# Programming in R

29 minute read

Source code downloads:     [ .Rmd ]     [ .R ]

## Overview

One of the main attractions of using the R (http://cran.at.r-project.org) environment is the ease with which users can write their own programs and custom functions. The R programming syntax is extremely easy to learn, even for users with no previous programming experience. Once the basic R programming control structures are understood, users can use the R language as a powerful environment to perform complex custom analyses of almost any type of data (Gentleman 2008).

## Why Programming in R?

• Powerful statistical environment and programming language
• Facilitates reproducible research
• Efficient data structures make programming very easy
• Ease of implementing custom functions
• Powerful graphics
• Access to fast growing number of analysis packages
• One of the most widely used languages in bioinformatics
• Is standard for data mining and biostatistical analysis
• Technical advantages: free, open-source, available for all OSs

## R Basics

The previous Rbasics tutorial provides a general introduction to the usage of the R environment and its basic command syntax. More details can be found in the R & BioConductor manual here.

## Code Editors for R

Several excellent code editors are available that provide functionalities like R syntax highlighting, auto code indenting and utilities to send code/functions to the R console.

Programming in R using RStudio
Programming in R using Vim or Emacs

## Finding Help

Reference list on R programming (selection)

## Control Structures

### Important Operators

#### Comparison operators

• == (equal)
• != (not equal)
• > (greater than)
• >= (greater than or equal)
• < (less than)
• <= (less than or equal)

#### Logical operators

• & (and)
• && (and)
• | (or)
• || (or)
• ! (not)

Note: & and && indicate logical AND, while | and || indicate logical OR. The shorter form performs element-wise comparisons of same-length vectors. The longer form evaluates left to right examining only the first element of each vector (can be of different lengths). Evaluation proceeds only until the result is determined. The longer form is preferred for programming control-flow, e.g. via if clauses.

### Conditional Executions: if Statements

An if statement operates on length-one logical vectors.

Syntax

if (TRUE) {
statements_1
} else {
statements_2
}

In the else component, avoid inserting newlines between } else. For details on how to best and consistently format R code, this style guide is a good start. In addition, the formatR package can be helpful.

Example

if (1==0) {
print(1)
} else {
print(2)
}
## [1] 2

Example 2

if (1==0) {
print(1)
} else if (1==2) {
print(2)
} else {
print(3)
}
## [1] 3

### Conditional Executions: ifelse Statements

The ifelse statement operates on vectors.

Syntax

ifelse(test, true_value, false_value)

Example

x <- 1:10
ifelse(x<5, sqrt(x), 0)
##  [1] 1.000000 1.414214 1.732051 2.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000

## Loops

### for loop

for loops iterate over elements of a looping vector.

Syntax

for(variable in sequence) {
statements
}

Example

mydf <- iris
myve <- NULL
for(i in seq(along=mydf[,1])) {
myve <- c(myve, mean(as.numeric(mydf[i,1:3])))
}
myve[1:8]
## [1] 3.333333 3.100000 3.066667 3.066667 3.333333 3.666667 3.133333 3.300000

Note: Inject into objecs is much faster than append approach with c, cbind, etc.

Example

myve <- numeric(length(mydf[,1]))
for(i in seq(along=myve)) {
myve[i] <- mean(as.numeric(mydf[i,1:3]))
}
myve[1:8]
## [1] 3.333333 3.100000 3.066667 3.066667 3.333333 3.666667 3.133333 3.300000

#### Conditional Stop of Loops

The stop function can be used to break out of a loop (or a function) when a condition becomes TRUE. In addition, an error message will be printed.

Example

x <- 1:10
z <- NULL
for(i in seq(along=x)) {
if (x[i] < 5) {
z <- c(z, x[i]-1)
print(z)
} else {
stop("values need to be < 5")
}
}

### while loop

Iterates as long as a condition is true.

Syntax

while(condition) {
statements
}

Example

z <- 0
while(z<5) {
z <- z + 2
print(z)
}
## [1] 2
## [1] 4
## [1] 6

### The apply Function Family

#### apply

Syntax

apply(X, MARGIN, FUN, ARGs)

Arguments

• X: array, matrix or data.frame
• MARGIN: 1 for rows, 2 for columns
• FUN: one or more functions
• ARGs: possible arguments for functions

Example

apply(iris[1:8,1:3], 1, mean)
##        1        2        3        4        5        6        7        8
## 3.333333 3.100000 3.066667 3.066667 3.333333 3.666667 3.133333 3.300000

#### tapply

Applies a function to vector components that are defined by a factor.

Syntax

tapply(vector, factor, FUN)

Example

iris[1:2,]
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
tapply(iris$Sepal.Length, iris$Species, mean)
##     setosa versicolor  virginica
##      5.006      5.936      6.588

#### sapply, lapply and vapply

The iterator functions sapply, lapply and vapply apply a function to vectors or lists. The lapply function always returns a list, while sapply returns vector or matrix objects when possible. If not then a list is returned. The vapply function returns a vector or array of type matching the FUN.VALUE. Compared to sapply, vapply is a safer choice with respect to controlling specific output types to avoid exception handling problems.

