R

A Basic Data Science Workflow

Developing a clean and easy analysis workflow takes a really, really long time. In this post, I outline the workflow that I have developed over the last few years.

Rebecca Barter

Developing a seamless, clean workflow for data analysis is harder than it sounds, especially because this is something that is almost never explicitly taught. Apparently we are all just supposed to “figure it out for ourselves”. For most of us, when we start our first few analysis projects, we basically have no idea how we are going to structure all of our files, or even what files we will need to make.

Coolors: choosing color schemes

A really cool website for choosing color palattes

Rebecca Barter

Choosing a color palette for a visualization can be one of the most time consuming parts for perfectionists like me. It can be surprisingly difficult to decide on a palette that is both visually appealing and practical, but fortunately there do exist websites to help! For example, Coolors shoots random, appealing, color palettes at you and you can swipe from one to the next with a hit of a space-bar.

Interactive visualization in R

Learn about creating interactive visualizations in R.

Rebecca Barter

Last week I gave an SGSA seminar on interactive visualizations in R. Here is a long-form version of the talk. Why be interactive? Interactivity allows the viewer to engage with your data in ways impossible by static graphs. With an interactive plot, the viewer can zoom into the areas the care about, highlight the data points that are relevant to them and hide the information that isn’t. Above all of that, making simple interactive plots are a sure-fire way to impress your coworkers!