Type:	Package
Title:	Utilities from and Interface to the 'Bioinfo-C' ('BIOS') Library
Version:	1.7.7
Date:	2026-01-22
Description:	Provides interface to the 'Bioinfo-C' (internal name: 'BIOS') library and utilities. 'ribiosUtils' is a Swiss-knife for computational biology in drug discovery, providing functions and utilities with minimal external dependency and maximal efficiency.
Depends:	R (≥ 3.4.0)
Imports:	methods, stats, utils
Suggests:	devtools, testthat
License:	GPL-3
URL:	https://github.com/bedapub/ribiosUtils, https://bedapub.github.io/ribiosUtils/
BugReports:	https://github.com/bedapub/ribiosUtils/issues
Encoding:	UTF-8
RoxygenNote:	7.3.3
NeedsCompilation:	yes
Packaged:	2026-01-28 09:10:07 UTC; david
Author:	Jitao David Zhang [aut, cre, ctb], Clemens Broger [aut, ctb], F.Hoffmann-La Roche AG [cph], Junio C Hamano [cph], Jean Thierry-Mieg [cph], Konrad Rudolph [cph], Richard Durbin [cph]
Maintainer:	Jitao David Zhang <jitao_david.zhang@roche.com>
Repository:	CRAN
Date/Publication:	2026-02-03 10:20:02 UTC

ribiosUtils: Utilities from and Interface to the 'Bioinfo-C' ('BIOS') Library

Description

Provides interface to the 'Bioinfo-C' (internal name: 'BIOS') library and utilities. 'ribiosUtils' is a Swiss-knife for computational biology in drug discovery, providing functions and utilities with minimal external dependency and maximal efficiency.

Author(s)

Maintainer: Jitao David Zhang jitao_david.zhang@roche.com (ORCID) [contributor]

Authors:

• Clemens Broger [contributor]

Other contributors:

• F.Hoffmann-La Roche AG [copyright holder]

• Junio C Hamano [copyright holder]

• Jean Thierry-Mieg [copyright holder]

• Konrad Rudolph [copyright holder]

• Richard Durbin [copyright holder]

See Also

Useful links:

• https://github.com/bedapub/ribiosUtils

• https://bedapub.github.io/ribiosUtils/

• Report bugs at https://github.com/bedapub/ribiosUtils/issues

Testing whether several objects are all identical with each other

Description

Given several objects, the function tests whether all of them are identical.

Usage

allIdentical(...)

Arguments

Value

Logical, whether all objects are the same

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

identical

Examples

test1 <- test2 <- test3 <- LETTERS[1:3]
allIdentical(test1, test2, test3)
allIdentical(list(test1, test2, test3))

num1 <- num2 <- num3 <- num4 <- sqrt(3)
allIdentical(num1, num2, num3, num4)

Apply isTopOrIncAndNotExcl filter to a matrix

Description

Apply isTopOrIncAndNotExcl filter to a matrix

Usage

applyTopOrIncAndNotExclFilter(matrix, MARGIN, top = 1, falseValue = 0, ...)

Arguments

Value

A matrix with the same dimnames but with elements not satisfying isTopOrIncAndNotExcl replaced by falseValue.

Examples

myMat <- matrix(c(1,2,3,4,8,7,6,5,12,9,11,10), nrow=3, byrow=TRUE,
   dimnames=list(c("A", "B", "C"), c("Alpha", "Beta", "Gamma", "Delta")))
print(myMat)
applyTopOrIncAndNotExclFilter(myMat, 1, top=2, falseValue=-1)
applyTopOrIncAndNotExclFilter(myMat, 2, top=2, falseValue=-1)
applyTopOrIncAndNotExclFilter(myMat, 2, top=2, falseValue=-1, decreasing=FALSE)
applyTopOrIncAndNotExclFilter(myMat, 1, top=2, falseValue=-1, incFunc=function(x) x%%2==0)
applyTopOrIncAndNotExclFilter(myMat, 1, top=2, falseValue=-1,
   incFunc=function(x) x%%2==0, excFunc=function(x) x<5)

Convert string-valued data frame or matrix into a numeric matrix

Description

Convert string-valued data frame or matrix into a numeric matrix

Usage

asNumMatrix(x)

Arguments

Value

A numeric matrix with the same dimension

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

testDf <- data.frame(a=c("2.34", "4.55"), b=c("7.33", "9.10"))
asNumMatrix(testDf)

testMatrix <- matrix(c("2.34", "4.55", "9E-3","-2.44", "7.33", "9.10"), nrow=2)
asNumMatrix(testMatrix)

Assert whether the required column names exist

Description

The function calls matchColumnName internally to match the column names.

Usage

assertColumnName(data.frame.cols, reqCols, ignore.case = FALSE)

Arguments

Value

If all required column names are present, their indices are returned *invisibly*. Otherwise an error message is printed.

Examples

myTestDf <- data.frame(HBV=1:3, VFB=0:2, BVB=4:6, FCB=2:4)
myFavTeams <- c("HBV", "BVB")
assertColumnName(myTestDf, myFavTeams)
myFavTeamsCase <- c("hbv", "bVb")
assertColumnName(myTestDf, myFavTeamsCase, ignore.case=TRUE)

Check dimensionality of contrast matrix

Description

Check dimensionality of contrast matrix

Usage

assertContrast(design, contrast)

Arguments

Value

Side effect is used: the function stops if the ncol(design) does not equal nrow(contrast)

Examples

design <- matrix(1:20, ncol=5)
contrast <- matrix(c(-1,1,0,0,0, 0,1,0,-1,0), nrow=5)
assertContrast(design, contrast)

Check dimensionality of design matrix

Description

Check dimensionality of design matrix

Usage

assertDesign(nsample, design)

Arguments

Value

Side effect is used: the function stops if sample size does not equal ncol(matrix)

Examples

nsample <- 4
design <- matrix(1:20, ncol=5)
assertDesign(nsample, design)

Check dimensionality of both design and contrast matrix

Description

Check dimensionality of both design and contrast matrix

Usage

assertDesignContrast(nsample, design, contrast)

Arguments

Value

Side effect is used: the function stops if there are errors in the dimensionalities

See Also

assertDesign, assertContrast

Examples

nsample <- 4
design <- matrix(1:20, ncol=5)
contrast <- matrix(c(-1,1,0,0,0, 0,1,0,-1,0), nrow=5)
assertDesignContrast(nsample, design, contrast)

Print BEDA project information

Description

Print BEDA project information

Usage

bedaInfo()

Value

A list, including pstore path, URL, git address, and user id The function is used at the end of the Rmarkdown report to print relevant information to help other colleagues finding relevant resources

Examples

bedaInfo()

Translate BiOmics-Pathology pstore path to URL

Description

Translate BiOmics-Pathology pstore path to URL

Usage

biomicsPstorePath2URL(path)

Arguments

Value

Character string of biomics pstore path The URL is only visible inside Roche

Examples

biomicsPstorePath2URL("/pstore/data/biomics/")

Set boundaries for numeric values, or perform a 0-1 normalization.

Description

The basic concepts of these functions are borrowed from the bound function in the Qt framework.

Usage

bound(x, low, high)

boundNorm(x, low = min(x, na.rm = TRUE), high = max(x, na.rm = TRUE))

Arguments

Details

bound sets the values smaller than low, or larger than high, to the value of low and high respectively.If no such values exist, the vector or matrix is returned unchanged.

boundNorm performs a 0-1 normalization. Input vector or matrix is transformed linearly onto the region defined between low and high, which has the unit length (1).

Value

A numeric vector or matrix, the same type as input.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

myVec <- c(2,4,3,-1,9,5,3,4)
bound(myVec, 0, 8)
boundNorm(myVec)
## boundNorm returns negative values if input values lie out of the
## given region between low and high
boundNorm(myVec, 0, 8)

myMat <- matrix(myVec, nrow=2)
myMat
bound(myMat, 0, 8)
boundNorm(myMat)
boundNorm(myMat, 0, 8)

Column bind by rownames

Description

Column bind by rownames

Usage

cbindByRownames(..., type = c("intersect", "union"))

rbindByColnames(..., type = c("intersect", "union"))

Arguments

Value

A matrix

Examples

mat1 <- matrix(1:9, nrow=3, byrow=FALSE,
               dimnames=list(LETTERS[1:3], LETTERS[1:3]))
mat2 <- matrix(1:9, nrow=3, byrow=FALSE,
               dimnames=list(LETTERS[2:4], LETTERS[4:6]))
mat3 <- matrix(1:9, nrow=3, byrow=FALSE,
               dimnames=list(LETTERS[c(2,4,5)], LETTERS[7:9]))
cbindByRownames(mat1, mat2, mat3, type="intersect")
cbindByRownames(mat1, mat2, mat3, type="union")
## it is also possible to pass a list
cbindByRownames(list(mat1, mat2, mat3), type="union")
mat4 <- matrix(1:9, nrow=3, byrow=FALSE,
               dimnames=list(LETTERS[1:3], LETTERS[1:3]))
mat5 <- matrix(1:9, nrow=3, byrow=FALSE,
               dimnames=list(LETTERS[4:6], LETTERS[2:4]))
mat6 <- matrix(1:9, nrow=3, byrow=TRUE,
               dimnames=list(LETTERS[7:9], LETTERS[c(2,4,6)]))
rbindByColnames(mat4, mat5, mat6, type="intersect")
rbindByColnames(mat4, mat5, mat6,  type="union")
## it is also possible to pass a list
rbindByColnames(list(mat4, mat5, mat6), type="union")

Check whether file(s) exist

Description

checkFile checks whether file exists, assertFile stops the program if files do not exist

Usage

checkFile(...)

assertFile(...)

Arguments

Details

assertFile is often used in scripts where missing a file would cause the script fail.

Value

checkFile returns logical vector. assertFile returns an invisible TRUE if files exist, otherwise halts and prints error messages.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

isDir and assertDir

Examples

myDesc <- system.file("DESCRIPTION", package="ribiosUtils")
myNEWS <- system.file("NEWS", package="ribiosUtils")
checkFile(myDesc, myNEWS)
assertFile(myDesc, myNEWS)

Print the chosen few items of a long vector

Description

Print the chosen few (the first and the last) items of a long vector

Usage

chosenFew(vec, start = 3, end = 1, collapse = ",")

Arguments

Value

A character string ready to be printed

Note

In case the vector is shorter than the sum of start and end, the whole vector is printed.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

lvec1 <- 1:100
chosenFew(lvec1)
chosenFew(lvec1, start=5, end=3)

svec <- 1:8
chosenFew(svec)
chosenFew(svec, start=5, end=4)

Close connections to all loggers This function closes all open connections set up by loggers It is automatically run at the end of the R session (setup by registerLog)

Description

Close connections to all loggers This function closes all open connections set up by loggers It is automatically run at the end of the R session (setup by registerLog)

Usage

closeLoggerConnections()

Value

Invisible NULL. Only side effect is used.

