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.

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. Thus, I plan to discuss a mixture of data-driven topics and insights here. I hope to use this blog as a place to write about things that interest me, but focus will likely be on statistics, research methods, open science, social psychology, and data science.

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