Examples

l <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE,FALSE,FALSE,TRUE))
lapply(l, mean)
## $a ## [1] 5.5 ## ##$beta
## [1] 4.535125
##
## $logic ## [1] 0.5 sapply(l, mean) ## a beta logic ## 5.500000 4.535125 0.500000 vapply(l, mean, FUN.VALUE=numeric(1)) ## a beta logic ## 5.500000 4.535125 0.500000 Often used in combination with a function definition: lapply(names(l), function(x) mean(l[[x]])) sapply(names(l), function(x) mean(l[[x]])) vapply(names(l), function(x) mean(l[[x]]), FUN.VALUE=numeric(1)) ### Improving Speed Performance of Loops Looping over very large data sets can become slow in R. However, this limitation can be overcome by eliminating certain operations in loops or avoiding loops over the data intensive dimension in an object altogether. The latter can be achieved by performing mainly vector-to-vecor or matrix-to-matrix computations. These vectorized operations run in R often over 100 times faster than the corresponding for() or apply() loops. In addition, one can make use of the existing speed-optimized C-level functions in R, such as rowSums, rowMeans, table, and tabulate. Moreover, one can design custom functions that avoid expensive R loops by using vector- or matrix-based approaches. Alternatively, one can write programs that will perform all time consuming computations on the C-level. The following code samples illustrate the time-performance differences among the different approaches of running iterative operations in R. #### 1. for loop with append versus inject approach The following runs a for loop where the result is appended in each iteration with the c() function. The corresponding cbind and rbind for two dimensional data objects would have a similar performance impact as c(). myMA <- matrix(rnorm(1000000), 100000, 10, dimnames=list(1:100000, paste("C", 1:10, sep=""))) results <- NULL system.time(for(i in seq(along=myMA[,1])) results <- c(results, mean(myMA[i,]))) user system elapsed 39.156 6.369 45.559 Now the for loop is run with an inject approach for storing the results in each iteration. results <- numeric(length(myMA[,1])) system.time(for(i in seq(along=myMA[,1])) results[i] <- mean(myMA[i,])) user system elapsed 1.550 0.005 1.556 As one can see from the output of system.time, the inject approach is 20-50 times faster. #### 2. apply loop versus rowMeans The following performs a row-wise mean calculation on a large matrix first with an apply loop and then with the rowMeans function. system.time(myMAmean <- apply(myMA, 1, mean)) user system elapsed 1.452 0.005 1.456 system.time(myMAmean <- rowMeans(myMA)) user system elapsed 0.005 0.001 0.006 Based on the results from system.time, the rowMeans approach is over 200 times faster than the apply loop. #### 3. apply loop versus vectorized approach In this example row-wise standard deviations are computed with an apply loop and then in a vectorized manner. system.time(myMAsd <- apply(myMA, 1, sd)) user system elapsed 3.707 0.014 3.721 myMAsd[1:4] 1 2 3 4 0.8505795 1.3419460 1.3768646 1.3005428 system.time(myMAsd <- sqrt((rowSums((myMA-rowMeans(myMA))^2)) / (length(myMA[1,])-1))) user system elapsed 0.020 0.009 0.028 myMAsd[1:4] 1 2 3 4 0.8505795 1.3419460 1.3768646 1.3005428 The vector-based approach in the last step is over 200 times faster than the apply loop. #### 4. Example of fast querying routine applied to a large matrix ##### (a) Create a sample matrix The following lfcPvalMA function creates a test matrix containing randomly generated log2 fold changes (LFCs) and p-values (here: pval or FDRs) for variable numbers of samples or test results. In biology this dataset mimics the results of an analysis of differentially expressed genes (DEGs) from several contrasts arranged in a single matrix (or data.frame). lfcPvalMA <- function(Nrow=200, Ncol=4, stats_labels=c("lfc", "pval")) { set.seed(1410) assign(stats_labels[1], runif(n = Nrow * Ncol, min = -4, max = 4)) assign(stats_labels[2], runif(n = Nrow * Ncol, min = 0, max = 1)) lfc_ma <- matrix(lfc, Nrow, Ncol, dimnames=list(paste("g", 1:Nrow, sep=""), paste("t", 1:Ncol, "_", stats_labels[1], sep=""))) pval_ma <- matrix(pval, Nrow, Ncol, dimnames=list(paste("g", 1:Nrow, sep=""), paste("t", 1:Ncol, "_", stats_labels[2], sep=""))) statsMA <- cbind(lfc_ma, pval_ma) return(statsMA[, order(colnames(statsMA))]) } degMA <- lfcPvalMA(Nrow=200, Ncol=4, stats_labels=c("lfc", "pval")) dim(degMA) ## [1] 200 8 degMA[1:4,] # Prints first 4 rows of DEG matrix generated as a test data set ## t1_lfc t1_pval t2_lfc t2_pval t3_lfc t3_pval t4_lfc t4_pval ## g1 -1.8476368 0.39486484 1.879310 0.7785999 0.1769551 0.9904342 0.1747932 0.9536679 ## g2 0.2542926 0.04188993 -1.629778 0.6379570 -1.9280792 0.6106041 -2.3599518 0.1950022 ## g3 3.4703657 0.73881357 2.047794 0.3129176 -3.8891714 0.1508787 3.7811606 0.2560303 ## g4 -2.8548158 0.99201512 -2.710385 0.1772805 1.0920515 0.2826038 3.9313225 0.5519854 ##### (b) Organize results in list To filter the results