MISSION 275

Bar Charts, Histograms, and Box Plots

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 this lesson, you will learn to visualize data distributions as you analyze movie reviews. To create the bar charts, histograms, and box plots, you will continue using ggplot2, the most popular R package for data viz. 

As you start this lesson, you will learn about a type of data visualization called a histogram, which is a type of plot used to visualize the distribution of a variable. You’ll learn how to create bar charts as well as histograms in ggplot2, how to interpret them, and when they can be most effectively used. 

Objectives

  • Learn to create bar charts, histograms, and box plots.
  • Understand the importance of data distributions.
  • Visualize distributions to understand data trends.

Mission Outline

1. Visualizing Distributions to Investigate Movie Review Bias
2. Comparing Averages Among Rating Sites
3. Visualizing Differences Among Groups Using Bar Charts
4. Using Histograms to Understand Distributions
5. Comparing Distributions of Multiple Variables: Faceted Plots
6. Comparing Distributions of Multiple Variables: Specifying Aesthetics
7. Visualizing Averages and Variation
8. Anatomy of a Box Plot
9. Deciding on a Visualization
10. Takeaways

r-data-viz

Course Info:

Advanced

The median completion time for this course is 5.6 hours. View details

This course is free and includes four missions and one guided project. This is the third course in the Data Analyst in R path.

START LEARNING FREE

Take a Look Inside