How to Change value of variable with dplyr
Last Updated :
03 Jun, 2024
The Dplyr package in R is a powerful tool for data manipulation and transformation. It provides a set of functions that allow you to perform common data manipulation tasks concisely and efficiently. One of these tasks is changing the value of a variable within a data frame. This article will guide you through various methods to change the value of a variable using dplyr.
Change the value of a variable with dplyr
dplyr is part of the tidyverse, a collection of R packages designed for data science. The key functions of dplyr that are commonly used include select(), filter(), mutate(), summarise(), and arrange(). Among these, mutate() is the primary function for modifying or creating new variables in a data frame.
Before diving into the examples, let's create a sample data frame to work with:
R
# Load dplyr package
library(dplyr)
# Create a sample data frame
data <- data.frame(
id = 1:5,
name = c("Ali", "Boby", "Charles", "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 Charles 35 85
4 4 David 40 87
5 5 Eva 45 90
Changing Variable Values with mutate()
The mutate()
function from the dplyr
package is a powerful tool for creating and modifying variables within a data frame.
1. Changing Values Based on a Condition
You can use mutate() along with ifelse() to change the values of a variable based on a condition.
R
# Change 'score' to 100 if 'age' is greater than 40
data <- data %>%
mutate(score = ifelse(age > 40, 100, score))
# Display the updated data frame
print(data)
Output:
id name age score
1 1 Ali 25 88
2 2 Boby 30 92
3 3 Charles 35 85
4 4 David 40 87
5 5 Eva 45 100
2. Using mutate() with Multiple Conditions
You can use case_when() for more complex conditional logic.
R
# Change 'score' based on multiple conditions
data <- data %>%
mutate(
score = case_when(
age <= 30 ~ score + 10,
age > 30 & age <= 40 ~ score + 5,
age > 40 ~ score + 15
)
)
# Display the updated data frame
print(data)
Output:
id name age score
1 1 Ali 25 98
2 2 Boby 30 102
3 3 Charles 35 90
4 4 David 40 92
5 5 Eva 45 115
3. Modifying Multiple Variables
You can modify multiple variables within a single mutate() call.
R
# Change 'age' and 'score' simultaneously
data <- data %>%
mutate(
age = age + 1,
score = score * 1.1
)
# Display the updated data frame
print(data)
Output:
id name age score
1 1 Ali 26 107.8
2 2 Boby 31 112.2
3 3 Charles 36 99.0
4 4 David 41 101.2
5 5 Eva 46 126.5
Using transmute() to Change and Drop Variables
If you want to change the values of variables and simultaneously drop others, you can use transmute(). This function works similarly to mutate() but only keeps the variables that are explicitly mentioned.
R
# Change 'score' and keep only 'id' and 'score'
data <- data %>%
transmute(
id,
score = score * 1.2
)
# Display the updated data frame
print(data)
Output:
id score
1 1 129.36
2 2 134.64
3 3 118.80
4 4 121.44
5 5 151.80
Using across() for Multiple Columns
The across() function allows you to apply the same transformation to multiple columns simultaneously.
R
# Create a new sample data frame
data <- data.frame(
id = 1:5,
math_score = c(88, 92, 85, 87, 90),
science_score = c(80, 85, 82, 84, 88)
)
# Load dplyr package
library(dplyr)
# Apply a transformation across multiple columns
data <- data %>%
mutate(across(c(math_score, science_score), ~ . * 1.1))
# Display the updated data frame
print(data)
Output:
id math_score science_score
1 1 96.8 88.0
2 2 101.2 93.5
3 3 93.5 90.2
4 4 95.7 92.4
5 5 99.0 96.8
Conclusion
The dplyr package in R offers a versatile and efficient way to change the value of variables within a data frame. Whether you need to modify single variables based on specific conditions, update multiple variables simultaneously, or apply transformations across multiple columns, dplyr provides intuitive functions such as mutate(), transmute(), case_when(), and across() to accomplish these tasks. Mastering these functions can significantly enhance your data manipulation capabilities, making your data analysis workflows more efficient and effective.
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