knitr::knit_hooks$set(document = function(x) {
gsub("```\n*```r\n*", "", x)
})
R has many powerful subset operators and mastering them will allow you to easily perform complex operation on any kind of dataset. Allows you to manipulate data very succinctly.
As the last section for this topic we'll cover:
x <- c(5.4, 6.2, 7.1, 4.8)
We can subset this in 5 ways
Using positive integers
x[1]
## [1] 5.4
x[c(3, 1)]
## [1] 7.1 5.4
# We can duplicate indices
x[c(1, 1)]
## [1] 5.4 5.4
# Real numbers are silently truncated to integers
x[c(2.1, 2.9)]
## [1] 6.2 6.2
Using negative integers
# skip the first element
x[-1]
## [1] 6.2 7.1 4.8
# skip the first and the fifth
x[-c(1, 5)]
## [1] 6.2 7.1 4.8
Using logical operators
x[c(TRUE, TRUE, FALSE, FALSE)]
## [1] 5.4 6.2
# or based on a condition
x[x > 3]
## [1] 5.4 6.2 7.1 4.8
x[which(x > 3)]
## [1] 5.4 6.2 7.1 4.8
# Also see `which.max()` and `which.min()`
with nothing
x[]
## [1] 5.4 6.2 7.1 4.8
# useful when dealing with 2 or higher dimensional objects
with zero
x[0]
## numeric(0)
# helpful for generating test data or creating empty objects
Referencing objects by their names
(y <- setNames(x, letters[1:4]))
## a b c d
## 5.4 6.2 7.1 4.8
y[c("d", "c", "a")]
## d c a
## 4.8 7.1 5.4
# We can also repeat indices
y[c("a", "a", "a")]
## a a a
## 5.4 5.4 5.4
# Names are always matched exactly, not partially
z <- c(abc = 1, def = 2)
z[c("a", "d")]
## <NA> <NA>
## NA NA
Subsetting a list works in exactly the same way as subsetting an atomic vector. Subsetting a list with [ will always return a list: [[ and $, as described below, let you pull out the components of the list.
x <- as.list(1:10)
x[1:5]
## [[1]]
## [1] 1
##
## [[2]]
## [1] 2
##
## [[3]]
## [1] 3
##
## [[4]]
## [1] 4
##
## [[5]]
## [1] 5
# What class is this object?
To extract individual elements inside a list, use the [[ operator
# to get element 5
x[[5]]
## [1] 5
class(x[[5]])
## [1] "integer"
another_variable <- x[[5]]
Or using their names
names(x) <- letters[1:5]
x$a
## [1] 1
x[["a"]]
## [1] 1
A matrix is subset with two arguments within single brackets, [], and separated by a comma. The first argument specifies the rows, and the second the columns.
a <- matrix(1:9, nrow = 3)
colnames(a) <- c("A", "B", "C")
a[1:2, ]
## A B C
## [1,] 1 4 7
## [2,] 2 5 8
a[c(T, F, T), c("B", "A")]
## B A
## [1,] 4 1
## [2,] 6 3
a[0, -2]
## A C
df <- data.frame(x = 1:3, y = 3:1, z = letters[1:3])
df[df$x == 2, ]
## x y z
## 2 2 2 b
df[c(1, 3), ]
## x y z
## 1 1 3 a
## 3 3 1 c
There are two ways to select columns from a data frame
df[c("x", "z")]
## x z
## 1 1 a
## 2 2 b
## 3 3 c
df[, c("x", "z")]
## x z
## 1 1 a
## 2 2 b
## 3 3 c
There's an important difference if you select a simple column: matrix subsetting simplifies by default, list subsetting does not.
df["x"]
## x
## 1 1
## 2 2
## 3 3
df[, "x"]
## [1] 1 2 3