Invariants for subsetting and subassignment

This vignette defines invariants for subsetting and subset-assignment for tibbles, and illustrates where their behaviour differs from data frames. The goal is to define a small set of invariants that consistently define how behaviors interact. Some behaviors are defined using functions of the vctrs package, e.g. vec_slice(), vec_recycle() and vec_as_index(). Refer to their documentation for more details about the invariants that they follow.

The subsetting and subassignment operators for data frames and tibbles are particularly tricky, because they support both row and column indexes, both of which are optionally missing. We resolve this by first defining column access with [[ and $, then column-wise subsetting with [, then row-wise subsetting, then the composition of both.

Conventions

In this article, all behaviors are demonstrated using one example data frame and its tibble equivalent:

library(vctrs)
library(tibble)
new_df <- function() {
  df <- data.frame(n = c(1L, NA, 3L, NA))
  df$c <- letters[5:8]
  df$li <- 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() #> n c li #> 1 1 e 9 #> 2 NA f 10, 11 #> 3 3 g 12, 13, 14 #> 4 NA h text

new_tbl() #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 NA f <int [2]> #> 3 3 g <int [3]> #> 4 NA h <chr [1]>

If the results are identical (after converting to a data frame if necessary), only the tibble result is shown.

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

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

Where needed, we also show examples with hierarchical columns containing a data frame or a matrix:

new_tbl2 <- function() {
  tibble(
    tb = tbl,
    m = diag(4)
  )
}
new_df2 <- function() {
  df2 <- new_tbl2()
  class(df2) <- "data.frame"
  class(df2$tb) <- "data.frame"
  df2
}
df2 <- new_df2()
tbl2 <- new_tbl2()

new_tbl() #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 NA f <int [2]> #> 3 3 g <int [3]> #> 4 NA h <chr [1]>

For subset assignment (subassignment, for short), we need a fresh copy of the data for each test. The with_*() functions (omitted here for brevity) allow for a more concise notation. These functions take an assignment expression, execute it on a fresh copy of the data, and return the data for printing. The first example prints what’s really executed, further examples omit this output.

with_df(df$n <- rev(df$n), verbose = TRUE) #> { #> df <- new_df() #> df$n <- rev(df$n) #> df #> } #> n c li #> 1 NA e 9 #> 2 3 f 10, 11 #> 3 NA g 12, 13, 14 #> 4 1 h text

with_tbl(tbl$n <- rev(tbl$n), verbose = TRUE) #> { #> tbl <- new_tbl() #> tbl$n <- rev(tbl$n) #> tbl #> } #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 NA e <dbl [1]> #> 2 3 f <int [2]> #> 3 NA g <int [3]> #> 4 1 h <chr [1]>

Column extraction

Definition of x[[j]]

x[[j]] is equal to .subset2(x, j).

	tbl[[1]] #> [1] 1 NA 3 NA
	.subset2(tbl, 1) #> [1] 1 NA 3 NA

NB: x[[j]] always returns an object of size nrow(x) if the column exists.

j must be a single number or a string, as enforced by .subset2(x, j).

df[[1:2]] #> [1] NA	tbl[[1:2]] #> Warning: The `j` argument of #> ``[[.tbl_df`()` can't be a vector of #> length 2 as of tibble 3.0.0. #> Recursive subsetting is deprecated for #> tibbles. #> [1] NA
df[[c("n", "c")]] #> Error in .subset2(x, i, exact = exact): #> subscript out of bounds	tbl[[c("n", "c")]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `c("n", "c")` has size 2 #> but must be size 1.
df[[TRUE]] #> [1] 1 NA 3 NA	tbl[[TRUE]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `TRUE` has the wrong #> type `logical`. #> [34mℹ[39m It must be numeric or character.
df[[mean]] #> Error in .subset2(x, i, exact = exact): #> invalid subscript type 'closure'	tbl[[mean]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `mean` has the wrong #> type `closure`. #> [34mℹ[39m It must be numeric or character.

NA indexes, numeric out-of-bounds (OOB) values, and non-integers throw an error:

df[[NA]] #> NULL	tbl[[NA]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `NA` can't be `NA`.
df[[NA_character_]] #> NULL	tbl[[NA_character_]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `NA_character_` can't be #> `NA`.
df[[NA_integer_]] #> NULL	tbl[[NA_integer_]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `NA_integer_` can't be #> `NA`.
df[[-1]] #> Error in .subset2(x, i, exact = exact): #> attempt to select more than one element #> in get1index <real>	tbl[[-1]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m The subscript `-1` has value -1 but #> must be a positive location.
df[[4]] #> Error in .subset2(x, i, exact = exact): #> subscript out of bounds	tbl[[4]] #> Error: Can't subset columns that don't #> exist. #> [31mx[39m The location 4 doesn't exist. #> [34mℹ[39m There are only 3 columns.
df[[1.5]] #> [1] 1 NA 3 NA	tbl[[1.5]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m Lossy cast from `1.5` <double> to #> <integer>.
df[[Inf]] #> NULL	tbl[[Inf]] #> Error: Must extract column with a single #> valid subscript. #> [31mx[39m Lossy cast from `Inf` <double> to #> <integer>.

