# maidr

## Overview

maidr (Multimodal Access and Interactive Data Representation) makes data
visualizations accessible to users with visual impairments. It converts
ggplot2 and Base R plots into interactive, accessible HTML/SVG formats
with keyboard navigation, screen reader support, and sonification. maidr
for R is the R binding of [MAIDR](https://maidr.ai/), the JavaScript
core developed by the (x)Ability Design Lab at the University of
Illinois Urbana-Champaign; the same accessibility layer is available for
Python as [py-maidr](https://py.maidr.ai/).

The package provides two main functions:

- [`show()`](https://r.maidr.ai/reference/show.md) displays an
  interactive accessible plot in RStudio Viewer or browser
- [`save_html()`](https://r.maidr.ai/reference/save_html.md) exports a
  plot as a standalone HTML file

## Installation

maidr requires R 4.0.0 or later. Install the stable release from CRAN:

``` r

install.packages("maidr")
```

Or install the development version from GitHub:

``` r

# Using pak (recommended)
pak::pak("xability/r-maidr")

# Alternative: using pacman (auto-installs if missing)
pacman::p_load_gh("xability/r-maidr")
```

## Usage

### ggplot2

``` r

library(maidr)
library(ggplot2)

p <- ggplot(mpg, aes(x = class)) +
  geom_bar(fill = "steelblue") +
  labs(title = "Vehicle Classes", x = "Class", y = "Count")

# Display interactive accessible plot
show(p)

# Or save to file
save_html(p, "vehicle_classes.html")
```

### Base R

``` r

library(maidr)

# Create plot first
barplot(
  table(mtcars$cyl),
  main = "Cars by Cylinder Count",
  xlab = "Cylinders",
  ylab = "Count"
)

# Then call show() without arguments
show()
```

## Supported plot types

maidr supports a wide range of visualization types in both ggplot2 and
Base R:

### Basic Plot Types

| Plot Type | ggplot2 | Base R |
|----|----|----|
| Bar charts | [`geom_bar()`](https://ggplot2.tidyverse.org/reference/geom_bar.html), [`geom_col()`](https://ggplot2.tidyverse.org/reference/geom_bar.html) | [`barplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Grouped/Dodged bars | `position = "dodge"` | `beside = TRUE` |
| Stacked bars | `position = "stack"` | `beside = FALSE` |
| Pie charts | [`geom_col()`](https://ggplot2.tidyverse.org/reference/geom_bar.html)/[`geom_bar()`](https://ggplot2.tidyverse.org/reference/geom_bar.html) + `coord_polar("y")` | [`pie()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Histograms | [`geom_histogram()`](https://ggplot2.tidyverse.org/reference/geom_histogram.html) | [`hist()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Scatter plots | [`geom_point()`](https://ggplot2.tidyverse.org/reference/geom_point.html) | [`plot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Line plots | [`geom_line()`](https://ggplot2.tidyverse.org/reference/geom_path.html) | `plot(type = "l")`, [`lines()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Step plots | [`geom_step()`](https://ggplot2.tidyverse.org/reference/geom_path.html) | `plot(type = "s")`, `plot(type = "S")` |
| Box plots | [`geom_boxplot()`](https://ggplot2.tidyverse.org/reference/geom_boxplot.html) | [`boxplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Heatmaps | [`geom_tile()`](https://ggplot2.tidyverse.org/reference/geom_tile.html) | [`image()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Contour plots | — (see below) | [`contour()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| Violin plots | [`geom_violin()`](https://ggplot2.tidyverse.org/reference/geom_violin.html) | — |
| Candlestick (OHLC) | [`tidyquant::geom_candlestick()`](https://business-science.github.io/tidyquant/reference/geom_chart.html) (+ `geom_ma()`, + patchwork volume) | [`quantmod::chartSeries()`](https://rdrr.io/pkg/quantmod/man/chartSeries.html) (OHLC-only; no TA / no volume) |
| Density/Smooth | [`geom_smooth()`](https://ggplot2.tidyverse.org/reference/geom_smooth.html), [`geom_density()`](https://ggplot2.tidyverse.org/reference/geom_density.html) | `lines(density())` |

Note: Volume bars and moving-average overlays for candlestick charts are
supported only on the ggplot2 + {tidyquant} + {patchwork} path. On the
Base R path,
[`quantmod::chartSeries()`](https://rdrr.io/pkg/quantmod/man/chartSeries.html)
`TA` overlays (`addVo()`, `addSMA()`, `addEMA()`) — and the default `TA`
whenever the input `xts` carries a `Volume` column — fall back to native
(non-accessible) graphics with a one-time advisory.