efficiently, it is usually best to store the two different stats (here lfc and pval) in separate matrices (here two) where each has the same dimensions and row/column ordering. Note, in this case a list is used to store the two matrices. degList <- list(lfc=degMA[ , grepl("lfc", colnames(degMA))], pval=degMA[ , grepl("pval", colnames(degMA))]) names(degList) ## [1] "lfc" "pval" sapply(degList, dim) ## lfc pval ## [1,] 200 200 ## [2,] 4 4 ##### (c) Combinatorial filter With the above generated data structure of two complementary matrices it is easy to apply combinatorial filtering routines that are both flexible and time-efficient (fast). The following example queries for fold changes of at least 2 (here lfc >= 1 | lfc <= -1) plus p-values of 0.5 or lower. Note, all intermediate and final results are stored in logical matrices. In addition to boolean comparisons, one can apply basic mathematical operations, such as calculating the sum of each cell across many matrices. This returns a numeric matix of integers representing the counts of TRUE values in each position of the considered logical matrices. Subsequently, one can perform summary and filtering routines on these count-based matrices which is convenient when working with large numbers of matrices. All these matrix-to-matrix comparisons are very fast to compute and require zero looping instructions by the user. queryResult <- (degList$lfc <= 1 | degList$lfc <= -1) & degList$pval <= 0.5
colnames(queryResult) <- gsub("_.*", "", colnames(queryResult)) # Adjust column names
queryResult[1:4,]
##       t1    t2    t3    t4
## g1  TRUE FALSE FALSE FALSE
## g2  TRUE FALSE FALSE  TRUE
## g3 FALSE FALSE  TRUE FALSE
## g4 FALSE  TRUE FALSE FALSE
##### (d) Extract query results
1. Retrieve row labels (genes) that match the query from the previous step in each column, and store them in a list.
matchingIDlist <- sapply(colnames(queryResult), function(x) names(queryResult[queryResult[ , x] , x]), simplify=FALSE)
matchingIDlist
## $t1 ## [1] "g1" "g2" "g5" "g6" "g11" "g16" "g18" "g19" "g21" "g23" "g24" "g31" "g36" ## [14] "g37" "g41" "g46" "g60" "g61" "g63" "g70" "g71" "g72" "g75" "g81" "g83" "g84" ## [27] "g88" "g91" "g97" "g98" "g100" "g102" "g103" "g104" "g110" "g111" "g112" "g113" "g114" ## [40] "g120" "g121" "g123" "g124" "g126" "g130" "g134" "g135" "g139" "g140" "g145" "g147" "g153" ## [53] "g157" "g158" "g159" "g160" "g162" "g170" "g171" "g172" "g173" "g175" "g178" "g183" "g184" ## [66] "g187" "g190" "g192" "g196" "g199" ## ##$t2
##  [1] "g4"   "g5"   "g7"   "g9"   "g10"  "g12"  "g16"  "g23"  "g34"  "g35"  "g39"  "g41"  "g44"
## [14] "g46"  "g47"  "g48"  "g49"  "g50"  "g51"  "g52"  "g56"  "g66"  "g75"  "g80"  "g81"  "g85"
## [27] "g88"  "g89"  "g90"  "g94"  "g99"  "g102" "g112" "g115" "g116" "g118" "g119" "g120" "g129"
## [40] "g144" "g145" "g148" "g152" "g155" "g156" "g160" "g164" "g165" "g167" "g168" "g170" "g172"
## [53] "g178" "g186" "g187" "g194" "g197"
##
## $t3 ## [1] "g3" "g6" "g7" "g9" "g12" "g15" "g23" "g25" "g26" "g27" "g38" "g43" "g52" ## [14] "g53" "g54" "g58" "g66" "g69" "g72" "g76" "g77" "g80" "g84" "g85" "g86" "g88" ## [27] "g89" "g90" "g91" "g99" "g100" "g107" "g110" "g122" "g124" "g125" "g129" "g134" "g139" ## [40] "g141" "g143" "g144" "g146" "g148" "g154" "g163" "g165" "g171" "g173" "g178" "g180" "g182" ## [53] "g188" "g190" "g193" "g195" ## ##$t4
##  [1] "g2"   "g5"   "g7"   "g8"   "g9"   "g12"  "g13"  "g15"  "g20"  "g21"  "g26"  "g28"  "g30"
## [14] "g31"  "g36"  "g37"  "g38"  "g47"  "g63"  "g64"  "g65"  "g67"  "g68"  "g69"  "g76"  "g77"
## [27] "g80"  "g85"  "g90"  "g98"  "g99"  "g101" "g105" "g106" "g120" "g123" "g125" "g126" "g129"
## [40] "g131" "g134" "g137" "g140" "g143" "g147" "g148" "g153" "g155" "g157" "g165" "g167" "g170"
## [53] "g171" "g172" "g174" "g175" "g176" "g178" "g181" "g182" "g188" "g192" "g199"
1. Return all row labels (genes) that match the above query across a specified number of columns (here 2). Note, the rowSums function is used for this, which performs the row-wise looping internally and extremely fast.
matchingID <- rowSums(queryResult) > 2
queryResult[matchingID, , drop=FALSE]
##         t1    t2    t3    t4
## g5    TRUE  TRUE FALSE  TRUE
## g7   FALSE  TRUE  TRUE  TRUE
## g9   FALSE  TRUE  TRUE  TRUE
## g12  FALSE  TRUE  TRUE  TRUE
## g23   TRUE  TRUE  TRUE FALSE
## g80  FALSE  TRUE  TRUE  TRUE
## g85  FALSE  TRUE  TRUE  TRUE
## g88   TRUE  TRUE  TRUE FALSE
## g90  FALSE  TRUE  TRUE  TRUE
## g99  FALSE  TRUE  TRUE  TRUE
## g120  TRUE  TRUE FALSE  TRUE
## g129 FALSE  TRUE  TRUE  TRUE
## g134  TRUE FALSE  TRUE  TRUE
## g148 FALSE  TRUE  TRUE  TRUE
## g165 FALSE  TRUE  TRUE  TRUE
## g170  TRUE  TRUE FALSE  TRUE
## g171  TRUE FALSE  TRUE  TRUE
## g172  TRUE  TRUE FALSE  TRUE
## g178  TRUE  TRUE  TRUE  TRUE
names(matchingID[matchingID])
##  [1] "g5"   "g7"   "g9"   "g12"  "g23"  "g80"  "g85"  "g88"  "g90"  "g99"  "g120" "g129" "g134"
## [14] "g148" "g165" "g170" "g171" "g172" "g178"

