Adding New Variables Using dplyr
The dplyr package provides a more intuitive and efficient way to manipulate data frames, including adding new variables.
1. Using mutate()
The mutate() function from dplyr is specifically designed for adding new variables or modifying existing ones.
# Load dplyr package
library(dplyr)
# Add new variables using mutate
data <- data %>%
mutate(
score_category = ifelse(score >= 90, "High", "Medium"),
score_double = score * 2
)
# Display the updated data frame
print(data)
Output:
id name age score pass age_group score_category score_double
1 1 Ali 25 88 No Young Medium 176
2 2 Boby 30 92 Yes Young High 184
3 3 Charlie 35 85 No Old Medium 170
4 4 David 40 87 No Old Medium 174
5 5 Eva 45 90 Yes Old High 180
2. Using mutate() with Custom Functions
You can also use custom functions within mutate() to create more complex new variables.
# Define a custom function to categorize age
age_category <- function(age) {
if (age < 30) {
return("Youth")
} else if (age <= 40) {
return("Adult")
} else {
return("Senior")
}
}
# Add a new variable 'age_category' using the custom function
data <- data %>%
mutate(age_category = sapply(age, age_category))
# Display the updated data frame
print(data)
Output:
id name age score pass age_group score_category score_double age_category
1 1 Ali 25 88 No Young Medium 176 Youth
2 2 Boby 30 92 Yes Young High 184 Adult
3 3 Charlie 35 85 No Old Medium 170 Adult
4 4 David 40 87 No Old Medium 174 Adult
5 5 Eva 45 90 Yes Old High 180 Senior
How to Add Variables to a Data Frame in R
In data analysis, it is often necessary to create new variables based on existing data. These new variables can provide additional insights, support further analysis, and improve the overall understanding of the dataset. R, a powerful tool for statistical computing and graphics, offers various methods for computing and adding new variables to a data frame. This article will guide you through different approaches to achieve this in R, using built-in functions as well as packages like dplyr.
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