## Overview

To facilitate exploring the package and employing it when working with colors, several graphical user interfaces (GUIs) are provided within the package as shiny apps . All of these GUIs/apps can be run locally from within R and are also provided at https://hclwizard.org/.

• Palette constructor: choose_palette() or hclwizard() or hcl_wizard().
• Color picker: choose_color() or equivalently hcl_color_picker().
• Color vision deficiency emulator: cvd_emulator().

In addition to the shiny version, the palette constructor app is also available as a Tcl/Tk GUI via the R package tcltk shipped with base R . The tcltk version can only be run locally and is considerably faster while the shiny version has a nicer interface with more features and can be run online. The choose_palette() function by default starts the tcltk version while hclwizard()/hcl_wizard() by default start the shiny version.

## Choose palettes with the HCL color model

The palette constructor GUI can either be started with hclwizard() (or equivalently hcl_wizard()) which by default starts the shiny version:

The tcltk version is started by default with choose_palette():

However, all defaults can be modified by setting gui = "tcltk" or gui = "shiny".

The GUIs interface the palette functions qualitative_hcl() for qualitative palettes, sequential_hcl() for sequential palettes with single or multiple hues, and diverging_hcl() for diverging palettes (composed from two single-hue sequential palettes). See the discussion of HCL-based color palettes for more details.

The GUIs allow for interactive modification of the arguments of the respective palette-generating functions, i.e., starting/ending hue (wavelength, type of color), minimal/maximal chroma (colorfulness), minimal/maximal luminance (brightness, amount of gray), and power transformations that control how quickly/slowly chroma and/or luminance are changed through the palette. Subsets of the parameters may not be applicable depending on the type of palette chosen. See qualitative_hcl() and Zeileis, Hornik, and Murrell (2009) for a more detailed explanation of the different arguments. Stauffer et al. (2015) provide more examples and guidance.

Optionally, the active palette can be illustrated by using a range of examples such as a map, heatmap, scatter plot, perspective 3D surface etc. To demonstrate different types of deficiencies, the active palette may be desaturated (emulating printing on a grayscale printer) and collapsed to emulate different types of color-blindness (without red-green or green-blue contrasts) using the simulate_cvd() functions. To facilitate generation of palettes for black/dark backgrounds, a “dark mode” of the GUIs is also available:

## Choose individual colors with the HCL color model

This GUI can be started with either choose_color() or equivalently hcl_color_picker().

It shows the HCL color space either as a hue-chroma plane for a given luminance value or as a luminance-chroma plane for a given hue. Colors can be entered by:

• Clicking on a color coordinate in the hue-chroma or luminance-chroma plane.
• Specifying the hue/chroma/luminance values via sliders.
• Entering an RGB hex code.

By repeating the selection a palette of colors can be constructed and returned within R for subsequent usage in visualizations.

## Emulate color vision deficiencies

This GUI can be started with cvd_emulator().

The GUI supports uploading a raster image in JPG or PNG format which is then checked for various kinds of color vision deficiencies at the selected severity. By default the severity is set to 100% and all supported kinds of color vision deficiency are checked for, i.e.,

• Monochromatic (desaturated grayscale).
• Deuteranope vision (green deficient).
• Protanope vision (red deficient).
• Tritanope vision (blue deficient).

## References

Chang, Winston, Joe Cheng, J. J. Allaire, Yihui Xie, and Jonathan McPherson. 2020. Shiny: Web Application Framework for r. https://CRAN.R-project.org/package=shiny.
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Stauffer, Reto, Georg J. Mayr, Markus Dabernig, and Achim Zeileis. 2015. “Somewhere over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations.” Bulletin of the American Meteorological Society 96 (2): 203–16. https://doi.org/10.1175/BAMS-D-13-00155.1.
Zeileis, Achim, Kurt Hornik, and Paul Murrell. 2009. “Escaping RGBland: Selecting Colors for Statistical Graphics.” Computational Statistics & Data Analysis 53: 3259–70. https://doi.org/10.1016/j.csda.2008.11.033.