Character OOB access is silent because a common package idiom is to check for the absence of a column with is.null(df[[var]]).

tbl[["x"]] #> NULL

Definition of x$name

x$name and x$"name" are equal to x[["name"]].

	tbl$n #> [1] 1 NA 3 NA
	tbl$"n" #> [1] 1 NA 3 NA
	tbl[["n"]] #> [1] 1 NA 3 NA

Unlike data frames, tibbles do not partially match names. Because df$x is rarely used in packages, it can raise a warning:

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

Column subsetting

Definition of x[j]

j is converted to an integer vector by vec_as_index(j, ncol(x), names = names(x)). Then x[c(j_1, j_2, ..., j_n)] is equivalent to tibble(x[[j_1]], x[[j_2]], ..., x[[j_3]]), keeping the corresponding column names. This implies that j must be a numeric or character vector, or a logical vector with length 1 or ncol(x).¹

tbl[1:2] #> # A tibble: 4 x 2 #> n c #> <int> <chr> #> 1 1 e #> 2 NA f #> 3 3 g #> 4 NA h

When subsetting repeated indexes, the resulting column names are undefined, do not rely on them.

df[c(1, 1)] #> n n.1 #> 1 1 1 #> 2 NA NA #> 3 3 3 #> 4 NA NA

tbl[c(1, 1)] #> # A tibble: 4 x 2 #> n n #> <int> <int> #> 1 1 1 #> 2 NA NA #> 3 3 3 #> 4 NA NA

For tibbles with repeated column names, subsetting by name uses the first matching column.

nrow(df[j]) equals nrow(df).

tbl[integer()] #> # A tibble: 4 x 0

Tibbles support indexing by a logical matrix, but only if all values in the returned vector are compatible.

df[is.na(df)] #> [[1]] #> [1] NA #> #> [[2]] #> [1] NA	tbl[is.na(tbl)] #> [1] NA NA
df[!is.na(df)] #> [[1]] #> [1] 1 #> #> [[2]] #> [1] 3 #> #> [[3]] #> [1] "e" #> #> [[4]] #> [1] "f" #> #> [[5]] #> [1] "g" #> #> [[6]] #> [1] "h" #> #> [[7]] #> [1] 9 #> #> [[8]] #> [1] 10 11 #> #> [[9]] #> [1] 12 13 14 #> #> [[10]] #> [1] "text"	tbl[!is.na(tbl)] #> Error: No common type for `n` <integer> #> and `c` <character>.

Definition of x[, j]

x[, j] is equal to x[j]. Tibbles do not perform column extraction if x[j] would yield a single column.

df[, 1] #> [1] 1 NA 3 NA	tbl[, 1] #> # A tibble: 4 x 1 #> n #> <int> #> 1 1 #> 2 NA #> 3 3 #> 4 NA
	tbl[, 1:2] #> # A tibble: 4 x 2 #> n c #> <int> <chr> #> 1 1 e #> 2 NA f #> 3 3 g #> 4 NA h

Definition of x[, j, drop = TRUE]

For backward compatiblity, x[, j, drop = TRUE] performs column extraction, returning x[j][[1]] when ncol(x[j]) is 1.

tbl[, 1, drop = TRUE] #> [1] 1 NA 3 NA

Row subsetting

Definition of x[i, ]

x[i, ] is equal to tibble(vec_slice(x[[1]], i), vec_slice(x[[2]], i), ...).²

tbl[3, ] #> # A tibble: 1 x 3 #> n c li #> <int> <chr> <list> #> 1 3 g <int [3]>

This means that i must be a numeric vector, or a logical vector of length nrow(x) or 1. For compatibility, i can also be a character vector containing positive numbers.

df[mean, ] #> Error in xj[i]: invalid subscript type #> 'closure'	tbl[mean, ] #> Error: Must subset rows with a valid #> subscript vector. #> [31mx[39m The subscript `mean` has the wrong #> type `closure`. #> [34mℹ[39m It must be logical, numeric, or #> character.
df[list(1), ] #> Error in xj[i]: invalid subscript type #> 'list'	tbl[list(1), ] #> Error: Must subset rows with a valid #> subscript vector. #> [31mx[39m The subscript `list(1)` has the wrong #> type `list`. #> [34mℹ[39m It must be logical, numeric, or #> character.
	tbl["1", ] #> # A tibble: 1 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]>

