Chapter 1, Installation and Introduction, teaches how to install the three most popular R visualization packages: ggplot2, ggvis, and plotly. This chapter introduces the ways a package can be installed: either from a CRAN repository or a GitHub one. You also get to know how to call functions from those packages while going through some examples demonstrating the basic framework of those packages.
Chapter 2, Plotting Two Continuous Variables, dives into scatterplots, the most popular way to plot two continuous variables. We learn how to change shapes, colors and sizes. The main problem imbued to scatterplots is called over plotting; this chapter explores related solutions. Also we see how to draw marginal plots and produce high-quality scatterplots.
Chapter 3, Plotting a Discrete Predictor and a Continuous Response, covers distributions over categories, a great way to explore your data. It can be handled using R to draw one box plots, bivariate dot plots, and violin plots. Learn how to draw and combine each of them. Meet nice additional features while learning the art of crafting to publish quality violin plots.
Chapter 4, Plotting One Variable, covers histograms, density plots, area plots, univariate dot plots, and univariate bar graphs. Learn how to brew these visuals under different R packages. Meet the useful modifications achieved by little code tweaks.
Chapter 5, Making Other Bivariate Plots. Trust this chapter to build bars of all kinds, line graphs, hexagon plots, and deploy variability estimates (error bars). Bars can be displayed as stacked, proportional, and/or side by side. Despite the wide variability of bars available, there are also some useful tricks to learn.
Chapter 6, Creating Maps, Demonstrating spatial correlation is mustily done through maps. Learn how points, lines, and polygons can be used to craft maps through R packages. Meet the John Snow cholera map, Tolkien's Middle Earth map, and an interactive globe of banking crisis. Also learn how to read shapefiles from temporary directories and how projection types can be coerced.
Chapter 7, Faceting, covers an important device that allows comprehension on more complex data relations. Recipes within this chapter teach how different kinds of visuals can easily reach out for faceting using ggplot2. Additionally, this chapter teaches how those can be coerced into interactive visuals by relying on the plotly package.
Chapter 8, Designing Three-Dimensional Plots. Although ggplot2 is not capable of drawing true 3D surfaces, it can draw their 2D representations. This chapter demonstrates the craft of contour, tile, and raster plots using ggplot2. It also demonstrates how to draw true and interactive 3D surfaces using plotly.
Chapter 9, Using Theming Packages, teaches how several themming packages can be used to coerce several themes to a ggplot. These range from themes inspired by The Simpsons and Star Trek to respectful academic journal themes. Also learn how to wrap a theme of your own into a function.
Chapter 10, Designing More Specialized Plots. Meet the complementary ggplot2 packages: ggforce, ggrepel, ggraph, and ggalt. These can be used to simple tasks like zooming in to more complex tasks like avoiding labels and texts inside the graph to overlay each other.
Chapter 11, Making Interactive Plots, explains how the ggiraph, gganimate, and tweenr packages can be used to create ggplot2 based animations and design interactive features. Animations are an useful way to denote how data variables have evolved with wonderful applications to all kinds of visuals.
Chapter 12, Building Shiny Dashboards. shiny allows the user to make interactive web applications direct from R. The package shinydashboard makes easier to create dash boards using shiny. This recipe demonstrates how to construct those while customizing styles, side bars, and more.
