How to Convert Character to Numeric in R

One common situation that data analysts and researchers face is the need to convert data from one type to another, specifically from character to numeric types. Such conversion is often required for carrying out mathematical operations or running statistical tests that require numeric data. In this extensive article, we’ll cover multiple facets of converting character to numeric types in R.

Table of Contents

1. Introduction to Data Types in R
2. The Necessity of Conversion
3. Utilizing as.numeric()
4. Handling Data Frames and Columns
5. Navigating Factors
6. Leverage sapply() and lapply()
7. Addressing Complex Scenarios
8. Specialized Packages
9. Conclusion

1. Introduction to Data Types in R

Firstly, let’s clarify the primary data types:

• Character: Represents textual data.
• Numeric: Encompasses both integers and decimal numbers.

2. The Necessity of Conversion

Why convert characters to numerics?

• Data Cleaning: Numbers can sometimes be stored as characters.
• Statistical Operations: Many statistical functions mandate numeric inputs.
• Visualization: Plots and charts often work better with numeric data.

3. Utilizing as.numeric( )

The direct method for this conversion is the as.numeric() function:

char_val <- "123.45"
num_val <- as.numeric(char_val)

4. Handling Data Frames and Columns

For those working with data frames, converting specific columns from character to numeric is a routine task:

# Sample data frame
df <- data.frame(character_col = c("1", "2", "3"))

# Conversion
df$numeric_col <- as.numeric(df$character_col)

5. Navigating Factors

Factors can be tricky. If a factor is directly converted using as.numeric(), it may return internal integer codes. A two-step conversion is often necessary:

factor_var <- factor(c("1", "2", "3"))
numeric_var <- as.numeric(as.character(factor_var))

6. Leverage sapply( ) and lapply( )

For more complex structures or multiple columns, sapply() and lapply() are invaluable:

df[] <- sapply(df, function(col) {
  if (is.character(col)) return(as.numeric(col))
  return(col)
})

7. Addressing Complex Scenarios

Sometimes, character strings contain non-numeric characters:

char_with_special <- "123.45$"
cleaned_char <- gsub("[^0-9.]", "", char_with_special)
numeric_value <- as.numeric(cleaned_char)

8. Specialized Packages

Packages like dplyr make data manipulation, including type conversion, a breeze:

library(dplyr)
df <- df %>%
  mutate(numeric_col = as.numeric(character_col))

9. Conclusion

Conversion from character to numeric in R, while straightforward, requires attention to nuances, especially with factors and data frames. This guide aimed to offer a comprehensive overview of the topic, ensuring you can handle such conversions with ease and confidence in your data analysis journey.