The map functions transform their input by applying a function to each element of a list or atomic vector and returning an object of the same length as the input.
map() always returns a list. See the modify() family for
versions that return an object of the same type as the input.
map_lgl(), map_int(), map_dbl() and map_chr() return an
atomic vector of the indicated type (or die trying). For these functions,
.f must return a length-1 vector of the appropriate type.
map_vec() simplifies to the common type of the output. It works with
most types of simple vectors like Date, POSIXct, factors, etc.
walk() calls .f for its side-effect and returns
the input .x.
map(.x, .f, ..., .progress = FALSE)
map_lgl(.x, .f, ..., .progress = FALSE)
map_int(.x, .f, ..., .progress = FALSE)
map_dbl(.x, .f, ..., .progress = FALSE)
map_chr(.x, .f, ..., .progress = FALSE)
map_vec(.x, .f, ..., .ptype = NULL, .progress = FALSE)
walk(.x, .f, ..., .progress = FALSE)A list or atomic vector.
A function, specified in one of the following ways:
A named function, e.g. mean.
An anonymous function, e.g. \(x) x + 1 or function(x) x + 1.
A formula, e.g. ~ .x + 1. You must use .x to refer to the first
argument. Only recommended if you require backward compatibility with
older versions of R.
A string, integer, or list, e.g. "idx", 1, or list("idx", 1) which
are shorthand for \(x) pluck(x, "idx"), \(x) pluck(x, 1), and
\(x) pluck(x, "idx", 1) respectively. Optionally supply .default to
set a default value if the indexed element is NULL or does not exist.
Additional arguments passed on to the mapped function.
We now generally recommend against using ... to pass additional
(constant) arguments to .f. Instead use a shorthand anonymous function:
This makes it easier to understand which arguments belong to which function and will tend to yield better error messages.
Whether to show a progress bar. Use TRUE to turn on
a basic progress bar, use a string to give it a name, or see
progress_bars for more details.
If NULL, the default, the output type is the common type
of the elements of the result. Otherwise, supply a "prototype" giving
the desired type of output.
The output length is determined by the length of the input. The output names are determined by the input names. The output type is determined by the suffix:
No suffix: a list; .f() can return anything.
_lgl(), _int(), _dbl(), _chr() return a logical, integer, double,
or character vector respectively; .f() must return a compatible atomic
vector of length 1.
_vec() return an atomic or S3 vector, the same type that .f returns.
.f can return pretty much any type of vector, as long as its length 1.
walk() returns the input .x (invisibly). This makes it easy to
use in a pipe. The return value of .f() is ignored.
Any errors thrown by .f will be wrapped in an error with class
purrr_error_indexed.
# Compute normal distributions from an atomic vector
1:10 |>
map(rnorm, n = 10)
#> [[1]]
#> [1] 1.6215527 2.1484116 -0.8218177 0.7526747 0.7558004 0.7172946
#> [7] 0.4463006 1.6289820 3.0650249 -0.6309894
#>
#> [[2]]
#> [1] 2.5124269 0.1369885 1.4779875 1.9473981 2.5429963 1.0859252 2.4681544
#> [8] 2.3629513 0.6954565 2.7377763
#>
#> [[3]]
#> [1] 4.888505 2.902555 2.064153 2.984050 2.173211 1.487600 3.935363 3.176489
#> [9] 3.243685 4.623549
#>
#> [[4]]
#> [1] 4.112038 3.866003 2.089913 3.720763 3.686554 5.067308 4.070035 3.360877
#> [9] 3.950035 3.748517
#>
#> [[5]]
#> [1] 5.444797 7.755418 5.046531 5.577709 5.118195 3.088280 5.862086 4.756763
#> [9] 4.793913 5.019178
#>
#> [[6]]
#> [1] 6.029561 6.549828 3.725885 8.682557 5.638779 6.213356 7.074346 5.334912
#> [9] 7.113952 5.754104
#>
#> [[7]]
#> [1] 5.822437 6.024149 8.065057 7.131671 7.488629 5.300549 5.529264 7.284150
#> [9] 8.337320 7.236696
#>
