# Datasets for categorical data analysis

#### Michael Friendly

#### 2023-08-21

The `vcdExtra` package contains 45 datasets, taken from the literature on categorical data analysis, and selected to illustrate various methods of analysis and data display. These are in addition to the 33 datasets in the vcd package.

To make it easier to find those which illustrate a particular method, the datasets in `vcdExtra` have been classified using method tags. This vignette creates an “inverse table”, listing the datasets that apply to each method. It also illustrates a general method for classifying datasets in R packages.

    library(dplyr)
    library(tidyr)
    library(readxl)

## Processing tags

Using the result of `vcdExtra::datasets(package="vcdExtra")` I created a spreadsheet, `vcdExtra-datasets.xlsx`, and then added method tags.

    dsets_tagged <- read_excel(here::here("inst", "extdata", "vcdExtra-datasets.xlsx"),
                               sheet="vcdExtra-datasets")

    dsets_tagged <- dsets_tagged |>
      dplyr::select(-Title, -dim) |>
      dplyr::rename(dataset = Item)

    head(dsets_tagged)
    ## # A tibble: 6 × 3
    ##   dataset   class      tags
    ##   <chr>     <chr>      <chr>
    ## 1 Abortion  table      loglinear;logit;2x2
    ## 2 Accident  data.frame loglinear; glm; logistic
    ## 3 AirCrash  data.frame reorder; ca
    ## 4 Alligator data.frame loglinear;multinomial;zeros
    ## 5 Bartlett  table      2x2;loglinear; homogeneity;oddsratio
    ## 6 Burt      data.frame ca

To invert the table, need to split tags into separate observations, then collapse the rows for the same tag.

    dset_split <- dsets_tagged |>
      tidyr::separate_longer_delim(tags, delim = ";") |>
      dplyr::mutate(tag = stringr::str_trim(tags)) |>
      dplyr::select(-tags)

    #' ## collapse the rows for the same tag
    tag_dset <- dset_split |>
      arrange(tag) |>
      dplyr::group_by(tag) |>
      dplyr::summarise(datasets = paste(dataset, collapse = "; ")) |> ungroup()

    # get a list of the unique tags
    unique(tag_dset$tag)
    ##  [1] "2x2"         "agree"       "binomial"    "ca"          "glm"
    ##  [6] "homogeneity" "lm"          "logistic"    "logit"       "loglinear"
    ## [11] "mobility"    "multinomial" "oddsratio"   "one-way"     "ordinal"
    ## [16] "poisson"     "reorder"     "square"      "zeros"

## Make this into a nice table

Another sheet in the spreadsheet gives a more descriptive `topic` for corresponding to each tag.

    tags <- read_excel(here::here("inst", "extdata", "vcdExtra-datasets.xlsx"),
                       sheet="tags")
    head(tags)
    ## # A tibble: 6 × 2
    ##   tag         topic
    ##   <chr>       <chr>
    ## 1 2x2         2 by 2 tables
    ## 2 agree       observer agreement
    ## 3 binomial    binomial distributions
    ## 4 ca          correspondence analysis
    ## 5 glm         generalized linear models
    ## 6 homogeneity homogeneity of association

Now, join this with the `tag_dset` created above.

    tag_dset <- tag_dset |>
      dplyr::left_join(tags, by = "tag") |>
      dplyr::relocate(topic, .after = tag)

    tag_dset |>
      dplyr::select(-tag) |>
      head()
    ## # A tibble: 6 × 2
    ##   topic                      datasets
    ##   <chr>                      <chr>
    ## 1 2 by 2 tables              Abortion; Bartlett; Heart
    ## 2 observer agreement         Mammograms
    ## 3 binomial distributions     Geissler
    ## 4 correspondence analysis    AirCrash; Burt; Draft1970table; Gilby; HospVisits;…
    ## 5 generalized linear models  Accident; Cormorants; DaytonSurvey; Donner; Draft1…
    ## 6 homogeneity of association Bartlett

### Add links to `help()`

We’re almost there. It would be nice if the dataset names could be linked to their documentation. This function is designed to work with the `pkgdown` site. There are different ways this can be done, but what seems to work is a link to `../reference/{dataset}.html` Unfortunately, this won’t work in the actual vignette.

    add_links <- function(dsets,
                          style = c("reference", "help", "rdrr.io"),
                          sep = "; ") {

      style <- match.arg(style)
      names <- stringr::str_split_1(dsets, sep)

      names <- dplyr::case_when(
        style == "help"      ~ glue::glue("[{names}](help({names}))"),
        style == "reference" ~ glue::glue("[{names}](../reference/{names}.html)"),
        style == "rdrr.io"   ~ glue::glue("[{names}](https://rdrr.io/cran/vcdExtra/man/{names}.html)")
      )
      glue::glue_collapse(names, sep = sep)
    }

## Make the table

Use `purrr::map()` to apply `add_links()` to all the datasets for each tag. (`mutate(datasets = add_links(datasets))` by itself doesn’t work.)

    tag_dset |>
      dplyr::select(-tag) |>
      dplyr::mutate(datasets = purrr::map(datasets, add_links)) |>
      knitr::kable()

topic | datasets
--- | ---
2 by 2 tables | Abortion; Bartlett; Heart
observer agreement | Mammograms
binomial distributions | Geissler
correspondence analysis | AirCrash; Burt; Draft1970table; Gilby; HospVisits; HouseTasks; Mental
generalized linear models | Accident; Cormorants; DaytonSurvey; Donner; Draft1970table; GSS; ICU; PhdPubs
homogeneity of association | Bartlett
linear models | Draft1970
logistic regression | Accident; Donner; ICU; Titanicp
logit models | Abortion; Cancer
loglinear models | Abortion; Accident; Alligator; Bartlett; Caesar; Cancer; Detergent; Dyke; Heckman; Hoyt; JobSat; Mice; TV; Titanicp; Toxaemia; Vietnam; Vote1980; WorkerSat
mobility tables | Glass; Hauser79; Mobility; Yamaguchi87
multinomial models | Alligator
odds ratios | Bartlett; Fungicide
one-way tables | CyclingDeaths; Depends; ShakeWords
ordinal variables | Draft1970table; Gilby; HairEyePlace; Hauser79; HospVisits; JobSat; Mammograms; Mental; Mice; Mobility; Yamaguchi87
Poisson distributions | Cormorants; PhdPubs
reordering values | AirCrash; Glass; HouseTasks
square tables | Glass; Hauser79; Mobility; Yamaguchi87
zero counts | Alligator; Caesar; PhdPubs; Vote1980

Voila!