Calculating non-pairwise correlation between different sets of columns of a dataset

Suppose to calculate correlation of dataset mtcars we can use corr <- round(cor(mtcars), 1), or calculate and plot a correlation matrix using corrplot(cor(mtcars). However, the product is pairwise correlation. Is it possible to calculate non-pairwise correlation between spesific set of columns i.e., in mtcars correlation between columns 1:3 (mpg, cyl, disp) VS column 4:8 (hp, drat, wt, qsec, vs).

Please suggest any method.

I have a data set containing 120 columns. I want to calculate/plot correlation matrix (Spearman correlate) of columns 1:50 with rest of the columns (i.e., 51-120). Please tell me the solution, if possible, by using cor, or packages i.e., corrplot, ggcorrplot.

Thanks in anticipation.

You can do this using the cor function, just need to specify the second parameter. Example:

vertical_axis <- 1:3
horizontal_axis <- 4:8
corr <- cor(mtcars[, vertical_axis], mtcars[, horizontal_axis])
corrplot(corr)

The result:

NOTE: Using the corrplot function with the order parameter will produce errors or a wrong plot. If you need to reorder the variables (ex.: with hclust), reorder the correlation matrix before passing it to corrplot.