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In statistics, we often use the Pearson correlation coefficient to measure the linear relationship between two variables.

However, sometimes we’re interested in understanding the relationship between two variables **while controlling for a third variable**.

For example, suppose we want to measure the association between the number of hours a student studies and the final exam score they receive, while controlling for the student’s current grade in the class.

In this case, we could use a **partial correlation **to measure the relationship between hours studied and final exam score.

This tutorial explains how to calculate partial correlation in R.

**Example: Partial Correlation in R**

Suppose we have the following data frame that displays the current grade, total hours studied, and final exam score for 10 students:

#create data frame df frame(currentGrade = c(82, 88, 75, 74, 93, 97, 83, 90, 90, 80), hours = c(4, 3, 6, 5, 4, 5, 8, 7, 4, 6), examScore = c(88, 85, 76, 70, 92, 94, 89, 85, 90, 93)) #view data frame df currentGrade hours examScore 1 82 4 88 2 88 3 85 3 75 6 76 4 74 5 70 5 93 4 92 6 97 5 94 7 83 8 89 8 90 7 85 9 90 4 90 10 80 6 93

To calculate the partial correlation between each pairwise combination of variables in the dataframe, we can use the **pcor()** function from the ppcor library:

library(ppcor) #calculate partial correlations pcor(df) $estimate currentGrade hours examScore currentGrade 1.0000000 -0.3112341 0.7355673 hours -0.3112341 1.0000000 0.1906258 examScore 0.7355673 0.1906258 1.0000000 $p.value currentGrade hours examScore currentGrade 0.00000000 0.4149353 0.02389896 hours 0.41493532 0.0000000 0.62322848 examScore 0.02389896 0.6232285 0.00000000 $statistic currentGrade hours examScore currentGrade 0.0000000 -0.8664833 2.8727185 hours -0.8664833 0.0000000 0.5137696 examScore 2.8727185 0.5137696 0.0000000 $n [1] 10 $gp [1] 1 $method [1] "pearson"

Here is how to interpret the output:

**Partial correlation between hours studied and final exam score:**

The partial correlation between hours studied and final exam score is **.191**, which is a small positive correlation. As hours studied increases, exam score tends to increase as well, assuming current grade is held constant.

The p-value for this partial correlation is **.623**, which is not statistically significant at α = 0.05.

**Partial correlation between current grade and final exam score:**

The partial correlation between current grade and final exam score is **.736**, which is a strong positive correlation. As current grade increases, exam score tends to increase as well, assuming hours studied is held constant.

The p-value for this partial correlation is **.024**, which is statistically significant at α = 0.05.

**Partial correlation between current grade and hours studied:**

The partial correlation between current grade and hours studied and final exam score is **-.311**, which is a mild negative correlation. As current grade increases, final exam score tends to decreases, assuming final exam score is held constant.

The p-value for this partial correlation is **0.415**, which is not statistically significant at α = 0.05.

The output also tells us that the method used to calculate the partial correlation was “pearson.”

Within the** pcor()** function, we could also specify “kendall” or “pearson” as alternative methods to calculate the correlations.

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Calculate Spearman Rank Correlation in R

How to Calculate Cross Correlation in R

How to Calculate Rolling Correlation in R

How to Calculate Point-Biserial Correlation in R