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🔬 R package: aroma.cn

Home Page: https://cran.r-project.org/package=aroma.cn

R 62.34% Makefile 3.64% TeX 0.46% Shell 0.29% REXX 33.27%

package r

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Update package title to use Title Case

From Writing R Extensions:

The mandatory 'Title' field should give a short description of the package. Some package listings may truncate the title to 65 characters. It should use title case (that is, use capitals for the principal words), not use any markup, not have any continuation lines, and not end in a period.

CLEANUP: Drop nbrOfFiles() methods

Replace nbrOfFiles() calls with length() calls for:

AbstractCurveNormalization
PairedPscbsModel
TumorBoostNormalization

CLEANUP: Import throw() from R.oo (>= 1.23.0)

Use Travis CI for R

METACRAN: Keep README.md so it appears on METACRAN

Drop README[.]md from .Rbuildignore such that the content of README.md is incorporated on the package page on METACRAN.

ROBUSTNESS: Add explicit 'stringsAsFactors' arguments [cbind, rbind]

$ for pkg in $pkgs; do echo "$pkg:"; (cd "$pkg"; grep -E "^[ \t]*[^#].*[cr]bind" -- */*.R | grep -vF stringsAsFactors;); echo; read -r -p "Press ENTER to continue ..."; done

aroma.cn:
R/AromaUnitPscnBinarySet.doSegmentByPairedPSCBS.R:    dataII <- cbind(dfT[,1:2], dfN[,1:2])
R/AromaUnitPscnBinarySet.doSegmentByPairedPSCBS.R:    data <- cbind(dataA, dataII)
R/C1C2.R:    rbind(c1c2, NA)
R/C1C2.R:  c1c2 <- Reduce(rbind, c1c2TList)
R/C1C2.R:  c1c2 <- cbind(c1c2, w=w)
R/NaiveFracBGenotyping.R:    cs[isDiploid] <- rowMins(abs(cbind(beta[isDiploid]-a, beta[isDiploid]-b)))
R/PairedPSCBS.CALL2.R:      df <- rbind(df, dfKK)
R/PairedPSCBS.CALL2.R:    df <- rbind(df, dfKK)
R/PairedPSCBS.CALL2.R:  segs <- cbind(segs, df)
R/PairedPSCBS.fitC1C2Peaks.R:  dd <- cbind(pList$C1$density[idxs[,1]], pList$C2$density[idxs[,2]])
R/PairedPSCBS.fitC1C2Peaks.R:  verbose && print(verbose, cbind(c1=c1, c2=c2, weights=w))
R/PairedPSCBS.NORM.R:    res <- as.matrix(cbind(l=l, b=b, w=w, k=k))
R/PeaksAndValleys.EXTS.R:  fitC <- cbind(fit, callId=NA_integer_, call=NA_real_)
R/PrincipalCurve.XTRAS.R:    xy <- cbind(y, yN)
R/PruneCNA.PLOT.R:            x <- cbind(x, length=x[,2]-x[,1])
R/segmentByPruneCBS.R:    regions <- rbind(regions3, regions4)
R/segmentByPruneCBS.R:    regions <- rbind(regions3, regions4)

CLEANUP: Use anyNA() - drop anyMissing()

Since package requires R (>= 3.x), we can now use anyNA() instead of matrixStats::anyMissing().

SUGGESTION: More efficient code using matrixStats (>= 0.50.0)

In matrixStats (>= 0.50.0), which is now on CRAN, we introduced support for subsetted calculations that are optimized both for speed and memory usage. There are now new arguments idxs, rows and cols for most functions which allows you to very easily make use of this.

For your package, I believe following functions could gain from this:

$aroma.cn$C1C2toAB
dxy <- colDiffs(xy[, 1:2])

by updating to something like:

$aroma.cn$C1C2toAB
dxy <- colDiffs(xy, cols=1:2)

and adding matrixStats (>= 0.50.0) to your DESCRIPTION file.

dsApplyInPairs() -> future.apply::future_mapply()

In order to full deprecate dsApplyInPairs() and dsApply() in R.filesets, we need to remove usage of dsApplyInPairs() in aroma.cn.

The most likely way forward with this is to replace dsApplyInPairs() with a futurized mapply() function, i.e. a future.apply::future_mapply(). Now, the latter does not exist yet, but is on the road map.

ROBUSTNESS: Make sure search file reg exp patterns have trailing $

Make sure all regular expressions used for searching / setting up file sets have trailing $. For instance, instead of:

  pattern <- sprintf("%s.asb", typeTags);
  res <- AromaUnitFracBCnBinarySet$byPath(outPath, pattern=pattern);

use

  pattern <- sprintf("%s[.]asb$", typeTags);
  res <- AromaUnitFracBCnBinarySet$byPath(outPath, pattern=pattern);

ROBUSTNESS: Add explicit 'stringsAsFactors' arguments [data.frame]

$ for pkg in $pkgs; do echo "$pkg:"; (cd "$pkg"; grep -E "^[ \t]*[^#].*data[.]frame" -- */*.R | grep -vF stringsAsFactors;); echo; read -r -p "Press ENTER to continue ..."; done

aroma.cn:
R/AromaUnitPscnBinarySet.doSegmentByPairedPSCBS.R:    dataA <- Arguments$getInstanceOf(dataA, "data.frame")
R/calculatePairedPSCNByGenotype.R:setMethodS3("calculatePairedPSCNByGenotype", "data.frame", function(data, ...) {
R/CBS.findAtomicAberrations.R:  res <- data.frame(
R/CopyNumberRegions.findAtomicAberrations.R:  res <- data.frame(
R/fitLoessKD.matrix.R:setMethodS3("fitLoessKD", "data.frame", function(X, ...) {
R/fitLoessKD.matrix.R:setMethodS3("normalizeLoessKD", "data.frame", function(X, ...) {
R/fitLoessKD.matrix.R:  XN <- as.data.frame(XN)
R/MultiSourceCopyNumberNormalization.R:  Y <- as.data.frame(Y)
R/PairedPSCBS.CALL2.R:  segs <- as.data.frame(fit)
R/PairedPSCBS.CALL2.R:    data=list(betaTN=betaTN, muN=muN, segments=as.data.frame(fit)),
R/PairedPSCBS.CALL2.R:      dfKK <- data.frame(
R/PairedPSCBS.CALL2.R:    dfKK <- data.frame(
R/PairedPSCBS.findAtomicAberrations.R:  res <- data.frame(
R/PairedPscbsModel.R:      data <- data.frame(chromosome=cp$chromosome, x=cp$position,
R/PairedPSCBS.NORM.R:    data=as.data.frame(fit),
R/RawAlleleBFractions.XTRAS.R:  data <- data.frame(total=2, fracB=beta)
R/segmentByPruneCBS.R:    regions <- as.data.frame(cnr)[,c("start","stop")]
R/segmentByPruneCBS.R:        data <- as.data.frame(cnrT)
R/segmentByPruneCBS.R:        data <- as.data.frame(cnr)
R/segmentByPruneCBS.R:    regions <- as.data.frame(cnr)[,c("start","stop")]
R/segmentByPruneCBS.R:        data <- as.data.frame(cnrT)
R/segmentByPruneCBS.R:        data <- as.data.frame(cnr)

FUTURE: Use do.call(fcn, ...) - not do.call("fcn", ...)

When calling a function via do.call(), specify the function by itself and not by its name, e.g.

## GOOD
y <- do.call(backgroundCorrect, args=args)

## BAD
y <- do.call("backgroundCorrect", args=args)

The former will cause backgroundCorrect() to be identified as a global object by the globals such that it is properly exported when evaluated by a future.

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