How to group and summarise a data frame in R

Grouping and summarising is essential for exploring data by categories.

With dplyr

library(dplyr)

# Group by department and calculate mean salary
avg_salary <- df %>%
  group_by(department) %>%
  summarise(mean_salary = mean(salary, na.rm = TRUE))

# Multiple summary statistics
summary_stats <- df %>%
  group_by(department) %>%
  summarise(
    mean_salary = mean(salary, na.rm = TRUE),
    median_salary = median(salary, na.rm = TRUE),
    n = n()
  )

With base R

# Using aggregate
avg_salary <- aggregate(salary ~ department, data = df, FUN = mean)

# Simple counts with table
dept_counts <- table(df$department)

With data.table

library(data.table)

dt <- as.data.table(df)

# Group and summarise
avg_salary <- dt[, .(mean_salary = mean(salary)), by = department]

Common Summary Functions

Function	Description
mean()	Average value
median()	Middle value
sum()	Total
min() / max()	Extremes
sd()	Standard deviation
n() / .N	Row count

See Also

• group_by() — dplyr grouping
• filter() — Filter rows
• mutate() — Add columns