# Julia for Data Analysis This repository contains source codes for the ["Julia for Data Analysis"](https://www.manning.com/books/julia-for-data-analysis?utm_source=bkamins&utm_medium=affiliate&utm_campaign=book_kaminski2_julia_3_17_22) book that is written by Bogumił Kamiński and is planned to be published in 2022 by [Manning Publications Co.](https://www.manning.com/) Extras: * in the `/exercises` folder for each book chapter you can find 10 additional exercises with solutions (they are meant for self study and are not discussed in the book) ## 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](https://julialang.org/downloads/) and [install](https://julialang.org/downloads/platform/) [Julia](https://julialang.org/); 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](https://github.com/bkamins/JuliaForDataAnalysis) 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 <kbd>]</kbd>, write `instantiate` and press <kbd>Enter</kbd> (this process will ensure that Julia properly configures the working environment for working with the codes from the book); in case you work on Julia newer than 1.7 running the `resolve` command also might be required; * press <kbd>Backspace</kbd>, write `exit()` and press <kbd>Enter</kbd>; 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 I explain there how to perform a correct configuration of your environment when doing: * integration with Python using the PyCall.jl package; * integration with R using the RCall.jl package; * installation of Plots.jl (which by default uses the GR Framework that requires installation of extra dependencies on operating system level under Linux). In particular, if you use [Visual Studio Code](https://code.visualstudio.com/) with [Julia extension](https://marketplace.visualstudio.com/items?itemName=julialang.language-julia) 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 <kbd>Alt</kbd>-<kbd>J</kbd> <kbd>Alt</kbd>-<kbd>O</kbd>) 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). ### Note for Linux users Installation of Julia under Linux requires that you choose the folder to which you extract the precompiled binaries you have downloaded. Next, assuming that you extracted Julia in, for example, the `/opt` folder, the simplest way to make sure that your system can find `julia` executable is to add it to your system `PATH` environment variable. A standard way to do it is to edit your `~/.bashrc` (or `~/.bash_profile`) file and add there the: ```text export PATH="$PATH:/opt/julia-1.7.2/bin" ``` line (assuming you have downloaded Julia 1.7.2 and extracted it to `/opt` folder). ## Organization of the code The codes for each chapter are stored in files named *chXX.jl*, where *XX* is chapter number. The exceptions are * 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); * appendix A, where the file name used is *appA.txt* because it also contains other instructions than only Julia code (in particular package manager mode instructions). 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: * it is recommended to use a machine with at least 8GB of RAM when working with the examples in this book (some examples require more RAM, which is clearly indicated in the book); * 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); when you move to a new chapter start a new Julia session; * 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; ## Accompanying materials There are the following videos that feature material related to this book: * [Analysis of Lichess puzzles database](https://www.twitch.tv/videos/1483517202) (a shortened version of material covered in chapters 8 and 9); also covered in [this blogpost](https://bkamins.github.io/julialang/2022/05/12/twitch.html); * [Analysis of GitHub developer graph](https://www.twitch.tv/videos/1527593261) (a shortened version of material covered in chapter 12) ## 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)