Fix suggestions from jarl

R/bootstrapCompartments.R

@@ -63,7 +63,7 @@ bootstrapCompartments <- function(
   # bootstrap and recompute compartments
   BiocParallel::bpprogressbar(BPPARAM) <- FALSE
   resamp.compartments <- bplapply(
-    1:ncol(bmeans),
+    seq_len(ncol(bmeans)),
     function(b) {
       # get the shrunken bins with new global mean
       boot.mean <- as.matrix(bmeans[, b])
@@ -80,7 +80,7 @@ bootstrapCompartments <- function(
       cor.bins <- getCorMatrix(s.bins, squeeze = !group)
 
       # Stupid check for perfect correlation with global mean
-      if (any(is.na(cor.bins$binmat.cor))) {
+      if (anyNA(cor.bins$binmat.cor)) {
         absig <- matrix(rep(NA, nrow(cor.bins$binmat.cor)))
       } else {
         absig <- getABSignal(cor.bins, assay = assay)

R/condenseRE.R

@@ -19,7 +19,7 @@ condenseRE <- function(obj) {
   # it will build a list of SummarizedExperiments with relevant information
   # from computing compartments
   if (!is(obj, "RaggedExperiment")) stop("Input needs to be a RaggedExperiment")
-  se_list <- lapply(1:length(assayNames(obj)), function(a) {
+  se_list <- lapply(seq_along(assayNames(obj)), function(a) {
     compactSummarizedExperiment(obj, i = a)
   })
 
@@ -64,11 +64,11 @@ condenseSE <- function(obj, sample.name = NULL) {
 
   # check and see how many samples we are extracting
   if (length(sample.name) == 1) {
-    obj.dense <- lapply(1:length(obj), .condenseGR, obj = obj, sample = sample.name)
+    obj.dense <- lapply(seq_along(obj), .condenseGR, obj = obj, sample = sample.name)
     return(Reduce("merge", obj.dense))
   } else {
     obj.dense.lst <- lapply(sample.name, function(s) {
-      obj.dense <- lapply(1:length(obj), .condenseGR, obj = obj, sample = s)
+      obj.dense <- lapply(seq_along(obj), .condenseGR, obj = obj, sample = s)
       return(Reduce("merge", obj.dense))
     })
     names(obj.dense.lst) <- sample.name

R/getBinMatrix.R

@@ -56,7 +56,7 @@ getBinMatrix <- function(
   genome = c("hg19", "hg38", "mm9", "mm10")
 ) {
 
-  if (any(is.na(mat))){
+  if (anyNA(mat)){
     stop("Matrix must not contain NAs")
   }
   if (nrow(mat) != length(genloc)){

R/getCompartments.R

@@ -38,9 +38,9 @@ getCompartments <- function(
   }
 
   boot_msg <- ""
-  if (bootstrap & boot.parallel) {
+  if (bootstrap && boot.parallel) {
     boot_msg <- sprintf("Bootstrapping in parallel with %d cores", bpnworkers(bpparams[[2]]))
-  } else if (bootstrap & !boot.parallel & group) {
+  } else if (bootstrap && !boot.parallel && group) {
     boot_msg <- "Not bootstrapping in parallel could take a long time..."
   }
 
@@ -153,7 +153,7 @@ getCompartments <- function(
 
   obj.cor <- getCorMatrix(obj.bins, squeeze = !group)
 
-  if (any(is.na(obj.cor$binmat.cor))) {
+  if (anyNA(obj.cor$binmat.cor)) {
     obj.cor$gr$pc <- matrix(rep(NA, nrow(obj.cor$binmat.cor)))
     obj.svd <- obj.cor$gr
   } else {

R/getDomainInflections.R

@@ -51,7 +51,7 @@ getDomainInflections <- function(
   end(tiles) <- suppressWarnings(end(tiles) - 1)
 
   # add a column for continuous runs!
-  mcols(tiles)$run <- seq(1:length(tiles))
+  mcols(tiles)$run <- seq(seq_along(tiles))
   mcols(tiles)$score <- NA
 
   # overlap
@@ -83,7 +83,7 @@ getDomainInflections <- function(
   }
 
   # loop through the non-contiguous space
-  grl <- lapply(1:length(non_contig), function(i) {
+  grl <- lapply(seq_along(non_contig), function(i) {
     message("Block processing non-contiguous space for block ", i)
     # get block of contiguous sequence
     if (non_contig[i] == non_contig[1]) {
@@ -132,7 +132,7 @@ getDomainInflections <- function(
 
   # subset the original granges
   # gr.inflect <- gr[gr.signs.diff,]
-  if (length(gr.signs) == 2 & any(gr.signs.bool)) {
+  if (length(gr.signs) == 2 && any(gr.signs.bool)) {
     gr.inflect <- gr[2, ] # special case
     gr.inflect.new <- GRanges(
       seqnames = seqnames(gr.inflect),

