Handling Non-ASCII Characters in R: A Step-by-Step Guide to Cleanup and Standardization

Handling Non-ASCII Characters in R

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When working with data from external sources, such as databases or files, you may encounter non-ASCII characters. These characters can be problematic when trying to manipulate the data in R.

The Problem


In the given example, the gene names contain non-ASCII characters (< and >) that are causing issues when trying to clean them up.

Solution


To fix this issue, you can use the gsub function to replace these characters with an empty string. Here’s how:

df %>% 
  mutate(clean_gene = gsub("<([[:alpha:]][[:alnum:]]*)(.[^>]*)>", "\\3", gene))

In this code, gsub replaces any sequence of one or more letters and numbers ([[:alpha:]][[:alnum:]]*) followed by zero or more characters that are not > (.[^>]*) with just the third character (\\3). The result is a clean gene name without any non-ASCII characters.

Applying to a List of Data Frames


If you have a list of data frames, you can apply this cleaning function to each one using the map function from the purrr package:

library(purrr)
library(dplyr)

list_of_dfs %>% 
  map(~mutate(., gene = gsub("<([[:alpha:]][[:alnum:]]*)(.[^>]*)>", "\\3", gene)))

This will clean up the gene names in each data frame in your list and return a new list with the cleaned data frames.

Example Use Case


Suppose you have a list of data frames containing gene information, but the gene names contain non-ASCII characters. You can use this code to clean up these genes:

# Create a sample list of data frames
df1 <- data.frame(gene = c("IL-12A/IL-12B", "&lt;U+00A0&gt;KLRK1"))
df2 <- data.frame(gene = c("IFNG", "&lt;U+00A0&gt;KLRK1"))

list_of_dfs <- list(df1, df2)

# Clean up the gene names
cleaned_list <- list_of_dfs %>% 
  map(~mutate(., gene = gsub("&lt;([[:alpha:]][[:alnum:]]*)(.[^&gt;]*)&gt;", "\\3", gene)))

print(cleaned_list)

This code creates a sample list of data frames, cleans up the gene names in each data frame using gsub, and returns a new list with the cleaned data frames.


Last modified on 2023-08-04