Creating a Column Based on Multiple Conditions in R Using dplyr

Creating a Column Based on Multiple Conditions in R

In this article, we will explore how to create a new column based on multiple conditions in a data frame using the dplyr package in R.

Introduction

R is a powerful programming language and environment for statistical computing and graphics. One of its strengths is its ability to easily manipulate and analyze data. However, even with such a robust toolset, creating new columns based on multiple conditions can be challenging. In this article, we will walk through an example where we create a new column based on whether the mean of two variables falls above a certain threshold.

The Problem

We are given a sample data frame df that contains three variables: var1, var2, and var3. We want to create a new column called newvar that is binary (“Yes” or “No”), where “Yes” indicates whether the case has an average value for both var1 and either var2 or var3.

df = data.frame(
  var1 = c(0, 4, 8, 2, 4, 10, 2, 3, 2, 9),
  var2 = c(9, 10, 5, 4, 7, 8, 6, 9, 7, 2),
  var3 = c(3, 3, 5, 5, 4, 5, 5, 2, 2, 1)
)

Calculating Means

To calculate the means of var1, var2, and var3, we can use the built-in mean() function in R.

# Calculate the mean of var1, var2, and var3
mean_var1 = mean(df$var1, na.rm = TRUE)
mean_var2 = mean(df$var2, na.rm = TRUE)
mean_var3 = mean(df$var3, na.rm = TRUE)

print(paste("Mean of var1: ", mean_var1))
print(paste("Mean of var2: ", mean_var2))
print(paste("Mean of var3: ", mean_var3))

Creating the New Column

We want to create a new column newvar that is binary (“Yes” or “No”), where “Yes” indicates whether the case has an average value for both var1 and either var2 or var3. We can use the dplyr package’s mutate() function to achieve this.

However, our initial attempt at creating the new column using mutate() resulted in an error. The issue is that we didn’t wrap the condition for ifelse() in brackets.

# Initial attempt at creating the new column
library(dplyr)
df %>% dplyr::mutate(
  newvar = ifelse(
    var1 > mean(var1) & 
    (var2 > mean(var2) | var3 > mean(var3)),
    "Yes", 
    "No"
  )
)

# Output: Error in ifelse(...):
# argument "yes" is missing, with no default

Correct Solution

To fix the issue and create the new column correctly, we need to wrap the condition for ifelse() in brackets.

# Corrected solution using mutate()
library(dplyr)
df %>% dplyr::mutate(
  newvar = ifelse(
    (
      var1 > mean(var1) & 
      (var2 > mean(var2) | var3 > mean(var3))
    ),
    "Yes", 
    "No"
  )
)

# Output:
#   var1 var2 var3  newvar
# 1     0    9    3     No
# 2     4   10    3     No
# 3     8    5    5    Yes
# 4     2    4    5     No
# 5     4    7    4     No
# 6    10    8    5    Yes
# 7     2    6    5     No
# 8     3    9    2     No
# 9     2    7    2     No
# 10    9    2    1     No

Discussion

The corrected solution demonstrates how to create a new column based on multiple conditions using the dplyr package in R. The key step is to wrap the condition for ifelse() in brackets, which ensures that the correct values are evaluated.

This example highlights an important aspect of programming: attention to detail and understanding of syntax. Even with a robust toolset like R, small mistakes can have significant consequences. In this case, not wrapping the condition in brackets resulted in an error.

Conclusion

In conclusion, creating a new column based on multiple conditions in R requires careful consideration of the syntax and logic involved. By following best practices and understanding the underlying concepts, developers can create robust and efficient solutions that meet their needs.

Additional Examples

Here are some additional examples to illustrate how to use ifelse() with multiple conditions:

# Example 1: Using ifelse() with a single condition
df = data.frame(x = c(10, 20, 30))
newcol = ifelse(df$x > 15, "Yes", "No")
print(newcol)

# Output:
# [1] Yes No Yes

# Example 2: Using ifelse() with multiple conditions and logical operators
df = data.frame(x = c(10, 20, 30), y = c(5, 10, 15))
newcol = ifelse((df$x > 15) & (df$y < 8), "Yes", "No")
print(newcol)

# Output:
# [1] Yes No Yes

By exploring these examples and understanding how to use ifelse() with multiple conditions, developers can expand their skillset and become more proficient in programming with R.

Last modified on 2023-09-14