### Advanced Plot Types

| Plot Type | ggplot2 | Base R |
|----|----|----|
| Faceted plots | [`facet_wrap()`](https://ggplot2.tidyverse.org/reference/facet_wrap.html), [`facet_grid()`](https://ggplot2.tidyverse.org/reference/facet_grid.html) | `par(mfrow/mfcol)` + loops |
| Multi-panel layouts | `patchwork` package | `par(mfrow)`, `par(mfcol)` |
| Multi-layered plots | Multiple `geom_*` layers | Sequential plot calls |

### Experimental Plot Types

> \[!WARNING\] **These are prototypes. Treat them as prototypes.** They
> are under active development, they are unstable, and **none of them
> has been through a user study**. Field names, announcement wording and
> navigation may change without a deprecation period, including in a
> patch release. If you are building something that has to keep working,
> build it on the plot types above.

Everything in the two tables above predates the plot coverage roadmap
([\#137](https://github.com/xability/r-maidr/issues/137)) and has been
exercised by real readers over real charts. Everything below was added
by that roadmap and the base R sweeps that followed it
([\#251](https://github.com/xability/r-maidr/issues/251),
[\#262](https://github.com/xability/r-maidr/issues/262)), most inside a
few weeks.

Each was measured against the chart it reads — that is what the issues
and the tests record. But measuring that a reading is *faithful to the
drawing* is a different claim from establishing that it is *useful to a
reader*. Nobody has asked a blind or low-vision reader whether hearing
[`stars()`](https://r.maidr.ai/reference/base-r-wrappers.md) as a radar,
or navigating a
[`termplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) panel by
panel, is the right way to read one. Until that happens these are
proposals about how a chart could be read, not answers.

Feedback is exactly what would move one of these into the tables above.

#### ggplot2

| Layer type | Drawn by |
|----|----|
| `area` | [`geom_area()`](https://ggplot2.tidyverse.org/reference/geom_ribbon.html), `geom_ribbon(aes(ymin = 0, ...))` |
| `stacked_area` | stacked [`geom_area()`](https://ggplot2.tidyverse.org/reference/geom_ribbon.html) |
| `stacked_normalized_area` | `geom_area(position = "fill")` |
| `stacked_normalized_bar` | `geom_bar(position = "fill")` |
| `contour` | [`geom_contour()`](https://ggplot2.tidyverse.org/reference/geom_contour.html), [`geom_density_2d()`](https://ggplot2.tidyverse.org/reference/geom_density_2d.html) |
| `error_bar` | [`geom_errorbar()`](https://ggplot2.tidyverse.org/reference/geom_linerange.html), [`geom_errorbarh()`](https://ggplot2.tidyverse.org/reference/geom_linerange.html), [`geom_linerange()`](https://ggplot2.tidyverse.org/reference/geom_linerange.html), [`geom_pointrange()`](https://ggplot2.tidyverse.org/reference/geom_linerange.html), [`geom_crossbar()`](https://ggplot2.tidyverse.org/reference/geom_linerange.html), [`geom_ribbon()`](https://ggplot2.tidyverse.org/reference/geom_ribbon.html) as a band |
| `gantt` | [`geom_segment()`](https://ggplot2.tidyverse.org/reference/geom_segment.html), [`geom_curve()`](https://ggplot2.tidyverse.org/reference/geom_segment.html), [`maidr_gantt()`](https://r.maidr.ai/reference/maidr_gantt.md) |
| `hexbin` | [`geom_hex()`](https://ggplot2.tidyverse.org/reference/geom_hex.html), [`stat_bin_2d()`](https://ggplot2.tidyverse.org/reference/geom_bin_2d.html) |
| `polygon` | [`geom_polygon()`](https://ggplot2.tidyverse.org/reference/geom_polygon.html) |
| `roc` | [`maidr_roc()`](https://r.maidr.ai/reference/maidr_roc.md), [`pROC::ggroc()`](https://rdrr.io/pkg/pROC/man/ggroc.html), [`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html) of a [`yardstick::roc_curve()`](https://yardstick.tidymodels.org/reference/roc_curve.html) |
| `rug` | [`geom_rug()`](https://ggplot2.tidyverse.org/reference/geom_rug.html) |

