Curly-Curly, the successor of Bang-Bang

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Writing functions that take data frame columns as arguments is a problem that most R users have been confronted with at some point. There are different ways to tackle this issue, and this blog post will focus on the solution provided by the latest release of the {rlang} package. You can read the announcement here, which explains really well what was wrong with the old syntax, and how the new syntax works now.

I have written about the problem of writing functions that use data frame columns as arguments three years ago and two year ago too. Last year, I wrote a blog post that showed how to map a list of functions to a list of datasets with a list of columns as arguments that used the !!quo(column_name) syntax (the !! is pronounced bang-bang). Now, there is a new sheriff in town, {{}}, introduced in {rlang} version 0.4.0 that makes things even easier. The suggested pronunciation of {{}} is curly-curly, but there is no consensus yet.

First, let’s load the {tidyverse}:

library(tidyverse)

Let’s suppose that I need to write a function that takes a data frame, as well as a column from this data frame as arguments:

how_many_na <- function(dataframe, column_name){
  dataframe %>%
    filter(is.na(column_name)) %>%
    count()
}

Let’s try this function out on the starwars data:

data(starwars)

head(starwars)
## # A tibble: 6 x 13
##   name  height  mass hair_color skin_color eye_color birth_year gender
##                               
## 1 Luke…    172    77 blond      fair       blue            19   male  
## 2 C-3PO    167    75        gold       yellow         112     
## 3 R2-D2     96    32        white, bl… red             33     
## 4 Dart…    202   136 none       white      yellow          41.9 male  
## 5 Leia…    150    49 brown      light      brown           19   female
## 6 Owen…    178   120 brown, gr… light      blue            52   male  
## # … with 5 more variables: homeworld , species , films ,
## #   vehicles , starships 

As you can see, there are missing values in the hair_color column. Let’s try to count how many missing values are in this column:

how_many_na(starwars, hair_color)
Error: object 'hair_color' not found

R cannot find the hair_color column, and yet it is in the data! Well, this is actually exactly the issue. The issue is that the column is inside the dataframe, but when calling the function with hair_color as the second argument, R is looking for a variable called hair_color that does not exist. What about trying with "hair_color"?

how_many_na(starwars, "hair_color")
## # A tibble: 1 x 1
##       n
##   
## 1     0

Now we get something, but something wrong!

One way to solve this issue, is to not use the filter() function, and instead rely on base R:

how_many_na_base <- function(dataframe, column_name){
  na_index <- is.na(dataframe[, column_name])
  nrow(dataframe[na_index, column_name])
}

how_many_na_base(starwars, "hair_color")
## [1] 5

This works, but not using the {tidyverse} at all is not an option, at least for me. For instance, the next function, which uses a grouping variable, would be difficult to implement without the {tidyverse}:

summarise_groups <- function(dataframe, grouping_var, column_name){
  dataframe %>%
    group_by(grouping_var) %>%  
    summarise(mean(column_name, na.rm = TRUE))
}

Calling this function results in the following error message:

Error: Column `grouping_var` is unknown

Before the release of {rlang} 0.4.0 this is was the solution:

summarise_groups <- function(dataframe, grouping_var, column_name){

  grouping_var <- enquo(grouping_var)
  column_name <- enquo(column_name)
  mean_name <- paste0("mean_", quo_name(column_name))

  dataframe %>%
    group_by(!!grouping_var) %>%  
    summarise(!!(mean_name) := mean(!!column_name, na.rm = TRUE))
}

The core of the function remained very similar to the version from before, but now one has to use the enquo()-!! syntax. While not overly difficult to use, it is cumbersome.

Now this can be simplified using the new {{}} syntax:

summarise_groups <- function(dataframe, grouping_var, column_name){

  dataframe %>%
    group_by({{grouping_var}}) %>%  
    summarise({{column_name}} := mean({{column_name}}, na.rm = TRUE))
}

Much easier and cleaner! You still have to use the := operator instead of = for the column name however. Also, from my understanding, if you want to modify the column names, for instance in this case return "mean_height" instead of height you have to keep using the enquo()-!! syntax.

Hope you enjoyed! If you found this blog post useful, you might want to follow me on twitter for blog post updates and buy me an espresso or paypal.me.

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