See Also

registerLog

Pairwise jaccard/overlap coefficient can be calculated efficiently using matrix Pairwise overlap coefficient of binary matrix by column

Description

Pairwise jaccard/overlap coefficient can be calculated efficiently using matrix Pairwise overlap coefficient of binary matrix by column

Usage

columnOverlapCoefficient(x, y = NULL)

Arguments

Value

A matrix of column-wise pairwise overlap coefficients of the binary matrix. NaN is reported when neither of the columns have any non-zero element.

Examples

set.seed(1887)
testMatrix1 <- matrix(rbinom(120, 1, 0.2), nrow=15)
columnOverlapCoefficient(testMatrix1)

testMatrix2 <- matrix(rbinom(150, 1, 0.2), nrow=15)
testMatrix12Poe <- columnOverlapCoefficient(testMatrix1, 
  testMatrix2)

Compare two vectors by set operations

Description

Basic set operations are used to compare two vectors

Usage

compTwoVecs(vec1, vec2)

Arguments

Value

A vector of six integer elements

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

year1 <- c("HSV", "FCB", "BVB", "S04", "FCN")
year2 <- c("HSV", "FCK", "S04")
compTwoVecs(year1, year2)

Calculate correlation coefficients using common rows of the two matrices

Description

Calculate correlation coefficients using common rows of the two matrices

Usage

corByRownames(mat1, mat2, ...)

Arguments

Value

A matrix of the dimension m \times n, where m and n are number of columns in mat1 and mat2, respectively. The matrix has an attribute, commonRownames, giving the common rownames shared by the two matrices.

Examples

myMat1 <- matrix(rnorm(24), nrow=6, byrow=TRUE,
  dimnames=list(sprintf("R%d", 1:6), sprintf("C%d", 1:4)))
myMat2 <- matrix(rnorm(35), nrow=7, byrow=TRUE,
  dimnames=list(sprintf("R%d", 7:1), sprintf("C%d", 1:5)))
corByRownames(myMat1, myMat2)

Count tokens by splitting strings

Description

Count tokens by splitting strings

Usage

countTokens(str, split = "\t", ...)

Arguments

Value

Integer vector: count of tokens in the strings

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

strsplit to split strings, or a convenient wrapper strtoken in this package.

Examples

myStrings <- c("HSV\t1887\tFavorite", "FCB\t1900", "FCK\t1948")
countTokens(myStrings)

## the function deals with factors as well
countTokens(factor(myStrings))

Create a directory if it does not exist, and then make sure the creation was successful.

Description

The function is particularly useful for scripting.

Usage

createDir(dir, showWarnings = FALSE, recursive = TRUE, mode = "0777")

Arguments

Value

Directory name (invisible)

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

tempdir <- tempdir()
createDir(tempdir)

Cumulative Jaccard Index

Description

Cumulative Jaccard Index

Usage

cumJaccardIndex(list)

cumJaccardDistance(list)

Arguments

Value

The cumulative Jaccard Index, a vector of values between 0 and 1, of the same length as the input list

The cumulative Jaccard Index is calculated by calculating the Jaccard Index of element i and the union of elements between 1 and i-1. The cumulative Jaccard Index of the first element is set as 0.0.

The cumulative Jaccard distance is defined in almost the same way, with the only difference the distance is returned. The value of the first element is 1.0.

Note

An advantage of using cumulative overlap coefficient over cumulative Jaccard Index is that it is monotonic: the value is garanteed to decrease from 1 to 0, whereas the cumulative Jaccard Index may not be monotic.

See Also

cumOverlapCoefficient

Examples

myList <- list(first=LETTERS[1:5], second=LETTERS[6:10], third=LETTERS[8:12], fourth=LETTERS[1:12])
cumJaccardIndex(myList)
cumJaccardDistance(myList)

Cumulative overlap coefficient

Description

Cumulative overlap coefficient

Usage

cumOverlapCoefficient(list)

cumOverlapDistance(list)

Arguments

Value

The cumulative overlap coefficients, a vector of values between 0 and 1, of the same length as the input list

The cumulative overlap coefficient is calculated by calculating the overlap coefficient of element i and the union of elements between 1 and i-1. The cumulative overlap coefficient of the first element is set as 0.0.

The cumulative overlap distance is defined in almost the same way, with the only difference the distance is returned. The value of the first element is 1.0. Pratically it is calculated by 1-cumOverlapCoefficient.

Since the denominator of the overlap coefficient is the size of the smaller set of the two, which is bound to be the size of element i, the cumulative overlap distance can be interpreted as the proportion of new items in each new element that are unseen in previous elements. Similarly, the cumulative overlap coefficient can be interpreted as the proportion of items in each new element that have been seen in previous elements. See examples below.

Note

An advantage of using cumulative overlap coefficient over cumulative Jaccard Index is that it is monotonic: the value is garanteed to decrease from 1 to 0, whereas the cumulative Jaccard Index may not be monotic.

Examples

myList <- list(first=LETTERS[1:5], second=LETTERS[6:10], third=LETTERS[8:12], fourth=LETTERS[1:12])
cumOverlapCoefficient(myList)
cumOverlapDistance(myList)

Proportion of cumulative sum over sum

Description

Proportion of cumulative sum over sum

Usage

cumsumprop(x)

Arguments

Value

the proportion cumulative sum over sum

Examples

x <- 1:4
cumsumprop(x) ## 0.1, 0.3, 0.6, 1

Cut a vector of numbers into interval factors.

Description

Three types of labels (levels) are supported: “cut.default” (Interval labels returned by cut as default), “left” (Left boundary of intervals), and “right” (Right boundary of intervals).

Usage

cutInterval(
  x,
  step = 1,
  labelOption = c("cut.default", "left", "right"),
  include.lowest = FALSE,
  right = TRUE,
  dig.lab = 3,
  ordered_result = FALSE,
  ...
)

Arguments

Value

A vector of factors

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

cut

Examples

testNum <- rnorm(100)
(testFac <- cutInterval(testNum, step=1, labelOption="cut.default"))

## compare the result to
(testFacCut <- cut(testNum, 10))

Cut a tree into groups of ordered sizes

Description

Cut a tree into groups of ordered sizes

Usage

cutreeIntoOrderedGroups(tree, k = NULL, h = NULL, decreasing = TRUE)

Arguments

Value

A named integer vector of cluster assignments, ordered by cluster size (largest first by default). If multiple values of k or h are provided, a matrix with one column per value.

See Also

cutree

Examples

hc <- hclust(dist(USArrests))
hck5 <- cutreeIntoOrderedGroups(hc, k = 5)
table(hck5)
## compare with cutree, which does not order the groups
table(cutree(hc, k=5))

hck25 <- cutreeIntoOrderedGroups(hc, k = 2:5)
apply(hck25, 2, table)

Get a factor vector for a data.frame

Description

The function try to assign a factor vector for a data.frame object. See details below.

Usage

dfFactor(df, sample.group)

Arguments

Details

The function tries to get a factor vector of the same length as the number of rows in the data.frame. The determination is done in the following order: Step 1: It tries to find a column in the data.frame with the name as given by sample.group. If found, this column is transformed into a factor if not and returned. Step 2: It tries to interpret the sample.group as an integer, as the index of the column in the data.frame giving the factor. Step 3: When sample.group itself is a vector of the same length as the data.frame, it is cast to factor when it is still not and returned.

Otherwise the program stops with error.

Value

A factor vector with the same length as the data.frame

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

df <- data.frame(gender=c("M", "M", "F", "F", "M"),
age=c(12,12,14,12,14), score=c("A", "B-", "C", "B-", "A"))
dfFactor(df, "gender")
dfFactor(df, "score")
dfFactor(df, 1L)
dfFactor(df, 2L)
dfFactor(df, df$score)

Convert factor columns in a data.frame into character strings

Description

Convert factor columns in a data.frame into character strings

Usage

dfFactor2Str(df)

Arguments

Value

A data.frame with factor columns coereced into character strings

Examples

exampleDf <- data.frame(Teams=c("HSV", "FCB", "FCB", "HSV"), 
             Player=c("Mueller", "Mueller", "Robben", "Holtby"), 
             scores=c(3.5, 1.5, 1.5, 1.0), stringsAsFactors=TRUE)
strDf <- dfFactor2Str(exampleDf)
stopifnot(identical(strDf[,1], c("HSV", "FCB", "FCB", "HSV")))
stopifnot(identical(exampleDf[,1], factor(c("HSV", "FCB", "FCB", "HSV"))))

Format labels for wells in microwell plates to equal widths

Description

Format labels for wells in microwell plates to equal widths

Usage

equateWellLabelWidth(wells)

Arguments

Value

A vector of the same length, with all labels adjusted to the equal width, with left-padding zeros added whenever it makes sense. If the input labels are already of the same length, no change is applied.

Examples

equateWellLabelWidth(c("A1", "C10", "D12"))

Get the base and extension(s) of file name(s)

Description

Many files have base and extensions in their names, for instance for the file mybook.pdf, the base is mybook and the extension is pdf. basefilename extname functions extract these information from one or more file names.

Usage

extname(x, ifnotfound = NA, lower.case = FALSE)

Arguments

Value

The base file name or the extension as characters, of the same length as the input file name character. In case that a file name does not contain a extension, NA will be returned.

Note

In case there are multiple dots in the input file name, the last field will be taken as the extension, and the rest as the base name. For instance for file test.out.txt, returned base name is test.out and extension is txt.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

extname("mybook.pdf")
extname("sequence.in.fasta")
extname(c("/path/mybook.pdf", "test.doc"))
extname("README")
extname("README", ifnotfound="")
extname("/path/my\ home/Holiday Plan.txt")

basefilename("mybook.pdf")
basefilename("sequence.in.fasta")
basefilename(c("/path/mybook.pdf", "test.doc"))
basefilename("README")
basefilename("/path/my\ home/Holiday Plan.txt")

basefilename("myBook.pdf", lower.case=TRUE)
extname("myBook.PDF", lower.case=TRUE)

Make the first alphabet of strings uppercase

Description

Make the first alphabet of strings uppercase

Usage

firstUp(str)

Arguments

Value

A vector of the same length, with the first alphabet in uppercase

See Also

toTitleCase

Examples

firstUp('test string')
firstUp(strsplit('many many years ago', ' ')[[1]])

Shorten strings to strings with a fix width of characters

Description

Shorten strings to strings with a fix width of characters

Usage

fixWidthStr(str, nchar = 8, align = c("left", "right"))

Arguments

Value

A vector of strings with fixed widths

Note

NA will be converted to characters and the same fixed width will be applied. The behavior is different from shortenStr, where NA is kept as it is.

