These functions work the same as purrr::modify() functions, but
allow you to modify in parallel.
Usage
future_modify(
.x,
.f,
...,
.options = furrr_options(),
.env_globals = parent.frame(),
.progress = FALSE
)
future_modify_at(
.x,
.at,
.f,
...,
.options = furrr_options(),
.env_globals = parent.frame(),
.progress = FALSE
)
future_modify_if(
.x,
.p,
.f,
...,
.else = NULL,
.options = furrr_options(),
.env_globals = parent.frame(),
.progress = FALSE
)Arguments
- .x
A vector.
- .f
A function specified in the same way as the corresponding map function.
- ...
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:# Instead of x |> future_map(f, 1, 2, collapse = ",") # do: x |> future_map(\(x) f(x, 1, 2, collapse = ","))This makes it easier to understand which arguments belong to which function and will tend to yield better error messages.
- .options
The
futurespecific options to use with the workers. This must be the result from a call tofurrr_options().- .env_globals
The environment to look for globals required by
.xand.... Globals required by.fare looked up in the function environment of.f.- .progress
A single logical. Should a progress bar be displayed? Only works with multisession, multicore, and multiprocess futures. Note that if a multicore/multisession future falls back to sequential, then a progress bar will not be displayed.
Warning: The
.progressargument will be deprecated and removed in a future version of furrr in favor of using the more robust progressr package.- .at
A logical, integer, or character vector giving the elements to select. Alternatively, a function that takes a vector of names, and returns a logical, integer, or character vector of elements to select.
![[Deprecated]](figures/lifecycle-deprecated.svg)
: if the tidyselect package is
installed, you can use vars()and tidyselect helpers to select elements.- .p
A single predicate function, a formula describing such a predicate function, or a logical vector of the same length as
.x. Alternatively, if the elements of.xare themselves lists of objects, a string indicating the name of a logical element in the inner lists. Only those elements where.pevaluates toTRUEwill be modified.- .else
A function applied to elements of
.xfor which.preturnsFALSE.
Details
From purrr:
Since the transformation can alter the structure of the input;
it's your responsibility to ensure that the transformation produces a valid
output. For example, if you're modifying a data frame, .f must preserve the
length of the input.
Examples
plan(multisession, workers = 2)
# Convert each col to character, in parallel
future_modify(mtcars, as.character)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21 6 160 110 3.9 2.62 16.46 0 1 4 4
#> Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
#> Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
#> Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#> Hornet Sportabout 18.7 8 360 175 3.15 3.44 17.02 0 0 3 2
#> Valiant 18.1 6 225 105 2.76 3.46 20.22 1 0 3 1
#> Duster 360 14.3 8 360 245 3.21 3.57 15.84 0 0 3 4
#> Merc 240D 24.4 4 146.7 62 3.69 3.19 20 1 0 4 2
#> Merc 230 22.8 4 140.8 95 3.92 3.15 22.9 1 0 4 2
#> Merc 280 19.2 6 167.6 123 3.92 3.44 18.3 1 0 4 4
#> Merc 280C 17.8 6 167.6 123 3.92 3.44 18.9 1 0 4 4
#> Merc 450SE 16.4 8 275.8 180 3.07 4.07 17.4 0 0 3 3
#> Merc 450SL 17.3 8 275.8 180 3.07 3.73 17.6 0 0 3 3
#> Merc 450SLC 15.2 8 275.8 180 3.07 3.78 18 0 0 3 3
#> Cadillac Fleetwood 10.4 8 472 205 2.93 5.25 17.98 0 0 3 4
#> Lincoln Continental 10.4 8 460 215 3 5.424 17.82 0 0 3 4
#> Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
#> Fiat 128 32.4 4 78.7 66 4.08 2.2 19.47 1 1 4 1
#> Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
#> Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.9 1 1 4 1
#> Toyota Corona 21.5 4 120.1 97 3.7 2.465 20.01 1 0 3 1
#> Dodge Challenger 15.5 8 318 150 2.76 3.52 16.87 0 0 3 2
#> AMC Javelin 15.2 8 304 150 3.15 3.435 17.3 0 0 3 2
#> Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
#> Pontiac Firebird 19.2 8 400 175 3.08 3.845 17.05 0 0 3 2
#> Fiat X1-9 27.3 4 79 66 4.08 1.935 18.9 1 1 4 1
#> Porsche 914-2 26 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.9 1 1 5 2
#> Ford Pantera L 15.8 8 351 264 4.22 3.17 14.5 0 1 5 4
#> Ferrari Dino 19.7 6 145 175 3.62 2.77 15.5 0 1 5 6
#> Maserati Bora 15 8 301 335 3.54 3.57 14.6 0 1 5 8
#> Volvo 142E 21.4 4 121 109 4.11 2.78 18.6 1 1 4 2
iris |>
future_modify_if(is.factor, as.character) |>
str()
#> 'data.frame': 150 obs. of 5 variables:
#> $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
#> $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
#> $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
#> $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
#> $ Species : chr "setosa" "setosa" "setosa" "setosa" ...
mtcars |>
future_modify_at(c(1, 4, 5), as.character) |>
str()
#> 'data.frame': 32 obs. of 11 variables:
#> $ mpg : chr "21" "21" "22.8" "21.4" ...
#> $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
#> $ disp: num 160 160 108 258 360 ...
#> $ hp : chr "110" "110" "93" "110" ...
#> $ drat: chr "3.9" "3.9" "3.85" "3.08" ...
#> $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
#> $ qsec: num 16.5 17 18.6 19.4 17 ...
#> $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
#> $ am : num 1 1 1 0 0 0 0 0 0 0 ...
#> $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
#> $ carb: num 4 4 1 1 2 1 4 2 2 4 ...