Reputation: 89
purrr::pmap for functions with multiple inputs and multiple return values
I am trying to set up a customized function with multiple inputs and multiple return values, and using this function with purrr::map on a data frame.
my sample data is:
test_data <-
tibble(x1 = 1:10,
x2 = 2:11,
x3 = 3:12,
x4 = x1 + x2 + x3)
this test_data looks like this:
# A tibble: 10 x 4
x1 x2 x3 x4
<int> <int> <int> <int>
1 1 2 3 6
2 2 3 4 9
3 3 4 5 12
4 4 5 6 15
5 5 6 7 18
6 6 7 8 21
7 7 8 9 24
8 8 9 10 27
9 9 10 11 30
10 10 11 12 33
Firstly, if my function only has one return value (output_3 in this case):
my_function_1 <-
function(var1, var2, var3, var4){
output_1 <- var1 + var2
output_2 <- var2 + var3
output_3 <- var1 + var2 + var3
output_4 <- var1 + var2 + var4
return(output_3)
}
I cam pmap this function using
my_results <-
dplyr::as.tbl(test_data) %>%
dplyr::mutate(output = purrr::pmap(list(var1 = x1, var2 = x2, var3 = x3, var4 = x4),
my_function_1)) %>%
tidyr::unnest()
the results looks like this:
my_results
# A tibble: 10 x 5
x1 x2 x3 x4 output
<int> <int> <int> <int> <int>
1 1 2 3 6 6
2 2 3 4 9 9
3 3 4 5 12 12
4 4 5 6 15 15
5 5 6 7 18 18
6 6 7 8 21 21
7 7 8 9 24 24
8 8 9 10 27 27
9 9 10 11 30 30
10 10 11 12 33 33
Now if my function has more than one return values, like
my_function_2 <-
function(var1, var2, var3, var4){
output_1 <- var1 + var2
output_2 <- var2 + var3
output_3 <- var1 + var2 + var3
output_4 <- var1 + var2 + var4
return(list(output_1, output_2, output_3, output_4))
}
How should I map this my_function_2 with purrr::map and add return columns to test_data, just like previous step with one return value?
I am also thinking to only have output results first (using the following code), and then join/bind with test_data:
pmap(list(test_data$x1,
test_data$x2,
test_data$x3,
test_data$x4),
my_function_2) %>%
flatten()
But the results is not in wanted format, like the following:
[[1]]
[1] 3
[[2]]
[1] 5
[[3]]
[1] 6
[[4]]
[1] 9
[[5]]
[1] 5
... ...
Could anybody remind me some potential solution to format the outputs and join with original test_data?
Upvotes: 2
Views: 2545
Answers (3)
Reputation: 47330
In your example the computations are vectorized so you don't need pmap and we can do the following :
library(tidyverse)
test_data %>%
mutate(!!!setNames(invoke(my_function_2,unname(.)),paste0("output_",1:4)))
# # A tibble: 10 x 8
# x1 x2 x3 x4 output_1 output_2 output_3 output_4
# <int> <int> <int> <int> <int> <int> <int> <int>
# 1 1 2 3 6 3 5 6 9
# 2 2 3 4 9 5 7 9 14
# 3 3 4 5 12 7 9 12 19
# 4 4 5 6 15 9 11 15 24
# 5 5 6 7 18 11 13 18 29
# 6 6 7 8 21 13 15 21 34
# 7 7 8 9 24 15 17 24 39
# 8 8 9 10 27 17 19 27 44
# 9 9 10 11 30 19 21 30 49
# 10 10 11 12 33 21 23 33 54
If you name your elements inside of my_function_2 (the simplest way is to use dplyr::lst instead of list it is even simpler:
my_function_2 <-
function(var1, var2, var3, var4){
output_1 <- var1 + var2
output_2 <- var2 + var3
output_3 <- var1 + var2 + var3
output_4 <- var1 + var2 + var4
return(lst(output_1, output_2, output_3, output_4))
}
test_data %>%
mutate(!!!invoke(my_function_2,unname(.)))
# # A tibble: 10 x 8
# x1 x2 x3 x4 output_1 output_2 output_3 output_4
# <int> <int> <int> <int> <int> <int> <int> <int>
# 1 1 2 3 6 3 5 6 9
# 2 2 3 4 9 5 7 9 14
# 3 3 4 5 12 7 9 12 19
# 4 4 5 6 15 9 11 15 24
# 5 5 6 7 18 11 13 18 29
# 6 6 7 8 21 13 15 21 34
# 7 7 8 9 24 15 17 24 39
# 8 8 9 10 27 17 19 27 44
# 9 9 10 11 30 19 21 30 49
# 10 10 11 12 33 21 23 33 54
Or if you need to use pmap because you use unvectorized operations in your real case :
test_data %>%
mutate(!!!pmap_dfr(unname(.),my_function_2))
Upvotes: 1
Reputation: 887531
A better option would be to create the return value as a tibble in the function and then just apply the pmap
library(purrr)
library(dplyr)
my_function_2 <-
function(var1, var2, var3, var4){
output_1 <- var1 + var2
output_2 <- var2 + var3
output_3 <- var1 + var2 + var3
output_4 <- var1 + var2 + var4
tibble::tibble(output_1, output_2, output_3, output_4)
}
pmap_dfr(list(test_data$x1,
test_data$x2,
test_data$x3,
test_data$x4),
my_function_2) %>%
bind_cols(test_data, .)
# A tibble: 10 x 8
# x1 x2 x3 x4 output_1 output_2 output_3 output_4
# <int> <int> <int> <int> <int> <int> <int> <int>
# 1 1 2 3 6 3 5 6 9
# 2 2 3 4 9 5 7 9 14
# 3 3 4 5 12 7 9 12 19
# 4 4 5 6 15 9 11 15 24
# 5 5 6 7 18 11 13 18 29
# 6 6 7 8 21 13 15 21 34
# 7 7 8 9 24 15 17 24 39
# 8 8 9 10 27 17 19 27 44
# 9 9 10 11 30 19 21 30 49
#10 10 11 12 33 21 23 33 54
Also, if the column names match the arguments of the function, we don't need to call each column separately
pmap_dfr(set_names(test_data, paste0("var", 1:4)), my_function_2) %>%
bind_cols(test_data, .)
Upvotes: 5
Reputation: 389155
One option is to return a vector from the function
my_function_2 <- function(var1, var2, var3, var4){
output_1 <- var1 + var2
output_2 <- var2 + var3
output_3 <- var1 + var2 + var3
output_4 <- var1 + var2 + var4
return(c(output_1, output_2, output_3, output_4))
}
and then use pmap_dfc and cbind to original dataframe
library(tidyverse)
bind_cols(test_data,
pmap_dfc(list(test_data$x1,
test_data$x2,
test_data$x3,
test_data$x4),
my_function_2) %>% t() %>% data.frame() %>%
set_names(paste0("x", 5:8)))
# A tibble: 10 x 8
# x1 x2 x3 x4 x5 x6 x7 x8
# <int> <int> <int> <int> <int> <int> <int> <int>
# 1 1 2 3 6 3 5 6 9
# 2 2 3 4 9 5 7 9 14
# 3 3 4 5 12 7 9 12 19
# 4 4 5 6 15 9 11 15 24
# 5 5 6 7 18 11 13 18 29
# 6 6 7 8 21 13 15 21 34
# 7 7 8 9 24 15 17 24 39
# 8 8 9 10 27 17 19 27 44
# 9 9 10 11 30 19 21 30 49
#10 10 11 12 33 21 23 33 54
Upvotes: 1
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