As demonstrated in the above query examples, by setting up the proper data structures (here two matrices with same dimensions), and utilizing vectorized (matrix-to-matrix) operations along with R’s built-in row* and col* stats function family (e.g. rowSums) one can design with very little code flexible query routines that also run very time-efficient.

## Functions

### Function Overview

A very useful feature of the R environment is the possibility to expand existing functions and to easily write custom functions. In fact, most of the R software can be viewed as a series of R functions.

Syntax to define function

myfct <- function(arg1, arg2, ...) {
function_body
}

Syntax to call functions

myfct(arg1=..., arg2=...)

The value returned by a function is the value of the function body, which is usually an unassigned final expression, e.g.: return()

### Function Syntax Rules

General

• Functions are defined by
1. The assignment with the keyword function
2. The declaration of arguments/variables (arg1, arg2, ...)
3. The definition of operations (function_body) that perform computations on the provided arguments. A function name needs to be assigned to call the function.

Naming

• Function names can be almost anything. However, the usage of names of existing functions should be avoided.

Arguments

• It is often useful to provide default values for arguments (e.g.: arg1=1:10). This way they don’t need to be provided in a function call. The argument list can also be left empty (myfct <- function() { fct_body }) if a function is expected to return always the same value(s). The argument ... can be used to allow one function to pass on argument settings to another.

Body

• The actual expressions (commands/operations) are defined in the function body which should be enclosed by braces. The individual commands are separated by semicolons or new lines (preferred).

Usage

• Functions are called by their name followed by parentheses containing possible argument names. Empty parenthesis after the function name will result in an error message when a function requires certain arguments to be provided by the user. The function name alone will print the definition of a function.

Scope

• Variables created inside a function exist only for the life time of a function. Thus, they are not accessible outside of the function. To force variables in functions to exist globally, one can use the double assignment operator: <<-

### Examples

Define sample function

myfct <- function(x1, x2=5) {
z1 <- x1 / x1
z2 <- x2 * x2
myvec <- c(z1, z2)
return(myvec)
}

Function usage

Apply function to values 2 and 5

myfct(x1=2, x2=5)
## [1]  1 25

Run without argument names

myfct(2, 5)
## [1]  1 25

Makes use of default value 5

myfct(x1=2)
## [1]  1 25

Print function definition (often unintended)

myfct
## function(x1, x2=5) {
##  z1 <- x1 / x1
##  z2 <- x2 * x2
##         myvec <- c(z1, z2)
##         return(myvec)
## }
## <bytecode: 0x56dd086c6988>

## Useful Utilities

### Debugging Utilities

Several debugging utilities are available for R. They include:

• traceback
• browser
• options(error=recover)
• options(error=NULL)
• debug

The Debugging in R page provides an overview of the available resources.

### Regular Expressions

R’s regular expression utilities work similar as in other languages. To learn how to use them in R, one can consult the main help page on this topic with ?regexp.

#### String matching with grep

The grep function can be used for finding patterns in strings, here letter A in vector month.name.

month.name[grep("A", month.name)]
## [1] "April"  "August"

#### String substitution with gsub

Example for using regular expressions to substitute a pattern by another one using a back reference. Remember: single escapes \ need to be double escaped \\ in R.

gsub('(i.*a)', 'xxx_\\1', "virginica", perl = TRUE)
## [1] "vxxx_irginica"

### Interpreting a Character String as Expression

Some useful examples

Generates vector of object names in session

myfct <- function(x) x^2
mylist <- ls()
n <- which(mylist %in% "myfct")
mylist[n]
## [1] "myfct"

Executes entry in position n as expression

get(mylist[n])
## function(x) x^2
get(mylist[n])(2)
## [1] 4

Alternative approach

eval(parse(text=mylist[n]))
## function(x) x^2

### Replacement, Split and Paste Functions for Strings

Selected examples

Substitution with back reference which inserts in this example _ character

x <- gsub("(a)","\\1_", month.name[1], perl=T)
x
## [1] "Ja_nua_ry"

Split string on inserted character from above

strsplit(x,"_")
## [[1]]
## [1] "Ja"  "nua" "ry"

Reverse a character string by splitting first all characters into vector fields

paste(rev(unlist(strsplit(x, NULL))), collapse="")
## [1] "yr_aun_aJ"

### Time, Date and Sleep

Selected examples

Return CPU (and other) times that an expression used (here ls)

system.time(ls())
##    user  system elapsed
##       0       0       0

Return the current system date and time

date()
## [1] "Thu Apr 14 09:21:47 2022"

Pause execution of R expressions for a given number of seconds (e.g. in loop)

