Solving infix_spaces_linter() issues (#38)

ocbe-uio · Oct 4, 2023 · 875ff08 · 875ff08
1 parent bc10c55
commit 875ff08
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Showing 28 changed files with 169 additions and 165 deletions.
diff --git a/R/DIscBIO-generic-ClustDiffGenes.R b/R/DIscBIO-generic-ClustDiffGenes.R
@@ -18,8 +18,8 @@
 #' @export
 #' @examples
 #' sc <- DISCBIO(valuesG1msTest)
-#' sc <- Clustexp(sc, cln=3, quiet=TRUE)
-#' cdiff <- ClustDiffGenes(sc, K=3, fdr=.3, export=FALSE)
+#' sc <- Clustexp(sc, cln = 3, quiet = TRUE)
+#' cdiff <- ClustDiffGenes(sc, K=3, fdr=.3, export = FALSE)
 #' str(cdiff)
 #' cdiff[[2]]
 

diff --git a/R/DIscBIO-generic-Clustexp.R b/R/DIscBIO-generic-Clustexp.R
@@ -32,7 +32,7 @@
 #' @return The DISCBIO-class object input with the cpart slot filled.
 #' @examples
 #' sc <- DISCBIO(valuesG1msTest) # changes signature of data
-#' sc <- Clustexp(sc, cln=2)
+#' sc <- Clustexp(sc, cln = 2)
 setGeneric("Clustexp", function(object, clustnr = 3, bootnr = 50,
     metric = "pearson", do.gap = TRUE, SE.method = "Tibs2001SEmax",
     SE.factor = .25, B.gap = 50, cln = 0, rseed = NULL, quiet = FALSE)
@@ -134,4 +134,4 @@ setMethod(
         object@cpart <- object@kmeans$kpart
         return(object)
     }
-)
+)
diff --git a/R/DIscBIO-generic-FinalPreprocessing.R b/R/DIscBIO-generic-FinalPreprocessing.R
@@ -10,8 +10,8 @@
 #' @return The DISCBIO-class object input with the FinalGeneList slot filled.
 #' @examples
 #' sc <- DISCBIO(valuesG1msTest)
-#' sc <- NoiseFiltering(sc, percentile=0.9, CV=0.2, export=FALSE)
-#' sc <- FinalPreprocessing(sc, GeneFlitering="NoiseF", export=FALSE)
+#' sc <- NoiseFiltering(sc, percentile = 0.9, CV = 0.2, export = FALSE)
+#' sc <- FinalPreprocessing(sc, GeneFlitering="NoiseF", export = FALSE)
 #'
 setGeneric(
     "FinalPreprocessing",

diff --git a/R/DIscBIO-generic-FindOutliers.R b/R/DIscBIO-generic-FindOutliers.R
@@ -23,7 +23,7 @@
 #'   of cells on each.
 #' @examples
 #' sc <- DISCBIO(valuesG1msTest)
-#' sc <- Clustexp(sc, cln=2) # K-means clustering
+#' sc <- Clustexp(sc, cln = 2) # K-means clustering
 #' FindOutliers(sc, K=2)
 #'
 setGeneric(
@@ -98,7 +98,7 @@ setMethod(
             outdistquant = outdistquant
         )
         ### calibrate background model
-	EXP<-object@expdata + 0.1
+	EXP <- object@expdata + 0.1
         m <- log2(apply(EXP, 1, mean))
         v <- log2(apply(EXP, 1, var))
         f <- m > -Inf & v > -Inf

diff --git a/R/DIscBIO-generic-NoiseFiltering.R b/R/DIscBIO-generic-NoiseFiltering.R
@@ -27,7 +27,7 @@
 #' @return The DISCBIO-class object input with the noiseF slot filled.
 #' @examples
 #' sc <- DISCBIO(valuesG1msTest) # changes signature of data
-#' sd_filtered <- NoiseFiltering(sc, export=FALSE)
+#' sd_filtered <- NoiseFiltering(sc, export = FALSE)
 #' str(sd_filtered)
 #'
 setGeneric(

diff --git a/R/DIscBIO-generic-Normalizedata.R b/R/DIscBIO-generic-Normalizedata.R
@@ -29,7 +29,7 @@
 #' # In this case this function is used to normalize the reads
 #' sc_normal <- Normalizedata(
 #'     sc, mintotal=1000, minexpr=0, minnumber=0, maxexpr=Inf, downsample=FALSE,
-#'     dsn=1, rseed=17000
+#'     dsn=1, rseed = 17000
 #' )
 #' summary(sc_normal@fdata)
 #'
@@ -93,4 +93,4 @@ setMethod(
         object@fdata <- x[apply(x, 1, max, na.rm = TRUE) < maxexpr, ]
         return(object)
     }
-)
+)
diff --git a/R/DIscBIO-generic-clustheatmap.R b/R/DIscBIO-generic-clustheatmap.R
@@ -18,10 +18,10 @@ setGeneric(
     function(
         object,
         clustering_method = "k-means",
-        hmethod="single",
-        rseed=NULL,
-        quiet=FALSE,
-        plot=TRUE)
+        hmethod = "single",
+        rseed = NULL,
+        quiet = FALSE,
+        plot = TRUE)
 	{
 	    standardGeneric("clustheatmap")
 	}

