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.

Stats with geoms

Smoothing

Grouping variables

Modifying stat_smooth

Modifying stat_smooth (2)

Stats: sum and quantile

Quantiles

Using stat_sum

Stats outside geoms

Preparations

Using position objects

Plotting variations

Statistics

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.

Coordinates

Zooming In

Aspect ratio I: 1:1 ratios

Aspect ratio II: setting ratios

Expand and clip

Coordinates vs. scales

Log-transforming scales

Adding stats to transformed scales

Double and flipped axes

Useful double axes

Flipping axes I

Flipping axes II

Polar coordinates

Pie charts

Wind rose plots

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 layer

Facet layer basics

Many variables

Formula notation

Facet labels and order

Labeling facets

Setting order

Facet plotting spaces

Variable plotting spaces I: continuous variables

Variable plotting spaces II: categorical variables

Facet wrap & margins

Wrapping for many levels

Margin plots

Facets

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 plots

Bar plots: dynamite plots

Bar plots: position dodging

Bar plots: Using aggregated data

Heatmaps use case scenario

Heat maps

Useful heat maps

Heat map alternatives

When good data makes bad plots

Suppression of the origin

Color blindness

Typical problems

Best Practices

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.

Introduction to Data Visualization with ggplot2

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

Intermediate Data Visualization with ggplot2

Associate Data Scientist in R

Data Visualization  in R

Color blindness

Intermediate Data Visualization with ggplot2

Hands-on interactive exercise