Exception: OOB values generate warnings instead of errors:

df[10, ] #> n c li #> NA NA <NA> NULL	tbl[10, ] #> Warning: The `i` argument of #> ``[.tbl_df`()` must lie in [0, rows] if #> positive, as of tibble 3.0.0. #> Use `NA` as row index to obtain a row #> full of `NA` values. #> # A tibble: 1 x 3 #> n c li #> <int> <chr> <list> #> 1 NA <NA> <NULL>
df["x", ] #> n c li #> NA NA <NA> NULL	tbl["x", ] #> Warning: The `i` argument of #> ``[.tbl_df`()` must use valid row names #> as of tibble 3.0.0. #> Use `NA` as row index to obtain a row #> full of `NA` values. #> # A tibble: 1 x 3 #> n c li #> <int> <chr> <list> #> 1 NA <NA> <NULL>

Unlike data frames, only logical vectors of length 1 are recycled.

df[c(TRUE, FALSE), ] #> n c li #> 1 1 e 9 #> 3 3 g 12, 13, 14

tbl[c(TRUE, FALSE), ] #> Error: Must subset rows with a valid #> subscript vector. #> [34mℹ[39m Logical subscripts must match the size #> of the indexed input. #> [31mx[39m The input has size 4 but the subscript #> `c(TRUE, FALSE)` has size 2.

NB: scalar logicals are recycled, but scalar numerics are not. That makes the x[NA, ] and x[NA_integer_, ] return different results.

	tbl[NA, ] #> # A tibble: 4 x 3 #> n c li #> <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 x 3 #> n c li #> <int> <chr> <list> #> 1 NA <NA> <NULL>

Definition of x[i, , drop = TRUE]

drop = TRUE has no effect when not selecting a single row:

df[1, , drop = TRUE] #> $n #> [1] 1 #> #> $c #> [1] "e" #> #> $li #> $li[[1]] #> [1] 9

tbl[1, , drop = TRUE] #> # A tibble: 1 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]>

Row and column subsetting

Definition of x[] and x[,]

x[] and x[,] are equivalent to x.³

Definition of x[i, j]

x[i, j] is equal to x[i, ][j].⁴

Definition of x[[i, j]]

i must be a numeric vector of length 1. x[[i, j]] is equal to x[i, ][[j]], or vctrs::vec_slice(x[[j]], i).⁵

This implies that j must be a numeric or character vector of length 1.

NB: vec_size(x[[i, j]]) always equals 1. Unlike x[i, ], x[[i, ]] is not valid.

Column update

Definition of x[[j]] <- a

If a is a vector then x[[j]] <- a replaces the jth column with value a.

a is recycled to the same size as x so must have size nrow(x) or 1. (The only exception is when a is NULL, as described below.) Recycling also works for list, data frame, and matrix columns.

	with_tbl(tbl[["li"]] <- list(0)) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 NA f <dbl [1]> #> 3 3 g <dbl [1]> #> 4 NA h <dbl [1]>
with_df2(df2[["tb"]] <- df[1, ]) #> Error in `[[<-.data.frame`(`*tmp*`, #> "tb", value = structure(list(n = 1L, : #> replacement has 1 row, data has 4	with_tbl2(tbl2[["tb"]] <- tbl[1, ]) #> # A tibble: 4 x 2 #> tb$n $c $li m[,1] [,2] [,3] #> <int> <chr> <list> <dbl> <dbl> <dbl> #> 1 1 e <dbl [1… 1 0 0 #> 2 1 e <dbl [1… 0 1 0 #> 3 1 e <dbl [1… 0 0 1 #> 4 1 e <dbl [1… 0 0 0 #> # … with 1 more variable: [,4] <dbl>
with_df2(df2[["m"]] <- df2[["m"]][1, , drop = FALSE]) #> Error in `[[<-.data.frame`(`*tmp*`, "m", #> value = structure(c(1, 0, 0, : #> replacement has 1 row, data has 4	with_tbl2(tbl2[["m"]] <- tbl2[["m"]][1, , drop = FALSE]) #> # A tibble: 4 x 2 #> tb$n $c $li m[,1] [,2] [,3] #> <int> <chr> <list> <dbl> <dbl> <dbl> #> 1 1 e <dbl [1… 1 0 0 #> 2 NA f <int [2… 1 0 0 #> 3 3 g <int [3… 1 0 0 #> 4 NA h <chr [1… 1 0 0 #> # … with 1 more variable: [,4] <dbl>

j must be a scalar numeric or a string, and cannot be NA. If j is OOB, a new column is added on the right hand side, with name repair if needed.