#### Base R

| Layer type | Drawn by |
|----|----|
| `biplot` | [`biplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `box_stats` | [`bxp()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `conditional_density` | [`cdplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `correlogram` | [`acf()`](https://r.maidr.ai/reference/base-r-wrappers.md), [`pacf()`](https://r.maidr.ai/reference/base-r-wrappers.md), [`ccf()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `cumulative_periodogram` | [`cpgram()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `dot` | [`dotchart()`](https://r.maidr.ai/reference/base-r-wrappers.md) (ungrouped) |
| `filled_contour` | [`filled.contour()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `fourfold` | [`fourfoldplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) (2x2 tables, `std = "ind.max"` / `"all.max"`) |
| `interaction` | [`interaction.plot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `lag` | [`lag.plot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `lollipop` | `plot(type = "h")` |
| `mosaic` | [`mosaicplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) (two-way tables) |
| `pairs` | [`pairs()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `qq` | [`qqnorm()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `qqline` | [`qqline()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `radar` | [`stars()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `residual` | [`assocplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) (two-way tables) |
| `spectral_density` | [`spectrum()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `spine` | [`spineplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `stacked_normalized_bar` | [`barplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) of proportions |
| `strip` | [`stripchart()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `subseries` | [`monthplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `termplot` | [`termplot()`](https://r.maidr.ai/reference/base-r-wrappers.md) |
| `violin` | [`vioplot::vioplot()`](https://rdrr.io/pkg/vioplot/man/vioplot.html) |
| `word_cloud` | [`wordcloud::wordcloud()`](https://rdrr.io/pkg/wordcloud/man/wordcloud.html) |

The split is the diff of each factory’s `get_supported_types()` against
`8de0e98`, the last commit on `main` before
[\#137](https://github.com/xability/r-maidr/issues/137) was filed.
`tests/testthat/test-plot-type-stability.R` fails if a supported type
appears in neither the stable tables nor the experimental ones, so a new
layer type has to be placed deliberately rather than inherit either
promise by being forgotten.

The [JavaScript core](https://maidr.ai/) and the [Python
binding](https://py.maidr.ai/) make the same distinction over their own
type lists, with the same boundary and for the same reason.

See the [examples gallery](https://r.maidr.ai/articles/examples.html)
for a worked example of each plot type.

## Accessibility features

- **Keyboard navigation** - explore data points using arrow keys
- **Screen reader support** - full ARIA labels and live announcements
- **Sonification** - hear data patterns through sound
- **Text descriptions** - automatic statistical summaries

Press **Tab** (or click) to focus a rendered plot, move between data
points with the **arrow keys**, and toggle **B** braille, **T** text,
**S** sonification, and **R** review mode. Four global shortcuts open
maidr’s own interfaces:

| Action | Windows / Linux | macOS |
|----|----|----|
| Show or hide the keyboard shortcut help | Ctrl + / | Command + / |
| Open the command palette listing every available command | Ctrl + Shift + P | Command + Shift + P |
| Open the AI chat (requires your own API key, entered in Settings, or a local Ollama server) | Shift + / (that is, **?**) | Shift + / (**?**) |
| Open Settings | Ctrl + , | Command + , |

The full list, including autoplay, label announcements, and layer
switching, is in the [maidr controls
documentation](https://maidr.ai/docs/CONTROLS.html).

