Visualise correlations amongst variables in your data as a heatmap

vis_cor(
  data,
  cor_method = "pearson",
  na_action = "pairwise.complete.obs",
  facet,
  ...
)

Arguments

data: data.frame
cor_method: correlation method to use, from cor: "a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated."
na_action: The method for computing covariances when there are missing values present. This can be "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs" (default). This option is taken from the cor function argument use.,
facet: bare unqouted variable to use for facetting
...: extra arguments you may want to pass to cor

ggplot2 object

vis_cor(airquality)

vis_cor(airquality, facet = Month)

vis_cor(mtcars)

if (FALSE) { # \dontrun{
# this will error
vis_cor(iris)
} # }