Summary

In this chapter, we looked at how Julia handles data input and output. We discussed how to perform simple I/O via the console and extended that to operating on text-based files on disk, extending this to structured data in the form of CSV and other DLM files. After, we looked at performing I/O operations on binary files and files formatted via the HDF5 and JLD file schemas.

Then, we considered interacting with datasets from Julia modules such as those contained in the RDatasets package and we saw how handling these leads naturally to Julia’s implementation of “R” style DataFrames, such as pandas in Python, and also of its sibling the time array.

Finally, we looked at how to visualize the datasets and further analyze the values by applying some simple statistical routines by computing descriptive metrics, kernel densities, and hypothesis testing.

Later in this book, we will introduce methods for dealing with data contained within databases and other...