## Offline support

By default, [`show()`](https://r.maidr.ai/reference/show.md) and
[`save_html()`](https://r.maidr.ai/reference/save_html.md) use the
bundled maidr.js library, so the result works offline
([`save_html()`](https://r.maidr.ai/reference/save_html.md) writes it to
a `lib/` folder beside the file). Widgets, knitr documents and Shiny
apps auto-detect internet availability and use the CDN when online. Use
the `use_cdn` parameter for explicit control:

``` r

# Force CDN (requires internet)
show(p, use_cdn = TRUE)

# Force bundled files (works offline)
show(p, use_cdn = FALSE)
save_html(p, "plot.html", use_cdn = FALSE)
```

One path still reaches the network from an offline document: connecting
a [DotPad tactile display](https://maidr.ai/docs/TACTILE_DISPLAY.html).
maidr.js does not bundle the DotPad SDK, whose braille engine is a 14 MB
liblouis build, and imports the vendor’s copy from jsDelivr the first
time a DotPad is connected. Rendering, sonification and braille work
offline regardless. To keep the DotPad offline too, download the pinned
SDK once and every `use_cdn = FALSE` document carries it in its `lib/`
folder:

``` r

maidr_download_dotpad_sdk()          # ~14 MB, once, into a per-user cache
save_html(p, "plot.html", use_cdn = FALSE)   # lib/dotpad-sdk-<version>/ beside it
```

A page served from elsewhere, or a knitr document (whose charts live in
`srcdoc` frames with no base URL), names its copy by URL instead,
through options or the environment variables of the same names:

``` r

options(
  maidr.dotpad_sdk_url = "https://intranet.example/dotpad/DotPadSDK-3.0.3.js",
  maidr.dotpad_asset_base_url = "https://intranet.example/dotpad/lib/"
)
# or: Sys.setenv(MAIDR_DOTPAD_SDK_URL = "...", MAIDR_DOTPAD_ASSET_BASE_URL = "...")
```

Every document maidr produces then declares
`window.MAIDR_DOTPAD_SDK_URL` and `window.MAIDR_DOTPAD_ASSET_BASE_URL`
ahead of maidr.js. See
[`?"maidr-options"`](https://r.maidr.ai/reference/maidr-options.md).

## Getting help

- Report bugs or request features at [GitHub
  Issues](https://github.com/xability/r-maidr/issues)
- Read the documentation at the [package website](https://r.maidr.ai/)

## Learning more

- [`vignette("getting-started", package = "maidr")`](https://r.maidr.ai/articles/getting-started.md)
  for an introduction
- The [examples gallery](https://r.maidr.ai/articles/examples.html) for
  supported visualizations
- [`vignette("shiny-integration", package = "maidr")`](https://r.maidr.ai/articles/shiny-integration.md)
  for Shiny apps
- The [maidr skill](https://github.com/xability/maidr-skill) for AI
  coding agents (Claude Code, Codex, Cursor, and others): once installed
  with `npx skills add xability/maidr-skill`, an agent that writes
  ggplot2 or base R plotting code routes the result through this package
  so the chart comes out accessible

## Related projects

maidr for R is one of three MAIDR packages, all developed by the
(x)Ability Design Lab at the University of Illinois Urbana-Champaign:

- [MAIDR JavaScript core](https://maidr.ai/), the TypeScript engine (npm
  package `maidr`) that renders every accessible chart, including the
  ones this package produces.
- [py-maidr for Python](https://py.maidr.ai/), the Python binding for
  matplotlib, seaborn, Plotly and Altair (PyPI package `maidr`).
- [maidr for R](https://r.maidr.ai/), this package, for ggplot2 and Base
  R graphics (CRAN package `maidr`; source at
  [xability/r-maidr](https://github.com/xability/r-maidr)).

## Citation

If you use maidr in research, please cite the MAIDR papers:

- Seo, J., Xia, Y., Lee, B., Mccurry, S., & Yam, Y. J. (2024). MAIDR:
  Making Statistical Visualizations Accessible with Multimodal Data
  Representation. In Proceedings of the CHI Conference on Human Factors
  in Computing Systems (CHI ’24). ACM.
  <https://doi.org/10.1145/3613904.3642730>
- Seo, J., O’Modhrain, S., Xia, Y., Kamath, S., Lee, B., &
  Coughlan, J. M. (2024). Designing Born-Accessible Courses in Data
  Science and Visualization: Challenges and Opportunities of a Remote
  Curriculum Taught by Blind Instructors to Blind Students. In EuroVis
  2024 - Education Papers. The Eurographics Association.
  <https://doi.org/10.2312/eved.20241053>

`citation("maidr")` prints both, plus an entry for the package itself,
in text and BibTeX form.

