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Subsetting

Source: vignettes/wip/subset.Rmd
subset.Rmd

There are many, many ways to subset data frames and tibbles.

Alignment
Drawing by Jacqueline Nolis

This vignette is an attempt to provide a comprehensive overview over the behavior of the subsetting operators $, [[ and [, highlighting where the tibble implementation differs from the data frame implementation.

library(tibble)
new_df <- function() {
  df <- data.frame(a = 1:4)
  df$b <- letters[5:8]
  df$cd <- list(9, 10:11, 12:14, "text")
  df
}
new_tbl <- function() {
  as_tibble(new_df())
}

Results of the same code for data frames and tibbles are presented side by side:

new_df()
#>   a b         cd
#> 1 1 e          9
#> 2 2 f     10, 11
#> 3 3 g 12, 13, 14
#> 4 4 h       text
 
new_tbl()
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>

In the following, if the results are identical (after converting to a data frame if necessary), only the tibble result is shown, as in the example below. This allows to spot differences easier.

new_tbl()
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>

Subsetting operations are read-only. The same objects are reused in all examples:

df <- new_df()
tbl <- new_tbl()

$

With $ subsetting, accessing a missing column gives a warning. Inexact matching is not supported:

tbl$a
#> [1] 1 2 3 4
 
tbl$"a"
#> [1] 1 2 3 4
 
tbl$a[2:3]
#> [1] 2 3
 
tbl$cd
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
df$c
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
tbl$c
#> Warning: Unknown or uninitialised
#> column: `c`.
#> NULL
 
df$d
#> NULL
 
tbl$d
#> Warning: Unknown or uninitialised
#> column: `d`.
#> NULL

[[

The exact argument is not supported by tibbles.

tbl[["a"]]
#> [1] 1 2 3 4
 
tbl[["cd", exact = TRUE]]
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
df[["cd", exact = FALSE]]
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
tbl[["cd", exact = FALSE]]
#> Warning: `exact` ignored.
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
tbl[["c", exact = TRUE]]
#> NULL
 
df[["c", exact = FALSE]]
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
tbl[["c", exact = FALSE]]
#> Warning: `exact` ignored.
#> NULL

With two indexes, a single element is returned. List columns are not unpacked by tibbles, the [[ only unpacks columns.

tbl[[2, "a"]]
#> [1] 2
 
df[[2, "cd"]]
#> [1] 10 11
 
tbl[[2, "cd"]]
#> [[1]]
#> [1] 10 11
 
df[[1:2, "cd"]]
#> Error in col[[i, exact = exact]]: subscript out of bounds
 
tbl[[1:2, "cd"]]
#> Error in `tbl[[1:2, "cd"]]`:
#> ! Can't extract row with `1:2`.
#> ✖ Subscript `1:2` must be size 1, not 2.
 
tbl[[2, "c"]]
#> NULL
 
df[[1:2, "c"]]
#> NULL
 
tbl[[1:2, "c"]]
#> Error in `tbl[[1:2, "c"]]`:
#> ! Can't extract row with `1:2`.
#> ✖ Subscript `1:2` must be size 1, not 2.

Exotic variants like recursive indexing are deprecated for tibbles.

df[[c(1, 2)]]
#> [1] 2
 
tbl[[c(1, 2)]]
#> 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.

[

With [ subsetting, tibbles always return a tibble. The drop argument is supported but has different defaults:

tbl["a"]
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
df["a", drop = FALSE]
#> Warning in `[.data.frame`(df, "a",
#> drop = FALSE): 'drop' argument will
#> be ignored
#>   a
#> 1 1
#> 2 2
#> 3 3
#> 4 4
 
tbl["a", drop = FALSE]
#> Warning: `drop` argument ignored
#> for subsetting a tibble with
#> `x[j]`, it has an effect only for
#> `x[i, j]`.
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
df["a", drop = TRUE]
#> Warning in `[.data.frame`(df, "a",
#> drop = TRUE): 'drop' argument will
#> be ignored
#>   a
#> 1 1
#> 2 2
#> 3 3
#> 4 4
 
