You can use the following methods to count the number of unique values by group in R:

**Method 1: Using Base R**

results <- aggregate(data=df, values_var~group_var, function(x) length(unique(x)))

**Method 2: Using dplyr**

library(dplyr) results <- df %>% group_by(group_var) %>% summarize(count = n_distinct(values_var))

**Method 3: Using data.table**

library(data.table) df <- data.table(df) results <- df[ , .(count = length(unique(values_var))), by = group_var]

Each method returns the exact same result, but the base R method tends to be significantly slower when working with large data frames.

The following examples show how to use each of these methods in practice with the following data frame:

#create data frame df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'C', 'C', 'C'), points=c(10, 10, 14, 14, 18, 19, 20, 20, 20)) #view data frame df team points 1 A 10 2 A 10 3 A 14 4 A 14 5 B 18 6 B 19 7 C 20 8 C 20 9 C 20

**Method 1: Count Unique Values by Group Using Base R**

The following code shows how to count the number of distinct points values for each team using base R:

#count unique points values by team results <- aggregate(data=df, points~team, function(x) length(unique(x))) #view results results team points 1 A 2 2 B 2 3 C 1

From the output we can see:

- There are
**2**unique points values for team A. - There are
**2**unique points values for team B. - There is
**1**unique points value for team C.

**Method 2: ****Count Unique Values by Group Using dplyr**

The following code shows how to count the number of distinct points values for each team using dplyr:

library(dplyr) #count unique points values by team results <- df %>% group_by(team) %>% summarize(count = n_distinct(points)) #view results results # A tibble: 3 x 2 team count 1 A 2 2 B 2 3 C 1

Notice that these results match the ones from the base R method.

**Method 3: ****Count Unique Values by Group Using data.table**

The following code shows how to count the number of distinct points values for each team using data.table:

library(data.table) #convert data frame to data table df <- data.table(df) #count unique points values by team results <- df[ , .(count = length(unique(points))), by = team] #view results results team count 1: A 2 2: B 2 3: C 1

Notice that these results match the ones from the previous two methods.

**Additional Resources**

The following tutorials explain how to perform other common operations using dplyr:

How to Recode Values Using dplyr

How to Replace NA with Zero in dplyr

How to Rank Variables by Group Using dplyr

How to Select the First Row by Group Using dplyr