Aliases: map map_lgl map_chr map_int map_dbl map_raw map_dfr map_df map_dfc walk
Keywords:
### ** Examples # Compute normal distributions from an atomic vector 1:10 %>% map(rnorm, n = 10)
[[1]] [1] 1.9387102 0.8783509 0.7880443 -0.3155046 1.4807586 1.3074779 [7] 0.5600396 1.4540053 -0.1517757 -0.1599775 [[2]] [1] 1.622899 2.101425 3.048021 1.480330 3.943505 2.291922 2.574481 3.176467 [9] 2.846962 2.608265 [[3]] [1] 4.222034 2.575776 2.394766 2.714443 4.043980 3.157874 3.869995 2.730669 [9] 1.809008 2.191912 [[4]] [1] 2.742911 4.931114 4.085140 5.252781 4.422800 2.458722 2.305174 4.207963 [9] 5.808217 4.747124 [[5]] [1] 7.238184 5.111757 4.329095 4.512909 4.417067 5.974941 3.670315 5.968359 [9] 4.121283 6.083598 [[6]] [1] 7.009298 5.070858 2.721285 5.785073 5.640342 5.643593 5.241538 5.428374 [9] 7.146001 5.806140 [[7]] [1] 7.270316 8.272042 6.572582 8.751402 7.598013 7.268362 6.576031 6.624514 [9] 6.535201 6.252226 [[8]] [1] 8.393489 7.188083 8.427215 7.822550 7.602798 8.791938 9.651898 8.006470 [9] 7.632077 6.799031 [[9]] [1] 8.706301 9.076514 9.617870 6.851333 9.150723 8.743793 8.976470 8.675278 [9] 9.092365 9.602405 [[10]] [1] 11.587027 9.886389 9.461007 10.096256 9.478319 10.546169 11.267051 [8] 7.894725 10.788795 9.827902
# You can also use an anonymous function 1:10 %>% map(function(x) rnorm(10, x))
[[1]] [1] 3.11999259 0.47059680 -0.07146338 1.71391817 1.38262048 1.48906002 [7] 1.29743397 1.73824037 -0.33413059 1.86265610 [[2]] [1] 1.6860447 2.0945895 2.6860537 1.0232517 1.4628065 -0.2215824 [7] 2.5866000 -0.3633533 2.0805896 2.4585248 [[3]] [1] 3.740159 1.929091 2.337720 3.015396 3.136365 2.990526 4.260697 1.729353 [9] 3.127813 3.725726 [[4]] [1] 4.520864 4.408656 2.949166 3.220031 3.528060 3.091445 3.187629 4.674529 [9] 2.914491 4.126285 [[5]] [1] 5.852932 5.024162 4.882690 4.598430 5.527226 4.639729 5.460618 6.444008 [9] 4.588359 4.870138 [[6]] [1] 5.847773 4.678494 6.245596 6.343604 4.103371 8.166449 4.383253 5.708647 [9] 6.693966 5.045618 [[7]] [1] 7.979777 6.379227 5.643726 6.800490 6.527120 7.381632 5.674401 7.686038 [9] 6.703946 7.833041 [[8]] [1] 7.720471 7.266269 7.329517 7.784647 7.997420 6.448698 8.250119 7.597152 [9] 7.001408 6.461876 [[9]] [1] 9.496748 8.424388 8.737797 7.948510 7.919841 8.439048 7.985533 [8] 10.213425 8.285159 8.167743 [[10]] [1] 10.219642 7.599881 10.209732 9.933109 8.530547 10.381494 9.677473 [8] 12.239091 9.484121 9.228268
# Or a formula 1:10 %>% map(~ rnorm(10, .x))
[[1]] [1] 0.4317073 0.4033545 -0.5392183 1.5412531 1.8638100 0.2732892 [7] 1.5872873 -0.1735912 -0.7542432 0.4689713 [[2]] [1] 1.3393881 2.5364489 2.2841910 2.1101714 1.1538849 3.8624358 1.6589356 [8] 0.8125566 1.2249548 1.4479032 [[3]] [1] 2.620370 3.153807 2.840385 2.044355 3.706701 3.188491 2.641205 3.980515 [9] 3.046637 4.109095 [[4]] [1] 4.361393 3.775508 4.710337 3.051586 4.519851 3.108516 3.804205 5.382209 [9] 3.562163 4.429751 [[5]] [1] 5.914777 5.894766 6.123667 4.275260 6.722845 3.763689 6.043463 4.799944 [9] 4.850801 4.269569 [[6]] [1] 5.103872 4.183706 6.538752 7.011154 5.817356 6.523622 6.602015 6.878266 [9] 5.657610 7.103528 [[7]] [1] 6.326683 5.963478 5.765856 7.666260 7.195439 6.998480 6.118748 7.208095 [9] 7.941284 7.546437 [[8]] [1] 6.875504 7.283789 7.483820 8.100358 7.592542 8.518645 7.903434 8.770284 [9] 9.042533 7.190983 [[9]] [1] 8.179616 9.273305 8.009386 9.120436 9.484311 9.853318 8.236515 [8] 7.275315 10.811816 9.021189 [[10]] [1] 9.188351 10.689925 9.245590 9.343178 10.905972 9.643291 9.350801 [8] 10.349509 11.665544 10.421839
# 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] 1.370136 1.870694 2.860474 4.405303 5.129947 6.121721 6.742919 7.555540 [9] 8.692043 9.612160
# 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)