Sys.sleep(1)

#### Example

##### Import of Specific File Lines with Regular Expression

The following example demonstrates the retrieval of specific lines from an external file with a regular expression. First, an external file is created with the cat function, all lines of this file are imported into a vector with readLines, the specific elements (lines) are then retieved with the grep function, and the resulting lines are split into vector fields with strsplit.

cat(month.name, file="zzz.txt", sep="\n")
x <- readLines("zzz.txt")
x[1:6]
## [1] "January"  "February" "March"    "April"    "May"      "June"
x <- x[c(grep("^J", as.character(x), perl = TRUE))]
t(as.data.frame(strsplit(x, "u")))
##                 [,1]  [,2]
## c..Jan....ary.. "Jan" "ary"
## c..J....ne..    "J"   "ne"
## c..J....ly..    "J"   "ly"

## Calling External Software

External command-line software can be called with system. The following example calls blastall from R

system("blastall -p blastp -i seq.fasta -d uniprot -o seq.blastp")

## Running R Scripts

### Possibilities for Executing R Scripts

#### R console

source("my_script.R")

#### Command-line

Rscript my_script.R # or just ./myscript.R after including shebang line #!/usr/bin/env Rscript and making it executable
R CMD BATCH my_script.R # Alternative way 1
R --slave < my_script.R # Alternative way 2

#### Passing arguments from command-line to R

Create an R script named test.R with the following content:

myarg <- commandArgs()
print(iris[1:myarg[6], ])

Then run it from the command-line like this:

Rscript test.R 10

In the given example the number 10 is passed on from the command-line as an argument to the R script which is used to return to STDOUT the first 10 rows of the iris sample data. If several arguments are provided, they will be interpreted as one string and need to be split in R with the strsplit function. A more detailed example can be found here.

## Building R Packages

This section has been moved to a dedicated tutorial on R package development here.

## Programming Exercises

### Exercise 1

#### for loop

Task 1.1: Compute the mean of each row in myMA by applying the mean function in a for loop.

myMA <- matrix(rnorm(500), 100, 5, dimnames=list(1:100, paste("C", 1:5, sep="")))
myve_for <- NULL
for(i in seq(along=myMA[,1])) {
myve_for <- c(myve_for, mean(as.numeric(myMA[i, ])))
}
myResult <- cbind(myMA, mean_for=myve_for)
myResult[1:4, ]
##          C1         C2         C3         C4         C5    mean_for
## 1 -1.202619 -0.1053735 -1.1074995  0.4269033 -1.3932608 -0.67636998
## 2  1.174138 -0.8991985 -1.2658862  1.0151751 -0.4075398 -0.07666225
## 3  1.357860 -1.2370991  0.7402124 -1.0277818  0.4851062  0.06365953
## 4  1.173512 -1.2724114  0.2270477 -1.5421106  0.7484669 -0.13309912

#### while loop

Task 1.2: Compute the mean of each row in myMA by applying the mean function in a while loop.

z <- 1
myve_while <- NULL
while(z <= length(myMA[,1])) {
myve_while <- c(myve_while, mean(as.numeric(myMA[z, ])))
z <- z + 1
}
myResult <- cbind(myMA, mean_for=myve_for, mean_while=myve_while)
myResult[1:4, -c(1,2)]
##           C3         C4         C5    mean_for  mean_while
## 1 -1.1074995  0.4269033 -1.3932608 -0.67636998 -0.67636998
## 2 -1.2658862  1.0151751 -0.4075398 -0.07666225 -0.07666225
## 3  0.7402124 -1.0277818  0.4851062  0.06365953  0.06365953
## 4  0.2270477 -1.5421106  0.7484669 -0.13309912 -0.13309912

Task 1.3: Confirm that the results from both mean calculations are identical

all(myResult[,6] == myResult[,7])
## [1] TRUE

#### apply loop

Task 1.4: Compute the mean of each row in myMA by applying the mean function in an apply loop

myve_apply <- apply(myMA, 1, mean)
myResult <- cbind(myMA, mean_for=myve_for, mean_while=myve_while, mean_apply=myve_apply)
myResult[1:4, -c(1,2)]
##           C3         C4         C5    mean_for  mean_while  mean_apply
## 1 -1.1074995  0.4269033 -1.3932608 -0.67636998 -0.67636998 -0.67636998
## 2 -1.2658862  1.0151751 -0.4075398 -0.07666225 -0.07666225 -0.07666225
## 3  0.7402124 -1.0277818  0.4851062  0.06365953  0.06365953  0.06365953
## 4  0.2270477 -1.5421106  0.7484669 -0.13309912 -0.13309912 -0.13309912

#### Avoiding loops

Task 1.5: When operating on large data sets it is much faster to use the rowMeans function

mymean <- rowMeans(myMA)
myResult <- cbind(myMA, mean_for=myve_for, mean_while=myve_while, mean_apply=myve_apply, mean_int=mymean)
myResult[1:4, -c(1,2,3)]
##           C4         C5    mean_for  mean_while  mean_apply    mean_int
## 1  0.4269033 -1.3932608 -0.67636998 -0.67636998 -0.67636998 -0.67636998
## 2  1.0151751 -0.4075398 -0.07666225 -0.07666225 -0.07666225 -0.07666225
## 3 -1.0277818  0.4851062  0.06365953  0.06365953  0.06365953  0.06365953
## 4 -1.5421106  0.7484669 -0.13309912 -0.13309912 -0.13309912 -0.13309912

To find out which other built-in functions for basic calculations exist, type ?rowMeans.