R/getShrinkageTargets.R

@@ -23,7 +23,7 @@ getShrinkageTargets <- function(obj, group) {
       msg <- paste(
         "Error while subsetting targets: provided column", column.type, "not found:"
       )
-      if (no_colnames & group.is_colnames) {
+      if (no_colnames && group.is_colnames) {
         message("The provided object does not have any column names - use column indices instead.")
       }
       message(msg)

R/imputeKNN.R

@@ -35,7 +35,7 @@ imputeKNN <- function(
   assay <- match.arg(assay)
 
   # stop early if there aren't any NAs to impute
-  if (!any(is.na(assay(obj)))) {
+  if (!anyNA(assay(obj))) {
     message("No NAs found. Nothing to impute.")
     return(obj)
   }

R/meanSmoother.R

@@ -32,7 +32,7 @@ meanSmoother <- function(mat, k = 1, iters = 2, delta = 0, weights = NULL) {
   eps <- delta + 1
   weights <- weights %||% rep(1, length(mat))
 
-  while (pos < iters & eps > delta) {
+  while (pos < iters && eps > delta) {
     mat0 <- mat
     pos <- pos + 1
     mat <- .meanSmoother.internal(mat0, weights = weights, k = k)

R/parallel.R

@@ -2,7 +2,7 @@
 #' inference and bootstrapping
 #' @keywords internal
 check_optim <- function(workers, group, chr_count, bootstrap) {
-  if (group & chr_count < workers[1]) {
+  if (group && chr_count < workers[1]) {
     flog.info(
       "Grouped inference with more outer workers than chromosomes leaves %d of %d workers unused",
       workers[1] - chr_count,
@@ -12,7 +12,7 @@ check_optim <- function(workers, group, chr_count, bootstrap) {
       flog.info("Consider using a single core for the outer worker and more cores for the inner bootstrap worker")
     }
   }
-  if (!group & bootstrap & workers[1] < workers[2]) {
+  if (!group && bootstrap && workers[1] < workers[2]) {
     flog.info("More outer (column-wise) than inner (bootstrap) workers is faster for single-cell inference")
   }
 }
@@ -29,7 +29,7 @@ check_worker_count <- function(bpparam, group, chr_count, bootstrap, avail_worke
 
   avail_msg <- sprintf("but your system has only %d cores", avail_workers)
   info_msg <- "See parallelly::availableCores(which = 'all') for more information on available resources"
-  if (workers[1] == 1 | workers[2] == 1) {
+  if (workers[1] == 1 || workers[2] == 1) {
     msg <- sprintf(
       "Requested %d %s workers %s\n%s",
       max(workers),
@@ -105,7 +105,7 @@ verify_workers <- function(n_workers) {
 get_nested_params <- function(BPPARAM, boot.parallel) {
   stopifnot("Only two BiocParallelParam objects can be used" = length(BPPARAM) <= 2)
   single_param <- length(BPPARAM) == 1
-  BPPARAM <- if (single_param & is.list(BPPARAM)) BPPARAM[[1]] else BPPARAM
+  BPPARAM <- if (single_param && is.list(BPPARAM)) BPPARAM[[1]] else BPPARAM
 
   if (boot.parallel) {
     if (single_param) {

R/preprocessArrays.R

@@ -45,7 +45,7 @@ preprocessArrays <- function(
   }
 
   # impute missing values if possible
-  if (any(is.na(minfi::getBeta(obj.opensea)))) {
+  if (anyNA(minfi::getBeta(obj.opensea))) {
     flog.debug("Imputing missing values.")
     obj.opensea <- imputeKNN(obj.opensea, assay = "array")
   }

R/transformTFIDF.R

@@ -23,7 +23,7 @@
 #'
 #' @export
 transformTFIDF <- function(mat, scale.factor = 1e5, binarize = FALSE) {
-  if (!is(mat, "matrix") & !is(mat, "Matrix")) {
+  if (!is(mat, "matrix") && !is(mat, "Matrix")) {
     stop("Input needs to be a matrix.")
   }
 

R/utils.R

@@ -62,7 +62,7 @@ flogit <- function(p, sqz = 0.000001) {
 #'
 #' @export
 fexpit <- function(x, sqz = 0.000001) {
-  midpt <- .5
+  midpt <- 0.5
   squoze <- exp(x) / (1 + exp(x))
   inflate <- 1 / (1 - (sqz * midpt))
   ((squoze - midpt) * inflate) + midpt
@@ -285,7 +285,7 @@ importBigWig <- function(
   }
 
   # it is now a GRanges object
-  if (any(is.na(seqlengths(bw.raw)))) stop("Imported bigwig does not have seqlengths")
+  if (anyNA(seqlengths(bw.raw))) stop("Imported bigwig does not have seqlengths")
 
   species <- switch(
     genome,