I’m Jennifer Dolatshahi, an evaluator at the New York City Department of Health and Mental Hygiene. In addition to my evaluation activities, I also teach internal workshops on beginner and intermediate R.
No matter how many other programs cross my desk, R remains one of my favorite tools for data manipulation, analysis, visualization, reporting, etc. I’ve been using R for 5 years and teaching it for 2, and I am still learning new ways to use it. Getting started can feel daunting (remembering how I coded early on can make me cringe!), but I’m here to share some great packages to help you dive right into data manipulation and basic analyses.
For data manipulation, I turn to the tidyverse, a group of packages developed by Hadley Wickham and others at R Studio. These packages, including dplyr and tidyr, use simple, intuitive commands and rely on the concept of “tidy” (read: well-organized & normalized) data. stringr deals with those messy character variables we all love to hate, and lubridate is great for working with date/time variables. While tidyverse has some limitations, it also provides a shared grammar and structure across multiple packages that makes it easy to find a solution to your data cleaning or data viz needs. It also has some options for basic analyses, like two-by-two tables, with dplyr::summarise() and tidyr::spread(). SQL users will see familiar commands in the form of _joins and case_when(). And don’t forget that pipes are your friends!
There are also some great packages outside of the tidyverse for basic analyses.
- psych, stats, and summarytools all provide options for a range of descriptive statistics and more advanced analyses.
- stargazer provides simple descriptive statistics on numeric variables and, when called on a regression object, has myriad options for presenting analysis results.
- swirl is an interactive package that helps you learn R. If you don’t come from a programming background, this package helps explain the structure and idiosyncrasies of the language.
For some examples of these packages in action, see my GitHub.
Rad Resources: You rarely if ever need to pay anything to learn R! The tidyverse has great resources, including R for Data Science, a free online text complete with examples and exercises, and the R Graphics Cookbook for your data viz needs. R Studio has cheat sheets on a variety of topics, and also posts webinars and videos from their annual conference.
Hot Tip: R comes with an amazing and robust online (and sometimes in person!) community to help you along the way. Google what you need to do and I promise someone has written a blog or stack overflow post with a solution. Find R meetups in your area, like R Ladies. And find out if your organization has a community of R users and get them talking, even if just on a listserv or slack channel.
I hope this helps get you started on your R journey, and definitely share those cool packages you discover along the way.
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