A collaborative visual post describing the benefits of working with tidy data
An art-filled learnr tutorial introducing some useful data wrangling functions in dplyr (part of the tidyverse)
The palmerpenguins data package provides a wonderful alternative to the iris data
Cross-posted from the RStudio Education blog.
Check out my new post for RStudio Ed, describing how I use the learnr package to facilitate teaching and learning for remote classes with R beginners!
I made this little self-tutorial last year because I was really excited about being able to more simply customize tables with the gt package by Rich Iannone.
I also quickly realized that I needed to do some simple examples to get started. Here are a few different gt tables that worked, using short examples with data existing in the datasets package or that I create here.
What I tried in these example:
By Allison Horst, Curtis Kephart, and Yanina Bellini
Birds of a Feather banner for RStudio::conf(2020)! The “Birds of a Feather” (BoF) sessions at the 2020 RStudio Conference were a place where R-users with similar backgrounds, interests, and aspirations could connect in a low-stress social setting. In other words - these sessions let “birds of a feather flock together.” Building on the 2019 conference swag, we rolled out a bunch of new BoF buttons for rstudio::conf 2020.
Since I started drawing R- and stats-related illustrations for my students in 2018, I have been overwhelmed by the reception from the #rstats community on twitter.
I am extremely excited to share that as of October 2019, I am RStudio’s first Artist-in-Residence!
You can read more about my motivation and goals for the position on the RStudio blog.
I look forward to sharing a whole bunch more art with you over the next year!
I just finished my sixth year of teaching intro stats and data analysis in R to environmental studies grad students. For the first five, I convinced myself that I shouldn’t share my instructor code keys with students before our weekly #rstats labs.
Here are a couple of my commonly regurgitated excuses, based on absolutely nothing:
“If I share the key beforehand, no one will show up to labs!
In the past I’ve always asked students to create their best possible graphs in ggplot2 to practice creating clear, engaging data visualizations. Recently, I’ve realized value in adding a few early exercises that encourage students to make their worst.
Why is it good to make ggplot2 graphs so (intentionally) bad?
Here are four ways that creating a purposefully disgusting graph promotes learning and exploration in ggplot2:
Making the worst graph encourages creativity while eliminating the stress of producing a perfect final visualization (unless, of course, it’s perfectly hideous) Making the worst graph requires extensive customization and code Making the worst graph means critically thinking about and identifying contributors to “bad” graphs Making the worst graph is surprisingly fun I was recently inspired to try my absolute dataviz worst by a #tidytuesday prompt using data from Sarah Leo’s efforts to improve imperfect graphs in The Economist.