### Exercise 2

#### Custom functions

Task 2.1: Use the following code as basis to implement a function that allows the user to compute the mean for any combination of columns in a matrix or data frame. The first argument of this function should specify the input data set, the second the mathematical function to be passed on (e.g. mean, sd, max) and the third one should allow the selection of the columns by providing a grouping vector.

myMA <- matrix(rnorm(100000), 10000, 10, dimnames=list(1:10000, paste("C", 1:10, sep="")))
myMA[1:2,]
##           C1         C2        C3            C4        C5        C6         C7         C8
## 1  0.1750576 -0.2826930 0.9290137  0.0830523803 1.4113331 -1.221849 -0.4663186  0.6170635
## 2 -0.8056727  0.1885557 0.4891758 -0.0003536156 0.8356054  1.406484  1.0363437 -0.1351562
##           C9       C10
## 1 0.58305534 0.1716817
## 2 0.09596605 0.4551591
myList <- tapply(colnames(myMA), c(1,1,1,2,2,2,3,3,4,4), list)
names(myList) <- sapply(myList, paste, collapse="_")
myMAmean <- sapply(myList, function(x) apply(myMA[,x], 1, mean))
myMAmean[1:4,]
##      C1_C2_C3      C4_C5_C6       C7_C8     C9_C10
## 1  0.27379276  0.0908453513  0.07537248  0.3773685
## 2 -0.04264706  0.7472451361  0.45059378  0.2755626
## 3  0.49411343 -0.4486259202 -0.08519687  1.0929435
## 4  0.06575562 -0.0007494971  0.07968924 -0.7374505

### Exercise 3

#### Nested loops to generate similarity matrices

Task 3.1: Create a sample list populated with character vectors of different lengths

setlist <- lapply(11:30, function(x) sample(letters, x, replace=TRUE))
names(setlist) <- paste("S", seq(along=setlist), sep="")
setlist[1:6]
## $S1 ## [1] "b" "s" "o" "w" "w" "x" "s" "z" "d" "f" "b" ## ##$S2
##  [1] "e" "w" "l" "y" "i" "l" "m" "q" "s" "r" "d" "s"
##
## $S3 ## [1] "h" "i" "t" "d" "f" "c" "u" "r" "v" "a" "n" "t" "v" ## ##$S4
##  [1] "o" "w" "c" "x" "c" "l" "a" "z" "c" "f" "n" "i" "j" "u"
##
## $S5 ## [1] "a" "z" "b" "t" "p" "l" "u" "z" "q" "x" "y" "h" "l" "j" "e" ## ##$S6
##  [1] "z" "v" "n" "v" "b" "m" "p" "p" "z" "g" "l" "j" "z" "z" "t" "e"

Task 3.2: Compute the length for all pairwise intersects of the vectors stored in setlist. The intersects can be determined with the %in% function like this: sum(setlist[[1]] %in% setlist[[2]])

setlist <- sapply(setlist, unique)
olMA <- sapply(names(setlist), function(x) sapply(names(setlist),
function(y) sum(setlist[[x]] %in% setlist[[y]])))
olMA[1:12,]
##     S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 S13 S14 S15 S16 S17 S18 S19 S20
## S1   8  3  2  5  3  2  4  4  4   5   8   5   5   5   6   5   4   6   4   4
## S2   3 10  3  3  4  3  5  3  3   5   4   6   6   8   7   5   5   8   7   8
## S3   2  3 11  6  4  3  5  4  4   7   4   8   4   5   8   6   6   6   7   8
## S4   5  3  6 12  6  4  8  4  4   8   8   7   7   7   8   9   6   8   9   9
## S5   3  4  4  6 13  7  3  6  6   8   7   8   5  10   8   9   6  10  10  10
## S6   2  3  3  4  7 11  4  5  5   5   5   7   4   8   7   6   8   8   9   8
## S7   4  5  5  8  3  4 11  5  6   5   6   6   7   6   9   6   7   7   8   9
## S8   4  3  4  4  6  5  5 12  7   4   7   6   5   7  10   7   8   8   9   7
## S9   4  3  4  4  6  5  6  7 11   6   8   6   3   8  10   6   6   8   8   8
## S10  5  5  7  8  8  5  5  4  6  14   7   9   5   9   9  10   7  11   9   8
## S11  8  4  4  8  7  5  6  7  8   7  13   7   6  10  10   9   8   9   8   8
## S12  5  6  8  7  8  7  6  6  6   9   7  15   5  10  10   9   9  11  10  10

Task 3.3 Plot the resulting intersect matrix as heat map. The image or the pheatmap functions can be used for this.

library(pheatmap); library("RColorBrewer")
pheatmap(olMA, color=brewer.pal(9,"Blues"), cluster_rows=FALSE, cluster_cols=FALSE, display_numbers=TRUE, number_format="%.0f", fontsize_number=10)
# image(olMA)

### Exercise 4

#### Build your own R package

Task 4.1: Save one or more of your functions to a file called script.R and build the package with the package.skeleton function.

package.skeleton(name="mypackage", code_files=c("script1.R"))

Task 4.2: Build tarball of the package

system("R CMD build mypackage")

Task 4.3: Install and use package

install.packages("mypackage_1.0.tar.gz", repos=NULL, type="source")
library(mypackage)
?myMAcomp # Opens help for function defined by mypackage

## Homework 5

See homework section here.

