Regardless of your programming language of preference, data visualization is paramount to understanding complex patterns in data. However, there are many steps that need to be taken before we can visualize the data. Therefore, this tutorial offers an illustration of the entire data visualization process in R and Python. To illustrate the process from importing to visualizing data, we will use data from the FiveThirtyEight Presidential Endorsement Tracker.
Given this focus on outlining the process of visualizing data, this tutorial does not offer a comprehensive explanation of the principles underlying data visualization. Some prior knowledge of data wrangling/visualization in either language before starting this tutorial is recommended before going through the examples in this tutorial. For an overview of tidyverse tools in R that will be useful to brush up on before starting this tutorial, you can go through some of the tidyverse primers from RStudio.
Therefore, this tutorial provides a very brief overview of:
- Importing and summarizing data
- Preparing data for visualization
- The next step: basic visualization with barplots and faceting
This tutorial uses several packages from the tidyverse library in R, as well as the reticulate package which provides a comprehensive set of tools for interoperability between Python and R. The Python libraries utilized in this tutorial are pandas for importing, summarizing, and manipulating data and plotnine for data visualization.