“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 not a totally crazy idea. R […]
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. As I looked through the tips, I realized that I could show how to do each of the five tips listed in the document in R. Many people come to […]
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 […]
Recoding Numeric Values to Character Values Automatically in R
After a recent Introduction to R workshop, a participant asked me a question. She works with data that comes to her with all numeric values, but these numeric actually represent character values. Every time she receives new data, she has to recode it manually in Excel, a situation she described to me via email: A […]
How to Clean Messy Data in R
If you work with data, you know that you often spend as much — if not more — time gathering, wrangling, and cleaning your data as you do analyzing it. A 2014 New York Times article cites the truism that data scientists spend at least half of their time cleaning data. R offers a wide […]
My R Journey: Harkanwal Singh
Harkanwal Singh is a data visualisation programmer, currently working at Westpac NZ. Previously, he established and led the data journalism desk at the New Zealand Herald. He is passionate about visualisation as code. He programs in R, JavaScript and Python. He enjoys teaching data visualisation in his own time. He loves reading words and code, […]