## Additional Exercises

### Pattern matching and positional parsing of equences

The following sample script patternSearch.R defines functions for importing sequences into R, retrieving reverse and complement of nucleotide sequences, pattern searching, positional parsing and exporting search results in HTML format. Sourcing the script will return usage instructions of its functions.

source("https://raw.githubusercontent.com/tgirke/GEN242/main/content/en/tutorials/rprogramming/scripts/patternSearch.R")

### Identify over-represented strings in sequence sets

Example functions for finding over-represented words in sets of DNA, RNA or protein sequences are defined in this script: wordFinder.R. Sourcing the script will return usage instructions of its functions.

source("https://raw.githubusercontent.com/tgirke/GEN242/main/content/en/tutorials/rprogramming/scripts/wordFinder.R")

## Object-Oriented Programming (OOP)

R supports several systems for object-oriented programming (OOP). This includes an older S3 system, and the more recently introduced R6 and S4 systems. The latter is the most formal version that supports multiple inheritance, multiple dispatch and introspection. Many of these features are not available in the older S3 system. In general, the OOP approach taken by R is to separate the class specifications from the specifications of generic functions (function-centric system). The following introduction is restricted to the S4 system since it is nowadays the preferred OOP method for package development in Bioconductor. More information about OOP in R can be found in the following introductions:

### Define S4 Classes

#### 1. Define S4 Classes with setClass() and new()

y <- matrix(1:10, 2, 5) # Sample data set
setClass(Class="myclass",
representation=representation(a="ANY"),
prototype=prototype(a=y[1:2,]), # Defines default value (optional)
validity=function(object) { # Can be defined in a separate step using setValidity
if(class(object@a)[1]!="matrix") {
return(paste("expected matrix, but obtained", class(object@a)))
} else {
return(TRUE)
}
}
)

The setClass function defines classes. Its most important arguments are

• Class: the name of the class
• representation: the slots that the new class should have and/or other classes that this class extends.
• prototype: an object providing default data for the slots.
• contains: the classes that this class extends.
• validity, access, version: control arguments included for compatibility with S-Plus.
• where: the environment to use to store or remove the definition as meta data.

#### 2. Create new class instance

The function new creates an instance of a class (here myclass).

myobj <- new("myclass", a=y)
myobj
## An object of class "myclass"
## Slot "a":
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10

If evaluated the following would return an error due to wrong input type (data.frame instead of matrix).

new("myclass", a=iris) # Returns error due to wrong input

The arguments of new are:

• Class: the name of the class
• ...: data to include in the new object with arguments according to slots in class definition

#### 3. Initialization method

A more generic way of creating class instances is to define an initialization method (more details below).

setMethod("initialize", "myclass", function(.Object, a) {
.Object@a <- a/a
.Object
})
new("myclass", a = y)
## An object of class "myclass"
## Slot "a":
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    1    1    1    1
## [2,]    1    1    1    1    1

#### 4. Usage and helper functions

The ‘@’ operator extracts the contents of a slot. Its usage should be limited to internal functions.

myobj@a
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10

Create a new S4 object from an old one.

initialize(.Object=myobj, a=as.matrix(cars[1:2,]))
## An object of class "myclass"
## Slot "a":
##   speed dist
## 1     1    1
## 2     1    1

If evaluated the removeClass function removes an object from the current session. This does not apply to associated methods.

removeClass("myclass")

#### 5. Inheritance

Inheritance allows to define new classes that inherit all properties (e.g. data slots, methods) from their existing parent classes. The contains argument used below allows to extend existing classes. This propagates all slots of parent classes.

setClass("myclass1", representation(a = "character", b = "character"))
setClass("myclass2", representation(c = "numeric", d = "numeric"))
setClass("myclass3", contains=c("myclass1", "myclass2"))
new("myclass3", a=letters[1:4], b=letters[1:4], c=1:4, d=4:1)
## An object of class "myclass3"
## Slot "a":
## [1] "a" "b" "c" "d"
##
## Slot "b":
## [1] "a" "b" "c" "d"
##
## Slot "c":
## [1] 1 2 3 4
##
## Slot "d":
## [1] 4 3 2 1
getClass("myclass1")
## Class "myclass1" [in ".GlobalEnv"]
##
## Slots:
##
## Name:          a         b
## Class: character character
##
## Known Subclasses: "myclass3"
getClass("myclass2")
## Class "myclass2" [in ".GlobalEnv"]
##
## Slots:
##
## Name:        c       d
## Class: numeric numeric
##
## Known Subclasses: "myclass3"
getClass("myclass3")
## Class "myclass3" [in ".GlobalEnv"]
##
## Slots:
##
## Name:          a         b         c         d
## Class: character character   numeric   numeric
##
## Extends: "myclass1", "myclass2"

#### 6. Coerce objects to another class

The following defines a coerce method. After this the standard as(..., "...") syntax can be used to coerce the new class to another one.

setAs(from="myclass", to="character", def=function(from) as.character(as.matrix(from@a)))
as(myobj, "character")
##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

#### 7. Virtual classes

Virtual classes are constructs for which no instances will be or can be created. They are used to link together classes which may have distinct representations (e.g. cannot inherit from each other) but for which one wants to provide similar functionality. Often it is desired to create a virtual class and to then have several other classes extend it. Virtual classes can be defined by leaving out the representation argument or including the class VIRTUAL as illustrated here:

setClass("myVclass")
setClass("myVclass", representation(a = "character", "VIRTUAL"))

#### 8. Introspection of classes

Useful functions to introspect classes include:

• getClass("myclass")
• getSlots("myclass")
• slotNames("myclass")
• extends("myclass2")

### Assign Generics and Methods

Generics and methods can be assigned with the methods setGeneric() and setMethod().

