Calculating the correlation between two variables by group in R is a powerful technique that allows you to analyze the relationships between variables within specific groups.

In this article, we will explore how to use the `dplyr`

package to calculate the correlation between two variables by group.

**Basic Syntax**

The basic syntax to calculate the correlation between two variables by group in R is as follows:

```
library(dplyr)
df %>%
group_by(group_var) %>%
summarize(cor=cor(var1, var2))
```

This syntax calculates the correlation between `var1`

and `var2`

, grouped by `group_var`

.

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**Example: Calculate Correlation By Group in R**

Suppose we have a data frame that contains information about basketball players on various teams:

```
# Create data frame
df <- data.frame(team=c('A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'),
points=c(108, 202, 109, 104, 104, 101, 200, 208),
assists=c(2, 7, 9, 3, 12, 10, 14, 21))
# View data frame
df
team points assists
1 A 108 2
2 A 202 7
3 A 109 9
4 A 104 3
5 B 104 12
6 B 101 10
7 B 200 14
8 B 208 21
```

We can use the following syntax from the `dplyr`

package to calculate the correlation between `points`

and `assists`

, grouped by `team`

:

```
library(dplyr)
df %>%
group_by(team) %>%
summarize(cor=cor(points, assists))
```

The output is:

```
# A tibble: 2 × 2
team cor
<chr> <dbl>
1 A 0.376
2 B 0.819
```

From the output, we can see:

- The correlation coefficient between
`points`

and`assists`

for team A is`.376`

. - The correlation coefficient between
`points`

and`assists`

for team B is`.819`

.

Since both correlation coefficients are positive, this tells us that the relationship between `points`

and `assists`

for both teams is positive.

**Conclusion**

In this article, we have demonstrated how to use the `dplyr`

package to calculate the correlation between two variables by group in R.

We have also shown how to apply this technique to a real-world example.

By calculating the correlation between two variables by group, you can gain valuable insights into the relationships between variables within specific groups.