See Also

shortenStr

Examples

inputStrs <- c("abc", "abcd", "abcde", "abcdefg", "NA", NA) 
outputStrs <- fixWidthStr(inputStrs, nchar=4)
stopifnot(all(nchar(outputStrs)==4))

Ensure the Truth of R Expressions and Print Defined Error Message if NOT

Description

If any of the expressions in ‘...’ are not all TRUE, stop is called, producing an error message defined by the msg parameter.

Usage

haltifnot(..., msg = "Error undefined. Please contact the developer")

Arguments

Details

The function is adapted from the stopifnot function, with the difference that the error message can be defined the programmer. With haltifnot error message can be more informative, which is desired for diagnostic and user-interation purposes.

Value

NULL if all statements in ... are TRUE

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

stop, warning and stopifnot

Examples

haltifnot(1==1, all.equal(pi, 3.14159265), 1<2) ## all TRUE
m <- matrix(c(1,3,3,1), 2,2)
haltifnot(m == t(m), diag(m) == rep(1,2)) ## all TRUE

op <- options(error = expression(NULL))
# "disable stop(.)"  << Use with CARE! >>
     
haltifnot(all.equal(pi, 3.141593),  2 < 2, all(1:10 < 12), "a" < "b",
          msg="not all conditions are TRUE. Please contact the devleoper")
options(op)# revert to previous error handler

head/tail function for matrix or data.frame

Description

These two functions reassembles head and tail, showing the first rows and columns of 2D data structures, e.g. matrix or data.frame.

Usage

headhead(x, m = 6L, n = 6L)

Arguments

Details

While head and tail can be applied to data.frame or matrix as well, they show all columns of the first (last) rows even if the matrix has a large number of columns. These two function, headhead and tailtail, circumvent this problem by showing only the first rows AND the first columns.

Value

The first rows/columns of the input object

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

head, tail

Examples

myMat <- matrix(rnorm(10000), nrow=10L)
head(myMat)
headhead(myMat)
tailtail(myMat)

Print head and tail elements of a vector

Description

This function prints head and tail elements of a vector for visualization purposes. See examples for its usage.

Usage

headtail(vec, head = 2, tail = 1, collapse = ", ")

Arguments

Details

Head and tail elements are concatenated with ellipsis, if there are any elements that are not shown in the vector.

Value

A character string representing the vector

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

head, tail

Examples

testVec1 <- LETTERS[1:10]
headtail(testVec1)
headtail(testVec1, head=3, tail=3)
headtail(testVec1, head=3, tail=3, collapse="|")

testVec2 <- letters[1:3]
headtail(testVec2, head=1, tail=1)
headtail(testVec2, head=2, tail=1)

Test whether two matrices are identical by values and by dim names

Description

Test whether two matrices are identical by values and by dim names

Usage

identicalMatrix(x, y, epsilon = 1e-12)

Arguments

Value

Logical

Examples

set.seed(1887); x <- matrix(rnorm(1000), nrow=10, dimnames=list(LETTERS[1:10],NULL))
set.seed(1887); y <- matrix(rnorm(1000), nrow=10, dimnames=list(LETTERS[1:10],NULL))
set.seed(1887); z <- matrix(rnorm(1000), nrow=10, dimnames=list(letters[1:10],NULL))
stopifnot(identicalMatrix(x,y))
stopifnot(!identicalMatrix(x,z))

Test whether two matrices have the same numerica values given certain accuracy

Description

Test whether two matrices have the same numerica values given certain accuracy

Usage

identicalMatrixValue(x, y, epsilon = 1e-12)

Arguments

Value

Logical

Examples

set.seed(1887); x <- matrix(rnorm(1000), nrow=10)
set.seed(1887); y <- matrix(rnorm(1000), nrow=10)
set.seed(1882); z <- matrix(rnorm(1000), nrow=10)
stopifnot(identicalMatrixValue(x,y))
stopifnot(!identicalMatrixValue(x,y+1E-5))
stopifnot(!identicalMatrixValue(x,y-1E-5))
stopifnot(!identicalMatrixValue(x,z))

Case-insensitive match and pmatch

Description

Case-insensitive match and pmatch functions, especially useful in parsing user inputs, e.g. from command line.

Usage

imatch(x, table, ...)

Arguments

Details

imatch and ipmatch works similar as match and pmatch, except that they are case-insensitive.

matchv, imatchv and ipmatchv are shot-cuts to get the matched value (therefore the ‘v’) if the match succeeded, or NA if not. match(x, table) is equivalent to table[match(x, table)]. See examples.

Value

imatch and ipmatch returns matching indices, or NA (by default) if the match failed.

matchv, imatchv and ipmatchv returns the matching element in table, or NA if the match failed. Note that when cases are different in x and table, the one in table will be returned. This is especially useful for cases where user's input has different cases as the internal options.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

match and pmatch

Examples

user.input <- c("hsv", "BvB")
user.input2 <- c("HS", "BV")
internal.options <- c("HSV", "FCB", "BVB", "FCN")

match(user.input, internal.options)
imatch(user.input, internal.options)
ipmatch(user.input, internal.options)
ipmatch(user.input2, internal.options)

matchv(user.input, internal.options)
matchv(tolower(user.input), tolower(internal.options))
imatchv(user.input, internal.options)
ipmatchv(user.input, internal.options)
ipmatchv(user.input2, internal.options)

Invert the names and elements of a list

Description

Invert the names and elements of a list

Usage

invertList(inputList, simplify = FALSE)

Arguments

Value

A list with values from the input becoming names and vice versa. When simplify=TRUE and there are no duplicated names, a named character vector is returned instead.

Examples

myList <- list("A"=c("a", "alpha"), "B"=c("b", "Beta"), "C"="c")
invertList(myList)
invertList(myList, simplify=TRUE)

Checks existing directory

Description

Checks whether given character strings point to valid directories

Usage

isDir(...)

checkDir(...)

assertDir(...)

Arguments

Details

isDir tests whether the given string represent a valid, existing directory. assertDir performs a logical test, and stops the program if the given string does not point to a given directory.

checkDir is synonymous to isDir

Value

isDir returns logical vector.

assertDir returns an invisible TRUE if directories exist, otherwise halts and prints error messages.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

file.info, checkFile and assertFile

Examples

dir1 <- tempdir()
dir2 <- tempdir()

isDir(dir1, dir2)
assertDir(dir1, dir2)

Tell whether an object is an error

Description

Determines whether an object is of class try-error

Usage

isError(x)

Arguments

Value

Logical value, TRUE if x inherits the try-error class.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

if(exists("nonExistObj")) rm(nonExistsObj)
myObj <- try(nonExistObj/5, silent=TRUE)
isError(myObj)

Whether an integer is odd (or even)

Description

Whether an integer is odd (or even)

Usage

isOdd(x)

isEven(x)

Arguments

Value

Logical, whether the input number is odd or even.

isOdd and isEven returns whether an integer is odd or even, respectively.

Examples

isOdd(3)
isEven(4)

Tell whether a character string is a Roche compound ID

Description

Tell whether a character string is a Roche compound ID

Usage

isRocheCompoundID(str)

Arguments

Value

A logical vector of the same length as str, indicating whether each element is a Roche compound ID or not.

Note

Short versions (RO[1-9]{2,7}) are supported.

Examples

isRocheCompoundID(c("RO1234567", "RO-1234567", 
                   "RO1234567-000", "RO1234567-000-000",
                   "ROnoise-000-000"))

Logical vector of being top or included and not excluded

Description

Logical vector of being top or included and not excluded

Usage

isTopOrIncAndNotExcl(x, top = 1, incFunc, excFunc, decreasing = TRUE)

Arguments

Value

A logical vector of the same length as the input x, indicating whether each element is being either top or included, and not excluded. The function can be used to keep top elements of a vector while considering both inclusion and exclusion criteria.

Examples

myVal <- c(2, 4, 8, 7, 1)
isTopOrIncAndNotExcl(myVal, top=1)
isTopOrIncAndNotExcl(myVal, top=3)
isTopOrIncAndNotExcl(myVal, top=3, incFunc=function(x) x>=2)
isTopOrIncAndNotExcl(myVal, top=3, excFunc=function(x) x%%2==1)
isTopOrIncAndNotExcl(myVal, top=3, incFunc=function(x) x>=2, excFunc=function(x) x%%2==1)
myVal2 <- c("a", "A", "a", "A", "A")
isTopOrIncAndNotExcl(myVal2, 2)
isTopOrIncAndNotExcl(myVal2, 2, incFunc=function(x) x=="A")
isTopOrIncAndNotExcl(myVal2, 4)
isTopOrIncAndNotExcl(myVal2, 4, excFunc=function(x) x=="a")

## the function returns all TRUEs if top is larger than the length of the vector
isTopOrIncAndNotExcl(myVal, top=9)

Calculate the Jaccard Index between two vectors

Description

Calculate the Jaccard Index between two vectors

Usage

jaccardIndex(x, y)

jaccardDistance(x, y)

Arguments

Value

The Jaccard Index, a number between 0 and 1

JaccardDistance is defined as 1-JaccardIndex.

Examples

myX <- 1:6
myY <- 4:9
jaccardIndex(myX, myY)
jaccardDistance(myX, myY)

myX <- LETTERS[1:5]
myY <- LETTERS[6:10]
jaccardIndex(myX, myY)
jaccardDistance(myX, myY)

KEEP ROWS WITH THE MAXIMUM STATISTIC

Description

A common task in expression analysis is to collapse multiple features that are mapped to the same gene by some statistic. This function does this job by keeping the matrix row (normally features) with the higheest statistic specified by the user.

Usage

keepMaxStatRow(
  matrix,
  keys,
  keepNArows = TRUE,
  stat = function(x) mean(x, na.rm = TRUE),
  levels = c("rownames", "attribute", "discard"),
  ...
)

Arguments

Details

isMaxStatRow returns a logical vector, with rows with maximal statistics each key as TRUE and otherwise as FALSE. keepMaxStatRowInd returns the integer indices of such rows. Finally keepMaxStatRow returns the resulting matrices.

