5 Subsetting

Author
Affiliation

Md Rasel Biswas

ISRT, University of Dhaka

1 Subsetting

  • R’s subsetting operators are fast and powerful and mastering them allows you concisely perform complex operations

  • Subsetting in R easy to learn but hard to master because you need to internalize a number of interrelated concepts

    • There are three subsetting operators, [[, [, and $

    • subsetting operators interact differently with different vector types (e.g. atomic vectors, lists, factors, matrices, and data frames)

    • Subsetting can be combined with assignment

2 Subsetting atomic vectors

  • We have already discussed how to select elements from an atomic vector using numerical and logical indexing previously 
age <- c(11, 9, 8, 10, 5)
age[c(2, 3, 5)]
#> [1] 9 8 5
age[-c(1, 3)]
#> [1]  9 10  5
age[age >= 8 & age <= 10]
#> [1]  9  8 10

3 Subsetting matrices

mat <- matrix(1:9, nrow = 3)
mat
#>      [,1] [,2] [,3]
#> [1,]    1    4    7
#> [2,]    2    5    8
#> [3,]    3    6    9

Select specific element of a matrix

mat[1, 3]
#> [1] 7
mat[1:2, 3]
#> [1] 7 8

Select rows or columns of a matrix

mat[ , 2:3]
#>      [,1] [,2]
#> [1,]    4    7
#> [2,]    5    8
#> [3,]    6    9
mat[c(1, 3), ]
#>      [,1] [,2] [,3]
#> [1,]    1    4    7
#> [2,]    3    6    9
mat
#>      [,1] [,2] [,3]
#> [1,]    1    4    7
#> [2,]    2    5    8
#> [3,]    3    6    9
mat[1, ] # returns a vector
#> [1] 1 4 7
mat[, 1] # returns a vector
#> [1] 1 2 3
# returns a matrix
mat[1, , drop = FALSE] 
#>      [,1] [,2] [,3]
#> [1,]    1    4    7
# returns a matrix
mat[, 1, drop = FALSE] 
#>      [,1]
#> [1,]    1
#> [2,]    2
#> [3,]    3

4 Subsetting data frames

# Creating a data frame
df <- data.frame(x = 1:4, y = letters[1:4], z = 11:14)
df
#>   x y  z
#> 1 1 a 11
#> 2 2 b 12
#> 3 3 c 13
#> 4 4 d 14
# Variable names
names(df)
#> [1] "x" "y" "z"
str(df)
#> 'data.frame':    4 obs. of  3 variables:
#>  $ x: int  1 2 3 4
#>  $ y: chr  "a" "b" "c" "d"
#>  $ z: int  11 12 13 14
# Row names
row.names(df)
#> [1] "1" "2" "3" "4"

Positional Indexing

df[1:2, 2:3] 
#>   y  z
#> 1 a 11
#> 2 b 12
df[2:3, ] # not specifying column index means we want all columns
#>   x y  z
#> 2 2 b 12
#> 3 3 c 13
df[ ,1:2] # similar
#>   x y
#> 1 1 a
#> 2 2 b
#> 3 3 c
#> 4 4 d

Extract a specific variable x from data frame

# first method
df$x
#> [1] 1 2 3 4
# second method
df[[1]]
#> [1] 1 2 3 4
# third method
df[["x"]]
#> [1] 1 2 3 4
# fourth method
df["x"]
#>   x
#> 1 1
#> 2 2
#> 3 3
#> 4 4
names(df)
#> [1] "x" "y" "z"
# Select the variables x and y
df[, c("x", "y")]
#>   x y
#> 1 1 a
#> 2 2 b
#> 3 3 c
#> 4 4 d
# Select the variables x and y
df[c("x", "y")]
#>   x y
#> 1 1 a
#> 2 2 b
#> 3 3 c
#> 4 4 d
# Selecting both columns and rows
df[1:2, c("x", "y")]
#>   x y
#> 1 1 a
#> 2 2 b

Logical Indexing

df
#>   x y  z
#> 1 1 a 11
#> 2 2 b 12
#> 3 3 c 13
#> 4 4 d 14
df[c(1, 3), ]
#>   x y  z
#> 1 1 a 11
#> 3 3 c 13
df$x > 2
#> [1] FALSE FALSE  TRUE  TRUE
# Rows that satisfy x>2
df[df$x > 2, ]
#>   x y  z
#> 3 3 c 13
#> 4 4 d 14

5 Exercise 5

  • How many variables are in mtcars? Show the list of these variables.

  • Extract the vector mpg from mtcars, and calculate its mean and standard deviation.

  • Check whether there is any missing value in wt of mtcars

  • Obtain a data frame with mpg > 22

  • Obtain a data frame from mtcars with gear=5 and cyl=4 and keep only the variables gear, and cyl

6 Subsetting lists

  • list() is the most flexible data structure of R, vectors of different lengths and/or a data frame can be included in a list

  • Data frame is a special case of a list and [[ ]] is useful for extracting elements of a list

  • List is considered as a heterogeneous vector as its elements could be of different types

  • The operators [[, [, and $ can be used to selecting elements from a list

Create a list

my_list <- list(1:3, "a", c(TRUE, FALSE, FALSE), c(2L, 5L, 9L))
str(my_list)
#> List of 4
#>  $ : int [1:3] 1 2 3
#>  $ : chr "a"
#>  $ : logi [1:3] TRUE FALSE FALSE
#>  $ : int [1:3] 2 5 9
my_list[[1]]
#> [1] 1 2 3
my_list[1]
#> [[1]]
#> [1] 1 2 3
typeof(my_list[[1]])
#> [1] "integer"
typeof(my_list[1])
#> [1] "list"
l2 <- list(x = 1:5,
           y = c(TRUE, FALSE),
           z = matrix(1:4, 2))
l2
#> $x
#> [1] 1 2 3 4 5
#> 
#> $y
#> [1]  TRUE FALSE
#> 
#> $z
#>      [,1] [,2]
#> [1,]    1    3
#> [2,]    2    4

Extracting x

l2$x
#> [1] 1 2 3 4 5
l2[["x"]]
#> [1] 1 2 3 4 5
l2[[1]]
#> [1] 1 2 3 4 5