In this chapter, we read in CSV files and performed a quick analysis of the data, including visualizations to help understand the data. Next, we considered some of the functions available in the dplyr package, including drawing a glimpse of the ranges of the data items, sampling a dataset, filtering out data, adding columns using mutate, and producing a summary. While doing so, we also started to use piping to more easily transfer the results of one operation into another operation. Lastly, we looked into the tidyr package to clean or tidy up our data into distinct columns and observations using the associated gather, separate, and spread functions.

In the next chapter, we will look at producing a dashboard under Jupyter.