如何在R语言中将形如‘Male25’的列拆分为性别与年龄两列？

Hey there! Splitting a column formatted like Male25 (letters for gender, digits for age) into two separate columns is a super common data cleaning task in R. Here are the most efficient, readable methods to get this done:

Method 1: Using tidyr::separate_wider_regex (Tidyverse Approach) #

If you're already working with the tidyverse (which most R users rely on for data manipulation), separate_wider_regex is the most intuitive way. It lets you explicitly define regex patterns for each new column:

# Load the tidyverse package (includes tidyr and dplyr)
library(tidyverse)

# Example data frame
df <- tibble(combined = c("Male25", "Female32", "Male19", "Female45"))

# Split the column
df_split <- df %>%
  separate_wider_regex(
    combined,
    patterns = list(
      gender = "[A-Za-z]+",  # Match all letters for gender
      age = "\\d+"          # Match all digits for age
    ),
    convert = TRUE  # Auto-convert age to numeric type
  )

# View the result
df_split

This method cleansly splits the column and handles type conversion in one step—perfect for streamlined workflows.

Method 2: Base R (No External Packages) #

If you prefer sticking to base R without loading extra libraries, use sub() to extract each component directly:

# Example data frame
df <- data.frame(combined = c("Male25", "Female32", "Male19", "Female45"))

# Extract gender (remove all digits from the string)
df$gender <- sub("\\d+", "", df$combined)

# Extract age (remove all letters, then convert to numeric)
df$age <- as.numeric(sub("[A-Za-z]+", "", df$combined))

# Check the result
df

This is lightweight and great for quick, one-off tasks where you don't want to pull in additional dependencies.

Method 3: Using stringr::str_extract (Tidyverse Alternative) #

Another flexible tidyverse option is using stringr's str_extract to pull out matching patterns individually:

library(stringr)
library(dplyr)

df <- tibble(combined = c("Male25", "Female32", "Male19", "Female45"))

df_split <- df %>%
  mutate(
    gender = str_extract(combined, "[A-Za-z]+"),
    age = as.numeric(str_extract(combined, "\\d+"))
  )

df_split

This approach gives you granular control over each extraction—handy if you need to adjust one column's logic without affecting the other.

Key Notes #

• All these methods assume your data follows the letters first, digits second pattern. If you have edge cases (like mixed case or non-standard entries), tweak the regex (e.g., use [A-Za-z]+ to handle both upper and lowercase).
• Always convert the age column to numeric with as.numeric() if you plan to do numerical analysis (like calculating averages or filtering by age ranges).

内容的提问来源于stack exchange，提问作者Neoleogeo