Introduction

In the previous chapter, we learned how to work with new datasets and get familiar with their data and structure. We also got hands-on experience of how to analyze and transform them using different data wrangling techniques such as filtering, sorting, and reshaping. All of these techniques will come in handy when working with further real-world datasets in the coming activities.

In this chapter, we will focus on various visualizations and identify which visualization is best for showing certain information for a given dataset. We will describe every visualization in detail and give practical examples, such as comparing different stocks over time or comparing the ratings for different movies. Starting with comparison plots, which are great for comparing multiple variables over time, we will look at their types (such as line charts, bar charts, and radar charts).

We will then move onto relation plots, which are handy for showing relationships among variables. We will cover scatter plots for showing the relationship between two variables, bubble plots for three variables, correlograms for variable pairs, and finally, heatmaps for visualizing multivariate data.

The chapter will further explain composition plots (used to visualize variables that are part of a whole), as well as pie charts, stacked bar charts, stacked area charts, and Venn diagrams. To give you a deeper insight into the distribution of variables, we will discuss distribution plots, describing histograms, density plots, box plots, and violin plots.

Finally, we will talk about dot maps, connection maps, and choropleth maps, which can be categorized into geoplots. Geoplots are useful for visualizing geospatial data. Let’s start with the family of comparison plots, including line charts, bar charts, and radar charts.

Note

The data used in this chapter has been provided to demonstrate the different types of plots available to you. In each case, the data itself will be revisited and explained more fully in a later chapter.