A comprehensive visualization toolkit built with coders of all skill levels and color-vision impaired audiences in mind. It allows creation of finely-tuned, publication-quality figures from single function calls.

Spawned out of the scRNAseq-focused dittoSeq-package, dittoViz contains most of it's visualization machinery with none of the added scRNAseq-related dependencies.

Visualizations include scatter plots, compositional bar plots, violin, box, and ridge plots, and more. Customization ranges from size and title adjustments to discrete-group circling and labeling, hidden data overlay upon cursor hovering via ggplotly() conversion, and many more, all with simple, discrete inputs.

Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors().

Installation:

install.packages("dittoViz")

Then there are a few options for getting aquainted with the package:

1. Read and follow along with the vignette available here!
2. View documentation and run examples inside R with, e.g. ?yPlot or example("yPlot").

Visualization functions are:

• scatterPlot()
• scatterHex()
• yPlot()
• barPlot()
• freqPlot()

Helper functions are:

• dittoColors()
• colLevels()

1. Carry over as much functionality from dittoSeq as makes sense for targeting data stored in a data.frame-type object (rather than in Seurat/SingleCellExperiemnt/SummarizedExperiment objects).

• Much of this work is now complete! ... But there are still a few missing bits, the biggest of which are dittoDotPlot() and dittoHeatmap() counterparts.

2. Implement dittoViz inside of a dittoSeq-v2.0 after it gets accepted into Bioconductor.
3. Python counterpart: a similarly named, largely equivalent, python companion which would target pandas.DataFrames using the plotnine ggplot-mimic and as similar syntax as possible.