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The post How to Count Distinct Values in R appeared first on Data Science Tutorials

How to Count Distinct Values in R?, using the n_distinct() function from dplyr, you can count the number of distinct values in an R data frame using one of the following methods.

With the given data frame, the following examples explain how to apply each of these approaches in practice.

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## How to Count Distinct Values in R

Let’s make a data frame

```df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
points=c(106, 106, 108, 110, 209, 209, 122, 212),
assists=c(203, 206, 204, 202, 24, 25, 125, 119))
df
team points assists
1    A    106     203
2    A    106     206
3    A    108     204
4    A    110     202
5    B    209      24
6    B    209      25
7    B    122     125
8    B    212     119```

### Approach 1: Count Distinct Values in One Column

The following code demonstrates how to count the number of distinct values in the ‘team’ column using n distinct().

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count the number of distinct values in the ‘team’ column

```library(dplyr)
n_distinct(df\$team)
 2```

In the ‘team’ column, there are two separate values.

### Approach 2: Count Distinct Values in All Columns

The following code demonstrates how to count the number of unique values in each column of the data frame using the sapply() and n distinct() functions.

count the number of distinct values in each column

```sapply(df, function(x) n_distinct(x))
team  points assists
2       6       8```

We can observe the following from the output:

In the ‘team’ column, there are two separate values.

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In the ‘points’ column, there are 6 different values.

The ‘assists’ column has 8 different values.

### Approach 3: Count Distinct Values by Group

The following code demonstrates how to count the number of distinct values by group using the n distinct() function.

count the number of different ‘points’ values by ‘team’

```df %>%
group_by(team) %>%
summarize(distinct_points = n_distinct(points))
team  distinct_points
<chr>           <int>
1 A                   3
2 B                   3```

We can observe the following from the output:

For team A, there are three different point values.

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For team B, there are three different point values.

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