This is an opinionated essay about good and bad practices in data visualization. Examples and explanations are below. The Scripts/ directory contains .Rmd files that generate the graphics shown below.

{colourpicker} gives you a colour picker widget that can be used in different contexts in R. You can use colourInput() to include a colour picker input in Shiny apps (or in R markdown documents). It ...

Metagenomic binning, the resolution of metagenomic sequence data into individual genomes, has been used to identify hundreds of thousands of genomes from microbiome samples 1,2,3,4,5,6. These studies ...

Dermatomyositis (DM) is a rare autoimmune disease characterized by severe muscle dysfunction, and the immune response of the muscles plays an important role in the development of DM. Currently, the ...

For everything from styling text and customizing color palettes to creating your own geoms, these ggplot2 add-ons deserve a place in your R data visualization toolkit. Plus, a bonus list of packages ...

Learn how to make the most of Observable JavaScript and the Observable Plot library, including a step-by-step guide to eight basic data visualization tasks in Plot. Built-in reactivity is one of ...

1 Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK 2 Pragmatic Clinical Trials Unit, Centre for Evaluation and Methods, Wolfson Institute of Population ...

Comparative genome- and proteome-wide screens yield large amounts of data. To efficiently present such datasets and to simplify the identification of hits, the results are often presented in a type of ...

Effect sizes are the most important outcome of empirical studies. Most articles on effect sizes highlight their importance to communicate the practical significance of results. For scientists ...