What’s New in R: September 14, 2026
Welcome to this week's edition of What's New in R! This week, we're featuring a package that adds tinting and shading aesthetics to your ggplot2 charts, a tool that reviews your code quality in a single report, and the second edition of a beloved book on statistical inference with R. Let's dive in!
ggtintshade
The {ggtintshade} package by William Kumler extends {ggplot2} so you can map the tint or shade of a color to an aesthetic, in addition to its hue. Why is that useful? It lets you visually group related data (say, subcategories within a larger category) by giving each group a hue and each member a lighter or darker version of it, something the usual advice of mapping to alpha does poorly since alpha can only lighten and introduces transparency issues. The package provides tintshade versions of familiar geoms like geom_col_tintshade() and geom_point_tintshade(), with matching scale functions, and it keeps your legend in sync automatically. If you've ever fiddled with manually calculated color variants to show nested groups, this handles it for you.
rsonar
The {rsonar} package by Damien Dotta gives you a quick review of your R code in a single interactive report, which is really helpful because it's often hard to know what you did well and what you didn't. It orchestrates {lintr}, {styler}, {covr}, and {goodpractice} under the hood, then rolls everything up into a technical debt rating from A to E and flags the high-priority files to fix first. You can run a full analysis with a single function call, or get an instant quality score right in your console. It also integrates with GitHub Actions and GitLab CI if you want checks running automatically.
Statistical Inference via Data Science
Modern Dive, the open-source textbook on statistical inference by Chester Ismay, Albert Y. Kim, and Arturo Valdivia, has been around for a while, and it's now out in a fully updated second edition. The book takes you from the foundations of R and data visualization with the tidyverse through data wrangling, regression modeling, and simulation-based statistical inference with the {infer} package. It's a great resource for learning statistical inference with R, whether you're brand new to it or read the first edition and want to see what's changed. If you've never checked it out, I highly recommend it.
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Got any ideas for resources I should feature in future issues of What’s New in R? Leave a comment below!
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