How to Handle Temporary Columns in Tidyverse Functions Without Overwriting Existing Data?

Question

I develop functions in R using tidyverse, where I often need to create temporary columns in the data frame for intermediate steps. However, I'm encountering issues where these temporary columns might overwrite columns already present in the user's data if they have the same name. Here's a minimal example to illustrate the problem:

fun <- function(data, col) {
  data |> 
    mutate(col2 = {{ col }} * 2) |> 
    filter(col2 <= 4) |>
    select(-col2)
}

iris |> fun(Petal.Length)

This function works fine unless col2 already exists in data, in which case it gets overwritten and removed, which we don't want. Of course this is a simple example and we could easily put the calculation for col2 directly into filter(), but this is just to show how such problems would occur.

I'm looking for a way to handle these temporary columns without risking overwriting any existing columns in the user's dataset. I've considered using uncommon naming conventions and dynamically generating unique column names, but these approaches either risk still conflicting with user data or reduce code readability.

Is there an established or recommended method in the tidyverse for handling such scenarios? How can I efficiently create and manage temporary columns in a data frame within a function without affecting the original dataset's structure or existing column names?

Answer 1

I don't think that there is a canonical way of dealing with temporary variables inside dplyr-based functions.

One way is to write a function that creates a unique column name stem. The function below create_tmp_col_name is, admittedly, a bit over-engineered, but in escence it hashes a string called "tmp_col" and tries to use this as variable stem. It's pretty unlikely that any dataset contains a variable named "2e2a0834ed8cd54664b8ba89f9b98ece". But in case it does, the function gets called recursively wrapping "tmp_col" in underscores, over and over again, until we find a hash value that is not used as variable stem.

We can then use this variable stem by creating as many temporary names as we need using paste(tmp_col, 1:n) where n is the number of names we need.

In the actual function we can use !! and sym or .data[col_nm] or embracing syntax "{{tmp_cols[1]}}" or just strings depending on whether it is a data-masking or tidy-selection function (see here).

We can delete all temporary variables with select(!starts_with(tmp_cols)).

library(dplyr)

# function that creates a unique variable stem that's not present in the current data
create_tmp_col_name <- function(data, name = "tmp_col") {
  
  tmp_col <- rlang::hash(name)
  
  if(!any(tmp_col == colnames(data))) {
    return(tmp_col)
  } 
  
  Recall(data = data,
         name = paste0("_", name, "_"))
  
}

fun <- function(data, col) {
  
  # createa a vector with unique temporary columns names
  tmp_cols <- paste0(create_tmp_col_name(data), 1:3)
  
  data |> 
    # create temporary columns subsetting vector of temporary columns
    mutate(!! tmp_cols[1] := {{ col }} * 2,
           !! tmp_cols[2] := {{ col }} / 4) |>
    # use `!! sym` notation to filter on temp columns
    filter(!! sym(tmp_cols[1]) <= 4,
           !! sym(tmp_cols[2])  > 0.4
           ) |>
    # deletes all temporary columns
    select(!starts_with(tmp_cols))
}

iris |> fun(Petal.Length)
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 1          5.4         3.9          1.7         0.4  setosa
#> 2          5.7         3.8          1.7         0.3  setosa
#> 3          5.4         3.4          1.7         0.2  setosa
#> 4          5.1         3.3          1.7         0.5  setosa
#> 5          4.8         3.4          1.9         0.2  setosa
#> 6          5.1         3.8          1.9         0.4  setosa

As already mentioned by the OP this approach is not very readable. Alternatively could think about this problem in a more base R way:

First create an empty copy of the the original data.frame just keeping the number of rows data_cp. Then we can add additional columns as we like, since there are no other columns present. Finally, we apply the filter operations to our original data based on the columns stored in our copied version.

In this case, writing more complex functions gets complicated, especially when we have a bunch of temporary variables and want to introduce some persistent variables into our original data (then the name problem arises again). But for pure filter or order operations this is probably the cleaner approach.

fun2 <- function(data, col) {
  
  # create a copy of your data.frame with 0 columns
  data_cp <- data[, NULL]
  
  # create new colums, names don't matter
  data_cp$col1 <- data[[col]] * 2
  data_cp$col2 <- data[[col]] / 4
  
  # do more data operations on `data_cp` here
  
  # apply changes to your orinial data
  data[which(data_cp$col1 <= 4 & data_cp$col2 > 0.4), ]
}

iris |>
  fun2("Petal.Length")

#>    Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#> 6           5.4         3.9          1.7         0.4  setosa
#> 19          5.7         3.8          1.7         0.3  setosa
#> 21          5.4         3.4          1.7         0.2  setosa
#> 24          5.1         3.3          1.7         0.5  setosa
#> 25          4.8         3.4          1.9         0.2  setosa
#> 45          5.1         3.8          1.9         0.4  setosa

^{Created on 2023-12-20 with reprex v2.0.2}