#> [[8]]
#> [1] 9.318293 8.523910 8.606748 7.890064 8.172182 7.909673 9.924343 9.298393
#> [9] 8.748791 8.556224
#>
#> [[9]]
#> [1] 8.451743 10.110535 6.387666 8.844306 9.433890 8.618049 9.424188
#> [8] 10.063102 10.048713 8.961897
#>
#> [[10]]
#> [1] 10.486149 11.672883 9.645639 10.946348 11.316826 9.703360 9.612786
#> [8] 9.214567 8.943263 9.204459
#>
# You can also use an anonymous function
1:10 |>
map(\(x) rnorm(10, x))
#> [[1]]
#> [1] -0.7562754 0.3094621 0.4414580 0.4633367 1.2271271 1.9784549
#> [7] 0.7911173 -0.3994105 1.2585373 0.5582005
#>
#> [[2]]
#> [1] 2.5685999 4.1268505 2.4248584 0.3157185 2.2494018 3.0728383 4.0393693
#> [8] 2.4494538 3.3918140 2.4265665
#>
#> [[3]]
#> [1] 3.107584 3.022295 3.603611 2.737349 2.471736 3.192149 1.853800 3.846185
#> [9] 3.081720 1.694883
#>
#> [[4]]
#> [1] 3.055088 4.454342 3.144797 3.713105 4.894962 4.067304 3.837324 3.172690
#> [9] 5.876506 4.766440
#>
#> [[5]]
#> [1] 5.979957 6.321781 3.880289 5.514600 3.490900 6.532741 5.429147 5.122103
#> [9] 3.861988 4.441985
#>
#> [[6]]
#> [1] 7.052539 6.677684 6.038500 5.643619 6.782844 6.804412 4.099939 6.935784
#> [9] 5.690948 6.263067
#>
#> [[7]]
#> [1] 5.209408 6.211741 5.866978 7.363653 6.714112 7.517669 6.897091 6.025930
#> [9] 8.270672 7.960865
#>
#> [[8]]
#> [1] 8.768721 9.035931 7.526113 6.724665 7.694379 10.211769 6.958332
#> [8] 6.853476 6.324673 9.525939
#>
#> [[9]]
#> [1] 9.554186 10.993110 8.845879 11.564408 10.061999 10.142695 10.123839
#> [8] 8.602999 8.176739 8.421115
#>
#> [[10]]
#> [1] 11.763789 10.132992 10.376499 11.138708 11.241263 10.612091 9.570620
#> [8] 11.360461 9.929143 9.727846
#>
# Simplify output to a vector instead of a list by computing the mean of the distributions
1:10 |>
map(rnorm, n = 10) |> # output a list
map_dbl(mean) # output an atomic vector
#> [1] 0.449421 2.083007 2.739160 4.144721 4.716806 5.978606 6.593186
#> [8] 8.619169 9.087989 10.312465
# Using set_names() with character vectors is handy to keep track
# of the original inputs:
set_names(c("foo", "bar")) |> map_chr(paste0, ":suffix")
#> foo bar
#> "foo:suffix" "bar:suffix"
# Working with lists
favorite_desserts <- list(Sophia = "banana bread", Eliott = "pancakes", Karina = "chocolate cake")
favorite_desserts |> map_chr(\(food) paste(food, "rocks!"))
#> Sophia Eliott Karina
#> "banana bread rocks!" "pancakes rocks!" "chocolate cake rocks!"
# Extract by name or position
# .default specifies value for elements that are missing or NULL
l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L))
l1 |> map("a", .default = "???")
#> [[1]]
#> [1] 1
#>
#> [[2]]
#> [1] "???"
#>
#> [[3]]
#> [1] "???"
#>
l1 |> map_int("b", .default = NA)
#> [1] NA 2 3
l1 |> map_int(2, .default = NA)
#> [1] NA 2 NA
# Supply multiple values to index deeply into a list
l2 <- list(
list(num = 1:3, letters[1:3]),
list(num = 101:103, letters[4:6]),
list()
)
l2 |> map(c(2, 2))
#> [[1]]
#> [1] "b"
#>
#> [[2]]
#> [1] "e"
#>
#> [[3]]
#> NULL
#>
# Use a list to build an extractor that mixes numeric indices and names,
# and .default to provide a default value if the element does not exist
l2 |> map(list("num", 3))
#> [[1]]
#> [1] 3
#>
#> [[2]]
#> [1] 103
#>
#> [[3]]
#> NULL
#>
l2 |> map_int(list("num", 3), .default = NA)
#> [1] 3 103 NA
# Working with data frames
# Use map_lgl(), map_dbl(), etc to return a vector instead of a list:
mtcars |> map_dbl(sum)
#> mpg cyl disp hp drat wt qsec vs
#> 642.900 198.000 7383.100 4694.000 115.090 102.952 571.160 14.000
#> am gear carb
#> 13.000 118.000 90.000
# A more realistic example: split a data frame into pieces, fit a
# model to each piece, summarise and extract R^2
mtcars |>
split(mtcars$cyl) |>
map(\(df) lm(mpg ~ wt, data = df)) |>
map(summary) |>
map_dbl("r.squared")
#> 4 6 8
#> 0.5086326 0.4645102 0.4229655