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  3. Introduction to Data Visualization with ggplot2
  • 1

    Introduction

    Free

    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.

  • 2

    Aesthetics

    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.

  • 3

    Geometries

    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.

  • 4

    Themes

    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.

Initializing

Exercise

Using annotate() for embellishments

In the previous exercise, we completed our basic plot. Now let's polish it by playing with the theme and adding annotations. In this exercise, you'll use annotate() to add text and a curve to the plot.

The following values have been calculated for you to assist with adding embellishments to the plot:

global_mean <- mean(gm2007_full$lifeExp)
x_start <- global_mean + 4
y_start <- 5.5
x_end <- global_mean
y_end <- 7.5

Our previous plot has been assigned to plt_country_vs_lifeExp.

Instructions 1/4

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Clean up the theme:

  • Add a classic theme to the plot with theme_classic().
  • Set axis.line.y, axis.ticks.y, and axis.title to element_blank().
  • Set the axis.text color to "black".
  • Remove the legend by setting legend.position to "none".