MISSION 276

Scatter Plots for Exploratory Analysis

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 in this data visualization in R course, 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 create and interpret scatter plots to explore relationships between variables. To create these plots, you will, as in previous lessons in this course, be using the popular ggplot2 package for data visualization. Then, you’ll learn how to optimize your plots to make them more meaningful and informative.

While learning about scatter plots, you’ll use movie rating data from Metacritic, Fandango, Rotten Tomatoes, and IMDB to get a sense of the differences in the way the four sites compute the movie ratings. With each concept, you'll be using our code running system with answer checking so you can ensure you've mastered each concept before moving to the next concept.

Objectives

  • Learn to use ggplot2 to create scatter plots.
  • Write a function to create multiple plots at once.
  • Interpret scatter plots to understand different types of relationships between variables.

Mission Outline

1. Importing and Modifying Data
2. Understanding Relationships Between Variables
3. Creating Informative Scatter Plots
4. Creating Multiple Scatter Plots
5. Write a Function to Create Multiple Scatter Plots
6. Learning from Scatter Plots
7. 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.

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