Data Visualization

This is the fourth part in an ongoing series on how and why you should be using R, especially if you are a social science researcher or education researcher, like me. If you missed the earlier ones, you can check out part 1 (Intro to R), part 2 (R Basics), and part 3 (Data Cleaning and Manipulation). This post will go into some more specifics relating to data visualization.

There are many ways to visualize your data using R. By far the most popular (and I think robust and flexible) is using the ggplot2 package. This post will talk a bit about why and how to visualize your data and some tips and basics to using R’s ggplot2 package to help you achieve your visualization goals.

Why visualize?

There are lots of reasons why you might want to visualize your data (or rather, why you should visualize your data). It can be a useful tool at various stages of research, and depending on where you are in your analysis process, different aspects of visualization might be more or less important to focus on. The way I see it, there are three main purposes for data visualization: examining your data, showing your data/findings, and sharing your data/findings.

What question are you trying to answer with your data? How can a visualization help you answer that? Do you have a really complex data set that is too hard to easily capture with a few numbers? Are you interested in variation and distribution rather than just means and medians? Are you exploring different relationships between variables and want to see how they interact?

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