Introduction to Data Visualization with ggplot2
Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.Start Course for Free
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The ability to produce meaningful and beautiful data visualizations is an essential part of your skill set as a data scientist. This course, the first R data visualization tutorial in the series, introduces you to the principles of good visualizations and the grammar of graphics plotting concepts implemented in the ggplot2 package. ggplot2 has become the go-to tool for flexible and professional plots in R. Here, we’ll examine the first three essential layers for making a plot - Data, Aesthetics and Geometries. By the end of the course you will be able to make complex exploratory plots.
In this chapter we’ll get you into the right frame of mind for developing meaningful visualizations with R. You’ll understand that as a communications tool, visualizations require you to think about your audience first. You’ll also be introduced to the basics of ggplot2 - the 7 different grammatical elements (layers) and aesthetic mappings.Introduction50 xpExplore and explain50 xpDrawing your first plot100 xpData columns types affect plot types100 xpThe grammar of graphics50 xpMapping data columns to aesthetics100 xpUnderstanding variables50 xpggplot2 layers50 xpAdding geometries100 xpChanging one geom or every geom100 xpSaving plots as variables100 xp
Aesthetic mappings are the cornerstone of the grammar of graphics plotting concept. This is where the magic happens - converting continuous and categorical data into visual scales that provide access to a large amount of information in a very short time. In this chapter you’ll understand how to choose the best aesthetic mappings for your data.Visible aesthetics50 xpAll about aesthetics: color, shape and size100 xpAll about aesthetics: color vs. fill100 xpAll about aesthetics: comparing aesthetics100 xpAesthetics for categorical & continuous variables50 xpUsing attributes50 xpAll about attributes: color, shape, size and alpha100 xpAll about attributes: conflicts with aesthetics100 xpGoing all out100 xpModifying aesthetics50 xpUpdating aesthetic labels100 xpSetting a dummy aesthetic100 xpAesthetics best practices50 xpAppropriate mappings50 xp
A plot’s geometry dictates what visual elements will be used. In this chapter, we’ll familiarize you with the geometries used in the three most common plot types you’ll encounter - scatter plots, bar charts and line plots. We’ll look at a variety of different ways to construct these plots.Scatter plots50 xpOverplotting 1: large datasets100 xpOverplotting 2: Aligned values100 xpOverplotting 3: Low-precision data100 xpOverplotting 4: Integer data100 xpHistograms50 xpDrawing histograms100 xpPositions in histograms100 xpBar plots50 xpPosition in bar and col plots100 xpOverlapping bar plots100 xpBar plots: sequential color palette100 xpLine plots50 xpBasic line plots100 xpMultiple time series100 xp
In this chapter, we’ll explore how understanding the structure of your data makes data visualization much easier. Plus, it’s time to make our plots pretty. This is the last step in the data viz process. The Themes layer will enable you to make publication quality plots directly in R. In the next course we'll look at some extra layers to add more variables to your plots.Themes from scratch50 xpMoving the legend100 xpModifying theme elements100 xpModifying whitespace100 xpTheme flexibility50 xpBuilt-in themes100 xpExploring ggthemes100 xpSetting themes100 xpPublication-quality plots100 xpEffective explanatory plots50 xpUsing geoms for explanatory plots100 xpUsing annotate() for embellishments100 xp
In the following tracksData Analyst with RData Scientist with RData Scientist Professional with RData Visualization with R
PrerequisitesIntroduction to the Tidyverse
Rick ScavettaSee More
Rick Scavetta is a co-founder of Scavetta Academy.
Rick Scavetta is a biologist, workshop trainer, freelance data scientist and co-founder of Scavetta Academy, a company dedicated to helping scientists better understand and visualize their data. Rick's practical, hands-on exposure to a wide variety of datasets has informed him of the many problems scientists face when trying to visualize their data.
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