correlate: Correlation Data Frame

Description

An implementation of stats::cor(), which returns a correlation data frame rather than a matrix. See details below. Additional adjustment include the use of pairwise deletion by default.

Usage

correlate(
  x,
  y = NULL,
  use = "pairwise.complete.obs",
  method = "pearson",
  diagonal = NA,
  quiet = FALSE
)

Value

A correlation data frame cor_df

Arguments

x: a numeric vector, matrix or data frame.
y: NULL (default) or a vector, matrix or data frame with compatible dimensions to x. The default is equivalent to y = x (but more efficient).
use: an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".
method: a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.
diagonal: Value (typically numeric or NA) to set the diagonal to
quiet: Set as TRUE to suppress message about method and use parameters.

Details

This function returns a correlation matrix as a correlation data frame in the following format:

A tibble (see tibble)
An additional class, "cor_df"
A "term" column
Standardized variances (the matrix diagonal) set to missing values by default (NA) so they can be ignored in calculations.

The use argument and its possible values are inherited from stats::cor():

"everything": NAs will propagate conceptually, i.e. a resulting value will be NA whenever one of its contributing observations is NA
"all.obs": the presence of missing observations will produce an error
"complete.obs": correlations will be computed from complete observations, with an error being raised if there are no complete cases.
"na.or.complete": correlations will be computed from complete observations, returning an NA if there are no complete cases.
"pairwise.complete.obs": the correlation between each pair of variables is computed using all complete pairs of those particular variables.

As of version 0.4.3, the first column of a cor_df object is named "term". In previous versions this first column was named "rowname".

There is a ggplot2::autoplot() method for quickly visualizing the correlation matrix, for more information see autoplot.cor_df().

Examples

Run this code

if (FALSE) {
correlate(iris)
}

correlate(iris[-5])

correlate(mtcars)
if (FALSE) {

# Also supports DB backend and collects results into memory

library(sparklyr)
sc <- spark_connect(master = "local")
mtcars_tbl <- copy_to(sc, mtcars)
mtcars_tbl %>%
  correlate(use = "pairwise.complete.obs", method = "spearman")
spark_disconnect(sc)
}

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