Miscellaneous Functions

• Looping functions
• Factors
• Missing Values

• Looping functions allow us to apply functions in an array, matrix or data frame.
• These functions are apply, tapply, sapply, and lapply
• Let us create a matrix for understanding these functions

mat=matrix(1:12,nrow=3)
dimnames(mat)=list(LETTERS[1:3],LETTERS[4:7])
mat

  D E F  G
A 1 4 7 10
B 2 5 8 11
C 3 6 9 12

• apply function takes 3 arguments
• (data structure, dimension, function to compute)
• dimension: 1=rowwise, 2=columnwise

a1=apply(mat,1,mean)
a1

  A   B   C 
5.5 6.5 7.5 

a2=apply(mat,2,mean)
a2

 D  E  F  G 
 2  5  8 11 

• tapply function takes 3 arguments
• (data vector, factor, function to compute)

x=1:10
f=c(rep(1,3),rep(2,5),rep(3,2))
f

 [1] 1 1 1 2 2 2 2 2 3 3

tapply(x,f,mean)

  1   2   3 
2.0 6.0 9.5 

• lapply function takes 2 arguments
• (list, function to compute)
• the output from lapply is also a list

x=list(a=1:5,b=c(4,6,5))
lapply(x,mean)

$a
[1] 3

$b
[1] 5

• Suppose we want to multiple files from a directory

setwd("C:/rpractice/shiny/AOL")
files= list.files(pattern="*.csv")
files # show filenames in the working directory

[1] "AOLCourse.csv" "results1.csv" 

rdf=lapply(files, read.csv) # files are loaded into rdf

• same as lapply except that the output is shown in simplied form

x=list(a=1:5,b=c(4,6,5))
sapply(x,mean)

a b 
3 5 

• Factors are used to represent categorical data.
• Factors can be unordered or ordered.
• Using factors with labels is better than using integers because factors are self-describing;
• Having a variable that has values “Male” and “Female” is better than a variable that has values 1 and 2.

x = factor(c("yes", "yes", "no", "yes", "no"))
x

[1] yes yes no  yes no 
Levels: no yes

table(x)

x
 no yes 
  2   3 

unclass(x)

[1] 2 2 1 2 1
attr(,"levels")
[1] "no"  "yes"

• Order of the levels can be set using the levels argument to factor()
• This can be important in linear modelling because the first level is used as the baseline level.

x = factor(c("yes", "yes", "no", "yes"), levels = c("yes", "no"))
x; unclass(x)

[1] yes yes no  yes
Levels: yes no

[1] 1 1 2 1
attr(,"levels")
[1] "yes" "no" 

Missing values are denoted by NA or NaN for undefined mathematical operations.

• is.na() is used to test objects if they are NA
• is.nan() is used to test for NaN (Not a Number)
• NA values have a class also, so there are integer NA, character NA, etc.
• A NaN value is also NA but the converse is not true

x = c(1, 2, NA, 10, 3)
is.na(x);is.nan(x)

[1] FALSE FALSE  TRUE FALSE FALSE

[1] FALSE FALSE FALSE FALSE FALSE

x = c(1, 2, NaN, NA, 4)
is.na(x);is.nan(x)

[1] FALSE FALSE  TRUE  TRUE FALSE

[1] FALSE FALSE  TRUE FALSE FALSE

A common task is to remove missing values (NAs).

x = c(1, 2, NA, 4, NA, 5)
bad = is.na(x)
x[!bad]

[1] 1 2 4 5