{"id":34,"date":"2026-03-03T06:51:46","date_gmt":"2026-03-03T06:51:46","guid":{"rendered":"https:\/\/gigz.pk\/r\/?post_type=lesson&p=34"},"modified":"2026-04-01T10:57:11","modified_gmt":"2026-04-01T10:57:11","slug":"mutate-and-summarize-functions","status":"publish","type":"lesson","link":"https:\/\/gigz.pk\/r\/lesson\/mutate-and-summarize-functions\/","title":{"rendered":"Mutate and Summarize Functions"},"content":{"rendered":"\n

The mutate()<\/code> and summarise()<\/code> functions in R, provided by the dplyr<\/code> package, are essential for creating new variables, transforming data, and generating summary statistics. Mastering these functions allows you to manipulate datasets efficiently and extract meaningful insights.<\/p>\n\n\n\n

1. The mutate() Function<\/strong><\/h1>\n\n\n\n

mutate()<\/code> is used to create new columns or modify existing ones in a data frame or tibble.<\/p>\n\n\n\n

Creating New Columns<\/strong><\/h3>\n\n\n\n
library(dplyr)data <- data.frame(
  Name = c(\"Alice\", \"Bob\", \"Charlie\"),
  Score = c(90, 85, 88),
  Age = c(25, 30, 28)
)# Add a new column for Score plus bonus points
data <- mutate(data, ScoreBonus = Score + 5)# Add a logical column to indicate if Score is passing (>= 88)
data <- mutate(data, Passed = Score >= 88)<\/pre>\n\n\n\n
Modifying Existing Columns<\/strong><\/h3>\n\n\n\n
You can also update existing columns:<\/p>\n\n\n\n
data <- mutate(data, Age = Age + 1)  # Increase all ages by 1<\/pre>\n\n\n\n
2. The summarise() Function<\/strong><\/h1>\n\n\n\n
summarise()<\/code> is used to calculate summary statistics for a dataset. It is often combined with group_by()<\/code> for grouped summaries.<\/p>\n\n\n\n
Basic Summary Statistics<\/strong><\/h3>\n\n\n\n
# Calculate average score and maximum age
summarise(data, AverageScore = mean(Score), MaxAge = max(Age))<\/pre>\n\n\n\n
Grouped Summaries with group_by()<\/strong><\/h3>\n\n\n\n
data <- mutate(data, Passed = Score >= 88)# Group by Passed status and calculate average age and score
data %>%
  group_by(Passed) %>%
  summarise(
    AverageAge = mean(Age),
    AverageScore = mean(Score)
  )<\/pre>\n\n\n\n
This calculates the average age and score for students who passed and those who didn\u2019t.<\/p>\n\n\n\n
3. Using mutate() and summarise() Together<\/strong><\/h1>\n\n\n\n
You can combine these functions in a pipeline to create new variables and then summarize them:<\/p>\n\n\n\n
data %>%
  mutate(ScoreAdjusted = Score + 2) %>%
  summarise(AverageAdjusted = mean(ScoreAdjusted))<\/pre>\n\n\n\n
4. Advantages of mutate() and summarise()<\/strong><\/h1>\n\n\n\n