	with_tbl(tbl[["x"]] <- 0) #> # A tibble: 4 x 4 #> n c li x #> <int> <chr> <list> <dbl> #> 1 1 e <dbl [1]> 0 #> 2 NA f <int [2]> 0 #> 3 3 g <int [3]> 0 #> 4 NA h <chr [1]> 0
with_df(df[[4]] <- 0) #> n c li V4 #> 1 1 e 9 0 #> 2 NA f 10, 11 0 #> 3 3 g 12, 13, 14 0 #> 4 NA h text 0	with_tbl(tbl[[4]] <- 0) #> # A tibble: 4 x 4 #> n c li ...4 #> <int> <chr> <list> <dbl> #> 1 1 e <dbl [1]> 0 #> 2 NA f <int [2]> 0 #> 3 3 g <int [3]> 0 #> 4 NA h <chr [1]> 0
with_df(df[[5]] <- 0) #> Warning in format.data.frame(if (omit) #> x[seq_len(n0), , drop = FALSE] else #> x, : corrupt data frame: columns will be #> truncated or padded with NAs #> n c li V5 #> 1 1 e 9 NULL 0 #> 2 NA f 10, 11 <NA> 0 #> 3 3 g 12, 13, 14 <NA> 0 #> 4 NA h text <NA> 0	with_tbl(tbl[[5]] <- 0) #> Error: Can't assign column 5 in a tibble #> with 3 columns.

df[[j]] <- a replaces the complete column so can change the type.

[[<- supports removing a column by assigning NULL to it.

Removing a nonexistent column is a no-op.

Definition of x$name <- a

x$name <- a and x$"name" <- a are equivalent to x[["name"]] <- a.⁶

	with_tbl(tbl$n <- 0) #> # A tibble: 4 x 3 #> n c li #> <dbl> <chr> <list> #> 1 0 e <dbl [1]> #> 2 0 f <int [2]> #> 3 0 g <int [3]> #> 4 0 h <chr [1]>
	with_tbl(tbl[["n"]] <- 0) #> # A tibble: 4 x 3 #> n c li #> <dbl> <chr> <list> #> 1 0 e <dbl [1]> #> 2 0 f <int [2]> #> 3 0 g <int [3]> #> 4 0 h <chr [1]>

$<- does not perform partial matching.

	with_tbl(tbl$l <- 0) #> # A tibble: 4 x 4 #> n c li l #> <int> <chr> <list> <dbl> #> 1 1 e <dbl [1]> 0 #> 2 NA f <int [2]> 0 #> 3 3 g <int [3]> 0 #> 4 NA h <chr [1]> 0
	with_tbl(tbl[["l"]] <- 0) #> # A tibble: 4 x 4 #> n c li l #> <int> <chr> <list> <dbl> #> 1 1 e <dbl [1]> 0 #> 2 NA f <int [2]> 0 #> 3 3 g <int [3]> 0 #> 4 NA h <chr [1]> 0

Column subassignment: x[j] <- a

• If j is missing, it’s replaced with seq_along(x)
• If j is logical vector, it’s converted to numeric with seq_along(x)[j].

a is a list or data frame

If inherits(a, "list") or inherits(a, "data.frame") is TRUE, then x[j] <- a is equivalent to x[[j[[1]]] <- a[[1]], x[[j[[2]]]] <- a[[2]], …

	with_tbl(tbl[1:2] <- list("x", 4:1)) #> # A tibble: 4 x 3 #> n c li #> <chr> <int> <list> #> 1 x 4 <dbl [1]> #> 2 x 3 <int [2]> #> 3 x 2 <int [3]> #> 4 x 1 <chr [1]>
	with_tbl(tbl[c("li", "x", "c")] <- list("x", 4:1, NULL)) #> # A tibble: 4 x 3 #> n li x #> <int> <chr> <int> #> 1 1 x 4 #> 2 NA x 3 #> 3 3 x 2 #> 4 NA x 1

If length(a) equals 1, then it is recycled to the same length as j.

	with_tbl(tbl[1:2] <- list(1)) #> # A tibble: 4 x 3 #> n c li #> <dbl> <dbl> <list> #> 1 1 1 <dbl [1]> #> 2 1 1 <int [2]> #> 3 1 1 <int [3]> #> 4 1 1 <chr [1]>
with_df(df[1:2] <- list(0, 0, 0)) #> Warning in `[<-.data.frame`(`*tmp*`, #> 1:2, value = list(0, 0, 0)): provided 3 #> variables to replace 2 variables #> n c li #> 1 0 0 9 #> 2 0 0 10, 11 #> 3 0 0 12, 13, 14 #> 4 0 0 text	with_tbl(tbl[1:2] <- list(0, 0, 0)) #> Error: `list(0, 0, 0)` can't be recycled #> to size 2. #> [31mx[39m It must be size 2 or 1, not 3.
with_df(df[1:3] <- list(0, 0)) #> n c li #> 1 0 0 0 #> 2 0 0 0 #> 3 0 0 0 #> 4 0 0 0	with_tbl(tbl[1:3] <- list(0, 0)) #> Error: `list(0, 0)` can't be recycled to #> size 3. #> [31mx[39m It must be size 3 or 1, not 2.

An attempt to update the same column twice gives an error.

with_df(df[c(1, 1)] <- list(1, 2)) #> Error in `[<-.data.frame`(`*tmp*`, c(1, #> 1), value = list(1, 2)): duplicate #> subscripts for columns

with_tbl(tbl[c(1, 1)] <- list(1, 2)) #> Error: Column index 1 is used more than #> once for assignment.

