Subsetting

In tibble: Simple Data Frames

knitr::opts_chunk$set(
  error = TRUE,
  collapse = TRUE,
  comment = "#>"
)

tibble:::set_dftbl_hooks()

options(
  lifecycle_disable_warnings = FALSE,
  lifecycle_verbose_soft_deprecation = TRUE,
  lifecycle_repeat_warnings = TRUE
)

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

[{ width=100% }](https://twitter.com/skyetetra/status/1160012260456062977)

Drawing by [Jacqueline Nolis](https://twitter.com/skyetetra/status/1160012260456062977)

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()

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_df()

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:

df$a
df$"a"
df$a[2:3]
df$cd
df$c
df$d

[[

The exact argument is not supported by tibbles.

df[["a"]]
df[["cd", exact = TRUE]]
df[["cd", exact = FALSE]]
df[["c", exact = TRUE]]
df[["c", exact = FALSE]]

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

df[[2, "a"]]
df[[2, "cd"]]
df[[1:2, "cd"]]
df[[2, "c"]]
df[[1:2, "c"]]

Exotic variants like recursive indexing are deprecated for tibbles.

df[[c(1, 2)]]

[

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

df["a"]
df["a", drop = FALSE]
df["a", drop = TRUE]
df[1]
df[0]
df[4]
df[NA]
df[NA_character_]
df[NA_integer_]

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

df[, "a"]
df[, "a", drop = FALSE]
df[, "a", drop = TRUE]
df[, 1]
df[, 0]
df[, 4]
df[, NA]
df[, NA_character_]
df[, NA_integer_]

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:

df[c("a", "b")]
df[character()]
df[1:2]
df[1:3]
df[1:4]
df[0:2]
df[-1:2]
df[-1]
df[-(1:2)]
df[integer()]
df[TRUE]
df[FALSE]
df[c(TRUE, TRUE, FALSE)]
df[c(FALSE, TRUE, FALSE)]
df[c(FALSE, TRUE)]
df[c(FALSE, TRUE, FALSE, TRUE)]

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

df[, c("a", "b")]
df[, character()]
df[, 1:2]
df[, 1:3]
df[, 1:4]
df[, 0:2]
df[, -1:2]
df[, -1]
df[, -(1:2)]
df[, integer()]
df[, TRUE]
df[, FALSE]
df[, c(TRUE, TRUE, FALSE)]
df[, c(FALSE, TRUE, FALSE)]
df[, c(FALSE, TRUE)]
df[, c(FALSE, TRUE, FALSE, TRUE)]

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.

df[1, ]
df[1, , drop = TRUE]
df[1:2, ]
df[0, ]
df[integer(), ]
df[5, ]
df[4:5, ]
df[-1, ]
df[-1:2, ]
df[NA, ]
df[NA_integer_, ]
df[c(NA, 1), ]

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

df[TRUE, ]
df[FALSE, ]
df[c(TRUE, FALSE), ]
df[c(TRUE, FALSE, TRUE), ]
df[c(TRUE, FALSE, TRUE, FALSE), ]
df[c(TRUE, FALSE, TRUE, FALSE, TRUE), ]

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

df[1, "a"]
df[1, "a", drop = FALSE]
df[1, "a", drop = TRUE]
df[1:2, "a"]
df[1:2, "a", drop = FALSE]
df[1:2, "a", drop = TRUE]
df[1, c("a", "b")]
df[1, c("a", "b"), drop = FALSE]
df[1, c("a", "b"), drop = TRUE]
df[1:2, c("a", "b")]

Indexes can be omitted altogether, no differences here:

df[]
df[,]

tibble documentation built on March 31, 2023, 11 p.m.