Last week, I decided to kill an afternoon by creating a Twitter bot. Why? Mostly, I was procrastinating on revisions for a manuscript and looking for a small R project to practice my programming skills. Creating a Twitter bot seemed like a great option: Bots can follow other users, retweet content from others, or post original content, and all of this is basically controlled by a script(s).
This project is surprisingly easy: If you’re familiar with R (e.

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. In today’s post, I will cover four more ways to calculating descriptive statistics and give some of my thoughts on these methods.

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. It’s been said R does a great job of making complex procedures simple, but it also has the tendency to make simple tasks complex.

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. In part I, I cover frequencies and tables. In parts II and III, I’ll cover descriptive statistics such as means, standard deviations, and the like.

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