For use see examples

Value

A numeric matrix with rows mapped to unique keys, selected by the maximum statistics. See examples below

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

myFun1 <- function(x) mean(x, na.rm=TRUE)
myFun2 <- function(x) sd(x, na.rm=TRUE)
mat1 <- matrix(c(1,3,4,-5,
                 0,1,2,3,
                 7,9,5,3,
                 0,1,4,3), ncol=4, byrow=TRUE)
keys1 <- c("A", "B", "A", "B")

isMaxStatRow(mat1, keys1, stat=myFun1)
isMaxStatRow(mat1, keys1, stat=myFun2)

keepMaxStatRowInd(mat1, keys1, stat=myFun1)
keepMaxStatRowInd(mat1, keys1, stat=myFun2)

keepMaxStatRow(mat1, keys1, stat=myFun1)
keepMaxStatRow(mat1, keys1, stat="myFun2")
keepMaxStatRow(mat1, keys1, stat="myFun2", levels="discard")
keepMaxStatRow(mat1, keys1, stat="myFun2", levels="attribute")

mat2 <- matrix(c(1,3,4,5,
                 0,1,2,3,
                 7,9,5,3,
                 0,1,4,3,
                 4,0,-1,3.1,
                 9,4,-3,2,
                 8,9,1,2,
                 0.1,0.2,0.5,NA,
                 NA, 4, 3,NA), ncol=4, byrow=TRUE,
               dimnames=list(LETTERS[1:9], NULL))
keys2 <- c("A", "B", "A", "B", NA, NA, "C", "A", "D")

isMaxStatRow(mat2, keys2, keepNArows=FALSE, stat=myFun1)
keepMaxStatRowInd(mat2, keys2, keepNArows=FALSE, stat=myFun1)

keepMaxStatRow(mat2, keys2, keepNArows=FALSE, stat=myFun1)
keepMaxStatRow(mat2, keys2, keepNArows=TRUE, stat=myFun1)
keepMaxStatRow(mat2, keys2, keepNArows=TRUE, stat=myFun1, levels="discard")
keepMaxStatRow(mat2, keys2, keepNArows=TRUE, stat=myFun1, levels="attribute")

Return last characters from strings

Description

Return last characters from strings

Usage

lastChar(str)

Arguments

Value

A vector of the same length, containing last characters

Examples

lastChar("Go tell it on the mountain")
lastChar(c("HSV", "FCB", "BVB"))

Load a library mutedly and quit (die) in case of failing

Description

The specified library is loaded mutedly by suppressing all messages. If the library is not found, or its version under the specification of minVer, the R session dies with a message.

Usage

libordie(package, minVer, missing.quit.status = 1, ver.quit.status = 1)

Arguments

Details

Only one package should be tested once.

Value

NULL if success, otherwise the session will be killed.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

The function calls qqmsg internally to kill the session

Examples

libordie(stats)
libordie("methods")
libordie(base, minVer="2.15-1")

Transform a list of character strings into a data.frame

Description

Transform a list of character strings into a data.frame

Usage

list2df(list, names = NULL, col.names = c("Name", "Item"))

Arguments

Value

A data.frame

Examples

myList <- list(HSV=c("Mueller", "Papadopoulos", "Wood"), FCB=c("Lewandowski", "Robben", "Hummels"),
               BVB=c("Reus", "Goetze", "Kagawa"))
list2df(myList, col.names=c("Club", "Player"))

Pairwise overlap coefficient of lists

Description

Pairwise overlap coefficient of lists

Usage

listOverlapCoefficient(x, y = NULL, checkUniqueNonNA = TRUE)

Arguments

Value

A matrix of column-wise pairwise overlap coefficients.

Examples

set.seed(1887)
testSets1 <- sapply(rbinom(10, size=26, prob=0.3), 
  function(x) sample(LETTERS, x, replace=FALSE))
names(testSets1) <- sprintf("List%d", seq(along=testSets1))
testSets1Poe <- listOverlapCoefficient(testSets1)
testSets1PoeNoCheck <- listOverlapCoefficient(testSets1, checkUniqueNonNA=FALSE)
stopifnot(identical(testSets1Poe, testSets1PoeNoCheck))

testSets2 <- sapply(rbinom(15, size=26, prob=0.3),
  function(x) sample(LETTERS, x, replace=FALSE))
names(testSets2) <- sprintf("AnotherList%d", seq(along=testSets2))
testSets12Poe <- listOverlapCoefficient(testSets1, testSets2)

Convert a long-format data frame into matrix

Description

Input data.frame must contain at least three columns: one contains row names (specified by row.col), one contains column names (column.col), and one contains values in matrix cells (value.col). The output is a 2D matrix.

Usage

longdf2matrix(
  df,
  row.col = 1L,
  column.col = 2L,
  value.col = 3L,
  missingValue = NULL
)

Arguments

Value

A 2D matrix equivalent to the long-format data frame

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

matrix2longdf

Examples

test.df <- data.frame(H=c("HSV", "BVB", "HSV", "BVB"),
A=c("FCB", "S04", "S04", "FCB"),
score=c(3, 1, 1, 0))
longdf2matrix(test.df, row.col=1L, column.col=2L, value.col=3L)

data(Indometh)
longdf2matrix(Indometh, row.col="time", column.col="Subject",value.col="conc")
longdf2matrix(Indometh, row.col="Subject", column.col="time", value.col="conc")

Match a column in data.frame to a master vector

Description

Given a vector known as master vcector, a data.frame and one column of the data.frame, the function matchColumnIndex matches the values in the column to the master vector, and returns the indices of each value in the column with respect to the vector. The function matchColumn returns whole or subset of the data.frame, with the matching column in the exact order of the vector.

Usage

matchColumn(vector, data.frame, column, multi = FALSE)

Arguments

Details

See more details below.

The function is used to address the following question: how can one order a data.frame by values of one of its columns, the order for which is given in a vector (known as “master vector”). matchColumnIndex and matchColumn provide thoroughly-tested implementation to address this question.

For one-to-one cases, where both the column and the vector have no duplicates and can be matched one-to-one, the question is straightforward to solve with the match function in R. In one-to-many or many-to-many matching cases, the parameter multi determines whether multiple rows matching the same value should be shown. If mutli=FALSE, then the sorted data.frame that are returned has exactly the same row number as the input vector; otherwise, the returned data.frame has more rows. See the examples below.

In either case, in the returned data.frame object by matchColumn, values in the column used for matching are overwritten by the master vector.If multi=TRUE, the order of values in the column is also obeying the order of the master vector, with exceptions of repeating values casued by mutliple matching.

The column parameter can be either character string or non-negative integers. In the exceptional case, where column=0L (“L” indicates integer), the row names of the data.frame is used for matching instead of any of the columns.

Both functions are NA-friendly, since NAs in neither vector nor column should break the code.

Value

For matchColumnIndex, if multi is set to FALSE, an integer vector of the same length as the master vector, indicating the order of the data.frame rows by which the column can be re-organized into the master vector. When multi is TRUE, the returning object is a list of the same length as the master vector, each item containing the index (indices) of data.frame rows which match to the master vector.

For matchColumn, a data.frame is always returned. In case multi=FALSE, the returning data frame has the same number of rows as the length of the input master vector, and the column which was specified to match contains the master vector in its order. If multi=TRUE, returned data frame can contain equal or more numbers of rows than the master vector, and multiple-matched items are repeated.

Note

When multi=TRUE, the indices within each list element (for matchColumnIndex) are returned in ascending order.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

See match for basic matching operations.

Examples

df <- data.frame(Team=c("HSV", "BVB", "HSC", "FCB", "HSV"),
                 Pkt=c(25,23,12,18,21),
                 row.names=c("C", "B", "A", "F", "E"))
teams <- c("HSV", "BVB", "BRE", NA)
ind <- c("C", "A", "G", "F", "C", "B", "B", NA)

matchColumnIndex(teams, df, 1L, multi=FALSE)
matchColumnIndex(teams, df, 1L, multi=TRUE)
matchColumnIndex(teams, df, "Team", multi=FALSE)
matchColumnIndex(teams, df, "Team", multi=TRUE)
matchColumnIndex(teams, df, 0, multi=FALSE)
matchColumnIndex(ind, df, 0, multi=FALSE)
matchColumnIndex(ind, df, 0, multi=TRUE)

matchColumn(teams, df, 1L, multi=FALSE)
matchColumn(teams, df, 1L, multi=TRUE)
matchColumn(teams, df, "Team", multi=FALSE)
matchColumn(teams, df, "Team", multi=TRUE)
matchColumn(ind, df, 0, multi=FALSE)
matchColumn(ind, df, 0, multi=TRUE)

Match a given vector to column names of a data.frame or matrix

Description

Match a given vector to column names of a data.frame or matrix

Usage

matchColumnName(data.frame.cols, reqCols, ignore.case = FALSE)

Arguments

Value

A vector of integers as indices

Examples

myTestDf <- data.frame(HBV=1:3, VFB=0:2, BVB=4:6, FCB=2:4)
myFavTeams <- c("HBV", "BVB")
matchColumnName(myTestDf, myFavTeams)
myFavTeamsCase <- c("hbv", "bVb")
matchColumnName(myTestDf, myFavTeamsCase, ignore.case=TRUE)
## NA will be returned in this case if ignore.case is set to FALSE
matchColumnName(myTestDf, myFavTeamsCase, ignore.case=FALSE)

Transform a matrix into a long-format data.frame

Description

The function converts a matrix into a long-format, three-column data.frame, containing row, columna nd value. Such ‘long’ data.frames can be useful in data visualization and modelling.

Usage

matrix2longdf(
  mat,
  row.names,
  col.names,
  longdf.colnames = c("row", "column", "value")
)

Arguments

Details

The function converts a matrix into a three-column, ‘long’ format data.frame containing row names, column names, and values of the matrix.

Value

A data.frame object with three columns: row, column and value. If the input matrix is of dimesion MxN, the returning data.frame is of the dimension MNx3.

Note

The length of row.names and col.names should be as the same as the matrix dimension. Otherwise the function raises warnings.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

test.mat <- matrix(1:12, ncol=4, nrow=3, dimnames=list(LETTERS[1:3],
LETTERS[1:4]))
print(test.mat)
print(matrix2longdf(test.mat))
print(matrix2longdf(test.mat, longdf.colnames=c("From", "To", "Time")))

Merge infrequent levels by setting the threshold of the proportion of cumulative sum over sum a.k.a. cumsumprop

Description

Merge infrequent levels by setting the threshold of the proportion of cumulative sum over sum a.k.a. cumsumprop

Usage

mergeInfreqLevelsByCumsumprop(
  classes,
  thr = 0.9,
  mergedLevel = "others",
  returnFactor = TRUE
)

Arguments

Value

A character string vector or a factor, of the same length as the input classes, but with potentially fewer levels.

Note

In case only one class is deemed as infrequent, its label is unchanged.

