Why does only case_when give different results in R?

Introduction

The case_when function in R is a powerful tool for conditional statements, allowing us to simplify our code and improve readability. However, there have been instances where users have reported unexpected behavior with this function. In this article, we will delve into the world of case_when and explore why it behaves differently compared to other conditional functions like if, ifelse, and even the built-in switch.

Understanding case_when

The case_when function was introduced in R 3.6.0 as part of the dplyr package, a popular data manipulation library. The basic syntax is:

dplyr::case_when(
  condition1 ~ value1,
  condition2 ~ value2,
  ...
)

In this example, condition1, condition2, and so on are logical conditions that evaluate to either TRUE or FALSE, while value1, value2, etc., are the values associated with each condition. If none of the conditions are met, the function defaults to a value specified in the last condition.

The internal workings of case_when involve several helper functions and data structures, which we’ll explore later in this article.

Comparing with Other Conditional Functions

To understand why case_when behaves differently from other conditional functions, let’s examine each one:

if

if (condition) {
  if body
}

In the if statement, a condition is evaluated, and if it returns TRUE, the code in the if body block is executed. If FALSE, the program continues to the next line of code.

ifelse

ifelse(
  condition,
  value_if_true,
  value_if_false
)

The ifelse function takes three arguments: a condition, a value if the condition is TRUE, and a value if the condition is FALSE. It returns one of these values based on the result of the condition.

switch

switch(
  expression,
  case1,
  [default],
  ...
)

The switch function takes an expression to be evaluated, followed by zero or more cases and a default value. When the expression matches a case, the corresponding code is executed, and if no matching case is found, the default value is returned.

Now that we’ve covered these basic conditional functions, let’s look closer at how case_when behaves differently.

The Issue with case_when

According to the provided Stack Overflow post, there seems to be an issue when using case_when with a condition that evaluates to a logical value itself. In this case, it appears that the function returns character(0) instead of initializing to NA.

Let’s dive deeper into why this happens.

The Role of Internal Helper Functions

To understand what’s going on inside case_when, we need to look at its internal helper functions, particularly compact_null and validate_case_when_length. These functions are responsible for:

• Checking the validity of conditions
• Validating case lengths

Here is an excerpt from the source code of case_when in dplyr:

fs <- dplyr:::compact_null(rlang::list2(...))
n <- length(fs)
error_call <- rlang::current_env()
if (n == 0) {
    abort("No cases provided.", call = error_call)
}

In the example given, fs is a list of conditions, and n represents its length. The function then checks if there are any cases (n > 0). If not, it throws an error.

Now, let’s examine what happens when the condition evaluates to character(0) versus a logical value:

x <- character(0)
dplyr::case_when(
  rlang::is_empty(x) ~ "Empty",
  !rlang::is_empty(x) ~ "Not empty"
)

#&gt; [1] "Empty"

dplyr::case_when(
  rlang::is_empty(x) ~ "Empty",
  !rlang::is_empty(x) ~ x
)

#&gt; [1] character(0)

The first example returns "Empty", as expected. However, in the second case, value[[1]][rep(NA_integer_, m)] is replaced by character(0).

The Importance of Internal Data Structures

This behavior can be attributed to how internal data structures are implemented within case_when. Specifically, it involves the following points:

• The use of vectorized operations for better performance
• Handling different types of inputs (logical, numeric, character)
• Managing case lengths and conditional checks

Understanding these technical aspects is essential in explaining why case_when behaves differently from other functions.

Conclusion

In conclusion, we’ve explored the world of R’s case_when function, examining its behavior compared to other conditional statements. We discovered that there seems to be an issue with using logical values as conditions within case_when, resulting in peculiar outcomes. By analyzing the internal helper functions and data structures behind this function, we gained a deeper understanding of why it behaves differently from other functions.

In addition to the explanation provided in the Stack Overflow post, our exploration here has highlighted the complexities involved in implementing conditional statements like case_when. This article serves as an educational resource for those who want to delve into the technical intricacies of R’s dplyr package and its internal workings.

Last modified on 2023-08-14