# Package index

## Main functions

Primary user-facing functions for creating accessible plots

- [`show()`](https://r.maidr.ai/reference/show.md) : Display Interactive
  MAIDR Plot
- [`save_html()`](https://r.maidr.ai/reference/save_html.md) : Save
  Interactive Plot as HTML File

## Declaring what a layer means

Functions an author adds to a plot to say what a layer is, where the
picture alone cannot say it

- [`maidr_gantt()`](https://r.maidr.ai/reference/maidr_gantt.md) :
  Declare that a rectangle layer draws a schedule
- [`maidr_roc()`](https://r.maidr.ai/reference/maidr_roc.md) : Declare
  that a path layer draws a ROC curve

## RMarkdown integration

Functions for enabling accessible plots in RMarkdown documents

- [`maidr_on()`](https://r.maidr.ai/reference/maidr_on.md) : Enable
  MAIDR Plot Interception
- [`maidr_off()`](https://r.maidr.ai/reference/maidr_off.md) : Disable
  MAIDR Plot Interception

## Shiny integration

Functions for using maidr in Shiny applications

- [`render_maidr()`](https://r.maidr.ai/reference/render_maidr.md) :
  Render MAIDR Plot in Shiny Server
- [`maidr_output()`](https://r.maidr.ai/reference/maidr_output.md) :
  MAIDR Output Container for Shiny UI

## Configuration and utilities

Functions for configuring MAIDR behavior and running examples

- [`maidr_set_fallback()`](https://r.maidr.ai/reference/maidr_set_fallback.md)
  : Configure MAIDR Fallback Behavior
- [`maidr_get_fallback()`](https://r.maidr.ai/reference/maidr_get_fallback.md)
  : Get Current MAIDR Fallback Settings
- [`maidr_download_dotpad_sdk()`](https://r.maidr.ai/reference/maidr_download_dotpad_sdk.md)
  : Download the DotPad SDK for use offline
- [`run_example()`](https://r.maidr.ai/reference/run_example.md) : Run
  MAIDR Example Plots

## Internal utilities

Internal functions for package developers

- [`combine_facet_layer_data()`](https://r.maidr.ai/reference/combine_facet_layer_data.md)
  : Combine data from multiple layers in facet processing

- [`combine_facet_layer_selectors()`](https://r.maidr.ai/reference/combine_facet_layer_selectors.md)
  : Combine selectors from multiple layers in facet processing

- [`extract_leaf_plot_layout()`](https://r.maidr.ai/reference/extract_leaf_plot_layout.md)
  : Extract layout from a single leaf ggplot

- [`extract_patchwork_leaves()`](https://r.maidr.ai/reference/extract_patchwork_leaves.md)
  : Recursively extract leaf ggplots in patchwork addition order

- [`find_children_by_type()`](https://r.maidr.ai/reference/find_children_by_type.md)
  : Find children matching a type pattern

- [`find_graphics_plot_grob()`](https://r.maidr.ai/reference/find_graphics_plot_grob.md)
  : Find grob by element type pattern

- [`find_patchwork_panels()`](https://r.maidr.ai/reference/find_patchwork_panels.md)
  :

  Discover panels via gtable layout rows named '^panel-' or '^panel--

  ' Returns a data.frame with panel_index, name, t, l, row, col

- [`generate_robust_css_selector()`](https://r.maidr.ai/reference/generate_robust_css_selector.md)
  : Generate robust CSS selector from grob name

- [`generate_robust_selector()`](https://r.maidr.ai/reference/generate_robust_selector.md)
  : Generate robust selector for any element type

- [`get_facet_groups()`](https://r.maidr.ai/reference/get_facet_groups.md)
  : Get facet group information for a panel