If a contains NULL values, the corresponding columns are removed after updating (i.e. position indexes refer to columns before any modifications).

with_tbl(tbl[1:2] <- list(NULL, 4:1)) #> # A tibble: 4 x 2 #> c li #> <int> <list> #> 1 4 <dbl [1]> #> 2 3 <int [2]> #> 3 2 <int [3]> #> 4 1 <chr [1]>

NA indexes are not supported.

Just like column updates, [<- supports changing the type of an existing column.

Appending columns at the end (without gaps) is supported. The name of new columns is determined by the LHS, the RHS, or by name repair (in that order of precedence).

	with_tbl(tbl[c("x", "y")] <- tibble("x", x = 4:1)) #> # A tibble: 4 x 5 #> n c li x y #> <int> <chr> <list> <chr> <int> #> 1 1 e <dbl [1]> x 4 #> 2 NA f <int [2]> x 3 #> 3 3 g <int [3]> x 2 #> 4 NA h <chr [1]> x 1
	with_tbl(tbl[3:4] <- list("x", x = 4:1)) #> # A tibble: 4 x 4 #> n c li x #> <int> <chr> <chr> <int> #> 1 1 e x 4 #> 2 NA f x 3 #> 3 3 g x 2 #> 4 NA h x 1
with_df(df[4] <- list(4:1)) #> n c li V4 #> 1 1 e 9 4 #> 2 NA f 10, 11 3 #> 3 3 g 12, 13, 14 2 #> 4 NA h text 1	with_tbl(tbl[4] <- list(4:1)) #> # A tibble: 4 x 4 #> n c li ...4 #> <int> <chr> <list> <int> #> 1 1 e <dbl [1]> 4 #> 2 NA f <int [2]> 3 #> 3 3 g <int [3]> 2 #> 4 NA h <chr [1]> 1
with_df(df[5] <- list(4:1)) #> Error in `[<-.data.frame`(`*tmp*`, 5, #> value = list(4:1)): new columns would #> leave holes after existing columns	with_tbl(tbl[5] <- list(4:1)) #> Error: Can't assign column 5 in a tibble #> with 3 columns.

Tibbles support indexing by a logical matrix, but only for a scalar RHS, and if all columns updated are compatible with the value assigned.

with_df(df[is.na(df)] <- 4) #> n c li #> 1 1 e 9 #> 2 4 f 10, 11 #> 3 3 g 12, 13, 14 #> 4 4 h text	with_tbl(tbl[is.na(tbl)] <- 4) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 4 f <int [2]> #> 3 3 g <int [3]> #> 4 4 h <chr [1]>
with_df(df[is.na(df)] <- 1:2) #> n c li #> 1 1 e 9 #> 2 1 f 10, 11 #> 3 3 g 12, 13, 14 #> 4 2 h text	with_tbl(tbl[is.na(tbl)] <- 1:2) #> Error: Subscript `is.na(tbl)` is a #> matrix, the data `1:2` must have size 1.
with_df(df[matrix(c(rep(TRUE, 5), rep(FALSE, 7)), ncol = 3)] <- 4) #> n c li #> 1 4 4 9 #> 2 4 f 10, 11 #> 3 4 g 12, 13, 14 #> 4 4 h text	with_tbl(tbl[matrix(c(rep(TRUE, 5), rep(FALSE, 7)), ncol = 3)] <- 4) #> Error: Assigned data `4` must be #> compatible with existing data. #> ℹ Error occurred for column `c`. #> x No common type for `value` <double> #> and `x` <character>.

a is another type of vector

If vec_is(a), then x[j] <- a is equivalent to x[j] <- list(a). This is primarily provided for backward compatbility.

	with_tbl(tbl[1] <- 0) #> # A tibble: 4 x 3 #> n c li #> <dbl> <chr> <list> #> 1 0 e <dbl [1]> #> 2 0 f <int [2]> #> 3 0 g <int [3]> #> 4 0 h <chr [1]>
	with_tbl(tbl[1] <- list(0)) #> # A tibble: 4 x 3 #> n c li #> <dbl> <chr> <list> #> 1 0 e <dbl [1]> #> 2 0 f <int [2]> #> 3 0 g <int [3]> #> 4 0 h <chr [1]>

Matrices are vectors, so they are also wrapped in list() before assignment. This consistently creates matrix columns, unlike data frames, which creates matrix columns when assigning to one column, but treats the matrix like a data frame when assigning to more than one column.

