Get a feel for how siuba works. Meet the spotifycharts.com data.
1. Data wrangling
Learn three key actions: filter for specific observations (rows), arrange observations, and mutate to add new columns of data. Combine sequences of actions together using pipes.
2. Data visualization
Use the library plotnine to create and customize scatter plots. Add text labels to your plots, and vary aesthetics such as color and size. Split plots with too much information into subplots with facets.
Summarize across observations using measures such as a minimum or mean. Use the group_by verb to calculate summaries for multiple groups of observations. Discover how summaries can lead to simpler, more readable plots.
4. 🚧 Plot types 🚧
Create line and bar plots for raw data. Use histograms and boxplots to examine distributions of data. Select between plots, based on your data and question at hand. (Note: this chapter is under construction)