Performing correlations and multivariate analysis

To analyze the relationship of more than two variables, you would need to conduct multivariate descriptive statistics, which allows the comparison of factors. Additionally, it prevents the effect of a single variable from distorting the analysis. In this recipe, we will discuss how to conduct multivariate descriptive statistics using a correlation and covariance matrix.

Getting ready

Ensure that mtcars has already been loaded into a DataFrame within an R session.

How to do it...

Perform the following steps:

1. Here, you can get the covariance matrix by inputting the first three variables in mtcars to the cov function:

        > cov(mtcars[1:3])
        Output:
                   mpg       cyldisp
        mpg  36.324103    -9.172379  -633.0972
        cyl  -9.172379     3.189516   199.6603
        disp -633.097208 199.660282 15360.7998

2. To obtain a correlation matrix of the dataset, we input the first three variables of mtcarsto the cor function:

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