In my last couple of posts, I discussed some of the ways R can summarize data. I started this discussion by demonstrating how to calculate frequencies and create data tables and then covered some common functions in base R and other packages that provide more detailed descriptive statistics.

Moving on from frequencies and tables, which were covered in part I, let’s now focus on other ways to summarize our data (e.g., mean, standard deviation). There are a lot of ways to divide a topic like descriptive statistics, and R can further complicate this seemingly simple task.

Well, I made it to my second blog post before I broke my goal of writing 2-4 posts a month. In fact, I completely missed the month of March. So, in an attempt to reestablish my (bi)weekly delivery of all things trivial, I’m starting a three-part series about conducting descriptive statistics in R.

Hello, world! Thanks for taking the time to read my first blog post! While I am trained as a social psychologist, I’m a big proponent of leveraging data science tools to understand the world around us.

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