This ggplot2 course builds on your knowledge from the introductory course to produce meaningful explanatory plots. Statistics will be calculated on the fly and you’ll see how Coordinates and Facets aid in communication. You’ll also explore details of data visualization best practices with ggplot2 to help make sure you have a sound understanding of what works and why. By the end of the course, you’ll have all the tools needed to make a custom plotting function to explore a large data set, combining statistics and excellent visuals.
A picture paints a thousand words, which is why R ggplot2 is such a powerful tool for graphical data analysis. In this chapter, you’ll progress from simply plotting data to applying a variety of statistical methods. These include a variety of linear models, descriptive and inferential statistics (mean, standard deviation and confidence intervals) and custom functions.
The Coordinates layers offer specific and very useful tools for efficiently and accurately communicating data. Here we’ll look at the various ways of effectively using these layers, so you can clearly visualize lognormal datasets, variables with units, and periodic data.Coordinates50 xpZooming In100 xpAspect ratio I: 1:1 ratios100 xpAspect ratio II: setting ratios100 xpExpand and clip100 xpCoordinates vs. scales50 xpLog-transforming scales100 xpAdding stats to transformed scales100 xpDouble and flipped axes50 xpUseful double axes100 xpFlipping axes I100 xpFlipping axes II100 xpPolar coordinates50 xpPie charts100 xpWind rose plots100 xp
Facets let you split plots into multiple panes, each displaying subsets of the dataset. Here you'll learn how to wrap facets and arrange them in a grid, as well as providing custom labeling.The facets layer50 xpFacet layer basics100 xpMany variables100 xpFormula notation100 xpFacet labels and order50 xpLabeling facets100 xpSetting order100 xpFacet plotting spaces50 xpVariable plotting spaces I: continuous variables100 xpVariable plotting spaces II: categorical variables100 xpFacet wrap & margins50 xpWrapping for many levels100 xpMargin plots100 xp
Now that you have the technical skills to make great visualizations, it’s important that you make them as meaningful as possible. In this chapter, you’ll review three plot types that are commonly discouraged in the data viz community: heat maps, pie charts, and dynamite plots. You’ll learn the pitfalls with these plots and how to avoid making these mistakes yourself.Best practices: bar plots50 xpBar plots: dynamite plots100 xpBar plots: position dodging100 xpBar plots: Using aggregated data100 xpHeatmaps use case scenario50 xpHeat maps100 xpUseful heat maps50 xpHeat map alternatives100 xpWhen good data makes bad plots50 xpSuppression of the origin50 xpColor blindness50 xpTypical problems100 xp
In the following tracksData Scientist with RData Scientist Professional with RData Visualization with R
PrerequisitesIntroduction to Data Visualization with ggplot2
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.