A picture is worth a thousand words. When analyzing data, a picture might be worth more than a million words. As you progress through your data analyst or data scientist career, you will encounter situations where you need to present your data findings to a group of people and creating a compelling visualization will make conveying your findings easier than through spoken language.

Throughout this lesson and subsequent lessons, you will gradually grow your data visualization skills to ensure you are prepared to land your first job in data! (If you'd like to read why you should learn data viz in R, we wrote about why in this blog post.)

In the second lesson of our data visualization course, you learn techniques for visualizing multiple variables using a single plot. Oftentimes, you'll find it beneficial to create multiple line graphs on a single plot.

To create multiple line graphs in R, you'll continue using the popular ggplot2 package for data visualization. You’ll continue using these new data viz skills to visualize differences in life expectancies of different American populations and how they have changed over the past 100 years.

Not only will you learn how to create and interpret a visualization, but you will also learn how to present your data that clearly answers the question you are trying to answer. As you go through this lesson, you might be wondering if there are any other tips for data visualization. For additional tips for data visualization beyond what's offered in this lesson, this blog post contains 11 additional tips to make your data visualization more aesthetically pleasing!

Objectives

  • Learn to visualize trends across multiple variables.
  • Practice manipulating data to create a graph.
  • Learn to understand when to use different data visualization techniques.

Lesson Outline

1. Visualizing Data for Multiple Populations
2. Manipulating the Data for Visualization
3. Graphing Life Expectancies for Men and Women: Multiple Panels
4. Graphing Life Expectancies for Men and Women on the Same Axes
5. Graphing a Subset of Data
6. Exploring the Data Further
7. Manipulating Multiple Line Graph Aesthetics
8. Deciding How to Present the Data
9. Next Steps
10. Takeaways