseqdata <- function(file, backgrounddose, check = TRUE,
   row.names(data) <- item <- as.character(d[2:nrowd, 1])
   (nitems <- nrow(data))
 
-  if (!missing(backgrounddose))
-  {
+  if (!missing(backgrounddose)) {
     dose <- dose * (dose > backgrounddose)
   }
 
@@ -150,7 +130,7 @@ RNAseqdata <- function(file, backgrounddose, check = TRUE,
   design <- table(dose, dnn = "")
   nbdoses <- length(design)
   nbpts <- sum(design)
-  if ((nbdoses < 4)| (nbpts < 8))
+  if ((nbdoses < 4) || (nbpts < 8))
     stop("Dromics cannot be used with a dose-response design 
     with less than four tested doses/concentrations or less than eight data points
          per dose-response curve.")
@@ -162,32 +142,29 @@ RNAseqdata <- function(file, backgrounddose, check = TRUE,
   # calculation of the means per dose
   fdose <- as.factor(dose)
   tdata <- t(data)
-  calcmean <- function(i)
-  {
+  calcmean <- function(i) {
     tapply(tdata[, i], fdose, mean)
   }
   s <- sapply(1:(nrowd - 1), calcmean)
   data.mean <- as.matrix(t(s))
-
-  calcsd <- function(i)
-  {
+
+  calcsd <- function(i) {
     tapply(tdata[, i], fdose, sd)
   }
   s <- sapply(1:(nrowd - 1), calcsd)
   data.sd <- as.matrix(t(s))
 
-  reslist <- list(data = data, dose = dose, item = item, 
-                  design = design, data.mean = data.mean, 
+  reslist <- list(data = data, dose = dose, item = item,
+                  design = design, data.mean = data.mean,
                   data.sd = data.sd,
                   transfo.method = transfo.method, raw.counts = raw.counts,
-                  containsNA = FALSE)  
+                  containsNA = FALSE)
 
   return(structure(reslist, class = "RNAseqdata"))
 }
 
 
-print.RNAseqdata <- function(x, ...)
-{
+print.RNAseqdata <- function(x, ...) {
   if (!inherits(x, "RNAseqdata"))
     stop("Use only with 'RNAseqdata' objects.")
 
@@ -196,35 +173,32 @@ print.RNAseqdata <- function(x, ...)
   print(x$design)
   cat("Number of items:", length(x$item), "\n")
 
-  if (length(x$item) > 20)
-  {
+  if (length(x$item) > 20) {
     cat("Identifiers of the first 20 items:\n")
     print(x$item[1:20])
-  } else
-  {
+  } else {
     cat("Identifiers of the items:\n")
     print(x$item)
   }
   cat(strwrap(paste0("Data were normalized with respect to library size 
                      and tranformed using the following method: ", x$transfo.method)), fill = TRUE)
 }
 
-plot.RNAseqdata <- function(x, range4boxplot = 0, ...) 
-{
+plot.RNAseqdata <- function(x, range4boxplot = 0, ...) {
   if (!inherits(x, "RNAseqdata"))
     stop("Use only with 'RNAseqdata' objects.")
 
   def.par <- graphics::par(no.readonly = TRUE)
   ymin.rc <- min(x$raw.counts)
   ymax.rc <- max(x$raw.counts)
-  graphics::par(mfrow = c(1,2), xaxt = "n")
-  graphics::boxplot(x$raw.counts, xlab = "Samples", ylab = "Raw counts", 
-          main = "Raw data", 
-          ylim = c(ymin.rc, ymax.rc), range = range4boxplot, ...) 
+  graphics::par(mfrow = c(1, 2), xaxt = "n")
+  graphics::boxplot(x$raw.counts, xlab = "Samples", ylab = "Raw counts",
+                    main = "Raw data",
+                    ylim = c(ymin.rc, ymax.rc), range = range4boxplot, ...)
   ymin.log <- min(x$data)
   ymax.log <- max(x$data)
-  graphics::boxplot(x$data, xlab = "Samples", ylab = "Signal", 
-          main = "Normalized and transformed data", 
-          ylim = c(ymin.log, ymax.log), range = range4boxplot, ...)   
-  graphics::par(def.par)    
+  graphics::boxplot(x$data, xlab = "Samples", ylab = "Signal",
+                    main = "Normalized and transformed data",
+                    ylim = c(ymin.log, ymax.log), range = range4boxplot, ...)
+  graphics::par(def.par)
 }