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.

Chicago red light camera violations In this post, I walk through a simple exploratory data analysis of red light camera violations in Chicago.
Data import Data downloaded from the Chicago Data Portal.

It’s been quite some time since I’ve written here, so I thought I would use one my of 2019 #rstats goals as an excuse to brush off the dust.

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.

In my last post, I refrained from standing on my Open Science soapbox; that’s not the case in this one. However, before I dig into why Open Science is near and dear to my heart, I want to take some time to cover some basic concepts.

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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