Examples

set.seed(1887)
myVals <- sample(c(rep("A", 4), rep("B", 3), rep("C", 2), "D"))
## in the example below, since A, B, C make up of 90% of the total,
## D is infrequent. Since it is alone, it is not merged
mergeInfreqLevelsByCumsumprop(myVals, 0.9) 
mergeInfreqLevelsByCumsumprop(myVals, 0.9, returnFactor=FALSE) ## return characters
## in the example below, since A and B make up 70% of the total, 
## and A, B, C 90%, they are all frequent and D is infrequent. 
## Following the logic above, no merging happens
mergeInfreqLevelsByCumsumprop(myVals, 0.8)
mergeInfreqLevelsByCumsumprop(myVals, 0.7) ## A and B are left, C and D are merged
mergeInfreqLevelsByCumsumprop(myVals, 0.5) ## A and B are left, C and D are merged
mergeInfreqLevelsByCumsumprop(myVals, 0.4) ## A is left
mergeInfreqLevelsByCumsumprop(myVals, 0.3) ## A is left

Multiple identical

Description

Testing whether multiple objects are identical

Usage

midentical(
  ...,
  num.eq = TRUE,
  single.NA = TRUE,
  attrib.as.set = TRUE,
  ignore.bytecode = TRUE,
  ignore.environment = FALSE,
  ignore.srcref = TRUE,
  extptr.as.ref = FALSE
)

Arguments

Details

midentical extends identical to test multiple objects instead of only two.

Value

A logical value, TRUE if all objects are identical

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

identical

Examples

set1 <- "HSV"
set2 <- set3 <- set4 <- c("HSV", "FCB")

midentical(set1, set2)
midentical(list(set1, set2))

midentical(set2, set3, set4)
midentical(list(set2, set3, set4))

## other options passed to identical
midentical(0, -0, +0, num.eq=FALSE)
midentical(0, -0, +0, num.eq=TRUE)

Multiple matching

Description

Multiple matching between two vectors. Different from R-native match function, where only one match is returned even if there are multiple matches, mmatch returns all of them.

Usage

mmatch(x, table, nomatch = NA_integer_)

Arguments

Details

Multiple matches can be useful in many cases, and there is no native R function for this purpose. User can write their own functions combining lapplying with match or %in%, our experience however shows that such non-vectorized function can be extremely slow, especially when the x or table vector gets longer.

mmatch delegates the multiple-matching task to a C-level function, which is optimized for speed. Internal benchmarking shows improvement of hundred fold, namely using mmatching costs about 1/100 of the time used by R-implementation.

Value

A list of the same length as the input x vector. Each list item contains the matching indices in ascending order (similar to match).

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>, C-code was adapted from the program written by Roland Schmucki.

See Also

match

Examples

vec1 <- c("HSV", "BVB", "FCB", "HSV", "BRE", "HSV", NA, "BVB")
vec2 <- c("FCB", "FCN", "FCB", "HSV", "BVB", "HSV", "FCK", NA, "BRE", "BRE")

mmatch(vec1, vec2)

## compare to match
match(vec1, vec2)

Operations for multiple sets

Description

Set operation functions in the base package, union, intersect and setdiff, can only be applied to binary manipulations involving two sets. Following functions, munion, mintersect and msetdiff, extend their basic versions to deal with multiple sets.

Usage

munion(...)

Arguments

Details

These functions apply set manipulations (union, intersect, or difference) in a sequential manner: the first two sets are considered first, then the third, the fourth and so on, till all sets have been visited.

Value

A vector of set operation results. Can be an empty vector if no results were returned.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

union, intersect and setdiff.

Examples

set1 <- c("HSV", "FCB", "BVB", "FCN", "HAN")
set2 <- c("HSV", "FCB", "BVB", "HAN")
set3 <- c("HSV", "BVB", "FSV")

munion(set1, set2, set3)
mintersect(set1, set2, set3)
msetdiff(set1, set2, set3)

## sets can be given in a list as well
munion(list(set1, set2, set3))
mintersect(list(set1, set2, set3))
msetdiff(list(set1, set2, set3))

Replace NA with FALSE

Description

Replace NA in a vector with FALSE

Usage

na.false(x)

Arguments

Value

Logical vector or matrix with NAs replaced by FALSE

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

myX <- c("HSV", "FCK", "FCN", NA, "BVB") res <- myX == "HSV" na.false(res)

Calculate pairwise distances between each pair of items in a list

Description

Calculate pairwise distances between each pair of items in a list

Usage

naivePairwiseDist(list, fun = jaccardIndex)

Arguments

Value

A symmetric matrix of dimension mxm, where m is the length of the list

This function is inefficient compared with matrix-based methods. It is exported just for education and for verifying results of matrix-based methods.

Examples

myList <- list(first=LETTERS[3:5], second=LETTERS[1:3], third=LETTERS[1:5], fourth=LETTERS[6:10])
naivePairwiseDist(myList, fun=jaccardIndex)
## despite of the name, any function that returns a number can work
naivePairwiseDist(myList, fun=jaccardDistance)

Ordered factor

Description

Build a factor using the order of input character strings

Usage

ofactor(x, ...)

Arguments

Value

Factor with levels in the same order of the input strings.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

factor

Examples

testStrings <- c("A", "C", "B", "B", "C")
(testFac <- factor(testStrings))
(testOfac <- ofactor(testStrings))

stopifnot(identical(levels(testOfac), c("A", "C", "B")))

Reorder the groups by their group size

Description

Reorder the groups by their group size

Usage

orderCutgroup(groups, decreasing = TRUE)

Arguments

Overlap coefficient, also known as Szymkiewicz-Simpson coefficient

Description

Overlap coefficient, also known as Szymkiewicz-Simpson coefficient

Usage

overlapCoefficient(x, y, checkUniqueNonNA = FALSE)

overlapDistance(x, y, checkUniqueNonNA = FALSE)

Arguments

Value

The overlap coefficient

See Also

jaccardIndex

overlapCofficient calculates the overlap coefficient, and overlapDistance is defined by 1-overlapCoefficient.

Examples

myX <- 1:6
myY <- 4:9
overlapCoefficient(myX, myY)

myY2 <- 4:10
overlapCoefficient(myX, myY2)
## compare the result with Jaccard Index
jaccardIndex(myX, myY2)

## overlapDistance
overlapDistance(myX, myY2)

Overwrite a directory

Description

Overwrite a directory

Usage

overwriteDir(dir, action = c("ask", "overwrite", "append", "no"))

Arguments

Value

If action is set to overwrite, the directory will be deleted recursively if it exists, a new directory with the same name will be created, and the function returns TRUE. If append is set, the function creates the directory if necessary and returns TRUE. If no is set, the function does nothing and returns.

If action is set to ask, user will be prompted for actions.

If overwrite is set, the directory will be removed and written anew.

If append is set, in contrast to overwrite, the directory and the files in it are not removed if they exists. In this case, files with the same name will be overwritten. Otherwise, new directories or files will be simply created. On the other hand, if the directory does not exist, it will be created.

If no is set, no action will be taken. The funciton returns FALSE.

Examples

createTempDir <- function() {
  tmpdir <- tempdir()
  tmpfile1 <- tempfile(tmpdir=tmpdir)
  tmpfile2 <- tempfile(tmpdir=tmpdir)

  writeLines("First file", tmpfile1)
  writeLines("Second file", tmpfile2)
  return(tmpdir)
}
newTempFile <- function(tmpdir) {
  writeLines("Third file", tempfile(tmpdir=tmpdir))
}

  tmpdir <- createTempDir()
  overwriteDir(tmpdir, action="ask")

  ## overwrite: delete the directory and create it a new
  tmpdir <- createTempDir()
  fileCount <- length(dir(tmpdir))
  dir(tmpdir) ## two files should be there
  overwriteDir(tmpdir, action="overwrite")
  newTempFile(tmpdir)
  dir(tmpdir) ## now there should be only one file
  stopifnot(length(dir(tmpdir))==1)

  ## append: append files, and overwrite if a file of the same name is there
  overwriteDir(tmpdir, action="append")
  newTempFile(tmpdir)
  dir(tmpdir) ## a new file is written
  stopifnot(length(dir(tmpdir))==2)

  ## no: no action, and returns FALSE
  noRes <- overwriteDir(tmpdir, action="no")
  stopifnot(!noRes)

Transform p-values to continuous scores with the absolute-log10 transformation

Description

The function maps p values between 0 and 1 to continuous scores ranging on R by the following equation: abs(log10(p))*sign

Usage

pAbsLog10Score(p, sign = 1, replaceZero = TRUE)

Arguments

Value

A numeric vector of transformed p-values using signed -log10 transformation.

See Also

pQnormScore, pScore, replaceZeroPvalue

Examples

testPvals <- c(0.001, 0.01, 0.05, 0.1, 0.5, 1)
pAbsLog10Score(testPvals)
testPvalSign <- rep(c(-1,1), 3)
pAbsLog10Score(testPvals, sign=testPvalSign)
testLog <- rep(c(TRUE, FALSE),3)
pAbsLog10Score(testPvals, testLog)

Transform p-values to continuous scores with the quantile function of the normal distribution

Description

Quantile function, also known as the inverse of cumulative distribution function of the normal distribution, is used to map p-values to continuous scores raging on R. The signs of the resulting scores are positive by default and are determined by the parameter sign.

Usage

pQnormScore(p, sign = 1, replaceZero = TRUE)

Arguments

Value

A numeric vector of transformed p-values using signed quantile normal transformation.

See Also

pAbsLog10Score, pScore, double

Examples

testPvals <- c(0.001, 0.01, 0.05, 0.1, 0.5, 1)
pQnormScore(testPvals)
testPvalSign <- rep(c(-1,1), 3)
pQnormScore(testPvals, sign=testPvalSign)
testLog <- rep(c(TRUE, FALSE),3)
pQnormScore(testPvals, testLog)

Transform p-values to continuous scores

Description

The function wraps other functions to map p values ranging on (0,1] to continuous scores ranging on R in a number of ways.

Usage

pScore(p, sign = 1, method = c("qnorm", "absLog10"), replaceZero = TRUE)

Arguments

Value

A numeric vector of transformed p-values using the specified method.

See Also

pAbsLog10Score, pQnormScore

Examples

testPvals <- c(0.001, 0.01, 0.05, 0.1, 0.5, 1)
pScore(testPvals, method="absLog10")
pScore(testPvals, method="qnorm")
testPvalSign <- rep(c(-1,1), 3)
pScore(testPvals, sign=testPvalSign, method="absLog10")
pScore(testPvals, sign=testPvalSign, method="qnorm")
testLog <- rep(c(TRUE, FALSE),3)
pScore(testPvals, testLog, method="absLog10")
pScore(testPvals, testLog, method="qnorm")

testPvals <- 10^seq(-5, 0, 0.05)
plot(pScore(testPvals, method="qnorm"),
     pScore(testPvals, method="absLog10"),
     xlab="pQnormScore", ylab="pAbsLog10Score"); abline(0,1, col="red", lty=2)

Calculate pairwise Jaccard Indices between each pair of items in a list

Description

Calculate pairwise Jaccard Indices between each pair of items in a list

Usage

pairwiseJaccardIndex(list)

pairwiseJaccardDistance(list)

Arguments

Value

A symmetric matrix of dimension mxm, where m is the length of the list

pairwiseJaccardDistance is defined as 1-pairwiseJaccardIndex.

