4.1 Selecting multiple elements
4.1.1 Atomic Vectors
- 6 ways to subset atomic vectors
Let’s take a look with an example vector.
Positive integer indices
# return elements at specified positions which can be out of order
x[c(4, 1)]
#> [1] 4.4 1.1
# duplicate indices return duplicate values
x[c(2, 2)]
#> [1] 2.2 2.2
# real numbers truncate to integers
# so this behaves as if it is x[c(3, 3)]
x[c(3.2, 3.8)]
#> [1] 3.3 3.3Negative integer indices
### excludes elements at specified positions
x[-c(1, 3)] # same as x[c(-1, -3)] or x[c(2, 4)]
#> [1] 2.2 4.4
### mixing positive and negative is a no-no
x[c(-1, 3)]
#> Error in x[c(-1, 3)]: only 0's may be mixed with negative subscriptsLogical Vectors
x[c(TRUE, TRUE, FALSE, TRUE)]
#> [1] 1.1 2.2 4.4
x[x < 3]
#> [1] 1.1 2.2
cond <- x > 2.5
x[cond]
#> [1] 3.3 4.4- Recyling rules applies when the two vectors are of different lengths
- the shorter of the two is recycled to the length of the longer
- Easy to understand if x or y is 1, best to avoid other lengths
Missing values (NA)
Nothing
Zero
Character vectors
# if name, you can use to return matched elements
(y <- setNames(x, letters[1:4]))
#> a b c d
#> 1.1 2.2 3.3 4.4
y[c("d", "b", "a")]
#> d b a
#> 4.4 2.2 1.1
# Like integer indices, you can repeat indices
y[c("a", "a", "a")]
#> a a a
#> 1.1 1.1 1.1
# When subsetting with [, names are always matched exactly
z <- c(abc = 1, def = 2)
z
#> abc def
#> 1 2
z[c("a", "d")]
#> <NA> <NA>
#> NA NA4.1.2 Lists
- Subsetting works the same way
[always returns a list[[and$let you pull elements out of a list
my_list <- list(a = c(T, F), b = letters[5:15], c = 100:108)
my_list
#> $a
#> [1] TRUE FALSE
#>
#> $b
#> [1] "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"
#>
#> $c
#> [1] 100 101 102 103 104 105 106 107 108Return a (named) list
Return a vector
l2 <- my_list[[2]]
l2
#> [1] "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"
l2b <- my_list$b
l2b
#> [1] "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o"Return a specific element
l3 <- my_list[[2]][3]
l3
#> [1] "g"
l4 <- my_list[['b']][3]
l4
#> [1] "g"
l4b <- my_list$b[3]
l4b
#> [1] "g"Visual Representation
See this stackoverflow article for more detailed information about the differences: https://stackoverflow.com/questions/1169456/the-difference-between-bracket-and-double-bracket-for-accessing-the-el
4.1.3 Matrices and arrays
You can subset higher dimensional structures in three ways:
- with multiple vectors
- with a single vector
- with a matrix
a <- matrix(1:12, nrow = 3)
colnames(a) <- c("A", "B", "C", "D")
# single row
a[1, ]
#> A B C D
#> 1 4 7 10
# single column
a[, 1]
#> [1] 1 2 3
# single element
a[1, 1]
#> A
#> 1
# two rows from two columns
a[1:2, 3:4]
#> C D
#> [1,] 7 10
#> [2,] 8 11
a[c(TRUE, FALSE, TRUE), c("B", "A")]
#> B A
#> [1,] 4 1
#> [2,] 6 3
# zero index and negative index
a[0, -2]
#> A C DSubset a matrix with a matrix
vals <- outer(1:5, 1:5, FUN = "paste", sep = ",")
vals
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] "1,1" "1,2" "1,3" "1,4" "1,5"
#> [2,] "2,1" "2,2" "2,3" "2,4" "2,5"
#> [3,] "3,1" "3,2" "3,3" "3,4" "3,5"
#> [4,] "4,1" "4,2" "4,3" "4,4" "4,5"
#> [5,] "5,1" "5,2" "5,3" "5,4" "5,5"
select <- matrix(ncol = 2, byrow = TRUE,
c(1, 1,
3, 1,
2, 4))
select
#> [,1] [,2]
#> [1,] 1 1
#> [2,] 3 1
#> [3,] 2 4
vals[select]
#> [1] "1,1" "3,1" "2,4"Matrices and arrays are just special vectors; can subset with a single vector (arrays in R stored column wise)
4.1.4 Data frames and tibbles
Data frames act like both lists and matrices
- When subsetting with a single index, they behave like lists and index the columns, so
df[1:2]selects the first two columns. - When subsetting with two indices, they behave like matrices, so
df[1:3, ]selects the first three rows (and all the columns).
