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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).

  • map_dfr() and map_dfc() return a data frame created by row-binding and column-binding respectively. They require dplyr to be installed.

  • The returned values of .f must be of length one for each element of .x. If .f uses an extractor function shortcut, .default can be specified to handle values that are absent or empty. See as_mapper() for more on .default.

  • walk() calls .f for its side-effect and returns the input .x.

Usage

map(.x, .f, ...)

map_lgl(.x, .f, ...)

map_chr(.x, .f, ...)

map_int(.x, .f, ...)

map_dbl(.x, .f, ...)

map_raw(.x, .f, ...)

map_dfr(.x, .f, ..., .id = NULL)

map_dfc(.x, .f, ...)

walk(.x, .f, ...)

Arguments

.x

A list or atomic vector.

.f

A function, formula, or vector (not necessarily atomic).

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments:

  • For a single argument function, use .

  • For a two argument function, use .x and .y

  • For more arguments, use ..1, ..2, ..3 etc

This syntax allows you to create very compact anonymous functions.

If character vector, numeric vector, or list, it is converted to an extractor function. Character vectors index by name and numeric vectors index by position; use a list to index by position and name at different levels. If a component is not present, the value of .default will be returned.

...

Additional arguments passed on to the mapped function.

.id

Either a string or NULL. If a string, the output will contain a variable with that name, storing either the name (if .x is named) or the index (if .x is unnamed) of the input. If NULL, the default, no variable will be created.

Only applies to _dfr variant.

Value

  • map() Returns a list the same length as .x.

  • map_lgl() returns a logical vector, map_int() an integer vector, map_dbl() a double vector, and map_chr() a character vector.

  • map_df(), map_dfc(), map_dfr() all return a data frame.

  • If .x has names(), the return value preserves those names.

  • The output of .f will be automatically typed upwards, e.g. logical -> integer -> double -> character.

  • walk() returns the input .x (invisibly). This makes it easy to use in pipe.

See also

map_if() for applying a function to only those elements of .x that meet a specified condition.

Other map variants: imap(), invoke(), lmap(), map2(), map_if(), modify()

Examples

# Compute normal distributions from an atomic vector
1:10 %>%
  map(rnorm, n = 10)
#> [[1]]
#>  [1] -0.33005266  1.16938020  0.51461548  0.50764934  0.77118524
#>  [6]  0.59358665  2.05278168 -0.84082226 -0.03995567  1.23740174
#> 
#> [[2]]
#>  [1] 0.4023269 1.3600169 1.6765932 0.4165575 3.0479110 2.9257833 2.8297412
#>  [8] 3.8556399 2.6632916 2.1175712
#> 
#> [[3]]
#>  [1] 2.920256 2.148360 3.190512 2.869541 3.932535 3.722460 3.688649
#>  [8] 2.996824 2.447696 3.391930
#> 
#> [[4]]
#>  [1] 3.928121 4.124139 5.690892 6.342451 4.331546 6.202030 4.418203
#>  [8] 4.626108 4.362771 4.645633
#> 
#> [[5]]
#>  [1] 5.510729 2.440301 4.987308 3.030024 3.565450 5.124508 4.282498
#>  [8] 6.967879 3.656479 6.198227
#> 
#> [[6]]
#>  [1] 7.477078 6.960283 4.990668 6.675381 5.092740 6.191480 6.937200
#>  [8] 4.670206 5.554705 6.060998
#> 
#> [[7]]
#>  [1] 5.689300 5.971133 4.787184 6.665550 6.901558 7.104542 5.073075
#>  [8] 6.597985 6.549152 6.843425
#> 
#> [[8]]
#>  [1] 8.922526 7.873473 6.377009 7.740205 8.399335 8.134439 6.814899
#>  [8] 8.459502 7.263681 7.355401
#> 
#> [[9]]
#>  [1] 10.045095  6.078727 10.334559 10.571744  8.522265  9.559503  8.404625
#>  [8] 10.304109  8.540005  9.338297
#> 
#> [[10]]
#>  [1] 11.081518 10.300762 10.103314 11.101992  9.746213  9.223091  7.714598
#>  [8] 11.717469  9.935019 11.055381
#> 

