Data sets in the heplots package"
In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

knitr::opts_chunk$set(
  message = FALSE,
  warning = FALSE,
  fig.height=5,
  fig.width=5,
  # results='hide',
  # fig.keep='none',
  fig.path='fig/datasets-',
  echo=TRUE,
  collapse = TRUE,
  comment = "#>"
)

set.seed(1071)
options(width=80, digits=5, continue="  ")
library(heplots)
library(candisc)
library(ggplot2)
library(dplyr)

The heplots package provides a large collection of data sets illustrating a variety of multivariate linear models with some an analyses, and graphical displays. The table below classifies these with method tags (@concept). The main methods are:

MANOVA: Multivariate analysis of variance
MANCOVA: Multivariate of covariance
MMRA: Multivariate multiple regression
cancor: Canonical correlation (using the candisc package)
candisc: Canonical discriminant analysis (using candisc)
repeated: Repeated measures designs
robust: Robust estimation of MLMs

In addition, a few examples illustrate special handling for linear hypotheses concerning factors:

ordered: ordered factors
contrasts: other contrasts

The dataset names are linked to the documentation with graphical output on the pkgdown website, [http://friendly.github.io/heplots/].

Dataset table

library(here)
library(dplyr)
library(tinytable)
#dsets <- read.csv(here::here("extra", "datasets.csv"))  # doesn't work in a vignette
dsets <- read.csv("https://raw.githubusercontent.com/friendly/heplots/master/extra/datasets.csv")
dsets <- dsets |> dplyr::select(-X) |> arrange(tolower(dataset))

# link dataset to pkgdown doc
refurl <- "http://friendly.github.io/heplots/reference/"

dsets <- dsets |>
  mutate(dataset = glue::glue("[{dataset}]({refurl}{dataset}.html)")) 

#tinytable::tt(dsets)
knitr::kable(dsets)

Concept table

This table can be inverted to list the datasets that illustrate each concept:

concepts <- dsets |>
  select(dataset, tags) |>
  tidyr::separate_longer_delim(tags, delim = " ") |>
  arrange(tags, dataset) |>
  summarize(datasets = toString(dataset), .by = tags) |>
  rename(concept = tags)

#tinytable::tt(concepts)
knitr::kable(concepts)