There are two basic ways to calculate summary statistics by group in R:

**Method 1: Use tapply() from Base R**

tapply(df$value_col, df$group_col, summary)

**Method 2: Use group_by() from dplyr Package**

**library(dplyr)
df %>%
group_by(group_col) %>%
summarize(min = min(value_col),
q1 = quantile(value_col, 0.25),
median = median(value_col),
mean = mean(value_col),
q3 = quantile(value_col, 0.75),
max = max(value_col))**

The following examples show how to use each method in practice.

**Method 1: Use tapply() from Base R**

The following code shows how to use the **tapply()** function from base R to calculate summary statistics by group:

#create data frame df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'), points=c(99, 68, 86, 88, 95, 74, 78, 93), assists=c(22, 28, 31, 35, 34, 45, 28, 31), rebounds=c(30, 28, 24, 24, 30, 36, 30, 29)) #calculate summary statistics of 'points' grouped by 'team' tapply(df$points, df$team, summary) $A Min. 1st Qu. Median Mean 3rd Qu. Max. 68.00 81.50 87.00 85.25 90.75 99.00 $B Min. 1st Qu. Median Mean 3rd Qu. Max. 74.0 77.0 85.5 85.0 93.5 95.0

**Method 2: Use group_by() from dplyr Package**

The following code shows how to use the **group_by()** and **summarize()** functions from the dplyr package to calculate summary statistics by group:

library(dplyr) #create data frame df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'), points=c(99, 68, 86, 88, 95, 74, 78, 93), assists=c(22, 28, 31, 35, 34, 45, 28, 31), rebounds=c(30, 28, 24, 24, 30, 36, 30, 29)) #calculate summary statistics of 'points' grouped by 'team' df %>% group_by(team) %>% summarize(min = min(points), q1 = quantile(points, 0.25), median = median(points), mean = mean(points), q3 = quantile(points, 0.75), max = max(points)) # A tibble: 2 x 7 team min q1 median mean q3 max 1 A 68 81.5 87 85.2 90.8 99 2 B 74 77 85.5 85 93.5 95

Notice that both methods return the exact same results.

It’s worth noting that the dplyr approach will likely be faster for large data frames but both methods will perform similarly on smaller data frames.

**Additional Resources**

The following tutorials explain how to perform other common grouping functions in R:

How to Create a Frequency Table by Group in R

How to Calculate the Sum by Group in R

How to Calculate the Mean by Group in R

How to Calculate the Sum by Group in R