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The process of calculating the correlation between multiple variables in R involves using statistical methods to determine the strength and direction of the relationships between two or more variables. This can be achieved by using functions such as cor() or cor.test() which take in a dataset and compute the correlation coefficient. The resulting value can range from -1 to 1, with a positive value indicating a positive correlation and a negative value indicating a negative correlation. By analyzing the correlation values, we can gain insights into the degree of linear relationship between the variables and make informed decisions in data analysis and modeling.
Calculate Correlation Between Multiple Variables in R
One way to quantify the relationship between two variables is to use the Pearson correlation coefficient, which is a measure of the linear association between two variables. It always takes on a value between -1 and 1 where:
- -1 indicates a perfectly negative linear correlation between two variables
- 0 indicates no linear correlation between two variables
- 1 indicates a perfectly positive linear correlation between two variables
This tutorial explains how to calculate the correlation between multiple variables in R, using the following data frame as an example:
#create data frame
df <- data.frame(a <- c(2, 3, 3, 5, 6, 9, 14, 15, 19, 21, 22, 23),
b <- c(23, 24, 24, 23, 17, 28, 38, 34, 35, 39, 41, 43),
c <- c(13, 14, 14, 14, 15, 17, 18, 19, 22, 20, 24, 26),
d <- c(6, 6, 7, 8, 8, 8, 7, 6, 5, 3, 3, 2))
Example 1: Correlation Between Two Variables
The following code shows how to calculate the correlation between two variables in the data frame:
cor(df$a, df$b) [1] 0.9279869
Example 2: Correlation Between Multiple Variables
The following code shows how to calculate the correlation between three variables in the data frame:
cor(df[, c('a', 'b', 'c')])
a b c
a 1.0000000 0.9279869 0.9604329
b 0.9279869 1.0000000 0.8942139
c 0.9604329 0.8942139 1.0000000
The way to interpret the output is as follows:
- The correlation between a and b is 0.9279869.
- The correlation between a and c is 0.9604329.
- The correlation between b and c is 0.8942139.
Example 3: Correlation Between All Variables
The following code shows how to calculate the correlation between all variables in a data frame:
cor(df)
a b c d
a 1.0000000 0.9279869 0.9604329 -0.7915488
b 0.9279869 1.0000000 0.8942139 -0.7917973
c 0.9604329 0.8942139 1.0000000 -0.8063549
d -0.7915488 -0.7917973 -0.8063549 1.0000000
Example 4: Correlation Between Only Numerical Variables
The following code shows how to calculate the correlation between only the numerical variables in a data frame:
cor(df[,unlist(lapply(df, is.numeric))]) a b c d a 1.0000000 0.9279869 0.9604329 -0.7915488 b 0.9279869 1.0000000 0.8942139 -0.7917973 c 0.9604329 0.8942139 1.0000000 -0.8063549 d -0.7915488 -0.7917973 -0.8063549 1.0000000
Example 5: Visualize Correlations
The following code shows how to create a pairs plot – a type of plot that lets you visualize the relationship between each pairwise combination of variables:
#load psych package library(psych) #create pairs plot pairs.panels(df)
