Overview

Teaching: 30 min
Exercises: 10 min

Questions

• How can I work with subsets of data in R?

Objectives

• To be able to subset vectors, factors, lists, and dataframes

• To be able to extract individual and multiple elements: by index, by name, using comparison operations

• To be able to skip and remove elements from various data structures.

Disclaimer: This tutorial is partially based on an excellent book Advanced R.. Read chapter 4 to learn how to subset other data structures.

Subsetting

R has many powerful subset operators and mastering them will allow you to easily perform complex operations on any kind of dataset.

There are six different ways we can subset any kind of object, and three different subsetting operators for the different data structures.

Let’s start with the workhorse of R: atomic vectors.

Subsetting atomic vectors

By position

Vector numbering in R starts at 1

In many programming languages (C and python, for example), the first element of a vector has an index of 0. In R, the first element is 1.

By name

Tip: Duplicated names

Although inexing by name is usually a much more reliable way to subset objects, notice, what will happen if we have several elements named “a”:

y <- c(5.4, 6.2, 7.1, 4.8, 7.5, 6.2)
names(y) <- c('a', 'b', 'c', 'a', 'e', 'a')
y[c("a", "c")]

  a   c 
5.4 7.1 

We’ll solve this problem in the next section.

By position, value, or name using logical vectors

Discussion 1

1. Predict and check the results of the following operations:

   x <- c(5.4, 6.2, 7.1, 4.8, 7.5, 6.2)
   names(x) <- c('a', 'b', 'c', 'd', 'e', 'f')
   x[-(2:4)]
   x[-2:4]
   -x[2:4]
   x[names(x) == "a"
   

2. Discuss with your neighbor subsetting using logical values. How does each of these commands work?

3. x[names(x) == "a"] x[which(names(x) == "a") produce the same results. How do these two commands differ from each other?

Tip: Getting help for operators

Remember you can search for help on operators by wrapping them in quotes: help("%in%") or ?"%in%".

Handling special values

At some point you will encounter functions in R which cannot handle missing, infinite, or undefined data.

There are a number of special functions you can use to filter out this data:

• is.na will return all positions in a vector, matrix, or data.frame containing NA.
• likewise, is.nan, and is.infinite will do the same for NaN and Inf.
• is.finite will return all positions in a vector, matrix, or data.frame that do not contain NA, NaN or Inf.
• na.omit will filter out all missing values from a vector

Subsetting lists: three functions: [, [[, and $.

Use [...] to return a subset of a list as a list.

If you want to subset a list, but not extract an element, then you will likely use [...].

lst <- list(1:3, "a", c(TRUE, FALSE, TRUE), c(2.3, 5.9))
names(lst) <- c("A","B","C","D")
lst[1]

$A
[1] 1 2 3

str(lst[1])

List of 1
 $ A: int [1:3] 1 2 3

Use [[...]] to extract an individual element of a list.

lst[[1]]

[1] 1 2 3

lst[["D"]]

[1] 2.3 5.9

str(lst[[1]])

 int [1:3] 1 2 3

You can’t extract more than one element at once nor use [[ ]] to skip elements:

lst[[1:3]]

Error in lst[[1:3]]: recursive indexing failed at level 2

lst[[-1]]

Error in lst[[-1]]: attempt to select more than one element in get1index <real>

Use $... to extract an element of a list by its name (you don’t need “” for the name):

lst$D # The `$` function is a shorthand way for extracting elements by name

[1] 2.3 5.9

Discussion 2

1. What is the difference between these two commands? Did you get the results you expected?

   lst <- list(1:3, "a", c(TRUE, FALSE, TRUE), c(2.3, 5.9))
   names(lst) <- c("A","B","C","D")
   # command 1:
   lst[1][2]
   # command 2:
   lst[[1]][2]
   

Subsetting dataframes

Remember the data frames are lists underneath the hood, so similar rules apply. However they are also two dimensional objects!

Subset columns from a dataset using [...].

Remember the data frames are lists underneath the hood, so similar rules apply.
However, the resulting object will be a data frame:

df <- data.frame(
  x = 1:3,
  y = c("a", "b", "c"),
  z = c(TRUE,FALSE,TRUE),
  stringsAsFactors = FALSE)
str(df)

'data.frame':	3 obs. of  3 variables:
 $ x: int  1 2 3
 $ y: chr  "a" "b" "c"
 $ z: logi  TRUE FALSE TRUE

df[1]

  x
1 1
2 2
3 3

Subset cells from a dataset using [...] with two numbers:

The first argument corresponds to rows and the second to columns (either or both of them can be skipped!):

df[1:2,2:3]

  y     z
1 a  TRUE
2 b FALSE

If we subset a single row, the result will be a data frame (because the elements are mixed types):

df[3,]

  x y    z
3 3 c TRUE

But for a single column the result will be a vector (this can be changed with the third argument, drop = FALSE).

df[,3]

[1]  TRUE FALSE  TRUE

Extract a single column using [[...]] or $... with the column name:

df$x

[1] 1 2 3

#or
df[["x"]]

[1] 1 2 3

Discussion 3

1. What is the difference between:

   df[,2]
   df[2]
   df[[2]]
   

2. How about

  df[3,2] #and
  df[[2]][3]

Key Points

• Access individual values by location using [].

• Access slices of data using [low:high].

• Access arbitrary sets of data using [c(...)].

• Use which to select subsets of data based on value.