tbl["a", drop = TRUE]
#> Warning: `drop` argument ignored
#> for subsetting a tibble with
#> `x[j]`, it has an effect only for
#> `x[i, j]`.
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
tbl[1]
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
tbl[0]
#> # A tibble: 4 × 0
 
df[4]
#> Error in `[.data.frame`(df, 4): undefined columns selected
 
tbl[4]
#> Error in `tbl[4]`:
#> ! Can't subset columns past the end.
#> ℹ Location 4 doesn't exist.
#> ℹ There are only 3 columns.
 
df[NA]
#> Error in `[.data.frame`(df, NA): undefined columns selected
 
tbl[NA]
#> Error in `tbl[NA]`:
#> ! Can't subset columns with `NA`.
#> ✖ Subscript `NA` can't contain missing values.
#> ✖ It has a missing value at location 1.
 
df[NA_character_]
#> Error in `[.data.frame`(df, NA_character_): undefined columns selected
 
tbl[NA_character_]
#> Error in `tbl[NA_character_]`:
#> ! Can't subset columns with `NA_character_`.
#> ✖ Subscript `NA_character_` can't contain missing values.
#> ✖ It has a missing value at location 1.
 
df[NA_integer_]
#> Error in `[.data.frame`(df, NA_integer_): undefined columns selected
 
tbl[NA_integer_]
#> Error in `tbl[NA_integer_]`:
#> ! Can't subset columns with `NA_integer_`.
#> ✖ Subscript `NA_integer_` can't contain missing values.
#> ✖ It has a missing value at location 1.

The same examples are repeated for two-dimensional indexing when omitting the row index:

df[, "a"]
#> [1] 1 2 3 4
 
tbl[, "a"]
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
tbl[, "a", drop = FALSE]
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
tbl[, "a", drop = TRUE]
#> [1] 1 2 3 4
 
df[, 1]
#> [1] 1 2 3 4
 
tbl[, 1]
#> # A tibble: 4 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
#> 3     3
#> 4     4
 
tbl[, 0]
#> # A tibble: 4 × 0
 
df[, 4]
#> Error in `[.data.frame`(df, , 4): undefined columns selected
 
tbl[, 4]
#> Error in `tbl[, 4]`:
#> ! Can't subset columns past the end.
#> ℹ Location 4 doesn't exist.
#> ℹ There are only 3 columns.
 
df[, NA]
#> Error in `[.data.frame`(df, , NA): undefined columns selected
 
tbl[, NA]
#> Error in `tbl[, NA]`:
#> ! Can't subset columns with `NA`.
#> ✖ Subscript `NA` can't contain missing values.
#> ✖ It has a missing value at location 1.
 
df[, NA_character_]
#> Error in `[.data.frame`(df, , NA_character_): undefined columns selected
 
tbl[, NA_character_]
#> Error in `tbl[, NA_character_]`:
#> ! Can't subset columns with `NA_character_`.
#> ✖ Subscript `NA_character_` can't contain missing values.
#> ✖ It has a missing value at location 1.
 
df[, NA_integer_]
#> Error in `[.data.frame`(df, , NA_integer_): undefined columns selected
 
tbl[, NA_integer_]
#> Error in `tbl[, NA_integer_]`:
#> ! Can't subset columns with `NA_integer_`.
#> ✖ Subscript `NA_integer_` can't contain missing values.
#> ✖ It has a missing value at location 1.

