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Last week I published the latest post in the My R Journey series. It is with Rika Gorn, Director of Data Analytics & Reporting at Covenant House International. I’ve followed Rika for a while on Twitter and was especially impressed by the tips and tricks she shared during her week as the We are R Ladies rotating curator.
Having now posted several My R Journey posts (see also the one done with Dana Wanzer as well as the one I wrote up about my own journey), I decided to take a look back at all of them to see what lessons are beginning to emerge.
Lesson #1: You don’t need to be a programmer to learn R. Rika put it really well when she said:
Every industry has tons and tons of data and learning how to work with it and learn from it happens everywhere, regardless of what your position is called.
Dana and I also do not come from programming backgrounds (she is an evaluator; I’m a trained anthropologist who’s also worked in evaluation). One of the unique aspects of R, unlike other parts of the tech world, is that its users come from such diverse fields (Tom Kelly wrote on Twitter that, with R users coming from so many fields, “We respect their expertise in other fields even if they’re new to R”). So, no matter where you come from, don’t feel that R is not for you!
Lesson #2: It’s hard to know how to get started learning R. One of the incredible things about R is that there are multiple ways to do anything. This is a blessing and a curse. When you’re new, it’s challenging to know which direction to go. As I wrote,
One of the benefits of R is that there are always multiple ways to do things. But this is super confusing when you’re learning! How do you know what the easiest way to do something is?
Lesson #3: Early on, it takes longer to do things in R. After a while, R is much faster than other options. Writing about her early experience with R, Dana wrote:
When I’m a full-time student working a bunch of part-time gigs, and much of the work I’m doing requiring a fairly quick turnaround, it’s difficult to justify spending an inordinate amount of time figuring out how to do something I could have easily done in Excel or SPSS.
But, as Dana has since written, she can now do work in R much, much faster than in Excel or SPSS. In fact, she can produce reports that previously took hours in minutes.
Lesson #4: The Tidyverse is a game changer. The Tidyverse is a collection of packages that make R much easier to use. All three R users highlighted so far are big Tidyverse users. And, for Dana and for me, realizing the Tidyverse existed and realizing how much easier it made our lives changed everything for us. Dana said that:
Learning the Tidyverse was when everything started to make sense to me and I could finally feel confident enough to fully transition to R.
Lesson #5: Packages exist for everything. The power of R comes from its extensibility. Built on top of the core software (aka “base R”) are thousands and thousands of packages. With this number of packages, if you ever ask yourself the question, “I wonder if R can do this?” the answer is almost certainly, “Yes!” I was reminded of this recently when someone posted a link to a function (i.e. a piece of a package) that calculates the date of Easter for a given year. This means that all of us can use code that others have written, saving tons and tons of time.
Lesson #6: Automating reporting is a key feature of R. I’ve written in the past that RMarkdown is R’s Killer Feature that new users often don’t appreciate. Using RMarkdown allows you go from data importing all the way to final reporting in the same tool. It’s something that Dana uses extensively, especially for reports that are largely similar to past ones (RMarkdown enables her to do these types of reports in minutes). Rika as well says that using RMarkdown has made her work “a lot more efficient and a lot faster.” As a result, she says that she’s “currently thinking a lot about automation for reporting.” And, since she doesn’t have to spend time on the little things that RMarkdown automates away, she has “more time to think about the actual data and how it affects users and clients.”
Lesson #7: The community makes learning R much easier. When Rika started learning R, she struggled with being the only person in her organization using it. As she put it:
Looking back, I really wish I had a physical person I could just turn to and ask, “Am I doing this right?”
For Rika, as well as for Dana and for me, the online R community has been instrumental in our learning. While it would be ideal to have others at your organization using R, for many this is simply not the case. So having a supportive and welcoming online community is the next best thing.