'cor_matrix' computing correlation matrix for numeric variables from data.
cor_matrix(data, variables = NULL, method = "pearson")A numeric matrix, a data frame or a vector.
An optional character string giving a variables for computing correlation matrix. This must be colnames from data.
A character string specifying the correlation coefficient to be calculated. Possible values are: 'pearson' - computes the Pearson correlation coefficient (default), 'spearman' - computes the Spearman rank correlation coefficient.
A correlation matrix is returned as a numeric matrix, which is a symmetric matrix with correlation coefficients between -1 and 1. The diagonal elements are always equal to 1.
library(toRpEDA)
#computing correlation matrix only from numeric variables
cor_matrix(data = iris)
#> Warning: Only numeric variables were used to calculate the correlation matrix.
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> Sepal.Length 1.0000000 -0.1175698 0.8717538 0.8179411
#> Sepal.Width -0.1175698 1.0000000 -0.4284401 -0.3661259
#> Petal.Length 0.8717538 -0.4284401 1.0000000 0.9628654
#> Petal.Width 0.8179411 -0.3661259 0.9628654 1.0000000
#computing correlation matrix from selected numeric variables
cor_matrix(data = mtcars, variables = c("cyl", "vs", "carb"))
#> cyl vs carb
#> cyl 1.0000000 -0.8108118 0.5269883
#> vs -0.8108118 1.0000000 -0.5696071
#> carb 0.5269883 -0.5696071 1.0000000
#computing correlation matrix from numeric variables using 'spearman' method
data <- data.frame(x = rnorm(100), y = rnorm(100), z = rnorm(100))
cor_matrix(data, method = 'spearman')
#> x y z
#> x 1.00000000 -0.07563156 0.08609661
#> y -0.07563156 1.00000000 -0.07695170
#> z 0.08609661 -0.07695170 1.00000000