Understanding the Case_When Function in R: How to Change Numerical Values in Multiple Columns

The case_when function is a powerful tool in R for handling conditional statements. It allows you to vectorize multiple if-else statements, making it easier to perform complex data transformations. However, one common issue users face when using case_when is that the default value of TRUE returns NA unless specified.

In this article, we will delve into the world of case_when and explore how to change numerical values in multiple columns while avoiding the return of NA. We’ll also discuss alternative approaches, including the use of mutate_at, replace, and mutate_if.

Introduction to Case_When

The case_when function is an R equivalent of the SQL CASE WHEN statement. It allows you to specify a series of conditions and corresponding actions for each condition. The function takes two main arguments: the expression(s) to be evaluated, and the corresponding values.

Here’s a basic example:

library(dplyr)
df %>% 
  mutate(x = case_when(
    x < 0 ~ -x,
    TRUE ~ x + 1
  ))

In this example, we’re defining two conditions: x is less than 0, and the corresponding action is to negate x. For all other cases, the value of x is increased by 1.

Default Values in Case_When

By default, the case_when function returns NA unless a specified condition is met. This can be problematic when working with numerical columns, as it can lead to unexpected results.

To illustrate this, let’s take a closer look at the example provided in the original question:

df <- tribble(
  ~X,     ~Y,    ~Z,
  "a",     0,     2,  
  "b",     5,     0, 
  "c",     0,     0, 
  "d",     3,     1, 
  "e",     0,     2, 
)

The user wants to change the values in columns Y and Z if they are zero. However, using case_when, the function returns NA for non-zero values.

Resolving the Issue: Specifying Default Values

One way to resolve this issue is to specify a default value with TRUE. This tells R to return the specified value unless a matching condition is met.

df <- df %>% 
  mutate(Y = case_when(
    Y == 0 ~ 0.0001,
    TRUE ~ Y
  ))

By adding TRUE as the default action, we ensure that non-zero values are returned instead of NA.

Alternative Approaches: Mutate_at and Replace

Another approach to avoid returning NA is to use the mutate_at function. This allows us to apply a specific function to multiple columns.

Here’s an example:

df <- df %>% 
  mutate_at(vars(Y, Z), ~ case_when(. == 0 ~ 0.0001, TRUE ~ .))

This code applies the case_when function to columns Y and Z, returning 0.0001 if the value is zero.

Alternatively, we can use the replace function to achieve the same result.

df <- df %>% 
  mutate_at(vars(Y, Z), ~ replace(., .== 0, 0.0001))

Alternative Approaches: Mutate_if

If we want to apply a specific function to all numeric columns, including Y and Z, we can use the mutate_if function.

df <- df %>% 
  mutate_if(is.numeric, ~ case_when(. == 0 ~ 0.0001, TRUE ~ .))

This code applies the case_when function to all numeric columns in the dataframe.

Conclusion

The case_when function is a powerful tool for handling conditional statements in R. However, its default behavior of returning NA unless specified can lead to unexpected results. By specifying default values with TRUE, using alternative approaches like mutate_at, replace, and mutate_if, we can avoid this issue.

In this article, we’ve explored how to change numerical values in multiple columns while avoiding the return of NA. We’ve also discussed alternative approaches and provided examples for each solution. With these techniques at your disposal, you’ll be better equipped to handle complex data transformations in R.

Last modified on 2023-07-16