191 lines
8.7 KiB
Markdown
191 lines
8.7 KiB
Markdown
# Julia for Data Analysis
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This repository contains source codes for the
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["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)
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book that has been written by Bogumił Kamiński and has been published by [Manning Publications Co.](https://www.manning.com/)
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## Contents
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* [Additional teaching materials](#additional-teaching-materials)
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* [Setting up your environment](#setting-up-your-environment)
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* [General instructions](#general-instructions)
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* [Note for Linux users](#note-for-linux-users)
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* [Dev Containers](#dev-containers)
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* [Organization of the code](#organization-of-the-code)
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* [Running the example codes](#running-the-example-codes)
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* [Accompanying materials](#accompanying-materials)
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* [Data used in the book](#data-used-in-the-book)
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* [Citation](#citation)
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* [Errata](#errata)
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## Additional teaching materials
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* in the `/exercises` folder for each book chapter you can find 10 additional
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exercises with solutions (they are meant for self study and are not discussed
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in the book)
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* in the `/lectures` folder for each book chapter you can find a Jupyter
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Notebook file with code from this chapter (note that the code is slightly
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adjusted in comparison to code contained in .jl files in the root folder
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to accommodate it for running in Jupyter Notebook).
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## Setting up your environment
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### General instructions
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In order to prepare the Julia environment before working with the materials
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presented in the book please perform the following setup steps:
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* [download](https://julialang.org/downloads/) and
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[install](https://julialang.org/downloads/platform/)
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[Julia](https://julialang.org/);
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all the codes were tested under Julia 1.7 (under never versions of Julia
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the code will work, but you might get warning messages when loading packages
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due to the fact that their versions are pinned in this repository);
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* make sure you can start Julia by running `julia` command in your terminal;
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* download [this repository](https://github.com/bkamins/JuliaForDataAnalysis)
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to a local folder on your computer;
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* start Julia in a folder containing the downloaded material using the command
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`julia --project`; the folder must
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contain the Project.toml and Manifest.toml files prepared for this book that
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allow Julia to automatically set up the project environment that will allow
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you to work with material presented in this book
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(a more detailed explanation what these files do and why they are required is
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given in appendix A to the book);
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* press <kbd>]</kbd>, write `instantiate` and press <kbd>Enter</kbd> (this
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process will ensure that Julia properly configures the working environment
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for working with the codes from the book); in some cases
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running the `resolve` command also might be required;
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* press <kbd>Backspace</kbd>, write `exit()` and press <kbd>Enter</kbd>;
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now you should exit Julia and everything is set up to work with the materials
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presented in the book.
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Additional instructions how to manage your Julia installation are given in
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appendix A to the book. In particular I explain there how to perform a correct
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configuration of your environment when doing:
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* integration with Python using the PyCall.jl package;
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* integration with R using the RCall.jl package;
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* installation of Plots.jl (which by default uses the GR Framework that requires
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installation of extra dependencies on operating system level under Linux).
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In particular, if you use
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[Visual Studio Code](https://code.visualstudio.com/) with
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[Julia extension](https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)
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then open the folder with the materials contained in this repository (you can
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open it in Folder/Open Folder... menu option). Then if you run
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*Start Julia REPL* command (e.g. under Windows its keyboard shortcut is
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<kbd>Alt</kbd>-<kbd>J</kbd> <kbd>Alt</kbd>-<kbd>O</kbd>)
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a proper project environment will be automatically activated (the Julia extension
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will use the Project.toml and Manifest.toml files that are present in this folder).
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### Note for Linux users
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Installation of Julia under Linux requires that you choose the folder to which
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you extract the precompiled binaries you have downloaded. Next, assuming that
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you extracted Julia in, for example, the `/opt` folder, the simplest way
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to make sure that your system can find `julia` executable is to add it to
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your system `PATH` environment variable. A standard way to do it is to
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edit your `~/.bashrc` (or `~/.bash_profile`) file and add there the:
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```text
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export PATH="$PATH:/opt/julia-1.7.2/bin"
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```
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line (assuming you have downloaded Julia 1.7.2 and extracted it to `/opt` folder).
