Understanding NaN in R: A Primer

NaN, or Not a Number, is a special value in R that represents an undefined or unreliable result. It’s commonly used to indicate missing data, invalid calculations, or outliers. In this blog post, we’ll explore how to handle NaN values when combining datasets.

What are tibbles?

A tibble is a type of data frame introduced in the tidyverse package. Tibbles are designed to be more flexible and efficient than traditional data frames, with features like column names as character vectors, automatic row numbering, and better performance.

The Problem: Combining NaN Values

When combining datasets using the map_dfr() function from the dplyr package, we often encounter errors when trying to combine columns with different data types. Specifically, when one value in a column is NaN (a character), it can’t be combined with other values in numeric or logical columns.

Example: A Real-World Scenario

Suppose we have 100 datasets, each containing the same column names and structure. We run a model 100 times, generating 100 separate tibbles. Each tibble has an AUC column that we want to combine using the map_dfr() function.

# Load necessary libraries
library(tidyverse)

# Create example data
model_list <- list()
cyls <- c(6, 4, 8, 12)
for (i in seq_along(cyls)) {
  run <- tryCatch({
    mod <- lm(mpg ~ hp, data = mtcars[mtcars$cyl == cyls[[i]],])
    tibble(cyl = cyls[[i]], r2 = summary(mod)$r.squared)
  }, error = e -> tibble(cyl = cyls[[i]], r2 = "NaN"))
  
  model_list[[i]] <- run
}

# Combine datasets using map_dfr()
combined_data <- map_dfr(model_list, ~ .x)

# Attempt to combine AUC column
combined_data

The Error

When we try to combine the AUC column using map_dfr(), we get an error:

Error in `dplyr::bind_rows()`:
! Can't combine '..1$r2' <double> and '..4$r2' <character>

Solution: Converting NaN to Logical

To solve this problem, we can convert any character NaN value to logical using the across() function from the dplyr package.

# Convert NaN values in AUC column to logical
combined_data <- map_dfr(model_list, ~ mutate(.x, across(where(is.character), as.logical)))

# Now we can combine datasets without errors!
combined_data

The Explanation

Here’s what happens when we convert character NaN values to logical:

• When across() detects a column with a character NaN value, it calls the as.logical() function.
• This function converts the NaN value to FALSE (logical FALSE).
• In the resulting dataset, all NaN values in the original AUC column are replaced with FALSE.
• Now we can combine datasets without errors using map_dfr(), as the logical values won’t conflict with numeric or other data types.

Conclusion

In this blog post, we explored how to handle NaN values when combining datasets in R. By converting character NaN values to logical, we can avoid errors and combine our datasets successfully. Remember to use across() and as.logical() functions from the dplyr package to tackle similar problems in your future data analysis tasks.

Additional Considerations

• Be mindful of data types when working with NaN values. In some cases, you may need to handle NaN differently depending on the specific requirements of your project.
• If you’re working with large datasets, consider using the dplyr package’s built-in functions for handling missing data, such as drop_na() or fill().
• Always check the documentation for each library function and package to ensure you’re using them correctly.

Example Use Case

Suppose we want to analyze customer purchase behavior based on their age group. We can use a similar approach to handle NaN values in our dataset:

# Load necessary libraries
library(tidyverse)

# Create example data
age_groups <- tibble(
  Age = c(25, 30, NA, 40),
  Income = c(50000, 60000, 70000, NA)
)

# Convert NaN values to logical
age_groups <- map_dfr(age_groups, ~ mutate(.x, across(where(is.character), as.logical)))

# Now we can analyze customer behavior without errors!
age_groups

In this example, we converted the Age column to logical by replacing NaN values with FALSE. This allows us to analyze customer purchase behavior based on their age group without encountering errors.

Last modified on 2024-03-03