Multiple columns can be queried by passing a vector of column indexes (names, positions, or even a logical vector). With the latter option, tibbles are a tad stricter:

tbl[c("a", "b")]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
tbl[character()]
#> # A tibble: 4 × 0
 
tbl[1:2]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
tbl[1:3]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>
 
df[1:4]
#> Error in `[.data.frame`(df, 1:4): undefined columns selected
 
tbl[1:4]
#> Error in `tbl[1:4]`:
#> ! Can't subset columns past the end.
#> ℹ Location 4 doesn't exist.
#> ℹ There are only 3 columns.
 
tbl[0:2]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
df[-1:2]
#> Error in `[.default`(df, -1:2): only 0's may be mixed with negative subscripts
 
tbl[-1:2]
#> Error in `tbl[-1:2]`:
#> ! Can't subset columns with `-1:2`.
#> ✖ Negative and positive locations can't be mixed.
#> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4.
 
tbl[-1]
#> # A tibble: 4 × 2
#>   b     cd       
#>   <chr> <list>   
#> 1 e     <dbl [1]>
#> 2 f     <int [2]>
#> 3 g     <int [3]>
#> 4 h     <chr [1]>
 
tbl[-(1:2)]
#> # A tibble: 4 × 1
#>   cd       
#>   <list>   
#> 1 <dbl [1]>
#> 2 <int [2]>
#> 3 <int [3]>
#> 4 <chr [1]>
 
tbl[integer()]
#> # A tibble: 4 × 0
 
tbl[TRUE]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>
 
tbl[FALSE]
#> # A tibble: 4 × 0
 
tbl[c(TRUE, TRUE, FALSE)]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
tbl[c(FALSE, TRUE, FALSE)]
#> # A tibble: 4 × 1
#>   b    
#>   <chr>
#> 1 e    
#> 2 f    
#> 3 g    
#> 4 h
 
df[c(FALSE, TRUE)]
#>   b
#> 1 e
#> 2 f
#> 3 g
#> 4 h
 
tbl[c(FALSE, TRUE)]
#> Error in `tbl[c(FALSE, TRUE)]`:
#> ! Can't subset columns with `c(FALSE, TRUE)`.
#> ✖ Logical subscript `c(FALSE, TRUE)` must be size 1 or 3, not 2.
 
df[c(FALSE, TRUE, FALSE, TRUE)]
#> Error in `[.data.frame`(df, c(FALSE, TRUE, FALSE, TRUE)): undefined columns selected
 
tbl[c(FALSE, TRUE, FALSE, TRUE)]
#> Error in `tbl[c(FALSE, TRUE, FALSE, TRUE)]`:
#> ! Can't subset columns with `c(FALSE, TRUE, FALSE, TRUE)`.
#> ✖ Logical subscript `c(FALSE, TRUE, FALSE, TRUE)` must be size 1 or 3, not 4.

The same examples are repeated for two-dimensional indexing when omitting the row index:

tbl[, c("a", "b")]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
tbl[, character()]
#> # A tibble: 4 × 0
 
tbl[, 1:2]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
tbl[, 1:3]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>
 
df[, 1:4]
#> Error in `[.data.frame`(df, , 1:4): undefined columns selected
 
tbl[, 1:4]
#> Error in `tbl[, 1:4]`:
#> ! Can't subset columns past the end.
#> ℹ Location 4 doesn't exist.
#> ℹ There are only 3 columns.
 
tbl[, 0:2]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
df[, -1:2]
#> Error in .subset(x, j): only 0's may be mixed with negative subscripts
 
tbl[, -1:2]
#> Error in `tbl[, -1:2]`:
#> ! Can't subset columns with `-1:2`.
#> ✖ Negative and positive locations can't be mixed.
#> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4.
 
tbl[, -1]
#> # A tibble: 4 × 2
#>   b     cd       
#>   <chr> <list>   
#> 1 e     <dbl [1]>
#> 2 f     <int [2]>
#> 3 g     <int [3]>
#> 4 h     <chr [1]>
 
df[, -(1:2)]
#> [[1]]
#> [1] 9
#> 
#> [[2]]
#> [1] 10 11
#> 
#> [[3]]
#> [1] 12 13 14
#> 
#> [[4]]
#> [1] "text"
 
tbl[, -(1:2)]
#> # A tibble: 4 × 1
#>   cd       
#>   <list>   
#> 1 <dbl [1]>
#> 2 <int [2]>
#> 3 <int [3]>
#> 4 <chr [1]>
 
