Is there a efficient way to mutate only on rows that meet a condition? Think mutate(when(condition))

Question

I am looking to apply a mutate only when certain conditions are met.

I know I can do this...

data2 <- data1 %>%
   group_by(a, b) %>%
   mutate(
      var1 = case_when(
         condition ~ TRUE,
         TRUE ~ FALSE,
         NA
         ),
      var2 = case_when(
         condition ~ TRUE,
         max(var28),
         var2
         ),
      var3 = case_when(
         condition ~ TRUE,
         "happy",
         var3
         ),
...more vars here....
)

What I would like is something that looks like this...

data2 <- data1 %>%
   group_by(a, b) %>%
   mutate(
      when(condition),
      var1 = FALSE,
      var2 = max(var28),
      var3 = "happy",
...more vars here....
)

Unfortunately mutate(across(when(condition))) did not work.

Any suggesions?

Answer 1

Another potential solution is to use across() and ifelse(), e.g. if the value in column x is less than four, perform the mutation:

library(tidyverse)
tibble(x = c(1:2, 4:5), y = 1:4) %>% 
  mutate(across(everything(), ~ ifelse(x < 4, -.x, .x)))
#> # A tibble: 4 × 2
#>       x     y
#>   <int> <int>
#> 1    -1    -1
#> 2    -2    -2
#> 3     4     3
#> 4     5     4

^{Created on 2021-11-24 by the reprex package (v2.0.1)}

You can also nest ifelse's together, e.g. if the value in column x is less than 4, or if a value in any column is equal to 3, mutate:

library(tidyverse)
tibble(x = c(1:2, 4:5), y = 1:4) %>% 
  mutate(across(everything(), ~ ifelse(x < 4, -.x,
                                       ifelse(.x == 3, .x + 10, .x))))
#> # A tibble: 4 × 2
#>       x     y
#>   <int> <dbl>
#> 1    -1    -1
#> 2    -2    -2
#> 3     4    13
#> 4     5     4

^{Created on 2021-11-24 by the reprex package (v2.0.1)}

And, so on:

library(tidyverse)
tibble(x = c(1:2, 4:5), y = 1:4) %>% 
  mutate(across(everything(), ~ ifelse(x < 4, -.x,
                                       ifelse(.x == 3, .x + 10,
                                              ifelse(.x >= 5, "outlier", .x)))))
#> # A tibble: 4 × 2
#>   x           y
#>   <chr>   <dbl>
#> 1 -1         -1
#> 2 -2         -2
#> 3 4          13
#> 4 outlier     4

^{Created on 2021-11-24 by the reprex package (v2.0.1)}

--

To do the mutation more efficiently, don't use dplyr::mutate. The ifelse() function is vectorized (more details: https://swcarpentry.github.io/r-novice-gapminder/09-vectorization/) and if you have a large dataframe, ifelse alone will very likely be faster than tidyverse functions, e.g.

library(tidyverse)
df <- tibble(x = c(1:2, 4:5), y = 1:4)
df$y <- ifelse(df$x < 4, -df$y, df$y)
df
#> # A tibble: 4 × 2
#>       x     y
#>   <int> <int>
#> 1     1    -1
#> 2     2    -2
#> 3     4     3
#> 4     5     4

Edit

Another potential option is to replace the values via assignment: df$y[df$x < 4] <- -(df$y); df$x[df$x < 4] <- -(df$x) (fast, but there are limitations).

Here is a quick benchmark of the suggested methods with 1 million rows:

library(tidyverse)
df <- tibble(x = sample(1:10, 1000000, replace = TRUE),
             y = sample(1:10, 1000000, replace = TRUE))

mutate_func <- function(df){
  df %>%
    mutate(across(everything(), ~ ifelse(x < 4, -.x, .x)))
}

ifelse_func <- function(df){
  df$y <- ifelse(df$x < 4, -df$y, df$y)
}

replacement_func <- function(df) {
  df$y[df$x < 4] <- -(df$y)
  df$x[df$x < 4] <- -(df$x)
}

mutate_when_func <- function(df) {
  mutate_when <- function(.data, when, ...) {
    dots <- enquos(...)
    names <- names(dots)
  
    mutate(.data, {
      test <- {{ when }}
    
      changed <- data.frame(!!!dots, stringsAsFactors = FALSE)
      out <- across(all_of(names))
      # assuming `changed` and `out` have the same data frame type
    
      out[test, ] <- changed[test, ]
      out
    })
  }

df %>% 
  mutate_when(x < 4, x = -x, y = -y)
}

library(microbenchmark)
result <- microbenchmark(mutate_func(df), ifelse_func(df),
                         mutate_when_func(df), replacement_func(df),
                         times = 10)
autoplot(result)

Answer 2

There is no functionality to do this in mutate, but Romain Francois has shared a function you could define yourself which does this:

library(dplyr, warn.conflicts = F)

mutate_when <- function(.data, when, ...) {
  dots <- enquos(...)
  names <- names(dots)
  
  mutate(.data, {
    test <- {{ when }}
    
    changed <- data.frame(!!!dots, stringsAsFactors = FALSE)
    out <- across(all_of(names))
    # assuming `changed` and `out` have the same data frame type

    out[test, ] <- changed[test, ]
    out
  })
  
}

tibble(x = 1:4, y = 1:4) %>% 
  mutate_when(x < 4, x = -x, y = -y)
#> # A tibble: 4 × 2
#>       x     y
#>   <int> <int>
#> 1    -1    -1
#> 2    -2    -2
#> 3    -3    -3
#> 4     4     4

^{Created on 2021-11-23 by the reprex package (v2.0.1)}