Equivalent of next in purrr::map_df - r

Question

I am looking for the equivalent of next in loops for a purrr::map_df call.
map_df plays nicely with dataframes that are NULL (as in the example below), so it works when I set Result <- NULL in my example below.
Could anyone suggest a general solution to my illustration below that would not require me setting Result <- NULL, but rather immediately go "next".
library(tidyverse)
set.seed(1000)
df <- data.frame(x = rnorm(100), y = rnorm(100), z = rep(LETTERS, 100))
Map_Func <- function(df) {
Sum_Num <- suppressWarnings(sqrt(sum(df$y)))
if( Sum_Num == "NaN" ) {
Result <- NULL
# I would like to have an equivalent to "next" here...
} else {
Result <- df %>% filter(y == max(y)) %>% mutate(Result = x*y)
}
Result
}
Test <- split(df, df$z) %>% map_df(~Map_Func(.))
In the code above, what can I use instead of Result <- NULL in the ugly if statement (i.e. I want to simply check a condition and effectively do a "next").

Answer 1

To exit a function you can use the return(<output>) command. This immediately exits the function with the output you define. The following gives the same output you were getting with your sample code.
library(tidyverse)
set.seed(1000)
df <- data.frame(x = rnorm(100), y = rnorm(100), z = rep(LETTERS, 100))
Map_Func <- function(df) {
Sum_Num <- suppressWarnings(sqrt(sum(df$y)))
if( Sum_Num == "NaN" ) {
return(NULL)
}
Result <- df %>% filter(y == max(y)) %>% mutate(Result = x*y)
}
Test <- split(df, df$z) %>% map_df(~Map_Func(.))

Answer 2

Logic wise not a very different solution than OP but trying to keep it clean by using separate functions. custom_check function is to check the condition for each group. Using map_if we apply the function Map_Func_true only when custom_check returns TRUE or else apply Map_Func_false which returns NULL and finally bind the rows.
library(tidyverse)
Map_Func_true <- function(df) {
df %>% filter(y == max(y)) %>% mutate(Result = x*y)
}
Map_Func_false <- function(df) { return(NULL) }
custom_check <- function(df) {
!is.nan(suppressWarnings(sqrt(sum(df$y))))
}
df %>%
group_split(z) %>%
map_if(., custom_check, Map_Func_true, .else = Map_Func_false) %>%
bind_rows()
# A tibble: 26 x 4
# x y z Result
# <dbl> <dbl> <fct> <dbl>
# 1 1.24 2.00 A 2.47
# 2 1.24 2.00 A 2.47
# 3 1.24 2.00 C 2.47
# 4 1.24 2.00 C 2.47
# 5 1.24 2.00 E 2.47
# 6 1.24 2.00 E 2.47
# 7 1.24 2.00 G 2.47
# 8 1.24 2.00 G 2.47
# 9 1.24 2.00 I 2.47
#10 1.24 2.00 I 2.47
# … with 16 more rows

Answer 3

Here's another way of looking at it using purrr::safely
Map_Func <- function(df) {
Sum_Num <- suppressWarnings(sqrt(sum(df$y)))
df %>% filter(y == max(y)) %>% mutate(Result = x*y)
}
Test <- split(df, df$z) %>%
map(safely(~Map_Func(.))) %>%
transpose() %>%
pluck("result") %>% # use 'error' here to get the error log
bind_rows()
This way the function becomes cleaner and you also get a nice log of errors