- To be able to subset vectors, factors, matrices, lists, and data frames
- To be able to extract individual and multiple elements
- To be able to skip and remove elements from various data structures.
Base R has many powerful subset operators. Mastering them will allow you to easily perform complex operations on any kind of dataset. Below is a brief list of ways you can subset data in base R.
- Accessing elements using their indices
- Subsetting by name
- Subsetting through other logical operations
Let's look at a few examples using the gapminder dataset.
To access a given column, you can call it by typing column position or indice in square brackets. For instance gapminder[3]. Let's just take a look at the first 6 rows by using head().
head(gapminder[3])
Source: local data frame [6 x 1]
pop
(dbl)
1 8425333
2 9240934
3 10267083
4 11537966
5 13079460
6 14880372
Likewise, we can subset by name by type the column name inside quotes inside a square bracket
head(gapminder[["pop"]])
Source: local data frame [6 x 1]
pop
(dbl)
1 8425333
2 9240934
3 10267083
4 11537966
5 13079460
6 14880372
Another way to call the column by name is to use the same $ nomenclature we used for plotting in base R.
head(gapminder$year)
[1] 8425333 9240934 10267083 11537966
[5] 13079460 14880372
To specify specific rows and columns, we must separate them by a ,. Also, you can use the : to specific multiple rows for columns. Nothing in front of or behind the comma means all rows or all columns, respectively.
gapminder[1:3,]
Returns the first three rows and all column.
country year pop continent lifeExp gdpPercap
1 Afghanistan 1952 8425333 Asia 28.801 779.4453
2 Afghanistan 1957 9240934 Asia 30.332 820.8530
3 Afghanistan 1962 10267083 Asia 31.997 853.1007
If that syntax doesn't not work well with your brain, have no fear. Hadley Wickham has come to your rescue!
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