	with_tbl(tbl[1] <- matrix(1:8, ncol = 2)) #> # A tibble: 4 x 3 #> n[,1] [,2] c li #> <int> <int> <chr> <list> #> 1 1 5 e <dbl [1]> #> 2 2 6 f <int [2]> #> 3 3 7 g <int [3]> #> 4 4 8 h <chr [1]>
with_df(df[1:2] <- matrix(1:8, ncol = 2)) #> n c li #> 1 1 5 9 #> 2 2 6 10, 11 #> 3 3 7 12, 13, 14 #> 4 4 8 text	with_tbl(tbl[1:2] <- matrix(1:8, ncol = 2)) #> # A tibble: 4 x 3 #> n[,1] [,2] c[,1] [,2] li #> <int> <int> <int> <int> <list> #> 1 1 5 1 5 <dbl [1]> #> 2 2 6 2 6 <int [2]> #> 3 3 7 3 7 <int [3]> #> 4 4 8 4 8 <chr [1]>

a is NULL

Entire columns can be removed. Specifying i is an error.

	with_tbl(tbl[1] <- NULL) #> # A tibble: 4 x 2 #> c li #> <chr> <list> #> 1 e <dbl [1]> #> 2 f <int [2]> #> 3 g <int [3]> #> 4 h <chr [1]>
	with_tbl(tbl[, 2:3] <- NULL) #> # A tibble: 4 x 1 #> n #> <int> #> 1 1 #> 2 NA #> 3 3 #> 4 NA
with_df(df[1, 2:3] <- NULL) #> Error in x[[jj]][iseq] <- vjj: #> replacement has length zero	with_tbl(tbl[1, 2:3] <- NULL) #> Error: `NULL` must be a vector, a bare #> list or a data frame.

a is not a vector

Any other type for a is an error. Note that if is.list(a) is TRUE, but inherits(a, "list") is FALSE, then a is considered to be a scalar. See ?vec_is and ?vec_proxy for details.

with_df(df[1] <- mean) #> Error in rep(value, length.out = n): #> attempt to replicate an object of type #> 'closure'

with_tbl(tbl[1] <- mean) #> Error: `mean` must be a vector, a bare #> list, a data frame or NULL.

with_df(df[1] <- lm(mpg ~ wt, data = mtcars)) #> Warning in `[<-.data.frame`(`*tmp*`, #> 1, value = structure(list(coefficients #> = c(`(Intercept)` = 37.285126167342, : #> replacement element 2 has 32 rows to #> replace 4 rows #> Warning in `[<-.data.frame`(`*tmp*`, #> 1, value = structure(list(coefficients #> = c(`(Intercept)` = 37.285126167342, : #> replacement element 3 has 32 rows to #> replace 4 rows #> Warning in `[<-.data.frame`(`*tmp*`, #> 1, value = structure(list(coefficients #> = c(`(Intercept)` = 37.285126167342, : #> replacement element 5 has 32 rows to #> replace 4 rows #> Warning in `[<-.data.frame`(`*tmp*`, #> 1, value = structure(list(coefficients #> = c(`(Intercept)` = 37.285126167342, : #> replacement element 7 has 5 rows to #> replace 4 rows #> Error in `[<-.data.frame`(`*tmp*`, 1, #> value = structure(list(coefficients = #> c(`(Intercept)` = 37.285126167342, : #> replacement element 10 has 3 rows, need #> 4

with_tbl(tbl[1] <- lm(mpg ~ wt, data = mtcars)) #> Error: `lm(mpg ~ wt, data = mtcars)` #> must be a vector, a bare list, a data #> frame or NULL.

Row subassignment: x[i, ] <- list(...)

x[i, ] <- a is the same as vec_slice(x[[j_1]], i) <- a[[1]], vec_slice(x[[j_2]], i) <- a[[2]], … .⁷

	with_tbl(tbl[2:3, ] <- tbl[1, ]) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 1 e <dbl [1]> #> 3 1 e <dbl [1]> #> 4 NA h <chr [1]>
	with_tbl(tbl[c(FALSE, TRUE, TRUE, FALSE), ] <- tbl[1, ]) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 1 e <dbl [1]> #> 3 1 e <dbl [1]> #> 4 NA h <chr [1]>

Only values of size one can be recycled.

	with_tbl(tbl[2:3, ] <- tbl[1, ]) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 1 e <dbl [1]> #> 3 1 e <dbl [1]> #> 4 NA h <chr [1]>
	with_tbl(tbl[2:3, ] <- list(tbl$n[1], tbl$c[1:2], tbl$li[1])) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 1 e <dbl [1]> #> 3 1 f <dbl [1]> #> 4 NA h <chr [1]>
with_df(df[2:4, ] <- df[1:2, ]) #> Error in `[<-.data.frame`(`*tmp*`, 2:4, #> , value = structure(list(n = c(1L, : #> replacement element 1 has 2 rows, need 3	with_tbl(tbl[2:4, ] <- tbl[1:2, ]) #> Error: Assigned data `tbl[1:2, ]` must #> be compatible with row subscript `2:4`. #> x 3 rows must be assigned. #> x Element 1 of assigned data has 2 rows. #> ℹ Only vectors of size 1 are recycled.

