How to Add Color to Titles Using the ggtext Package

If you read experts on data visualization, you’ll start to see some common suggestions. One of these is use titles not just to describe your chart, but to explain your findings. Instead of generic titles like “Change in Math Scores, 2017-2018 to 2018-2019,” they suggest, try something like, “Most Students Improved Their Math Scores From

How to Download Data in R

When you work with most data analysis tools, the first step in any project is to download your data. Your workflow might look something like this: Go to the website where the data is locatedFind the data you needDownload the data to your computerCopy the data to where you need to in

R is Not Just for Big Data

“Oh yeah, I thought about learning R, but my data isn’t that big so it’s not worth it.”  I’ve heard that line more times than I can count. There is a common perception among non-R users that R is only worth learning if you work with “big data.” It’s

Data Cleaning Tips in R

I recently came across a set of data cleaning tips in Excel from EvaluATE, which provides support for people looking to improve their evaluation practice. Screenshot of the Excel Data Cleaning Tips As I looked through the tips, I realized that I could show how to do each of

My R Journey: Ryan Ames

When I came across Ryan Ames’ LinkedIn profile, I was struck by how similar his work is to my own. Based in Portland (like me), he works at the intersection of data science and program evaluation (the field I worked in prior to starting R for the Rest of Us). My curiosity piqued at seeing

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