Julia for Data Analysis

This repository contains source codes for the “Julia for Data Analysis” book that is written by Bogumił Kamiński and is planned to be published in 2022 by Manning Publications Co..

Setting up your environment

In order to prepare the Julia environment before working with the materials presented in the book please perform the following setup steps: * download and install Julia; all the codes were tested under Julia 1.7; * make sure you can start Julia by running julia command in your terminal; * download this repository to a local folder on your computer; * start Julia in a folder containing the downloaded material using the command julia --project; the folder must contain the Project.toml and Manifest.toml files prepared for this book that allow Julia to automatically set up the project environment that will allow you to work with material presented in this book (a more detailed explanation what these files do and why they are required is given in Appendix A to the book); * press ], write instantiate and press Enter (this process will ensure that Julia properly configures the working environment for working with the codes from the book); * press Backspace, write exit() and press Enter; now you should exit Julia and everything is set up to work with the materials presented in the book.

Additional instructions how to manage your Julia installation are given in Appendix A to the book.

In particular, if you use Visual Stuido Code with Julia extenssion then open the folder with the materials contained in this repository (you can open it in Folder/Open Folder… menu option). Then if you run Start Julia REPL command (e.g. under Windows its keyboard shortcut is Alt-J Alt-O) a proper project environment will be automatically activated (the Julia extension will use the Project.toml and Manifest.toml files that are present in this folder).

Organization of the code

The codes for each chapter are stored in files named chXX.jl, where XX is chapter number. The only exception is chapter 14, where additionally a separate ch14_server.jl is present along with ch14.jl (the reason is that in this chapter we create a web service and the ch14_server.jl contains the server-side code that should be run in a separate Julia process).

Solutions to the exercises that are presented in appendix B in the book are stored in appB.jl file. These solutions assume that they are executed in the same Julia session as the codes from the chapter where the question was posted (so that appropriate variables and functions are defined and appropriate packages are loaded).

Running the example codes

To work with codes from some given chapter: * start a fresh Julia session using the julia --project command in a folder containing the downloaded material (or alternatively use Visual Studio Code to activate the appropriate project environment automatically); * execute the commands sequentially as they appear in the file; the codes were prepared in a way that you do not need to restart Julia when working with material from a single chapter, unless it is explicitly written in the instructions to restart Julia (some of the codes require this); * before each code there is a comment allowing you to locate the relevant part of the book where it is used; if in the code there is a blank line between consecutive code sections this means that in the book these codes are separated by the text of the book explaining what the code does

Data used in the book

For your convenience I additionally stored data files that we use in this book. They are respectively: * movies.dat (for chapter 6, shared on GitHub repository https://github.com/sidooms/MovieTweetings under MIT license) * puzzles.csv.bz2 (for chapter 8, available puzzles at https://database.lichess.org/. The data is distributed under Creative Commons CC0 license) * git_web_ml.zip (for chapter 12, available on Stanford Large Network Dataset Collection website https://snap.stanford.edu/data/github-social.html under GPL-3.0 License) * owensboro.zip (for chapter 13, available at The Stanford Open Policing Project under the Open Data Commons Attribution License)