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Hands-On Data Visualization with ggplot2

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

How to create engaging and well-designed visualizations in R

Communicating data through proper visualization is critical for data scientists and business decisions. Creating data visualizations of high quality in an efficient and preferably reproducible way is an essential tool in a data scientist's toolbox.

This live training provides everything one needs to know to create and customize numerous chart types with ggplot2, the most famous package for data visualization with R. The course also covers the most important steps and helpful tricks to create visually appealing and informative graphs both in theory and in practice using ggplot2. The course is designed for participants with none to minimal prior experience in R and data visualization and more experienced R users. As part of the training, attendees can download the course examples and exercises as Rmarkdown notebooks from the course webpage.

After completing the course, participants will be able to create a vast range of data visualizations with a custom design in R including custom colors and shapes, adding annotations and images, advanced legend design, and multi-panel figures.

What you’ll learn and how you can apply it

  • Load data into R and visualize it with the help of the powerful ggplot2 library.
  • Customize your ggplot2 output as you like, including theming, colors, annotations, and many more.
  • Get to know useful extension libraries that will boost your workflow and improve your ggplot2 design.
  • Use Rmarkdown notebooks to build reproducible reports.
  • Get insights how to prepare and wrangle data with the popular tidyverse package collection

This live event is for you because...

  • Anyone interested in creating visually appealing charts with R and ggplot2—no matter if you have no or minimal experience with R.
  • Data scientists aiming to use the powerful ggplot2 library in their workflow for exploration and communication.
  • Business managers that aim to quickly gain data insights based on reproducible and elegantly formatted charts and reports.

Prerequisites

Course Set-up

  • Download the most recent version of R and Rstudio and follow the installation steps (for more detailed instructions see Chapter 2 of “R Programming for Statistics and Data Science” by 365 Careers
  • Install the following R libraries: tidyverse, ggrepel, ggtext, patchwork, ggforce, sf, rnaturalearth, cowplot, ragg, magick.
  • To install the libraries, run the following code in the R console of Rstudio: install.packages(“tidyverse”, “ggrepel”, “ggtext”, “patchwork”, “ggforce”, “sf”, “rnaturalearth”, “cowplot”, “ragg”, “magick”)
  • Download the course material

Recommended Preparation

Data Wrangling with the tidyverse (helpful but not required)

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: The Grammar of Graphics (60 min)

  • Motivation: The ggplot2 Showcase
  • Loading Data into R
  • The Grammar of Graphics
  • Our First ggplot
  • Q&A
  • Includes an exercise on (i) loading data and (ii) mapping of aesthetics.
  • Break (5 min)

Segment 2: Build Plots Layer by Layer (60 min)

  • Create any Chart Type
  • Use Statistical Transformations
  • Plot Spatial Maps
  • Q&A
  • Includes an exercise on (i) geometries and (ii) statistical transformations, and (iii) projections.
  • Break (10 min)

Segment 3: Polish Your Visualization (60 min)

  • Adjust Colors and Guides
  • Use Template Designs
  • Create Custom Themes
  • Q&A
  • Includes an exercise on (i) customizing colors, (ii) changing legends, and (iii) creating custom themes.
  • Break (5 min)

Segment 4: Provide Context with Annotations (45 min)

  • Insert Titles and Labels
  • Basic and Advanced Text Labeling
  • Add Images and Illustrations
  • Q&A
  • Includes an exercise on (i) text labeling and (ii) adding images.
  • Break (10 min)

Segment 5: Create Multi-Panel Figures (30 min)

  • Create Small Multiples (Facets)
  • Combine Multiple Plots with {patchwork}
  • Q&A
  • Includes an exercise on (i) facets and (ii) the use of {patchwork}.

Course wrap-up and next steps (15 min)

Your Instructor

  • Cedric Scherer

    Dr. Cedric Scherer is a graduated computational ecologist with a passion for design. In 2020, he combined his expertise in analyzing and visualizing large data sets in R with his passion to become a freelance data visualization specialist. Cédric has created visualizations across all disciplines, purposes, and styles and regularly teaches data visualization principles, R, and ggplot2. Due to regular participation to social data challenges, he is now well known for complex and visually appealing figures, entirely made with ggplot2, that look as they have been created with a vector design tool.

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Skill covered

ggplot