Whenever you use software, you are also using a tool that was crafted by someone else. Is it kind? Does it matter? What makes a tool kind?
Made with YAML, strings, and glue.
How I took one sad plot and made it better, and what I learned from it.
Recent updates in the R Markdown family
Come hear from 3 R-Ladies on how to make R pedagogy work for different groups of people!
Aprendiendo sin una red
A 2-hour workshop for RStudio certified trainers on how to make shareable slides and websites with R Markdown.
How to get your materials online on short notice.
Making it tidy isn’t the end of your data’s story…
“Literate programming” is an approach to writing software programs that weaves together the source code and documentation at the time of creation. The idea is to create programs that are easier for users to understand. But they are also easier for programmers to work on and maintain. In this talk, I will describe how data scientists can be inspired by this programming approach and start what I refer to as “literate projecting.
In this talk, Alison will talk about one plot’s life cycle, from a sad Powerpoint slide to an Excel chart and finally to the finished product made with the ggplot2 package in R. Along the way, she will discuss why each version of the plot fails in different ways and how each iteration improved on the last one. Latest event This talk was most recently given at the University of South Wales on 2019/10/04.
Data science educators have a unique opportunity to teach students the skills they need in their future careers. We know that practical skills matter, like being able to wrangle, explore, analyze, and visualize data (preferably using code), but what is easy to overlook is teaching students how to communicate about data science with other people. Being able to communicate about data, code, and insights gained are important skills we can strengthen in the classroom to make a real impact on students.
Why and how to teach students to create their own website
Using one dataset from The Great British Bake Off, I show eleven ways to visualize one dataset using eleven different versions of “tidy data.” Take-away messages: Tidy data is the start of your data wrangling journey, not the end There is not a single “tidy” version of a dataset Tidy data does make you more nimble!
Inspired by the book “Big Magic: Creative Living Beyond Fear” by Elizabeth Gilbert, Alison will talk about the five essential ingredients needed to creatively learn R and why these elements are also essential for advanced users to take their R skills to the next level. You will hear practical advice for when, where, and how to start a project in R, and how your learning can add value- both to your own knowledge and to contribute to the larger community of R learners.
Join us on April 6 for a walk-through of how to take a sad plot and make it better by Alison Hill, who co-teaches the CS631 Data Visualization course. Alison will take us through one plot’s life cycle, from a sad Powerpoint slide to an Excel chart and finally to the finished product made with the ggplot2 package in R. We will discuss why each version of the plot fails in different ways, how each iteration improved on the last one, and which data visualization principles are at work in the final plot to communicate a clear scientific story.