# Data sets in the heplots package

#### Michael Friendly

#### 2026-08-23

## Documenting package datasets

Datasets used in package examples are such an important part of making a package understandable and usable, but is often overlooked. In developing the `heplots` package I collected a large collection of data sets illustrating a variety of multivariate linear models with some an analyses, and graphical displays. Each of these have much more than the usual stub examples, that often look like:

    data(dataset)
    # str(dataset); plot(dataset)

But `.Rd`, and now `roxygen`, don’t make it easy to work with numerous datasets in a package, or, more importantly, to document what they illustrate. I’m showing the work to create this vignette, in case these ideas are useful to others.

In this release, I started with a file generated by:

    vcdExtra::datasets("heplots") |> head(4)
    #>        Item      class    dim                         Title
    #> 1 AddHealth data.frame 4344x3 Adolescent Mental Health Data
    #> 2   Adopted data.frame   62x6              Adopted Children
    #> 3      Bees data.frame  246x6   Captive and maltreated bees
    #> 4  Diabetes data.frame  145x6              Diabetes Dataset

Then, in the roxygen documentation, I added `@concept` tags to classify these datasets according to methods used. (`@concept` entries are indexed with the package, so they work via `help.search()`) For example, the documentation for the `AddHealth` data contains these lines:

    #' @name AddHealth
    #' @docType data
     ...
    #' @keywords datasets
    #' @concept MANOVA
    #' @concept ordered

With standard processing, these concepts along with the keywords, appear in the Index section of the manual constructed by `devtools::build_manual`. In the `pkgdown` site for this package, they are also searchable in the search box.

With a bit of extra processing, I created a dataset datasets.csv used below.

How this is done is described in this StackExchange question. The first step is to extract the `concept` tags from the `man/*.Rd` files:

    concepts <- readLines(pipe("grep concept man/*.Rd")) %>%
      grep("concept{", ., fixed = TRUE, value = TRUE) %>%
      read.table(text = ., sep = "{", comment.char = "}",
        col.names = c("dataset", "tags")) %>%
      separate(dataset, c(NA, "dataset"), extra = "drop") %>%
      summarize(tags = paste(tags, collapse = " "), .by = dataset) %>%
      arrange(dataset)

    head(concepts)

Then, I use `vcdExtra::datasets` to extract title and other information for the datasets in the package, munge the result and `left_join` with `concepts`:

    dsets <- vcdExtra::datasets("heplots")[, c("Item", "dim", "Title")]
    rowcols <- as.data.frame(stringr::str_split_fixed(dsets$dim,"x", 2))
    colnames(rowcols) <- c("rows", "cols")

    dsets <- cbind(dsets, rowcols) |>
      rename(dataset = Item) |>
      select(-dim) |>
      relocate(c(rows, cols), .after=dataset) |>
      left_join(concepts, by = "dataset")

For use in pkgdown documentation, I also turn the name of the dataset into a hyperlink to the documentation.

    refurl <- "http://friendly.github.io/candisc/reference/"

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

## Methods

The main methods used in the example datasets are shown in the table below:

- 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, analyzed from the multivariate perspective
- 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)"))

    #knitr::kable(dsets)
    tinytable::tt(dsets)  |> format_tt(markdown = TRUE)

dataset | rows | cols | title | tags
--- | --- | --- | --- | ---
AddHealth | 4344 | 3 | Adolescent Health Data | MANOVA ordered
Adopted | 62 | 6 | Adopted Children | MMRA repeated
Bees | 246 | 6 | Captive and maltreated bees | MANOVA
Diabetes | 145 | 6 | Diabetes Dataset | MANOVA
dogfood | 16 | 3 | Dogfood Preferences | MANOVA contrasts candisc
FootHead | 90 | 7 | Head measurements of football players | MANOVA contrasts
Headache | 98 | 6 | Treatment of Headache Sufferers for Sensitivity to Noise | MANOVA repeated
Hernior | 32 | 9 | Recovery from Elective Herniorrhaphy | MMRA candisc
Iwasaki_Big_Five | 203 | 7 | Personality Traits of Cultural Groups | MANOVA
mathscore | 12 | 3 | Math scores for basic math and word problems | MANOVA
MockJury | 114 | 17 | Effects Of Physical Attractiveness Upon Mock Jury Decisions | MANOVA candisc
NeuroCog | 242 | 10 | Neurocognitive Measures in Psychiatric Groups | MANOVA candisc
NLSY | 243 | 6 | National Longitudinal Survey of Youth Data | MMRA
oral | 56 | 5 | Effect of Delay in Oral Practice in Second Language Learning | MANOVA
Oslo | 332 | 14 | Oslo Transect Subset Data | MANOVA candisc
Overdose | 17 | 7 | Overdose of Amitriptyline | MMRA cancor
Parenting | 60 | 4 | Father Parenting Competence | MANOVA contrasts
peng | 333 | 8 | Size measurements for adult foraging penguins near Palmer Station | MANOVA
Plastic | 20 | 5 | Plastic Film Data | MANOVA
Pottery2 | 48 | 12 | Chemical Analysis of Romano-British Pottery | MANOVA candisc
Probe | 11 | 5 | Response Speed in a Probe Experiment | MANOVA repeated
RatWeight | 27 | 6 | Weight Gain in Rats Exposed to Thiouracil and Thyroxin | MANOVA repeated
ReactTime | 10 | 6 | Reaction Time Data | repeated
Rohwer | 69 | 10 | Rohwer Data Set | MMRA MANCOVA
RootStock | 48 | 5 | Growth of Apple Trees from Different Root Stocks | MANOVA contrasts
Sake | 30 | 10 | Taste Ratings of Japanese Rice Wine (Sake) | MMRA
schooldata | 70 | 8 | School Data | MMRA robust
Skulls | 150 | 5 | Egyptian Skulls | MANOVA contrasts
SocGrades | 40 | 10 | Grades in a Sociology Course | MANOVA candisc
SocialCog | 139 | 5 | Social Cognitive Measures in Psychiatric Groups | MANOVA candisc
TIPI | 1799 | 16 | Data on the Ten Item Personality Inventory | MANOVA candisc
VocabGrowth | 64 | 4 | Vocabulary growth data | repeated
WeightLoss | 34 | 7 | Weight Loss Data | repeated

## 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)

    #knitr::kable(concepts)
    tinytable::tt(concepts) |> format_tt(markdown = TRUE)

concept | datasets
--- | ---
MANCOVA | Rohwer
MANOVA | AddHealth, Bees, Diabetes, FootHead, Headache, Iwasaki_Big_Five, MockJury, NeuroCog, Oslo, Parenting, Plastic, Pottery2, Probe, RatWeight, RootStock, Skulls, SocGrades, SocialCog, TIPI, dogfood, mathscore, oral, peng
MMRA | Adopted, Hernior, NLSY, Overdose, Rohwer, Sake, schooldata
cancor | Overdose
candisc | Hernior, MockJury, NeuroCog, Oslo, Pottery2, SocGrades, SocialCog, TIPI, dogfood
contrasts | FootHead, Parenting, RootStock, Skulls, dogfood
ordered | AddHealth
repeated | Adopted, Headache, Probe, RatWeight, ReactTime, VocabGrowth, WeightLoss
robust | schooldata