Summary

In this chapter, we first discussed sources of open data, which included The Bureau of Labor Statistics, the Census Bureau, Professor French's data library, the Federal Reserve's data library, and the UCI Machine Learning Depository. After that, we showed you how to input data; how to deal with missing data; how to sort, slice, and dice the datasets; and how to merge different datasets. Data output was discussed in detail. For different languages, such as Python, R, and Julia, several relevant packages for data manipulation were introduced and discussed.

In Chapter 4, Data Visualization, we will discuss data visualization in R, Python, and Julia separately. To make our visual presentation more eye catching, we will show how you to generate simple graphs and bar charts, as well as how to add trend lines and legends. Other explanations will include how to save graphs to an external file, and we will discuss several packages related to data visualization in R, Python, and Julia.