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Revision 6 vom 2015-05-01 10:48:36

location: RstatisTik / RstatisTikPortal / RcourSe / CourseOutline / FunctionsInR / ApplyR

Introduction

Every function in R has three important characteristics:

You can see all three parts if you type the name of the function without brackets. Exceptions are primitives. Primitive functions, like sum(), call C code directly with .Primitive() and contain no R code. Therefore their formals(), body(), and environment() are all NULL.

Functions

   1 > chisq.test
   2 function (x, y = NULL, correct = TRUE, p = rep(1/length(x), length(x)),
   3 DNAME <- deparse(substitute(x))
   4 if (is.data.frame(x))
   5 expected = E, residuals = (x - E)/sqrt(E), stdres = (x - ...
   6 
   7 > sum
   8 function (..., na.rm = FALSE)  .Primitive("sum")

Function Arguments

Arguments are matched

By default, R function arguments are lazy, they are only evaluated if they are actually used:

   1 > f <- function(x) {
   2 f <- function(x) {
   3 +   10
   4 + }
   5 > f(stop("This is an error!"))
   6 [1] 10
   7 >

Implicit Loops

Introduction

A common application of loops is to apply a function to each element of a set of values and collect the results in a single structure. In R this is mainly done by the higher order functions:

lapply()

   1 > lapply(dat,mean)
   2 [1] 6753.636
   3 [1] 5433.182
   4 > sapply(dat,mean)

apply()

   1 > x<-1:12
   2 > dim(x)<-c(2,2,3)
   3 > apply(x,3,quantile) ## calculate the quantiles

tapply()

For example in the quine data frame, we can summarize Days classify by Eth and Lrn as follows:

   1 > tapply(Days,list(Eth,Lrn),mean)
   2 AL       SL
   3 A 18.57500 24.89655
   4 N 13.25581 10.82353

   1 m <- matrix(rnorm(100),nrow=10)

   1 > aggregate(Days ~ Sex + Eth, data=quine,mean)
   2 Sex Eth     Days
   3 1   F   A 20.92105
   4 2   M   A 21.61290
   5 3   F   N 10.07143
   6 4   M   N 14.71429
   7 > aggregate(Days ~ Sex + Eth, data=quine,summary)
   8 Sex Eth Days.Min. Days.1st Qu. Days.Median Days.Mean Days.3rd Qu. Days.Max.
   9 1   F   A      0.00         5.25       13.50     20.92        30.25     81.00
  10 2   M   A      2.00         9.50       16.00     21.61        33.00     57.00
  11 3   F   N      0.00         5.00        7.00     10.07        14.00     37.00
  12 4   M   N      0.00         3.50        8.00     14.71        19.50     69.00

Function Exercises (Verzani)

Function Exercises (Verzani) Solutions

   1 > avg.dist <- function(x){
   2 +     xbar <- mean(x)
   3 +     mean(abs(x-xbar))
   4 + }

Function Exercises (Verzani) Solutions

   1 > f <- function(x){
   2 +     mean(x**2) - mean(x)**2
   3 + }
   4 > f(1:10)
   5 [1] 8.25

Function Exercises (Verzani) Solutions

   1 > iseven <- function(x){
   2 +     x %% 2 == 0
   3 + }
   4 > iseven(1:10)
   5 [1] FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE
   6 > isodd <- function(x){
   7 +     !iseven(x)
   8 + }
   9 > isodd(1:10)
  10 [1]  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE

Function Exercises (Verzani) Solutions

   1 > isprime <- function(x){
   2 +     if(x == 2) return(TRUE)
   3 +     !(0 %in% (x %% (2:(x-1))))
   4 + }
   5 > isprime(2)
   6 [1] TRUE
   7 > isprime(5)
   8 [1] TRUE
   9 > isprime(15)
  10 [1] FALSE