Mastering dplyr's mutate Function with Conditions for Data Manipulation in R

Introduction to Using dplyr mutate with Conditions Based on Multiple Columns

In this article, we will delve into the world of dplyr, a popular R package for data manipulation and analysis. We will explore how to use the mutate() function in conjunction with conditional statements to create new columns based on multiple conditions.

Background: The Problem with cbind()

When working with data frames in R, it’s common to encounter matrices or other types of data structures that may not be compatible with dplyr functions. One such issue arises when using the cbind() function to join two data sets together.

# Load necessary libraries
library(dplyr)

# Create a sample matrix
V1 <- c(NA, 1, 2, 0, 0)
V2 <- c(0, 0, 2, 1, 1)
V3 <- c(NA, 0, 2, 1, 0)

# Use cbind() to join the data
V <- cbind(V1, V2, V3)

# Convert V to a data frame for easier manipulation
V <- as.data.frame(V)

The Issue with Using mutate()

When we use mutate() with our sample data, we encounter an issue:

# Use mutate() to create a new column
V <- mutate(V, V4 = ifelse(V1 == 2 | V2 == 2 | V3 == 2, 2,
                           ifelse(V1 == 1 | V2 == 1 | V3 == 1, 1,
                                  ifelse(V1 == 0 | V2 == 0 | V3 == 0, 0, NA))))

The problem arises when the data contains missing values (NA). The mutate() function does not handle these cases correctly.

A Solution Using case_when()

To solve this issue, we can use the case_when() function from dplyr, which allows us to specify multiple conditions in a more readable and maintainable way:

# Use case_when() instead of ifelse()
V <- mutate(V, V4 = case_when(
  V1 == 2 | V2 == 2 | V3 == 2 ~ 2,
  V1 == 1 | V2 == 1 | V3 == 1 ~ 1,
  V1 == 0 | V2 == 0 | V3 == 0 ~ 0
))

This approach is more readable and maintainable than using ifelse() with multiple conditions.

Using data.frame(), data_frame(), or tibble()

Another important point to note is that we should use one of the three functions (data.frame(), data_frame(), or tibble()) instead of cbind(). This is because dplyr functions expect a data frame, not a matrix.

# Use data.frame() or tibble()
V <- mutate(V, V4 = case_when(
  V1 == 2 | V2 == 2 | V3 == 2 ~ 2,
  V1 == 1 | V2 == 1 | V3 == 1 ~ 1,
  V1 == 0 | V2 == 0 | V3 == 0 ~ 0
))

# Alternatively, use data_frame()
V <- data_frame(V4 = case_when(
  V1 == 2 | V2 == 2 | V3 == 2 ~ 2,
  V1 == 1 | V2 == 1 | V3 == 1 ~ 1,
  V1 == 0 | V2 == 0 | V3 == 0 ~ 0
))

# Alternatively, use tibble()
V <- tibble(V4 = case_when(
  V1 == 2 | V2 == 2 | V3 == 2 ~ 2,
  V1 == 1 | V2 == 1 | V3 == 1 ~ 1,
  V1 == 0 | V2 == 0 | V3 == 0 ~ 0
)))

Conclusion

In conclusion, using dplyr’s mutate() function with conditions based on multiple columns can be achieved using the case_when() function. It is also important to note that we should use one of the three functions (data.frame(), data_frame(), or tibble()) instead of cbind() when working with data frames in R.