Conditional Mutating with Regex in dplyr using RowSum

Introduction

In this article, we will explore how to use regular expressions (regex) and the dplyr package in R to conditionally mutate a data frame while performing calculations. Specifically, we’ll focus on creating a new measure that sums across certain columns, excluding specific values.

Background

The dplyr package provides a powerful and flexible way to manipulate data frames in R. One of its key features is the ability to perform operations on rows or columns using various functions such as mutate, select, and rowSums. When working with data frames, it’s often necessary to conditionally apply these operations based on certain criteria.

In this article, we’ll demonstrate how to use regex to exclude specific values when summing up certain columns. We’ll also explore alternative approaches to achieve the same result.

Step 1: Importing Required Libraries

Before we begin, let’s import the required libraries:

library(dplyr)
library(readTable)

Step 2: Creating a Sample Data Frame

First, we need to create a sample data frame to work with. Let’s use the read.table() function to load some data:

test <- read.table(text = "total_score_1 total_score_2 partner_total_score_1 total_score_3 total_score_4 letter
                   1 -1 1 1 -1 B
                   1 1 1 -1 1 C
                   -1 -1 -1 -1 1 A", header = T)

Step 3: Identifying Columns to Sum

We need to identify the columns that we want to sum. In this case, we’re interested in total_score_1, total_score_2, and total_score_4. We can use the grep() function to select these columns:

selected_cols <- test[, grep("total_score", names(test))]

Step 4: Creating a New Column with Sums

Now, we want to create a new column that sums up the values in selected_cols while treating -1 as 0. We can use the rowSums() function along with the replace() function:

test %>% 
  mutate(net_correct = select(., setdiff(contains("total_score"), contains("partner"))) %>%
    replace(., . == -1, 0) %>%
    rowSums())

This will produce the desired output:

#   total_score_1 total_score_2 partner_total_score_1 total_score_3 total_score_4 letter net_correct
#1               1             -1                       1              1            -1       B           2
#2               1              1                       1              -1            1       C           3
#3              -1             -1                      -1              -1            1       A           1

Explanation of the Code

Let’s break down the code to understand what’s happening:

select(., setdiff(contains("total_score"), contains("partner"))): This selects all columns that contain “total_score” but not “partner”. The setdiff() function returns a vector of indices for these columns.
replace(., . == -1, 0): This replaces all values in the selected columns with 0 if they’re equal to -1. This effectively treats -1 as 0 when summing up the values.
rowSums(): This calculates the sum of each row based on the modified values.

Alternative Approach: Using `filter()` and `summarise()`

Alternatively, you can use the filter() and summarise() functions to achieve the same result:

test %>%
  filter(!str_detect(., "partner")) %>%
  summarise(net_correct = sum(rowSums(select(., str_detect(., "total_score")))))

This approach is more concise but may be slightly less efficient.

Using Regular Expressions with `grepl()`

Another way to achieve the same result is by using regular expressions with grepl():

test %>% 
  mutate(net_correct = select(., grepl("total_score", names(.))) %>%
    replace(., . == -1, 0) %>%
    rowSums())

This approach uses grepl() to check if the column name contains “total_score” and then applies the same logic as before.

Conclusion

In this article, we explored how to use regular expressions and the dplyr package in R to conditionally mutate a data frame while performing calculations. We demonstrated three alternative approaches to achieve the same result: using grep(), filter() and summarise(), or grepl(). By mastering these techniques, you can easily manipulate your data frames and perform complex operations with ease.

Best Practices

When working with regex in R, keep the following best practices in mind:

Always test your regex patterns against sample data to ensure they’re working as expected.
Use grepl() instead of grep() for more flexibility and control over the matching process.
Consider using character classes (e.g., [abc]) or word boundaries (\b) to make your regex patterns more readable and efficient.

By following these guidelines and mastering the techniques outlined in this article, you’ll be well on your way to becoming a proficient R user!

Last modified on 2024-02-03