#### 1. Accessor functions

This avoids the usage of the @ operator.

setGeneric(name="acc", def=function(x) standardGeneric("acc"))
## [1] "acc"
setMethod(f="acc", signature="myclass", definition=function(x) {
return(x@a)
})
acc(myobj)
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    1    3    5    7    9
## [2,]    2    4    6    8   10

#### 2. Replacement methods

1. Using custom accessor function with acc <- syntax.
setGeneric(name="acc<-", def=function(x, value) standardGeneric("acc<-"))
## [1] "acc<-"
setReplaceMethod(f="acc", signature="myclass", definition=function(x, value) {
x@a <- value
return(x)
})
## After this the following replace operations with 'acc' work on new object class
acc(myobj)[1,1] <- 999 # Replaces first value
colnames(acc(myobj)) <- letters[1:5] # Assigns new column names
rownames(acc(myobj)) <- letters[1:2] # Assigns new row names
myobj
## An object of class "myclass"
## Slot "a":
##     a b c d  e
## a 999 3 5 7  9
## b   2 4 6 8 10
1. Replacement method using [ operator, here [...] <- syntax.
setReplaceMethod(f="[", signature="myclass", definition=function(x, i, j, value) {
x@a[i,j] <- value
return(x)
})
myobj[1,2] <- 999
myobj
## An object of class "myclass"
## Slot "a":
##     a   b c d  e
## a 999 999 5 7  9
## b   2   4 6 8 10

#### 3. Behavior of bracket operator

The behavior of the bracket [ subsetting operator can be defined as follows.

setMethod(f="[", signature="myclass",
definition=function(x, i, j, ..., drop) {
x@a <- x@a[i,j]
return(x)
})
myobj[1:2, 1:3] # Standard subsetting works now on new class
## An object of class "myclass"
## Slot "a":
##     a   b c
## a 999 999 5
## b   2   4 6

#### 4. Print behavior

A convient summary printing behavior for a new class should always be defined.

setMethod(f="show", signature="myclass", definition=function(object) {
cat("An instance of ", "\"", class(object), "\"", " with ", length(acc(object)[,1]), " elements", "\n", sep="")
if(length(acc(object)[,1])>=5) {
print(as.data.frame(rbind(acc(object)[1:2,], ...=rep("...", length(acc(object)[1,])), acc(object)[(length(acc(object)[,1])-1):length(acc(object)[,1]),])))
} else {
print(acc(object))
}
})
myobj # Prints object with custom method
## An instance of "myclass" with 2 elements
##     a   b c d  e
## a 999 999 5 7  9
## b   2   4 6 8 10

#### 5. Define custom methods

The following gives an example for defining a data specific method, here randomizing row order of matrix stored in new S4 class.

setGeneric(name="randomize", def=function(x) standardGeneric("randomize"))
## [1] "randomize"
setMethod(f="randomize", signature="myclass", definition=function(x) {
acc(x)[sample(1:length(acc(x)[,1]), length(acc(x)[,1])), ]
})
randomize(myobj)
##     a   b c d  e
## b   2   4 6 8 10
## a 999 999 5 7  9

#### 6. Plotting method

Define a graphical plotting method for new class and allow users to access it with R’s generic plot function.

setMethod(f="plot", signature="myclass", definition=function(x, ...) {
barplot(as.matrix(acc(x)), ...)
})
plot(myobj)

#### 7. Utilities to inspect methods

Important inspection methods for classes include:

• showMethods(class="myclass")
• findMethods("randomize")
• getMethod("randomize", signature="myclass")
• existsMethod("randomize", signature="myclass")

## Session Info

sessionInfo()
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Debian GNU/Linux 10 (buster)
##
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.8.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.8.0
##
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8
##  [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base
##
## other attached packages:
## [1] RColorBrewer_1.1-2 pheatmap_1.0.12    BiocStyle_2.22.0
##
## loaded via a namespace (and not attached):
##  [1] knitr_1.37          magrittr_2.0.2      munsell_0.5.0       colorspace_2.0-2
##  [5] R6_2.5.1            rlang_1.0.2         fastmap_1.1.0       highr_0.9
##  [9] stringr_1.4.0       tools_4.1.3         grid_4.1.3          gtable_0.3.0
## [13] xfun_0.30           cli_3.1.0           jquerylib_0.1.4     htmltools_0.5.2
## [17] yaml_2.3.5          digest_0.6.29       lifecycle_1.0.1     bookdown_0.24
## [21] BiocManager_1.30.16 codetools_0.2-18    sass_0.4.0          evaluate_0.15
## [25] rmarkdown_2.13      blogdown_1.8.2      stringi_1.7.6       compiler_4.1.3
## [29] bslib_0.3.1         scales_1.1.1        jsonlite_1.8.0

## References

Gentleman, Robert. 2008. R Programming for Bioinformatics (Chapman & Hall/CRC Computer Science & Data Analysis). 1 edition. Chapman; Hall/CRC. http://www.amazon.com/Programming-Bioinformatics-Chapman-Computer-Analysis/dp/1420063677.

Last modified 2022-05-23: some edits (297444b11)