# You can also use an anonymous function
1:10 %>%
  map(function(x) rnorm(10, x))
#> [[1]]
#>  [1]  1.51073576  1.44189335  0.59996713 -0.31657382  0.68946511
#>  [6]  2.42577099  3.41264039  0.61613253 -0.63238493  0.03027404
#> 
#> [[2]]
#>  [1] 0.7715523 2.8458921 2.8601112 3.4155083 3.0038757 1.6716353 1.4697428
#>  [8] 2.3065674 1.6010469 1.5371903
#> 
#> [[3]]
#>  [1] 3.601926 1.320165 2.031204 2.684637 2.636041 2.589475 2.882947
#>  [8] 3.685277 2.861452 2.718697
#> 
#> [[4]]
#>  [1] 4.841020 2.246810 2.919041 4.167942 3.263133 3.918789 4.508736
#>  [8] 3.216942 4.128173 6.428031
#> 
#> [[5]]
#>  [1] 5.799355 5.223523 4.232092 5.085557 5.550274 6.023162 7.024083
#>  [8] 5.514186 4.080235 4.071218
#> 
#> [[6]]
#>  [1] 6.288740 6.143478 6.439365 6.044262 6.999850 6.122501 5.424881
#>  [8] 5.919893 5.956319 7.137929
#> 
#> [[7]]
#>  [1] 5.505915 5.212710 7.341394 9.733964 8.154294 5.005580 7.437616
#>  [8] 7.270275 5.339835 7.113426
#> 
#> [[8]]
#>  [1] 8.736630 8.097831 8.386212 8.352851 9.279395 8.937042 7.847974
#>  [8] 7.457611 6.871598 8.225599
#> 
#> [[9]]
#>  [1] 7.889781 6.722619 8.180109 9.045687 9.075177 7.990847 8.913990
#>  [8] 8.608053 8.059512 9.938671
#> 
#> [[10]]
#>  [1]  8.559418  9.431864 10.233176 10.138606 10.916264 10.831297  9.122666
#>  [8]  9.920924  9.611978 10.887876
#> 

# Or a formula
1:10 %>%
  map(~ rnorm(10, .x))
#> [[1]]
#>  [1]  1.3705025 -0.8475769 -1.0836770  1.4754134  1.0154841  0.7195109
#>  [7] -1.9092978  1.6748726  0.5151697 -0.3697344
#> 
#> [[2]]
#>  [1] 2.542304 3.053414 3.322603 1.912702 2.647517 1.342681 2.145057
#>  [8] 3.149783 2.661671 2.428057
#> 
#> [[3]]
#>  [1] 1.860268 3.969342 3.400718 3.616287 3.424801 4.595332 2.924648
#>  [8] 2.771398 3.085583 1.950685
#> 
#> [[4]]
#>  [1] 2.968975 3.549948 4.893061 4.176134 3.371122 3.862740 4.238757
#>  [8] 3.328407 3.441108 4.942307
#> 
#> [[5]]
#>  [1] 5.243776 4.972958 3.792386 4.683212 5.422046 4.267013 4.504570
#>  [8] 5.783662 4.608725 4.170206
#> 
#> [[6]]
#>  [1] 5.500425 8.071588 6.502481 4.053161 6.704480 5.630803 7.967160
#>  [8] 5.274310 5.872336 7.245665
#> 
#> [[7]]
#>  [1] 8.153672 7.545571 5.659837 7.646695 7.025130 6.913479 7.132289
#>  [8] 6.537969 7.131020 7.472515
#> 
#> [[8]]
#>  [1] 7.236522 6.970660 7.217738 7.917837 8.218194 7.067140 5.599998
#>  [8] 7.569683 7.532446 6.345580
#> 
#> [[9]]
#>  [1]  9.631674  8.724814  9.149855  9.178104  8.327091  9.919226  8.543936
#>  [8] 10.565467  8.705109  9.212858
#> 
#> [[10]]
#>  [1] 10.828820 10.622347 10.276298 11.267222 11.296271 10.312032  9.472046
#>  [8]  8.413754 10.447023 10.225880
#> 

# 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]  1.169039  2.476506  2.779545  4.039582  5.367765  6.500865  7.203717
#>  [8]  8.242913  8.844403 10.140176

# 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(~ paste(.x, "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(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  map(summary) %>%
  map_dbl("r.squared")
#>         4         6         8 
#> 0.5086326 0.4645102 0.4229655 

# If each element of the output is a data frame, use
# map_dfr to row-bind them together:
mtcars %>%
  split(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  map_dfr(~ as.data.frame(t(as.matrix(coef(.)))))
#>   (Intercept)        wt
#> 1    39.57120 -5.647025
#> 2    28.40884 -2.780106
#> 3    23.86803 -2.192438
# (if you also want to preserve the variable names see
# the broom package)