tbl[, integer()]
#> # A tibble: 4 × 0
 
tbl[, TRUE]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>
 
tbl[, FALSE]
#> # A tibble: 4 × 0
 
tbl[, c(TRUE, TRUE, FALSE)]
#> # A tibble: 4 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f    
#> 3     3 g    
#> 4     4 h
 
df[, c(FALSE, TRUE, FALSE)]
#> [1] "e" "f" "g" "h"
 
tbl[, c(FALSE, TRUE, FALSE)]
#> # A tibble: 4 × 1
#>   b    
#>   <chr>
#> 1 e    
#> 2 f    
#> 3 g    
#> 4 h
 
df[, c(FALSE, TRUE)]
#> [1] "e" "f" "g" "h"
 
tbl[, c(FALSE, TRUE)]
#> Error in `tbl[, c(FALSE, TRUE)]`:
#> ! Can't subset columns with `c(FALSE, TRUE)`.
#> ✖ Logical subscript `c(FALSE, TRUE)` must be size 1 or 3, not 2.
 
df[, c(FALSE, TRUE, FALSE, TRUE)]
#> Error in `[.data.frame`(df, , c(FALSE, TRUE, FALSE, TRUE)): undefined columns selected
 
tbl[, c(FALSE, TRUE, FALSE, TRUE)]
#> Error in `tbl[, c(FALSE, TRUE, FALSE, TRUE)]`:
#> ! Can't subset columns with `c(FALSE, TRUE, FALSE, TRUE)`.
#> ✖ Logical subscript `c(FALSE, TRUE, FALSE, TRUE)` must be size 1 or 3, not 4.

Row subsetting with integer indexes works almost identical. Out-of-bounds subsetting is not recommended and may lead to an error in future versions. Another special case is subsetting with [1, , drop = TRUE] where the data frame implementation returns a list.

tbl[1, ]
#> # A tibble: 1 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
 
df[1, , drop = TRUE]
#> $a
#> [1] 1
#> 
#> $b
#> [1] "e"
#> 
#> $cd
#> $cd[[1]]
#> [1] 9
 
tbl[1, , drop = TRUE]
#> # A tibble: 1 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
 
tbl[1:2, ]
#> # A tibble: 2 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
 
tbl[0, ]
#> # A tibble: 0 × 3
#> # … with 3 variables: a <int>,
#> #   b <chr>, cd <list>
 
tbl[integer(), ]
#> # A tibble: 0 × 3
#> # … with 3 variables: a <int>,
#> #   b <chr>, cd <list>
 
tbl[5, ]
#> # A tibble: 1 × 3
#>       a b     cd    
#>   <int> <chr> <list>
#> 1    NA NA    <NULL>
 
tbl[4:5, ]
#> # A tibble: 2 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     4 h     <chr [1]>
#> 2    NA NA    <NULL>
 
tbl[-1, ]
#> # A tibble: 3 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     2 f     <int [2]>
#> 2     3 g     <int [3]>
#> 3     4 h     <chr [1]>
 
df[-1:2, ]
#> Error in xj[i]: only 0's may be mixed with negative subscripts
 
tbl[-1:2, ]
#> Error in `tbl[-1:2, ]`:
#> ! Can't subset rows with `-1:2`.
#> ✖ Negative and positive locations can't be mixed.
#> ℹ Subscript `-1:2` has 2 positive values at locations 3 and 4.
 
tbl[NA, ]
#> # A tibble: 4 × 3
#>       a b     cd    
#>   <int> <chr> <list>
#> 1    NA NA    <NULL>
#> 2    NA NA    <NULL>
#> 3    NA NA    <NULL>
#> 4    NA NA    <NULL>
 
tbl[NA_integer_, ]
#> # A tibble: 1 × 3
#>       a b     cd    
#>   <int> <chr> <list>
#> 1    NA NA    <NULL>
 
tbl[c(NA, 1), ]
#> # A tibble: 2 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1    NA NA    <NULL>   
#> 2     1 e     <dbl [1]>

