Correlation By Group in R

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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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