I am currently cleaning a large dataset in R and I noticed that several columns that should be numeric were imported as characters because of some missing value symbols. I need to change these to numeric and also convert some category columns into factors for a machine learning model I'm building. What are the standard functions to change data types, and how do I handle the "NAs introduced by coercion" warning that pops up when the conversion fails?
3 answers
The most common way to change data types in R is by using the "as.family" of functions. For example, as.numeric(), as.character(), as.logical(), and as.factor() are your primary tools. If you are working with a dataframe, you can transform a specific column using df$column <- as.numeric(df$column).
Regarding the "NAs introduced by coercion" warning, this happens when R encounters a string (like "Unknown") that it cannot turn into a number. It replaces that value with NA. To handle this properly, I recommend cleaning non-numeric characters using gsub() before you attempt the conversion. This ensures you don't lose data unintentionally during the cleaning process.
Are you trying to convert these types across the entire dataframe at once, or are you manually specifying each column? I ask because if you have fifty columns to change, using a loop or the mutate(across()) function from the tidyverse library is significantly faster and less prone to copy-paste errors than doing them one by one.
For factors specifically, always check your levels after conversion. Use levels(df$column) to make sure the categories are in the right order, especially for ordinal data.
Jennifer is spot on. I’ll add to that: if you are converting a factor back to a number, you must use as.numeric(as.character(factor_col)). If you call as.numeric() directly on a factor, R will return the underlying integer codes (1, 2, 3...) instead of the actual values, which is a very common trap!
Robert, I'm actually using the tidyverse for this project. Could you explain how the mutate_if or across syntax works for changing all character columns to factors? I find the syntax for the across function a bit confusing compared to base R, especially when I need to pass additional arguments to the conversion function.