In easystats/see: Model Visualisation Toolbox for 'easystats' and 'ggplot2'

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  warning = FALSE,
  message = FALSE
)

pkgs <- c("correlation", "ggplot2", "ggraph", "poorman")
successfully_loaded <- vapply(pkgs, requireNamespace, FUN.VALUE = logical(1L), quietly = TRUE)
can_evaluate <- all(successfully_loaded)

if (can_evaluate) {
  knitr::opts_chunk$set(eval = TRUE)
  vapply(pkgs, require, FUN.VALUE = logical(1L), quietly = TRUE, character.only = TRUE)
} else {
  knitr::opts_chunk$set(eval = FALSE)
}

This vignette can be referred to by citing the package:

citation("see")

Introduction

correlation is an easystats package focused on correlation analysis. It's lightweight, easy to use, and allows for the computation of many different types of correlation, including:

✅ Pearson's correlation
✅ Spearman's rank correlation
✅ Kendall's rank correlation
✅ Biweight midcorrelation
✅ Distance correlation
✅ Percentage bend correlation
✅ Shepherd's Pi correlation
✅ Blomqvist’s coefficient
✅ Hoeffding’s D
✅ Gamma correlation
✅ Gaussian rank correlation
✅ Point-Biserial and biserial correlation
✅ Winsorized correlation
✅ Polychoric correlation
✅ Tetrachoric correlation
✅ Multilevel correlation

An overview and description of these correlations types is available here. Moreover, many of these correlation types are available as partial or within a Bayesian framework.

Setup

library(correlation)
library(see)
library(ggplot2)

Correlation Scatterplot

(related function documentation)

It is easy to visualize correlation tests with correlation and see.

result <- cor_test(iris, "Sepal.Length", "Petal.Width")

plot(result)

We can even customize that to make it more beautiful:

plot(result,
  point = list(
    aes = list(color = "Petal.Width", size = "Sepal.Length"),
    alpha = 0.66
  ),
  smooth = list(color = "black", se = FALSE)
) +
  see::theme_modern() +
  see::scale_color_material_c(palette = "rainbow", guide = "none") +
  scale_size_continuous(guide = "none")

Correlation Matrix

(related function documentation)

The default output for correlation() is a detailed overview including test statistic, p-values and confidence intervals. A shorter summary in matrix-layout can be obtained by using summary().

result <- correlation(iris)

result

summary(result)

The result from summary() can be used to create a plot.

s <- summary(result)
plot(s)

To change the style of geoms, use the show_data-argument.

plot(s, show_data = "points")

And a "redundant" summary can be plotted as well:

s <- summary(result, redundant = TRUE)

s

plot(s)

The corrlation function also provides a convenient way to change names for selected variables:

plot(summary(correlation(
  data = mtcars[c("wt", "mpg", "drat")],
  rename = c("weight", "miles per gallon", "rear axle ratio")
)))

Gaussian Graphical Models (GGMs)

To create a Gaussian Graphical Models plot, the library ggraph needs to be loaded first.

library(ggraph)
result <- correlation(mtcars, partial = TRUE)

result

plot(result)

easystats/see documentation built on March 1, 2025, 3:54 p.m.