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### Dev Containers
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Folder `/.devcontainer` contains configuration files for
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[Dev Containers](https://containers.dev).
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* For use with Github Codespaces, please follow the instruction at
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[Creating a codespace for a repository](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace-for-a-repository).
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* For local/'remote host' usage with Visual Studio Code, please follow the
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instructions at
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[Developing inside a Container](https://code.visualstudio.com/docs/devcontainers/containers).
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## Organization of the code
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The codes for each chapter are stored in files named *chXX.jl*, where *XX* is
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chapter number. The exceptions are
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* chapter 14, where additionally a separate *ch14_server.jl* is present along
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with *ch14.jl* (the reason is that in this chapter we create a web service and
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the *ch14_server.jl* contains the server-side code that should be run in a
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separate Julia process);
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* appendix A, where the file name used is *appA.txt* because it also
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contains other instructions than only Julia code (in particular package
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manager mode instructions).
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Solutions to the exercises that are presented in appendix B in
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the book are stored in *appB.jl* file. These solutions assume that they are
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executed in the same Julia session as the codes from the chapter where the
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question was posted (so that appropriate variables and functions are defined
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and appropriate packages are loaded).
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## Running the example codes
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To work with codes from some given chapter:
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* it is recommended to use a machine with at least 8GB of RAM when working
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with the examples in this book (some examples require more RAM, which
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is clearly indicated in the book);
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* start a fresh Julia session using the `julia --project` command in a folder
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containing the downloaded material (or alternatively use Visual Studio Code
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to activate the appropriate project environment automatically);
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* execute the commands sequentially as they appear in the file;
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the codes were prepared in a way that you do not need to restart Julia
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when working with material from a single chapter, unless it is explicitly
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written in the instructions to restart Julia (some of the codes require this);
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when you move to a new chapter start a new Julia session;
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* before each code there is a comment allowing you to locate the relevant part
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of the book where it is used; if in the code there is a blank line between
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consecutive code sections this means that in the book these codes are
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separated by the text of the book explaining what the code does;
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## Accompanying materials
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There are the following videos that feature material related to this book:
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* [Analysis of Lichess puzzles database](https://www.twitch.tv/videos/1483517202)
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(a shortened version of material covered in chapters 8 and 9); also covered
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in [this blogpost](https://bkamins.github.io/julialang/2022/05/12/twitch.html);
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* [Analysis of GitHub developer graph](https://www.twitch.tv/videos/1527593261)
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(a shortened version of material covered in chapter 12)
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## Data used in the book
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For your convenience I additionally stored data files that we use in this book.
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They are respectively:
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* movies.dat (for chapter 6, shared on GitHub repository
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<https://github.com/sidooms/MovieTweetings> under MIT license)
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* puzzles.csv.bz2 (for chapter 8, available puzzles at
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<https://database.lichess.org/>. The data is distributed under
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Creative Commons CC0 license);
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* git_web_ml.zip (for chapter 12, available on
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Stanford Large Network Dataset Collection website
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<https://snap.stanford.edu/data/github-social.html> under GPL-3.0 License)
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* owensboro.zip (for chapter 13, available at The Stanford Open Policing Project
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under the Open Data Commons Attribution License)
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## Citation
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#### Plain text (Chicago style):
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Kamiński, Bogumił. 2023. *Julia for Data Analysis*. Manning.
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#### BibTeX:
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```
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@book{Kaminski2023,
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title = "Julia for Data Analysis",
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author = "Kamiński, Bogumił",
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year = 2023,
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publisher = "Manning",
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address = "Shelter Island, NY"
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}
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```
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## Errata
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You can find errata for the book in [this file](errata.md).
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