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.

Compute and Add new Variables to a Data Frame in R

In data analysis and manipulation, adding new variables to a data frame is a common task. This allows you to create new insights, summarize data, or prepare it for further analysis. In R, this can be efficiently done using the mutate() function from the dplyr package, but you can also achieve it using base R functions.

Before we delve into computing and adding new variables, let's create a sample data frame to work with:

# Create a sample data frame
data <- data.frame(
  id = 1:5,
  name = c("Ali", "Boby", "Charlie", "David", "Eva"),
  age = c(25, 30, 35, 40, 45),
  score = c(88, 92, 85, 87, 90)
)

# Display the data frame
print(data)

Output:

  id    name age score
1  1     Ali  25    88
2  2    Boby  30    92
3  3 Charlie  35    85
4  4   David  40    87
5  5     Eva  45    90

Adding New Variables Using Base R

In base R, you can add new variables to a data frame by assigning a new column name to a vector of values. This vector can be the result of a transformation of existing columns or can be independently created.

1. Adding a Variable Directly

You can add a new variable directly to the data frame by creating a new column and assigning values to it.

# Add a new variable 'pass' based on 'score'
data$pass <- ifelse(data$score >= 90, "Yes", "No")
# Display the updated data frame
print(data)

Output:

  id    name age score pass
1  1     Ali  25    88   No
2  2    Boby  30    92  Yes
3  3 Charlie  35    85   No
4  4   David  40    87   No
5  5     Eva  45    90  Yes

2. Using transform()

The transform() function can also be used to add new variables to a data frame.

# Add a new variable 'age_group' using transform
data <- transform(data, age_group = ifelse(age < 35, "Young", "Old"))
# Display the updated data frame
print(data)

Output:

  id    name age score pass age_group
1  1     Ali  25    88   No     Young
2  2    Boby  30    92  Yes     Young
3  3 Charlie  35    85   No       Old
4  4   David  40    87   No       Old
5  5     Eva  45    90  Yes       Old

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

Adding New Variables Using data.table

The data.table package is another powerful tool for data manipulation in R, known for its speed and efficiency.

1. Using := Operator

The := operator in data.table is used to add or modify columns by reference.

# Load data.table package
library(data.table)
# Convert data frame to data.table
data <- as.data.table(data)
# Add new variables using :=
data[, pass_new := ifelse(score >= 90, "Pass", "Fail")]
data[, age_decade := floor(age / 10) * 10]
# Display the updated data table
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
   pass_new age_decade
1:     Fail         20
2:     Pass         30
3:     Fail         30
4:     Fail         40
5:     Pass         40

Conclusion

Adding new variables to a data frame in R is a common task in data analysis, which can be accomplished using various methods depending on your needs and preferences. Whether you prefer base R functions, the dplyr package for a more readable and chainable syntax, or the data.table package for speed and efficiency, R provides robust tools for creating and manipulating variables. Understanding these methods allows you to enhance your datasets, derive new insights, and conduct more thorough analyses.

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Article Tags :

• R Language
• R Basics
• AI-ML-DS With R