1. Apprendre
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  3. Introduction to Data Visualization with ggplot2
  • 1

    Introduction

    Gratuit

    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

Exercice

Updating aesthetic labels

In this exercise, you'll modify some aesthetics to make a bar plot of the number of cylinders for cars with different types of transmission.

You'll also make use of some functions for improving the appearance of the plot.

  • labs() to set the x- and y-axis labels. It takes strings for each argument.
  • scale_fill_manual() defines properties of the color scale (i.e. axis). The first argument sets the legend title. values is a named vector of colors to use.

Instructions 1/3

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  • 1

    Set the x-axis label to "Number of Cylinders", and the y-axis label to "Count" using the x and y arguments of labs(), respectively.

  • 2

    Implement a custom fill color scale using scale_fill_manual(). Set the first argument to "Transmission", and values to palette.

  • 3

    Modify the code to set the position to dodge so that the bars for transmissions are displayed side by side.