cannot coerce type 'closure' to vector of type 'character'

Guri Bee

2 min read

·

19-10-2024

1

0

Share

"Cannot coerce type 'closure' to vector of type 'character':" Unraveling R's Error Message

Have you ever encountered the error message "cannot coerce type 'closure' to vector of type 'character'" in your R code? This cryptic message can be frustrating, especially for beginners. Let's break it down and explore how to address this common R error.

Understanding the Error:

This error occurs when you try to use a function (a "closure" in R parlance) in a context where R expects a character vector. Functions are not directly interchangeable with character strings. Imagine trying to use a recipe as a list of ingredients – it wouldn't work!

Common Causes:

Here are some common scenarios where this error might pop up:

1. Incorrectly Passing Functions to Functions: This happens when you feed a function directly into a function that expects a character vector as input. For example:

# Incorrect:
my_function(function(x) x + 1) 

# Correct:
my_function("function(x) x + 1") # Assuming 'my_function' expects a string

2. Using Functions Where Character Vectors Are Expected: Sometimes we try to use functions in places where character vectors are needed, like within c(), paste(), or string manipulation functions.

# Incorrect:
my_vector <- c(mean, "Hello", 2)  # 'mean' is a function

# Correct:
my_vector <- c("mean", "Hello", 2) # Using the string "mean"

3. Misusing lapply or sapply: lapply and sapply iterate over elements of a list or vector. You might get this error if you try to apply a function to a list containing functions instead of data.

# Incorrect:
my_list <- list(mean, sd, sum)
result <- lapply(my_list, function(x) x(1:10)) # Trying to apply each function to data

# Correct:
result <- lapply(my_list, function(x) as.character(deparse(x)))  # Converting functions to character strings

Solutions:

1. Check Function Arguments: Double-check the documentation of the function you're using to ensure you're providing the correct input types. Most functions have specific requirements for their arguments.

2. Convert Functions to Strings: If you need a string representation of the function name, use deparse() to convert the function into a string.

function_name <- deparse(mean)  # Result: "mean"

3. Use lapply or sapply Correctly: If you intend to apply functions to data, make sure the list or vector contains data elements, not functions.

4. Use Anonymous Functions: Sometimes, creating a new function using an anonymous function (function(x) { ... }) can be a cleaner solution than passing a named function.

Illustrative Example (From a Github Issue)

Consider this code snippet from a Github issue https://github.com/tidyverse/dplyr/issues/4253:

data %>% mutate(new_column = if_else(condition, mean(column_name), NA_real_))

The error occurs because mean(column_name) tries to calculate the mean within the if_else() function, resulting in a function instead of a scalar value.

Solution:

Use a separate function call to calculate the mean outside if_else():

data %>% mutate(new_column = if_else(condition, mean(column_name), NA_real_))

Key Takeaways:

• R distinguishes between functions and character vectors.
• Understand what your functions expect as inputs.
• Use deparse() to convert functions to character strings when necessary.
• Carefully apply lapply and sapply to avoid applying functions to functions.

By understanding the error message and its root causes, you can effectively debug your code and ensure smooth operation in your R projects.