Shokolatte Tachikawa has completed

# Intermediate Data Visualization with ggplot2

4 hours
4,350 XP

## Course Description

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.

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1. 1

### Statistics

Free

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.

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Stats with geoms
50 xp
Smoothing
100 xp
Grouping variables
100 xp
Modifying stat_smooth
100 xp
Modifying stat_smooth (2)
100 xp
Stats: sum and quantile
50 xp
Quantiles
100 xp
Using stat_sum
100 xp
Stats outside geoms
50 xp
Preparations
100 xp
Using position objects
100 xp
Plotting variations
100 xp
2. 2

### Coordinates

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.

3. 3

### Facets

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.

4. 4

### Best Practices

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.

### GroupTraining 2 or more people?

Collaborators

Prerequisites

Introduction to Data Visualization with ggplot2
Rick Scavetta

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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