Row subsetting with logical indexes also works almost identical, the index vector must have length one or the number of rows with tibbles.

tbl[TRUE, ]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>
 
tbl[FALSE, ]
#> # A tibble: 0 × 3
#> # … with 3 variables: a <int>,
#> #   b <chr>, cd <list>
 
df[c(TRUE, FALSE), ]
#>   a b         cd
#> 1 1 e          9
#> 3 3 g 12, 13, 14
 
tbl[c(TRUE, FALSE), ]
#> Error in `tbl[c(TRUE, FALSE), ]`:
#> ! Can't subset rows with `c(TRUE, FALSE)`.
#> ✖ Logical subscript `c(TRUE, FALSE)` must be size 1 or 4, not 2.
 
df[c(TRUE, FALSE, TRUE), ]
#>   a b         cd
#> 1 1 e          9
#> 3 3 g 12, 13, 14
#> 4 4 h       text
 
tbl[c(TRUE, FALSE, TRUE), ]
#> Error in `tbl[c(TRUE, FALSE, TRUE), ]`:
#> ! Can't subset rows with `c(TRUE, FALSE, TRUE)`.
#> ✖ Logical subscript `c(TRUE, FALSE, TRUE)` must be size 1 or 4, not 3.
 
tbl[c(TRUE, FALSE, TRUE, FALSE), ]
#> # A tibble: 2 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     3 g     <int [3]>
 
df[c(TRUE, FALSE, TRUE, FALSE, TRUE), ]
#>     a    b         cd
#> 1   1    e          9
#> 3   3    g 12, 13, 14
#> NA NA <NA>       NULL
 
tbl[c(TRUE, FALSE, TRUE, FALSE, TRUE), ]
#> Error in `tbl[c(TRUE, FALSE, TRUE, FALSE,
#>     TRUE), ]`:
#> ! Can't subset rows with `c(TRUE, FALSE, TRUE, FALSE, TRUE)`.
#> ✖ Logical subscript `c(TRUE, FALSE, TRUE, FALSE, TRUE)` must be size 1 or 4, not 5.

Indexing both row and column works more or less the same, except for drop:

df[1, "a"]
#> [1] 1
 
tbl[1, "a"]
#> # A tibble: 1 × 1
#>       a
#>   <int>
#> 1     1
 
tbl[1, "a", drop = FALSE]
#> # A tibble: 1 × 1
#>       a
#>   <int>
#> 1     1
 
tbl[1, "a", drop = TRUE]
#> [1] 1
 
df[1:2, "a"]
#> [1] 1 2
 
tbl[1:2, "a"]
#> # A tibble: 2 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
 
tbl[1:2, "a", drop = FALSE]
#> # A tibble: 2 × 1
#>       a
#>   <int>
#> 1     1
#> 2     2
 
tbl[1:2, "a", drop = TRUE]
#> [1] 1 2
 
tbl[1, c("a", "b")]
#> # A tibble: 1 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e
 
tbl[1, c("a", "b"), drop = FALSE]
#> # A tibble: 1 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e
 
df[1, c("a", "b"), drop = TRUE]
#> $a
#> [1] 1
#> 
#> $b
#> [1] "e"
 
tbl[1, c("a", "b"), drop = TRUE]
#> # A tibble: 1 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e
 
tbl[1:2, c("a", "b")]
#> # A tibble: 2 × 2
#>       a b    
#>   <int> <chr>
#> 1     1 e    
#> 2     2 f

Indexes can be omitted altogether, no differences here:

tbl[]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>
 
tbl[,]
#> # A tibble: 4 × 3
#>       a b     cd       
#>   <int> <chr> <list>   
#> 1     1 e     <dbl [1]>
#> 2     2 f     <int [2]>
#> 3     3 g     <int [3]>
#> 4     4 h     <chr [1]>