These functions are variants of map() that iterate over multiple arguments
simultaneously. They are parallel in the sense that each input is processed
in parallel with the others, not in the sense of multicore computing. They
share the same notion of "parallel" as base::pmax() and base::pmin().
map2() and walk2() are specialised for the two argument case; pmap()
and pwalk() allow you to provide any number of arguments in a list. Note
that a data frame is a very important special case, in which case pmap()
and pwalk() apply the function .f to each row. map_dfr(), pmap_dfr()
and map2_dfc(), pmap_dfc() return data frames created by row-binding
and column-binding respectively. They require dplyr to be installed.
map2(.x, .y, .f, ...) map2_lgl(.x, .y, .f, ...) map2_int(.x, .y, .f, ...) map2_dbl(.x, .y, .f, ...) map2_chr(.x, .y, .f, ...) map2_raw(.x, .y, .f, ...) map2_dfr(.x, .y, .f, ..., .id = NULL) map2_dfc(.x, .y, .f, ...) walk2(.x, .y, .f, ...) pmap(.l, .f, ...) pmap_lgl(.l, .f, ...) pmap_int(.l, .f, ...) pmap_dbl(.l, .f, ...) pmap_chr(.l, .f, ...) pmap_raw(.l, .f, ...) pmap_dfr(.l, .f, ..., .id = NULL) pmap_dfc(.l, .f, ...) pwalk(.l, .f, ...)
| .x, .y | Vectors of the same length. A vector of length 1 will be recycled. |
|---|---|
| .f | A function, formula, or vector (not necessarily atomic). If a function, it is used as is. If a formula, e.g.
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 |
| ... | Additional arguments passed on to the mapped function. |
| .id | Either a string or Only applies to |
| .l | A list of vectors, such as a data frame. The length of |
An atomic vector, list, or data frame, depending on the suffix.
Atomic vectors and lists will be named if .x or the first
element of .l is named.
If all input is length 0, the output will be length 0. If any input is length 1, it will be recycled to the length of the longest.
Note that arguments to be vectorised over come before .f,
and arguments that are supplied to every call come after .f.
#> [[1]] #> [1] 11 #> #> [[2]] #> [1] 21 #> #> [[3]] #> [1] 31 #># Or just map2(x, y, `+`)#> [[1]] #> [1] 11 #> #> [[2]] #> [1] 21 #> #> [[3]] #> [1] 31 #>#> [[1]] #> [1] 111 #> #> [[2]] #> [1] 221 #> #> [[3]] #> [1] 331 #># Matching arguments by position pmap(list(x, y, z), function(first, second, third) (first + third) * second)#> [[1]] #> [1] 1010 #> #> [[2]] #> [1] 4020 #> #> [[3]] #> [1] 9030 #>#> [[1]] #> [1] 1010 #> #> [[2]] #> [1] 4020 #> #> [[3]] #> [1] 9030 #># Split into pieces, fit model to each piece, then predict by_cyl <- mtcars %>% split(.$cyl) mods <- by_cyl %>% map(~ lm(mpg ~ wt, data = .)) map2(mods, by_cyl, predict)#> $`4` #> Datsun 710 Merc 240D Merc 230 Fiat 128 Honda Civic #> 26.47010 21.55719 21.78307 27.14774 30.45125 #> Toyota Corolla Toyota Corona Fiat X1-9 Porsche 914-2 Lotus Europa #> 29.20890 25.65128 28.64420 27.48656 31.02725 #> Volvo 142E #> 23.87247 #> #> $`6` #> Mazda RX4 Mazda RX4 Wag Hornet 4 Drive Valiant Merc 280 #> 21.12497 20.41604 19.47080 18.78968 18.84528 #> Merc 280C Ferrari Dino #> 18.84528 20.70795 #> #> $`8` #> Hornet Sportabout Duster 360 Merc 450SE Merc 450SL #> 16.32604 16.04103 14.94481 15.69024 #> Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial #> 15.58061 12.35773 11.97625 12.14945 #> Dodge Challenger AMC Javelin Camaro Z28 Pontiac Firebird #> 16.15065 16.33700 15.44907 15.43811 #> Ford Pantera L Maserati Bora #> 16.91800 16.04103 #># Vectorizing a function over multiple arguments df <- data.frame( x = c("apple", "banana", "cherry"), pattern = c("p", "n", "h"), replacement = c("P", "N", "H"), stringsAsFactors = FALSE ) pmap(df, gsub)#> [[1]] #> [1] "aPPle" #> #> [[2]] #> [1] "baNaNa" #> #> [[3]] #> [1] "cHerry" #>pmap_chr(df, gsub)#> [1] "aPPle" "baNaNa" "cHerry"# Use `...` to absorb unused components of input list .l df <- data.frame( x = 1:3, y = 10:12, z = letters[1:3] ) plus <- function(x, y) x + y if (FALSE) { # this won't work pmap(df, plus) } # but this will plus2 <- function(x, y, ...) x + y pmap_dbl(df, plus2)#> [1] 11 13 15# The "p" for "parallel" in pmap() is the same as in base::pmin() # and base::pmax() df <- data.frame( x = c(1, 2, 5), y = c(5, 4, 8) ) # all produce the same result pmin(df$x, df$y)#> [1] 1 2 5map2_dbl(df$x, df$y, min)#> [1] 1 2 5pmap_dbl(df, min)#> [1] 1 2 5# If you want to bind the results of your function rowwise, use: # map2_dfr() or pmap_dfr() ex_fun <- function(arg1, arg2){ col <- arg1 + arg2 x <- as.data.frame(col) } arg1 <- 1:4 arg2 <- 10:13 map2_dfr(arg1, arg2, ex_fun)#> col #> 1 11 #> 2 13 #> 3 15 #> 4 17# If instead you want to bind by columns, use map2_dfc() or pmap_dfc() map2_dfc(arg1, arg2, ex_fun)#> col col1 col2 col3 #> 1 11 13 15 17