Examples

myList <- list(first=LETTERS[3:5], second=LETTERS[1:3], third=LETTERS[1:5], fourth=LETTERS[6:10])
pairwiseJaccardIndex(myList)

poormanPJI <- function(list) {
  sapply(list, function(x) sapply(list, function(y) jaccardIndex(x,y)))
}
stopifnot(identical(pairwiseJaccardIndex(myList), poormanPJI(myList)))

Calculate pairwise overlap coefficients between each pair of items in a list

Description

Calculate pairwise overlap coefficients between each pair of items in a list

Usage

pairwiseOverlapDistance(list)

pairwiseOverlapCoefficient(list)

Arguments

Value

A symmetric matrix of dimension mxm, where m is the length of the list

pairwiseOverlapDistance is defined the pairwise overlap distance.

Examples

myList <- list(first=LETTERS[3:5], second=LETTERS[1:3], third=LETTERS[1:5], fourth=LETTERS[6:10])
pairwiseOverlapCoefficient(myList)
pairwiseOverlapDistance(myList)

poormanPOC <- function(list) {
  sapply(list, function(x) sapply(list, function(y) overlapCoefficient(x,y)))
}
stopifnot(identical(pairwiseOverlapCoefficient(myList), poormanPOC(myList)))

Print a decimal number in procent format

Description

Print a decimal number in procent format

Usage

percentage(x, fmt = "1.1")

Arguments

Value

Character string

Examples

percentage(c(0,0.1,0.25,1))
percentage(c(0,0.1,0.25,1), fmt="1.4")
percentage(c(0,-0.1,0.25,-1), fmt="+1.1")

Print BEDAinfo object

Description

Print BEDAinfo object

Usage

## S3 method for class 'BEDAinfo'
print(x, ...)

Arguments

Value

Invisible NULL, only side effect is used

Examples

print(bedaInfo())

Rearrange columns to put some columns to far left

Description

This function is helpful to export tables where certain columns are desired to be placed to the most left of the data.frame

Usage

putColsFirst(data.frame, columns)

Arguments

Value

data.frame with re-arranged columns

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

clubs <- data.frame(Points=c(21,23,28,24), Name=c("BVB", "FCB", "HSV",
"FCK"), games=c(12,11,11,12))
putColsFirst(clubs, c("Name"))
putColsFirst(clubs, c("Name", "games"))

Decode password with function implemented with pwencode

Description

Decode password encypted with pwencode.

Usage

pwdecode(password)

Arguments

Details

See pwdecode function documentation in BIOS for implemetnation details.

Note that since R does not support strings embedding null values (\000), the password to be decoded has to be given with two slashes, e.g. ‘ \001\000\129\235’.

Value

Decoded character string, or empty string if decoding fails

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>. The C library code was written by Detlef Wolf.

Examples

mycode <- " \\001\\000\\141\\314\\033\\033\\033\\033\\033\\142\\303\\056\\166\\311\\037\\042"
pwdecode(mycode)

Encode a password

Description

Encode a password

Usage

pwencode(label = "VAR", key)

Arguments

Value

Character string, encoded password

Quitely Quit with Messages

Description

Quitely quit R with messages in non-interactive sessions

Usage

qqmsg(..., status = 0, save = FALSE, runLast = TRUE)

Arguments

Details

The function prints messages in any case, and quits R if the current session is non-interactive, e.g. in the command-line running Rscript mode

Value

Invisible NULL, only side effect is used.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

quit

Examples

## the example should not run because it will lead the R session to quit
## Not run: 
  qqmsg()
  qqmsg("die", status=0)
  qqmsg("Avada kedavra", status=-1)
  qqmsg("Crucio!", "\n", "Avada kedavra", status=-100)

## End(Not run)

Quietly runs a system command

Description

Quietly runs a system command: the output is internalized and returned as an invisible variable, and the standard error output is ignored.

Usage

qsystem(command)

Arguments

Details

The function runs the system command in a quiet mode. The function can be useful in CGI scripts, for instance

Value

(Invisibly) the internalized output of the command

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

dateIntern <- system("date")

Sort numeric factor levels by values

Description

Factor variables with numbers as levels are alphabetically ordered by default, which requires rearrangements for various purposes, e.g. modelling or visualizations. This function re-orders levels of numeric factor variables numerically.

Usage

refactorNum(x, decreasing = FALSE)

Arguments

Value

A factor variable, with sorted numeric values as levels

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

(nums <- factor(c("2","4","24","1","2","125","1","2","125")))
(nums.new <- refactorNum(nums))

The functions registerLog and doLog provide a simple mechanism to handle loggings (printing text messages to files or other types of connections) in R.

Description

Users can register arbitrary numbers of loggers with registerLog, and the functions take care of low-level details such as openning and closing the connections.

Usage

registerLog(..., append = FALSE)

Arguments

Details

Input parameters can be either character strings or connections (such as the objects returned by stdout() or pipe().

If a character string is registered as a logger, it is assumed as a file name (user must make sure that it is writable/appendable). In case the file exists, new logging messages will be appended; otherwise if the file does not exists, it will be created and the logging messages will be written to the file.

A special case is the parameter value "-": it will be interpreted as standard output.

if a connection is registered as a logger, it must be writable in order to write the logging messages.

Each parameter will be converted to a connection object, which will be closed (when applicable) automatically before R quits.

If the parameter is missing (or set to NA or NULL), no logging will take place.

Value

No value returned: its side effect is used.

Note

Currently, the loggers are stored in a variable in the namespace of ribiosUtils named RIBIOS_LOGGERS. This is only for internal use of the package and may change any time, therefore users are not advised to manipulate this variable directly.

To clear the registered loggers, use clearLog.To flush the registered loggers, use flushLog. Usually it is not necessary to use flushLog in R scripts, since by program exit the active R session will automatically flush and close the connections (in addition, frequent flushing may decrease the program's efficiency). However, if used in interactive sessions, sometimes flushLog is needed to force R write all log files to all connections that are registered.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

doLog writes messages iteratively to each connection registered by registerLog.

Examples

  logfile1 <- tempfile()
  logfile2 <- tempfile()
  logcon3 <- stdout()
  if(.Platform$OS.type == "unix") {
    registerLog("/dev/null")
  } else {
    registerLog(tempfile())
  }
  registerLog(logfile1)
  registerLog(logfile2)
  registerLog(logcon3)

  doLog("Start logging")
  doLog("Do something...")
  doLog("End logging")

  flushLog() ## usually not needed, see notes

  txt1 <- readLines(logfile1)
  txt2 <- readLines(logfile2)

  cat(txt1)
  cat(txt2)

  clearLog()

  registerLog(logfile1, logfile2, logcon3)

  doLog("Start logging - round 2")
  doLog("Do something again ...")
  doLog("End logging - for good")

  flushLog() ## usually not needed, see notes

  txt1 <- readLines(logfile1)
  txt2 <- readLines(logfile2)

  cat(txt1)
  cat(txt2)

  ## clean up files and objects to close unused connections
  closeLoggerConnections()

Relevel a factor by a named or unnamed vector.

Description

This function wraps relevelsByNamedVec for named vector and relevelsByNotNamedVec for not named vectors

Usage

relevels(
  x,
  refs,
  missingLevels = c("pass", "warning", "error"),
  unrecognisedLevels = c("warning", "pass", "error")
)

Arguments

Value

A vector of factor

See Also

relevelsByNamedVec and relevelsByNotNamedVec

Examples

oldFactor <- factor(c("A", "B", "A", "C", "B"), levels=LETTERS[1:3])
refLevels <- c("B", "C", "A")
refDict <- c("A"="a", "B"="b", "C"="c")
newFactor <- relevels(oldFactor, refLevels)
stopifnot(identical(newFactor, factor(c("A", "B", "A", "C", "B"), levels=c("B", "C", "A"))))
newFactor2 <-  relevels(oldFactor, refDict)
stopifnot(identical(newFactor2, factor(c("a", "b", "a", "c", "b"), levels=c("a", "b", "c"))))

Relevel a factor by a named vector.

Description

If names contain character strings other than the levels in the old factor and warning is set to TRUE, a warning will be raised.

Usage

relevelsByNamedVec(
  x,
  refs,
  missingLevels = c("pass", "warning", "error"),
  unrecognisedLevels = c("warning", "pass", "error")
)

Arguments

Details

The levels of the factor are the names of the ref vector, and the order of the ref vector matters: it is the levels of the new factor.

Value

A vector of factor

Examples

oldFactor <- factor(c("A", "B", "A", "C", "B"), levels=LETTERS[1:3])
factorDict <- c("A"="a", "B"="b", "C"="c")
newFactor <- relevelsByNamedVec(oldFactor, factorDict)
stopifnot(identical(newFactor, factor(c("a", "b", "a", "c", "b"), levels=c("a", "b", "c"))))
## TODO: test warning and error

Relevel a factor by a unnamed vector.

Description

If names contain character strings other than the levels in the old factor and warning is set to TRUE, a warning will be raised

Usage

relevelsByNotNamedVec(
  x,
  refs,
  missingLevels = c("pass", "warning", "error"),
  unrecognisedLevels = c("warning", "pass", "error")
)

Arguments

Value

A vector of factor

Examples

oldFactor <- factor(c("A", "B", "A", "C", "B"), levels=LETTERS[1:3])
refLevels <- c("B", "C", "A")
newFactor <- relevelsByNotNamedVec(oldFactor, refLevels)
stopifnot(identical(newFactor, factor(c("A", "B", "A", "C", "B"), levels=c("B", "C", "A"))))
## TODO: test warning and error

Reload a package

Description

Reload a package by first detaching and loading the library.

Usage

reload(pkg)

Arguments

Value

Side effect is used.

Note

So far only character is accepted

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

detach and library

Examples

## the example should not run because it will reload the package
## Not run: 
  reload(ribiosUtils)

## End(Not run)

Remove columns

Description

Remove columns from a data.frame object

Usage

removeColumns(data.frame, columns, drop = FALSE)

Arguments

Details

The function is equivalent to the subsetting operation with brackets. It provides a tidy programming interface to manupulate data.frames.

