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.

Introduction

Explore and explain

Drawing your first plot

Data columns types affect plot types

The grammar of graphics

Mapping data columns to aesthetics

Understanding variables

ggplot2 layers

Adding geometries

Changing one geom or every geom

Saving plots as variables

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 aesthetics

All about aesthetics: color, shape and size

All about aesthetics: color vs. fill

All about aesthetics: comparing aesthetics

Aesthetics for categorical & continuous variables

Using attributes

All about attributes: color, shape, size and alpha

All about attributes: conflicts with aesthetics

Going all out

Modifying aesthetics

Updating aesthetic labels

Setting a dummy aesthetic

Aesthetics best practices

Appropriate mappings

Aesthetics

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 plots

Overplotting 1: large datasets

Overplotting 2: Aligned values

Overplotting 3: Low-precision data

Overplotting 4: Integer data

Histograms

Drawing histograms

Positions in histograms

Bar plots

Position in bar and col plots

Overlapping bar plots

Bar plots: sequential color palette

Line plots

Basic line plots

Multiple time series

Geometries

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 scratch

Moving the legend

Modifying theme elements

Modifying whitespace

Theme flexibility

Built-in themes

Exploring ggthemes

Setting themes

Publication-quality plots

Effective explanatory plots

Using geoms for explanatory plots

Using annotate() for embellishments

Themes

Diamonds

Iris

Recession

Fish

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.

Introduction to the Tidyverse

Learn to produce meaningful and beautiful data visualizations with ggplot2 by understanding the grammar of graphics.

Introduction to Data Visualization with ggplot2

Associate Data Scientist in R

Data Analyst  in R

Data Visualization  in R

Explore and explain

Introduction to Data Visualization with ggplot2

Hands-on interactive exercise