diff --git a/R/DIscBIO-generic-comptSNE.R b/R/DIscBIO-generic-comptSNE.R
@@ -13,14 +13,14 @@
 #' @return The DISCBIO-class object input with the tsne slot filled.
 #' @examples
 #' sc <- DISCBIO(valuesG1msTest) # changes signature of data
-#' sc <- Clustexp(sc, cln=2) # data must be clustered before plottin
-#' sc <- comptSNE(sc, max_iter=30)
+#' sc <- Clustexp(sc, cln = 2) # data must be clustered before plottin
+#' sc <- comptSNE(sc, max_iter = 30)
 #' head(sc@tsne)
 #'
 	setGeneric(
 	name = "comptSNE",
 	def = function(
-		object, rseed=NULL, max_iter=5000, epoch=500, quiet=FALSE, ...
+		object, rseed = NULL, max_iter = 5000, epoch = 500, quiet = FALSE, ...
 	)
 	{
 		standardGeneric("comptSNE")
@@ -68,4 +68,4 @@ setMethod(
 		}
 		return(object)
 	}
-)
+)
diff --git a/R/DIscBIO-generic-plotGap.R b/R/DIscBIO-generic-plotGap.R
@@ -23,7 +23,7 @@ setMethod(
             y_limits <- c(y_lo, y_up)
         }
         plot(
-            gap, las=1, ylim = y_limits, main="Gap Statistics"
+            gap, las = 1, ylim = y_limits, main = "Gap Statistics"
         )
     }
-)
+)
diff --git a/R/DIscBIO-generic-plottSNE.R b/R/DIscBIO-generic-plottSNE.R
@@ -30,7 +30,7 @@ setMethod(
 		# Plotting
 		# ======================================================================
 		col <- c("black", "blue", "green", "red", "yellow", "gray")
-	LEN<-length(levels(factor(part)))
+	LEN <- length(levels(factor(part)))
 		plot(
 			x,
 			las = 1,

diff --git a/R/DIscBIO-generic-pseudoTimeOrdering.R b/R/DIscBIO-generic-pseudoTimeOrdering.R
@@ -10,9 +10,9 @@
 #' @return The DISCBIO-class object input with the kordering slot filled.
 setGeneric("pseudoTimeOrdering", function(
 	object,
-	quiet=FALSE,
-	export=FALSE,
-	filename="Cellular_pseudo-time_ordering"
+	quiet = FALSE,
+	export = FALSE,
+	filename = "Cellular_pseudo-time_ordering"
 	)
 	{
 		standardGeneric("pseudoTimeOrdering")

diff --git a/R/J48DTeval.R b/R/J48DTeval.R
@@ -74,10 +74,10 @@ J48DTeval <- function(
         )
         colnames(j48.confusion.matrix) <- c(First, Second)
         if (!quiet) print(j48.confusion.matrix)
-        j48.sn <- round(SN(j48.confusion.matrix),digits=2)
-        j48.sp <- round(SP(j48.confusion.matrix),digits=2)
-        j48.acc <- round(ACC(j48.confusion.matrix),digits=2)
-        j48.mcc <- round(MCC(j48.confusion.matrix),digits=2)
+        j48.sn <- round(SN(j48.confusion.matrix),digits = 2)
+        j48.sp <- round(SP(j48.confusion.matrix),digits = 2)
+        j48.acc <- round(ACC(j48.confusion.matrix),digits = 2)
+        j48.mcc <- round(MCC(j48.confusion.matrix),digits = 2)
 
         if (!quiet) {
             message(

diff --git a/R/NetAnalysis.R b/R/NetAnalysis.R
@@ -23,10 +23,10 @@ NetAnalysis <- function(
     names <- rownames(betweenness.table)
     rownames(betweenness.table) <- NULL
     degree <- degree(gg)
-    
-    Duplicated<-data[duplicated(data),]
-    if(length(Duplicated[,1])>0){
-        degree=degree/2
+
+    Duplicated <- data[duplicated(data),]
+    if(length(Duplicated[, 1]) > 0){
+        degree <- degree / 2
     }
 
     degree.table <- data.frame(degree)

diff --git a/R/Networking.R b/R/Networking.R
@@ -46,7 +46,7 @@ Networking <- function(
 		"\n",
 		paste0(y)
 	)
-	Network <- readPNG(FileName, native=TRUE)
+	Network <- readPNG(FileName, native = TRUE)
 	set_plot_dimensions <- function(width_choice, height_choice) {
 		opar <- options(
 			repr.plot.width = width_choice,

diff --git a/R/PlotMBpca.R b/R/PlotMBpca.R
@@ -14,7 +14,7 @@
 #' @return A plot of the PCA.
 #' @export
 
-PlotMBpca <- function(object, type="order", g=NULL, n=NULL) {
+PlotMBpca <- function(object, type = "order", g = NULL, n = NULL) {
     # ==========================================================================
     # Validation
     # ==========================================================================

diff --git a/R/RpartEVAL.R b/R/RpartEVAL.R
@@ -74,10 +74,10 @@ RpartEVAL <- function(data, num.folds = 10, First = "CL1", Second = "CL2",
         )
         colnames(Rpart.confusion.matrix) <- c(First, Second)
         if (!quiet) print(Rpart.confusion.matrix)
-        Rpart.sn <- round(SN(Rpart.confusion.matrix),digits=2)
-        Rpart.sp <- round(SP(Rpart.confusion.matrix),digits=2)
-        Rpart.acc <- round(ACC(Rpart.confusion.matrix),digits=2)
-        Rpart.mcc <- round(MCC(Rpart.confusion.matrix),digits=2)
+        Rpart.sn <- round(SN(Rpart.confusion.matrix),digits = 2)
+        Rpart.sp <- round(SP(Rpart.confusion.matrix),digits = 2)
+        Rpart.acc <- round(ACC(Rpart.confusion.matrix),digits = 2)
+        Rpart.mcc <- round(MCC(Rpart.confusion.matrix),digits = 2)
 
         if (!quiet) {
             message(