Value

data.frame with specified columns removed

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

clubs <- data.frame(Points=c(21,23,28,24), Name=c("BVB", "FCB", "HSV",
"FCK"), games=c(12,11,11,12))
removeColumns(clubs,c("Name"))

Remove rows or column by function

Description

Remove rows or column by function

Usage

removeColumnsByFunc(matrix, removeFunc)

removeRowsByFunc(matrix, removeFunc)

Arguments

Value

A matrix with rows or columns whose return value of removeFunc is TRUE

Examples

myMat <- matrix(c(1, 3 ,5, 4, 5, 6, 7, 9, 11), byrow=FALSE, nrow=3)
removeColumnsByFunc(myMat, removeFunc=function(x) any(x %% 2 == 0))
removeRowsByFunc(myMat, removeFunc=function(x) any(x %% 2 == 0))

Remove columns in a matrix that contains one or more NAs

Description

Remove columns in a matrix that contains one or more NAs

Usage

removeColumnsWithNA(mat)

Arguments

Value

A matrix, with columns containing one or more NAs removed

Examples

myMat <- matrix(c(1:9, NA, 10:17), nrow=6, byrow=TRUE,
  dimnames=list(sprintf("R%d", 1:6), sprintf("C%d", 1:3)))
removeColumnsWithNA(myMat)

Remove invariable columns from a data frame or matrix

Description

Columns with one unique value are invariable. The functions help to remove such columns from a data frame (or matrix) in order to highlight the variables.

Usage

removeInvarCol(df)

Arguments

Details

removeInvarCol the data frame removing invariable column(s).

isVarCol and isInvarCol are helper functions, returning a logical vector indicating the variable and invariable columns respectively.

Value

isVarCol and isInvarCol return a logical vector indicating the variable and invariable columns respectively.

removeInvarCol removes invariable columns.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

 testDf <- data.frame(a=1:4, b=7, c=LETTERS[1:4])
isVarCol(testDf)
isInvarCol(testDf)
removeInvarCol(testDf)

Remove rows in a matrix that contains one or more NAs

Description

Remove rows in a matrix that contains one or more NAs

Usage

removeRowsWithNA(mat)

Arguments

Value

A matrix, with rows containing one or more NAs removed

Examples

myMat <- matrix(c(1:9, NA, 10:17), nrow=6, byrow=TRUE,
  dimnames=list(sprintf("R%d", 1:6), sprintf("C%d", 1:3)))
removeRowsWithNA(myMat)

Replace column names in data.frame

Description

Replace column names in data.frame

Usage

replaceColumnName(data.frame, old.names, new.names)

Arguments

Value

Data.frame with column names updated

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

clubs <- data.frame(Points=c(21,23,28,24), Name=c("BVB", "FCB", "HSV",
"FCK"), games=c(12,11,11,12))
replaceColumnName(clubs, c("Points", "games"), c("Punkte", "Spiele"))

Replace p-values of zero

Description

Replace p-values of zero

Usage

replaceZeroPvalue(p, factor = 1)

Arguments

Value

A numeric vector of the same length as the input vector, with zeros replaced by the minimal absolute double value defined by the machine multiplied by the factor.

Note

Values under the minimal positive double value are considered zero and replaced.

Examples

ps <- seq(0,1,0.1)
replaceZeroPvalue(ps)
replaceZeroPvalue(ps, factor=2)

A temporary directory which (1) every machine in the cluster has access to and (2) has sufficient space

Description

A temporary directory which (1) every machine in the cluster has access to and (2) has sufficient space

Usage

ribiosTempdir()

Value

a character string of the directory name

See Also

ribiosTempfile

A temporary file which (1) every machine in the cluster has access to and (2) there is sufficient space

Description

A temporary file which (1) every machine in the cluster has access to and (2) there is sufficient space

Usage

ribiosTempfile(pattern = "file", tmpdir = ribiosTempdir(), fileext = "")

Arguments

Value

a character string of the file name

See Also

ribiosTempdir

ribiosUtils

Description

ribiosUtils is a swiss-knife package providing misc utilities

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>, with inputs from Clemens Broger, Martin Ebeling, Laura Badi and Roland Schmucki

Remove temporary files at a specified time interval from now

Description

Send a at job to remove (probably temporary) files in the future with a specified time interval from now

Usage

rmat(..., days = NULL, hours = NULL, minutes = NULL, dry = TRUE)

Arguments

Details

The command will delete files, and there is usually no way to get deleted files back. So make sure you know what you are doing!

Days, hours, and minutes can be given in a mixed way: they will be summed up to give the interval.

Value

(Invisibly) the output of the at job.

Note

Since the command uses at internally, it is unlikely the command will work in the Windows system “out of box”.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

qsystem for running system commands quietly.

Examples

tmp1 <- tempfile()
tmp2 <- tempfile()
rmat(tmp1, tmp2, minutes=1)

Extract core identifiers from Roche compound IDs

Description

Extract core identifiers from Roche compound IDs

Usage

rocheCore(str, short = FALSE)

Arguments

Value

Core identifiers if the element is a Roche compound ID, the original element otherwise Non-character input will be converted to character strings first.

See Also

isRocheCompoundID

Examples

rocheCore(c("RO1234567-001", "RO1234567-001-000", "RO1234567", 
    "ROnoise-001", "anyOther-not-affected"))
rocheCore(c("RO1234567-001", "RO1234567-001-000", "RO1234567",
    "ROnoise-001","anyOther-not-affected"), short=TRUE)

S3 method for row-scaling

Description

S3 method for row-scaling

Usage

rowscale(x, center = TRUE, scale = TRUE)

Arguments

Value

The input object with rows scaled

Scale a matrix by row

Description

Scaling a matrix by row can be slightly slower due to a transposing step.

Usage

## S3 method for class 'matrix'
rowscale(x, center = TRUE, scale = TRUE)

Arguments

Value

A matrix with each row scaled.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

scale

Examples

mat <- matrix(rnorm(20), nrow=4)
rs.mat <- rowscale(mat)

print(mat)
print(rs.mat)
rowMeans(rs.mat)
apply(rs.mat, 1L, sd)

rowscale(mat, center=FALSE, scale=FALSE) ## equal to mat
rowscale(mat, center=TRUE, scale=FALSE)
rowscale(mat, center=FALSE, scale=TRUE)

Scale a table by row

Description

Scaling a table by row can be slightly slower due to a transposing step.

Usage

## S3 method for class 'table'
rowscale(x, center = TRUE, scale = TRUE)

Arguments

Value

A table with each row scaled.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

scale

Examples

letterDf <- data.frame(from=c("A", "A", "B", "C"), to=c("A", "B", "C", "A"))
tbl <- table(letterDf$from, letterDf$to)
tblRowscale <- rowscale(tbl)

print(tbl)
print(tblRowscale)
rowMeans(tblRowscale)
apply(tblRowscale, 1L, sd)

rowscale(tbl, center=FALSE, scale=FALSE) ## equal to mat
rowscale(tbl, center=TRUE, scale=FALSE)
rowscale(tbl, center=FALSE, scale=TRUE)

Reverse rank order

Description

Reverse rank order

Usage

rrank(x, ...)

## Default S3 method:
rrank(x, ...)

Arguments

Value

A vector of numbers of the same length as the input, giving reverse rank orders.

The function returns the reverse rank order, i.e. in the descending order

See Also

rank

Examples

testVec <- c(3,6,4,5)
rank(testVec)
rrank(testVec)

Get reverse rank orders in each column

Description

Get reverse rank orders in each column

Usage

## S3 method for class 'matrix'
rrank(x, ...)

Arguments

Value

A matrix of the same dimension and attributes of the input matrix, with reverse rank orders of each column

Examples

testMatrix <- matrix(c(3,6,4,5,2,4,8,3,2,5,4,7), ncol=3, byrow=FALSE)
rrank(testMatrix)

Return a matrix that highlights reverse rank orders of features of interest by column

Description

Return a matrix that highlights reverse rank orders of features of interest by column

Usage

rrankInd(matrix, ind, inValue = 1L, outValue = 0L, ...)

Arguments

Value

A matrix of the same dimension and attributes of the input matrix, each column contains a vector of inValue and outValue. Positions that match the reverse ranks of matrix values indexed by ind are assigned the inValue, otherwise, the outValue.

The function can be used to visualize the reverse ranks of features of interest (rows of the input matrix) in each sample (columns of the input matrix). This is for instance useful for rank plots of features for gene-set enrichment analysis.

Imagine that all features indexed by ind are the larger than all other features in each sample, then the returned matrix will have value 1 in the first rows (the number determined by the features indxed by ind), and 0 in the rest rows.

See Also

rank

Examples

testMatrix <- matrix(c(3,6,4,5,2,4,8,3,2,5,4,7), ncol=3, byrow=FALSE)
print(testMatrix)
testInd <- c(2,4)
## verify that the command below returns 1 in positions occupied by 
## the reverse ranks of the values indexed by testInd
rrankInd(testMatrix, testInd)
testIndBool <- c(FALSE, TRUE, FALSE, TRUE)
rrankInd(testMatrix, testIndBool)

Reverse setdiff

Description

reverse setdiff, i.e. rsetdiff(x,y) equals setdiff(y,x)

Usage

rsetdiff(x, y)

Arguments

Value

Similar to setdiff, but with elements in y but not in x

Author(s)

Jitao David Zhang

Examples

testVec1 <- LETTERS[3:6]
testVec2 <- LETTERS[5:7]
rsetdiff(testVec1, testVec2)

Prepare R for an interactive script

Description

The function prepares R for an interactive session (e.g. in a script). Currently it defines behaviour in case of errors: a file named “ribios.dump” is written.

Usage

scriptInit()

Value

Side effect is used.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

options

Examples

## do not run unless the script mode is needed

  scriptInit()

Functions for command-line Rscript debugging

Description

These functions are used to debug command-line executable Rscripts in R sessions

Usage

setDebug()

Details

setDebug sets the environmental variable RIBIOS_SCRIPT_DEBUG as TRUE. unsetDebug unsets the variable. isDebugging checks whether the variable is set or not. isIntDebugging tests whether the scripts runs interactively or runs in the debugging mode. The last one can be useful when debugging Rscript in a R session.

A programmer wishing to debug a Rscript can explicitly set (or unset) the RIBIOS_SCRIPT_DEBUG variable in order to activate (inactivate) certain trunks of codes. This can be automated via isDebugging, or probably more conveniently, by isIntDebugging: if the script runs in an interactive mode, or the debugging flag is set, the function returns TRUE.