For compatibility, only a warning is issued for indexing beyond the number of rows. Appending rows right at the end of the existing data is supported, without warning.

	with_tbl(tbl[5, ] <- tbl[1, ]) #> # A tibble: 5 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 NA f <int [2]> #> 3 3 g <int [3]> #> 4 NA h <chr [1]> #> 5 1 e <dbl [1]>
	with_tbl(tbl[5:7, ] <- tbl[1, ]) #> # A tibble: 7 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 NA f <int [2]> #> 3 3 g <int [3]> #> 4 NA h <chr [1]> #> 5 1 e <dbl [1]> #> 6 1 e <dbl [1]> #> 7 1 e <dbl [1]>
with_df(df[6, ] <- df[1, ]) #> n c li #> 1 1 e 9 #> 2 NA f 10, 11 #> 3 3 g 12, 13, 14 #> 4 NA h text #> 5 NA <NA> NULL #> 6 1 e 9	with_tbl(tbl[6, ] <- tbl[1, ]) #> Error: Can't assign row 6 in a tibble #> with 4 rows.
with_df(df[-5, ] <- df[1, ]) #> n c li #> 1 1 e 9 #> 2 1 e 9 #> 3 1 e 9 #> 4 1 e 9	with_tbl(tbl[-5, ] <- tbl[1, ]) #> Error: Can't negate rows that don't #> exist. #> [31mx[39m The location 5 doesn't exist. #> [34mℹ[39m There are only 4 rows.
with_df(df[-(5:7), ] <- df[1, ]) #> n c li #> 1 1 e 9 #> 2 1 e 9 #> 3 1 e 9 #> 4 1 e 9	with_tbl(tbl[-(5:7), ] <- tbl[1, ]) #> Error: Can't negate rows that don't #> exist. #> [31mx[39m The locations 5, 6, and 7 don't exist. #> [34mℹ[39m There are only 4 rows.
with_df(df[-6, ] <- df[1, ]) #> n c li #> 1 1 e 9 #> 2 1 e 9 #> 3 1 e 9 #> 4 1 e 9	with_tbl(tbl[-6, ] <- tbl[1, ]) #> Error: Can't negate rows that don't #> exist. #> [31mx[39m The location 6 doesn't exist. #> [34mℹ[39m There are only 4 rows.

For compatibility, i can also be a character vector containing positive numbers.

with_tbl(tbl[as.character(1:3), ] <- tbl[1, ]) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 1 e <dbl [1]> #> 3 1 e <dbl [1]> #> 4 NA h <chr [1]>

Row and column subassignment

Definition of x[i, j] <- a

x[i, j] <- a is equivalent to x[i, ][j] <- a.⁸

Subassignment to x[i, j] is stricter for tibbles than for data frames. x[i, j] <- a can’t change the data type of existing columns.

with_df(df[2:3, 1] <- df[1:2, 2]) #> n c li #> 1 1 e 9 #> 2 e f 10, 11 #> 3 f g 12, 13, 14 #> 4 <NA> h text	with_tbl(tbl[2:3, 1] <- tbl[1:2, 2]) #> Error: Assigned data `tbl[1:2, 2]` must #> be compatible with existing data. #> ℹ Error occurred for column `n`. #> x No common type for `value` <character> #> and `x` <integer>.
with_df(df[2:3, 2] <- df[1:2, 3]) #> Warning in `[<-.data.frame`(`*tmp*`, #> 2:3, 2, value = list(9, 10:11)): #> provided 2 variables to replace 1 #> variables #> n c li #> 1 1 e 9 #> 2 NA 9 10, 11 #> 3 3 9 12, 13, 14 #> 4 NA h text	with_tbl(tbl[2:3, 2] <- tbl[1:2, 3]) #> Error: Assigned data `tbl[1:2, 3]` must #> be compatible with existing data. #> ℹ Error occurred for column `c`. #> x No common type for `value` <list> and #> `x` <character>.
with_df(df[2:3, 3] <- df2[1:2, 1]) #> Warning in `[<-.data.frame`(`*tmp*`, #> 2:3, 3, value = structure(list(n = #> c(1L, : provided 3 variables to replace #> 1 variables #> n c li #> 1 1 e 9 #> 2 NA f 1 #> 3 3 g NA #> 4 NA h text	with_tbl(tbl[2:3, 3] <- tbl2[1:2, 1]) #> Error: Assigned data `tbl2[1:2, 1]` must #> be compatible with existing data. #> ℹ Error occurred for column `li`. #> x No common type for `value` <tbl_df< #> n : integer #> c : character #> li: list #> >> and `x` <list>.
with_df2(df2[2:3, 1] <- df2[1:2, 2]) #> Warning in matrix(value, n, p): data #> length [8] is not a sub-multiple or #> multiple of the number of columns [3] #> tb.n tb.c tb.li m.1 m.2 m.3 m.4 #> 1 1 e 9 1 0 0 0 #> 2 1 0 0 0 1 0 0 #> 3 0 1 0 0 0 1 0 #> 4 NA h text 0 0 0 1	with_tbl2(tbl2[2:3, 1] <- tbl2[1:2, 2]) #> Error: Assigned data `tbl2[1:2, 2]` must #> be compatible with existing data. #> ℹ Error occurred for column `tb`. #> x No common type for `value` #> <double[,4]> and `x` <tbl_df< #> n : integer #> c : character #> li: list #> >>.
	with_tbl2(tbl2[2:3, 2] <- tbl[1:2, 1]) #> # A tibble: 4 x 2 #> tb$n $c $li m[,1] [,2] [,3] #> <int> <chr> <list> <dbl> <dbl> <dbl> #> 1 1 e <dbl [1… 1 0 0 #> 2 NA f <int [2… 1 1 1 #> 3 3 g <int [3… NA NA NA #> 4 NA h <chr [1… 0 0 0 #> # … with 1 more variable: [,4] <dbl>

