In February, I asked you to help me figure out what to do next with R for the Rest of Us. As I wrote at the time: Moving forward, I want to focus my efforts on the types of learning that resonate with you. I’m also thinking about offering opportunities to learn about particular topics that I think people might be interested in (e.g. mapping with R). But again, I don’t know which topics will resonate with my audience unless I ask you. Nearly 200 of you were generous enough to respond to the...
“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 is a common tool among people who work with big data. But just because those who work with big data use R does not mean that R is not valuable for the rest of us. I rarely work with datasets larger than a few hundred...
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 R from Excel so having a set of R to Excel equivalents (also see this post on a similar topic) is helpful. The tips are not intended to be comprehensive, but they do show some common things that people...
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 a professional doppelgänger, I reached out to ask if he'd answer some questions about his path to R. He kindly agreed and you can see the results below. Ryan also shared a bit about his professional and...
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 lot of the variables share the same code (we use a few different likert scales for groups of questions) and then others may only be used once (gender, race, etc.). I've been trying...
Data Cleaning with R Interested in learning more about data cleaning with R? We now have a full course devoted to the topic. 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 range of options for dealing with dirty data. The collection of packages known as the...
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, both preferably of others. Why did you decide to learn R? In 2012, I convinced the editors in parent company of my newspaper to hire me as a data...
Updated March 12, 2022 with info on the reactablefmtr and gtextras packages. It doesn't matter how good your data analysis is if you don't effectively communicate your results. In most reports, communication of results happens through a combination of data visualization and tables. Along with narrative text, these make up the bulk of content used to communicate results. Clear writing is a topic for another venue. Data visualization in R is a huge topic (and one covered expertly in Kieran...
Jon Harmon is one of the friendliest, most helpful people you'll come across in the R community (and, in a community known for being incredibly welcoming , that's saying something!). If you're involved at all with the R for Data Science Online Learning Community , you've likely seen his posts. There, he tries to "pay it forward" for all the awesome help he's gotten learning R over the last few years. If you follow the #TidyTuesday social data project , you may have heard about the Tidy...
R is a functional programming language. Most of the time, you use other people's functions. But, as you become a more experienced R user, you may want to start making your own functions. For most people, the idea of making your first function comes when you realize you are copying and pasting code over and over. Maybe you're doing the same data wrangling on multiple data frames. Maybe you're making a nearly identical graph several times. The initial impetus to make a function is usually that...