library(palmerpenguins)
penguins <- penguins
# single index selects first two columns
two_cols <- penguins[2:3] # or penguins[c(2,3)]
head(two_cols)
#> # A tibble: 6 × 2
#> island bill_length_mm
#> <fct> <dbl>
#> 1 Torgersen 39.1
#> 2 Torgersen 39.5
#> 3 Torgersen 40.3
#> 4 Torgersen NA
#> 5 Torgersen 36.7
#> 6 Torgersen 39.3
# equivalent to the above code
same_two_cols <- penguins[c("island", "bill_length_mm")]
head(same_two_cols)
#> # A tibble: 6 × 2
#> island bill_length_mm
#> <fct> <dbl>
#> 1 Torgersen 39.1
#> 2 Torgersen 39.5
#> 3 Torgersen 40.3
#> 4 Torgersen NA
#> 5 Torgersen 36.7
#> 6 Torgersen 39.3
# two indices separated by comma (first two rows of 3rd and 4th columns)
penguins[1:2, 3:4]
#> # A tibble: 2 × 2
#> bill_length_mm bill_depth_mm
#> <dbl> <dbl>
#> 1 39.1 18.7
#> 2 39.5 17.4
# Can't do this...
penguins[[3:4]][c(1:4)]
#> Error:
#> ! The `j` argument of `[[.tbl_df()` can't be a vector of length 2 as of
#> tibble 3.0.0.
#> ℹ Recursive subsetting is deprecated for tibbles.
# ...but this works...
penguins[[3]][c(1:4)]
#> [1] 39.1 39.5 40.3 NA
# ...or this equivalent...
penguins$bill_length_mm[1:4]
#> [1] 39.1 39.5 40.3 NASubsetting a tibble with [ always returns a tibble
4.1.5 Preserving dimensionality
- Data frames and tibbles behave differently
- tibble will default to preserve dimensionality, data frames do not
- this can lead to unexpected behavior and code breaking in the future
- Use
drop = FALSEto preserve dimensionality when subsetting a data frame or use tibbles
tb <- tibble::tibble(a = 1:2, b = 1:2)
# returns tibble
str(tb[, "a"])
#> tibble [2 × 1] (S3: tbl_df/tbl/data.frame)
#> $ a: int [1:2] 1 2
tb[, "a"] # equivalent to tb[, "a", drop = FALSE]
#> # A tibble: 2 × 1
#> a
#> <int>
#> 1 1
#> 2 2
# returns integer vector
# str(tb[, "a", drop = TRUE])
tb[, "a", drop = TRUE]
#> [1] 1 2df <- data.frame(a = 1:2, b = 1:2)
# returns integer vector
# str(df[, "a"])
df[, "a"]
#> [1] 1 2
# returns data frame with one column
# str(df[, "a", drop = FALSE])
df[, "a", drop = FALSE]
#> a
#> 1 1
#> 2 2Factors
Factor subsetting drop argument controls whether or not levels (rather than dimensions) are preserved.