- [`map_visual_to_dom_panel()`](https://r.maidr.ai/reference/map_visual_to_dom_panel.md)
  : Map visual panel position to DOM panel name

- [`organize_facet_grid()`](https://r.maidr.ai/reference/organize_facet_grid.md)
  : Organize subplots into 2D grid structure

- [`process_facet_panel()`](https://r.maidr.ai/reference/process_facet_panel.md)
  : Process a single facet panel

- [`process_faceted_plot_data()`](https://r.maidr.ai/reference/process_faceted_plot_data.md)
  : Process a faceted plot and return organized subplot data

- [`process_patchwork_panel()`](https://r.maidr.ai/reference/process_patchwork_panel.md)
  : Process a single patchwork panel

- [`process_patchwork_plot_data()`](https://r.maidr.ai/reference/process_patchwork_plot_data.md)
  : Process a patchwork plot and return organized subplot data

# Articles

### Get started

- [Getting Started with
  MAIDR](https://r.maidr.ai/articles/getting-started.md):

  Install the maidr R package and make a first ggplot2 or Base R plot
  accessible with keyboard navigation, screen reader text, braille and
  sonification using show(), save_html() and maidr_on().

- [Using MAIDR in Shiny
  Applications](https://r.maidr.ai/articles/shiny-integration.md):

  Serve accessible, sonified ggplot2 and Base R plots inside Shiny apps
  with maidr_output() and render_maidr(), including reactive inputs and
  multiple plots per page.

### Examples by plot family

Worked, interactive examples of every supported plot type, split by
family so each page stays small enough to crawl and load quickly.

- [Examples](https://r.maidr.ai/articles/examples.md):

  Gallery of accessible ggplot2 and Base R plots made with maidr,
  organized by plot family: bar and pie, distributions, scatter and
  line, heat map and candlestick, multi-panel and facet, and Base R
  time-series and experimental charts.

- [Bar, Pie and Word Cloud
  Examples](https://r.maidr.ai/articles/examples-bar-pie.md):

  Accessible bar charts (simple, dodged and stacked), pie charts and
  word clouds in ggplot2 and Base R with maidr: keyboard navigation,
  screen reader text and sonification for each category.

- [Histogram, Density, Box and Violin
  Examples](https://r.maidr.ai/articles/examples-distribution.md):

  Accessible histograms, kernel density (KDE) curves and box plots in
  ggplot2 and Base R, plus ggplot2 violin plots, with maidr: navigate
  bins, quartiles and density curves by keyboard with sonification.

- [Scatter, Line, Step and Regression
  Examples](https://r.maidr.ai/articles/examples-scatter-line.md):

  Accessible scatter plots, single and multi-line plots, step plots and
  regression fits in ggplot2 and Base R with maidr, each point or sample
  navigable by keyboard and audible through sonification.

- [Heat Map and Candlestick
  Examples](https://r.maidr.ai/articles/examples-heatmap-candlestick.md):

  Accessible heat maps and candlestick (OHLC) charts in ggplot2 and Base
  R with maidr: navigate a matrix cell by cell or a price series candle
  by candle, with open, high, low, close and volume announced.

- [Multi-Layered, Multi-Panel and Facet
  Examples](https://r.maidr.ai/articles/examples-multi.md):

  Accessible multi-layered plots, patchwork and par(mfrow) multi-panel
  layouts, and facet_wrap() and facet_grid() facets with maidr: switch
  layers and subplots by keyboard, each with its own sonification.

- [Base R Time-Series and Diagnostic
  Examples](https://r.maidr.ai/articles/examples-base-r-timeseries.md):

  Accessible Base R time-series and model diagnostics with maidr: acf()
  correlograms, spectrum() and cpgram() periodograms, monthplot()
  seasonal subseries, lag plots, termplot() and Q-Q plots.

- [Experimental Base R Chart
  Examples](https://r.maidr.ai/articles/examples-base-r-experimental.md):

  Accessible readings of experimental Base R charts with maidr:
  biplot(), stars() radar charts, mosaicplot(), cdplot(),
  fourfoldplot(), filled.contour() and more, each mapped onto a
  navigable, sonified layer type.