Adam's Blogroll: click through to the author's blog
The easystats R package in on my to-try list.
easystats is a collection of R packages, which aims to provide a unifying and consistent framework to tame, discipline, and harness the scary R statistics and their pesky models.
Apparently it’s been around since 2022, but it’s new to me.
Blind users do not have access to the graphical output from R without printing the content of graphics windows to an embosser of some kind. This is not as immediate as is required for efficient access to statistical output. The functions here are created so that blind people can make even better use of R. This includes the text descriptions of graphs, convenience functions to replace the functionality offered in many GUI front ends, and experimental functionality for optimising graphical content to prepare it for embossing as tactile images.
Has anyone tried this yet? It sounds really useful.
Tags: accessibility, braille, R
Posted by in accessibility, braille, Coding, R
Matthew Jané made a small R package called Theme Park, which is meant to supply movie-based themes for ggplot. For now, it just has Barbie and Oppenheimer themes.
Tags: Barbie, ggplot, Matthew Jané, Oppenheimer, R, theme
Posted by in Barbie, ggplot, Matthew Jané, Oppenheimer, R, software, theme
If you’re looking to switch or just want to expand your skills, this starter guide by Stephanie Lo provides some translations:
Are you curious about delving into the world of R programming? While Python remains the dominant choice amongst the data science community, with approximately 60% of developers using it in 2022, there are instances where R may pop up now and again. That’s because R is optimized for statistics and data. If you, like me, have a foundation in Python but now encounter job listings and internal company tasks that demand R skills, this article aims to break that down. We will explore the fundamental distinctions between Python and R and wrap the project into a data cleaning and visualization tutorial to ensure a smooth transition to R.
I mostly use R, but have always found it helpful to know some Python, especially when there’s some fun library to try.
Tags: Python, R, Stephanie Lo
Posted by in Coding, Python, R, Stephanie Lo
Achim Zeileis and Paul Murrell provide a rundown of the more user-friendly color palettes available in R by default since version 4.0. The new palettes make it easier to see differences and less like saturated output from an old computer.
To show snow cover across the United States, Althea Archer for the USGS used hexbins, but instead of hexbins, she used snowflakes. Archer provided her R code and outlined her process in a blog post, which is something I’m not used to seeing from a government agency. I like it.
Tags: Althea Archer, R, snow, USGS
I have two course-related updates on FlowingData. First, there’s a new course on visualizing data with R. Second, I updated the Visualization for Clarity course so that you can more easily get feedback from me on how to make a better chart.
Members get instant access to the new courses. If you’re not a member yet, you can find information here.
If you’re already a member (thank you), log in and you can have at it.
The first version that I wrote several years ago was an effort to consolidate my visualization tutorials so that those new to charts in R had a more step-by-step way to follow along.
This second version of the course is completely rewritten with new examples, exercises, quizzes, and resources all in one place. So it should be easier to follow along. You can also keep track of what you’ve completed and pick up where you left off. I hope it’s helpful.
Find more details on the Visualization in R course here.
I have another course, Visualization for Clarity, which is tool-independent. It’s more focused on making data graphics that help people understand data better than it is on the tools to work with data.
There are a few new wrinkles to this course. There are more exercises and resources. There are quizzes.
But the best part is that there’s a final project that you can send to me. If you send your project by November 30, 2022, I’ll send back comments on how I might make it better (or how great the work is).
When I was new to visualization, I made charts and stared at them for a while. I knew there was something to improve, but I wasn’t sure what it was, so I’d tinker a lot. Tinkering is time well-spent, but feedback from others helps with less random tinkering. I hope it’s helpful.
Find more details on Visualization for Clarity here.
FlowingData is proudly 100% member-supported, and I’d like to keep it that way forever. If you’re not a member, I’d appreciate your support. You can see membership perks here.
Twitter isn’t in a great place right now, so maybe you want to do something with your account and your tweets. Julia Silge outlines how to delete your tweets with R:
If you are looking to remove yourself from Twitter, you can delete your account, but I’ve seen some folks say a better initial move may be to delete the content from your account (perhaps including followers and following), and then take your account private or deactivate it. In this blog post, I’ll walk through how to use rtweet to automate some of these steps.
Social media in general hasn’t been my thing for a few years now, so I’m not sure what I’m going to do, but you can find me on Mastadon. Or we can go back to blogs Xanga-style, and I’d be okay with that.
Tags: Julia Silge, R, Twitter
Posted by in Coding, Julia Silge, R, twitter
Introduction to Data Science, by Harvard biostatistics professor Rafael A. Irizarry, is an open source book that provides, as you might have guessed, an introduction to data science:
The demand for skilled data science practitioners in industry, academia, and government is rapidly growing. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning.
Tags: book, data science, R, Rafael Irizarry
Posted by in Book, data science, R, Rafael Irizarry, statistics
RStudio, the company behind the IDE of the same name, are changing their name to Posit:
Our charter defines our mission as the creation of free and open source software for data science, scientific research, and technical communication. This mission intentionally goes beyond “R for Data Science”—we hope to take the approach that’s succeeded with R and apply it more broadly. We want to build a company that is around in 100 years time that continues to have a positive impact on science and technical communication. We’ve only just started along this road: we’re experimenting with tools for Python and our new Quarto(opens in a new tab) project aims to impact scientific communication far beyond data science.
It doesn’t seem that long ago when I heard about some new IDE for R that might make programming easier. It’s been cool to see the company grow over the years, and I take it as an indicator for the growth of data work in general, which goes beyond the software or a single language.