How to write custom pipe-friendly functions?

user_13874036 0
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I'm trying to create pipe-friendly functions using magrittr

For example, I tried to write a custom function to calculate the mean of a column:

library(magrittr)
custom_function <-
  function(.data, x) {
   mean(.data$x)
  }

mtcars %>%
 custom_function(mpg)

But I'm getting this error:

Error in (function(x, i, exact) if (is.matrix(i)) as.matrix(x)[[i]] else .subset2(x,  : 
  object 'mpg' not found

Maybe my reference to the variable is not working. How do I fix this .data$x ?

magrittr r
2 Answers

.data$x does not refer to a column whose name is held in a variable x but refers to a column called "x". Use .data[[x]] to refer to the column whose name is the character string held in variable x and call your function using character string "mpg".

library(magrittr)
custom_function <- function(.data, x) mean(.data[[x]])

mtcars %>% custom_function("mpg")
## [1] 20.09062
user_5165480

In base R, we can change the $ to [[ and convert the unquoted column names to character with deparse/substitute

custom_function <- function(.data, x) {
  mean(.data[[deparse(substitute(x))]])
 }

Now, we apply the function

mtcars %>%
  custom_function(mpg)
#[1] 20.09062

The issue with $ is that it is literally checking the column name 'x' without the associative value it stored. Thus, it is failing and returns NULL

With tidyverse, we can use the curly-curly operator ({{}}) to do the evaluation within summarise. As we need only a single summarised output, summarise can return that single value whereas if we need to create a new column in the original dataset, we need mutate. After we create summarised column, just pull that column as a vector

custom_function <- function(.data, x) {
     .data %>%
         summarise(out = mean({{x}})) %>%
         pull(out)
}

mtcars %>% 
   custom_function(mpg)
[1] 20.09062
user_37322710
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