One of the reasons that R is a top language for data science is that it’s great for data visualization. R users can take advantage of the wildly popular ggplot2 package to turn massive data sets into easily-readable charts in just a few lines of code. That can be incredibly valuable for presenting your data, but more importantly, when it’s done right, data viz is a tool for helping you understand what the data is telling you.
That’s why we just launched a new course dedicated to teaching you how to do data visualization in R.
In the course, you’ll learn to use ggplot2 with real-world data to construct five different popular chart types (including a version of the chart you see above). But more importantly, you’ll learn when to use different charts and how to interpret them correctly.
And of course, you’ll also get some practice writing clean, efficient R code using the fundamentals you learned in our introductory R courses, in addition to learning some new coding tricks!
The course includes four all-new missions and a new guided project. As you work through it, you’ll build and analyze line graphs to assess life expectancy in various US population groups, use histograms, bar charts, box plots, and scatter plots to investigate bias in movie reviews, and then use your new skills to answer questions about the causes of wildfires (with real-world data, of course).
And like the R courses we launched last month, this course is written by R native Dr. Rose Martin, which means that you’ll be learning modern, production-ready R code.
It’s critical for finding insight
Visualizing your data should be an important step in almost every data science project, because looking at it can reveal trends, patterns, and problems, that you might not notice any other way. Particularly once you’ve learned why and how to work with the popular chart formats covered in this course, you’ll be able to use them to glean insight from your data that would be easy to miss with no visualization (or the wrong type of visualization).
It’s also key for communication
Learning to do data viz isn’t just for your own benefit, though. In the majority of data science jobs, you’re going to have to present your findings to others (including people who aren’t data scientists or statisticians). In these presentations, your data is only as valuable as it is understandable, and the insights you’ve uncovered could fall on deaf ears if your audience can’t see what you’re talking about.
Data visualization is one of your best communication tools, because for the average person, a chart is much easier to understand than numbers and tables, particularly if it is well designed (here are some quick-and-dirty chart design tips). People in management positions may not have the technical knowledge to understand your code, but they’re typically very familiar with reading charts and graphs, so being good with data viz helps you speak their language.
Dive in to data viz
If you’re ready to dive in, click here to get started with the course. The first three missions are free!
If you’re not quite ready yet, our introductory R courses are completely free, and they’ll give you the foundation you need to dive into this data viz course with confidence once you’ve completed them.