Value

setDebug and unsetDebug returns an invisible value indicating whether the variable setting (unsetting) was successful.

isDebugging and isIntDebugging returns logical values.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

unsetDebug()
print(isDebugging())
setDebug()
print(isDebugging())
unsetDebug()
print(isDebugging())
print(isIntDebugging())

Shorten Roche compounds identifiers

Description

Shorten Roche compounds identifiers

Usage

shortenRocheCompoundID(str)

Arguments

Value

Character strings of the same length as the input, with all core identifiers shortened

In contrast to rocheCore, which only handles character strings that are valid Roche compound identifiers, this function takes any input string and performs a gsub operation to shorten Roche core numbers. Therefore, it even works when only a substring matches the pattern of a Roche compound name.

Examples

shortenRocheCompoundID(c("RO1234567-001", "RO1234567-001-000", 
"RO1234567", "ROnoise-001", "anyOther-not-affected", 
"RO1234567 and RO9876543 are two imaginary compounds."))

Shorten strings to a given number of characters

Description

Shorten strings to a given number of characters

Usage

shortenStr(str, nchar = 8)

Arguments

Value

A vector of strings of the same length as the input, with each string shortened to the desired length

Strings with more characters than nchar will be shortened.

Note

NA will be kept as they are

Examples

inputStrs <- c("abc", "abcd", "abcde", NA)
shortenStr(inputStrs, nchar=4)
## expected outcome: abc, abcd, abcd..., NA

Keep silent by suppressing warnings and messages

Description

The function is used to keep the command silent by suppressing warnings and messages

Usage

silencio(...)

Arguments

Value

The same as the function call

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

suppressWarnings, suppressMessages

Examples

wsqrt <- function(x) {warning("Beep");message("Calculating square");return(x^2)}
silencio(wsqrt(3))

Sort a numeric vector and filter by a threshold of cumsumprop

Description

Sort a numeric vector and filter by a threshold of cumsumprop

Usage

sortAndFilterByCumsumprop(x, thr = 0.9)

Arguments

Value

Another numeric vector, likely shorter than x, items whose cumsumprop is equal or lower than thr. The rest items are summed into one new item, with the name rest

This function can be useful to extract from a long numeric vector the largest items that dominate the sum of the vector

Examples

x <- c("A"=1,"B"=2,"C"=3,"D"=4,"E"=400,"F"=500)
sortAndFilterByCumsumprop(x, thr=0.99) ## F and E should be returned

Sort data.frame rows by values in specified columns

Description

Sort rows of an data.frame by values in specified columns.

Usage

sortByCol(
  data.frame,
  columns,
  na.last = TRUE,
  decreasing = TRUE,
  orderAsAttr = FALSE
)

Arguments

Details

Columns can be specified by integer indices, logical vectors or character names.

Value

Sorted data.frame

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

sample.df <- data.frame(teams=c("HSV", "BVB", "FCB", "FCN"),pts=c(18,17,17,9), number=c(7,7,6,6))
sortByCol(sample.df, 1L)
sortByCol(sample.df, 1L, decreasing=FALSE)

sortByCol(sample.df, c(3L, 1L))
sortByCol(sample.df, c(3L, 1L), decreasing=FALSE)
sortByCol(sample.df, c(3L, 2L))

sortByCol(sample.df, c(TRUE, FALSE, TRUE))

sortByCol(sample.df, c("teams", "pts"))
sortByCol(sample.df, c("pts", "number", "teams"))
sortByCol(sample.df, c("pts", "teams", "number"))

Sort matrix by dim names

Description

Rearrange rows and columns of a matrix by dim names

Usage

sortByDimnames(x, row.decreasing = FALSE, col.decreasing = FALSE)

Arguments

Value

Resorted matrix or data frame

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

testMat <- matrix(1:16, nrow=4, dimnames=list(c("B", "D", "A", "C"), c("t", "f", "a", "g")))
sortByDimnames(testMat)
sortByDimnames(testMat, row.decreasing=TRUE, col.decreasing=FALSE)

Tokenize strings by character

Description

Tokenize strings by character in a similar way as the strsplit function in the base package. The function can return a matrix of tokenized items when index is missing. If index is given, tokenized items in the selected position(s) are returned. See examples.

Usage

strtoken(x, split, index, ...)

Arguments

Value

A matrix if index is missing, NULL, or contains more than one integer indices; otherwise a character vector.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

References

The main body of the function is modified from the strsplit2 function in the limma package.

See Also

strsplit

Examples

myStr <- c("HSV\t1887", "FCB\t1900", "FCK\t1948")
strsplit(myStr, "\t")

strtoken(myStr, "\t")
strtoken(myStr, "\t", index=1L)
strtoken(myStr, "\t", index=2L)

myFac <- factor(myStr)
strtoken(myFac, "\t")
strtoken(myFac, "\t", index=1L)

Repeat garbage-collecting until all resource is freed

Description

stubborngc repeats collecting garbage untill no more resource can be freed

Usage

stubborngc(verbose = FALSE, reset = TRUE)

Arguments

Value

Side effect is used.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

gc

Examples

stubborngc()

Subset a data.frame by column name, allowing differences in cases

Description

The function calls assertColumnName internally to match the column names.

Usage

subsetByColumnName(data.frame, reqCols, ignore.case = FALSE)

Arguments

Value

If all required column names are present, the data.frame object will be subset to include only these columns and the result data.frame is returned. Otherwise an error message is printed.

Examples

myTestDf <- data.frame(HBV=1:3, VFB=0:2, BVB=4:6, FCB=2:4)
myFavTeams <- c("HBV", "BVB")
subsetByColumnName(myTestDf, myFavTeams)
myFavTeamsCase <- c("hbv", "bVb")
subsetByColumnName(myTestDf, myFavTeamsCase, ignore.case=TRUE)

Summarizing rows/columns by a factor

Description

Apply a function to summarize rows/columns that assigned to the same level by a factor vector.

Usage

summarizeRows(matrix, factor, fun = mean, ...)

Arguments

Details

NA levels are neglected, and corresponding rows/columns will not contribute to the summarized matrix.

summarizeCols is synonymous to summarizeColumns

Value

A matrix, the dimension will be determined by the number of levels of the factor vector.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

my.matrix <- matrix(1:25, nrow=5)
print(my.matrix)

my.factor <- factor(c("A", "B", "A", "C", "B"))
summarizeRows(matrix=my.matrix, factor=my.factor, fun=mean)
summarizeRows(matrix=my.matrix, factor=my.factor, fun=prod)
summarizeColumns(matrix=my.matrix, factor=my.factor, fun=mean)
summarizeColumns(matrix=my.matrix, factor=my.factor, fun=prod)

## NA values in factor
my.na.factor <- factor(c("A", "B", "A", "C", NA))
summarizeRows(matrix=my.matrix, factor=my.na.factor, fun=mean)
summarizeRows(matrix=my.matrix, factor=my.na.factor, fun=prod)
summarizeColumns(matrix=my.matrix, factor=my.na.factor, fun=mean)
summarizeColumns(matrix=my.matrix, factor=my.na.factor, fun=prod)

Trim leading and tailing spaces from string

Description

The function trims leading and/or tailing spaces from string(s), using C function implemented in the BIOS library.

Usage

trim(x, left = " \n\r\t", right = " \n\r\t")

Arguments

Details

left and right can be set to NULL. In such cases no trimming will be performed.

Value

Trimmed string(s)

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

myStrings <- c("This is a fine day\n",
               " Hallo Professor!",
               "  NUR DER HSV  ")
trim(myStrings)

Length of unique elements in a vector

Description

Length of unique elements in a vector

Usage

uniqueLength(x, incomparables = FALSE)

Arguments

Value

An integer indicating the number of unique elements in the input vector

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

unique

Examples

test.vec1 <- c("HSV", "FCB", "BVB", "HSV", "BVB")
uniqueLength(test.vec1)

test.vec2 <- c(1L, 2L, 3L, 5L, 3L, 4L, 2L, 1L, 5L)
ulen(test.vec2)

Make a vector free of NA and unique

Description

Make a vector free of NA and unique

Usage

uniqueNonNA(x)

Arguments

Value

A unique vector without NA

Examples

testVec <- c(3,4,5,NA,3,5)
uniqueNonNA(testVec)

Print messages conditional on the verbose level

Description

The verbose level can be represented by non-negative integers. The larger the number is, the more verbose is the program: it prints then more messages for users' information.

Usage

verbose(..., global = 1L, this = 1L)

Arguments

Details

This function decides whether or not to print a message, dependent on the global verbose level and the specific level of the message. If the specific level is larger than the global level, the message is suppresed; otherwise it is printed. see the details section for an example.

Suppose the global verbose level is set to 5, and two messages have levels of 1 and 7 repsectively. Since 1 suggests a low-threshold of being verbose, the first message is printed; whereas the message of level 7 is only printed when the program should run in a more verbose way (7,8,9,...{}), it is suppressed in the current global verbose level.

Value

The function is used for its side effect by printing messages.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

Examples

Gv <- 5L
verbose("Slightly verbosing", global=Gv, this=1L)
verbose("Moderately verbosing", global=Gv, this=5L)
verbose("Heavily verbosing", global=Gv, this=9L)

Translate well index numbers to well positions

Description

Translate well index numbers to well positions

Usage

wellIndex2position(ind, format = c("96", "384"))

Arguments

Value

A data.frame containing three columns: input WellIndex, Row (characters) and Column (integers)

Examples

wellIndex2position(1:96, format="96")
wellIndex2position(c(3,2,5,34,85, NA), format="96")
wellIndex2position(1:384, format="384")

System user name

Description

System user name

Usage

whoami()

Value

System user name

Examples

whoami()

Write text as log to a connection

Description

The function writeLog can be used to log outputs and/or running status of scripts to one connection. To use it one does not need to run registerLog first.

Usage

writeLog(fmt, ..., con = stdout(), level = 0)

Arguments

Details

In contrast, doLog can be used to log on multiple connections that are registered by registerLog. Therefore, to register logger(s) with registerLog is a prerequisite of calling doLog. Internally doLog calls writeLog sequentially to make multiple-connection logging.

Value

Side effect is used.

Author(s)

Jitao David Zhang <jitao_david.zhang@roche.com>

See Also

registerLog to register more than one loggers so that doLog can write to them sequentially.

Examples

  writeLog("This is the start of a log")
  writeLog("Message 1", level=1)
  writeLog("Message 1.1", level=2)
  writeLog("Message 1.2", level=2)
  writeLog("Message 2", level=1)
  writeLog("Message 3", level=1)
  writeLog("Message 3 (special)", level=4)
  writeLog("End of the log");

  ## log with format
  writeLog("This is Message %d", 1)
  writeLog("Square of 2 is %2.2f", sqrt(2))

  ## NA is handled automatically
  writeLog("This is a not available value: %s", NA, level=1)
  writeLog("This is a NULL value: %s", NULL, level=1)