This means that columns initialized with NA (which is a logical) cannot be later filled with values of a different type. Use the correct type of NA to initialize columns.

with_df({df$x <- NA; df[2:3, "x"] <- 3:2}) #> n c li x #> 1 1 e 9 NA #> 2 NA f 10, 11 3 #> 3 3 g 12, 13, 14 2 #> 4 NA h text NA	with_tbl({tbl$x <- NA; tbl[2:3, "x"] <- 3:2}) #> Error: Assigned data `3:2` must be #> compatible with existing data. #> ℹ Error occurred for column `x`. #> x Lossy cast from `value` <integer> to #> `x` <logical>. #> * Locations: 1, 2.
	with_tbl({tbl$x <- NA_integer_; tbl[2:3, "x"] <- 3:2}) #> # A tibble: 4 x 4 #> n c li x #> <int> <chr> <list> <int> #> 1 1 e <dbl [1]> NA #> 2 NA f <int [2]> 3 #> 3 3 g <int [3]> 2 #> 4 NA h <chr [1]> NA

For new columns, x[i, j] <- a fills the unassigned rows with NA.

with_df(df[2:3, "n"] <- 1) #> n c li #> 1 1 e 9 #> 2 1 f 10, 11 #> 3 1 g 12, 13, 14 #> 4 NA h text	with_tbl(tbl[2:3, "n"] <- 1) #> # A tibble: 4 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 1 f <int [2]> #> 3 1 g <int [3]> #> 4 NA h <chr [1]>
	with_tbl(tbl[2:3, "x"] <- 1) #> # A tibble: 4 x 4 #> n c li x #> <int> <chr> <list> <dbl> #> 1 1 e <dbl [1]> NA #> 2 NA f <int [2]> 1 #> 3 3 g <int [3]> 1 #> 4 NA h <chr [1]> NA
with_df(df[2:3, "n"] <- NULL) #> Error in x[[jj]][iseq] <- vjj: #> replacement has length zero	with_tbl(tbl[2:3, "n"] <- NULL) #> Error: `NULL` must be a vector, a bare #> list or a data frame.

Likewise, for new rows, x[i, j] <- a fills the unassigned columns with NA.

with_tbl(tbl[5, "n"] <- list(0L)) #> # A tibble: 5 x 3 #> n c li #> <int> <chr> <list> #> 1 1 e <dbl [1]> #> 2 NA f <int [2]> #> 3 3 g <int [3]> #> 4 NA h <chr [1]> #> 5 0 <NA> <NULL>

Definition of x[[i, j]] <- a

i must be a numeric vector of length 1. x[[i, j]] <- a is equivalent to x[i, ][[j]] <- a.⁹

NB: vec_size(a) must equal 1. Unlike x[i, ] <-, x[[i, ]] <- is not valid.

---

1. x[j][[jj]] is equal to x[[ j[[jj]] ]], in particular x[j][[1]] is equal to x[[j]] for scalar numeric or integer j.↩

2. Row subsetting x[i, ] is not defined in terms of x[[j]][i] because that definition does not generalise to matrix and data frame columns. For efficiency and backward compatibility, i is converted to an integer vector by vec_as_index(i, nrow(x)) first.↩

3. x[,] is equivalent to x[] because x[, j] is equivalent to x[j].↩

4. A more efficient implementation of x[i, j] would forward to x[j][i, ].↩

5. Cell subsetting x[[i, j]] is not defined in terms of x[[j]][[i]] because that definition does not generalise to list, matrix and data frame columns. A more efficient implementation of x[[i, j]] would check that j is a scalar and forward to x[i, j][[1]].↩

6. $ behaves almost completely symmetrically to [[ when comparing subsetting and subassignment.↩

7. x[i, ] is symmetrically for subset and subassignment.↩

8. x[i, j] is symmetrically for subsetting and subassignment. A more efficient implementation of x[i, j] <- a would forward to x[j][i, ] <- a.↩

9. x[[i, j]] is symmetrically for subsetting and subassignment. An efficient implementation would check that i and j are scalar and forward to x[i, j][[1]] <- a.↩