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The post Correlation By Group in R appeared first on Data Science Tutorials

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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.

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