# Chapter 1 Getting Started

Everything starts somewhere, though many physicists disagree.

— Terry Pratchett

As with many research projects, the first step in our Zipf’s Law analysis is to download the research data and install the required software. Before doing that, it’s worth taking a moment to think about how we are going to organize everything. We will soon have a number of books from Project Gutenberg in the form of a series of text files, plots we’ve produced showing the word frequency distribution in each book, as well as the code we’ve written to produce those plots and to document and release our software package. If we aren’t organized from the start, things could get messy later on.

## 1.1 Project Structure

Project organization is like a diet: everyone has one, it’s just a question of whether it’s healthy or not. In the case of a project, “healthy” means that people can find what they need and do what they want without becoming frustrated. This depends on how well organized the project is and how familiar people are with that style of organization.

As with good coding style, small pieces in predictable places with readable names are easier to find and use than large chunks that vary from project to project and have names like “stuff”. We can be messy while we are working and then tidy up later, but experience teaches that we will be more productive if we make tidiness a habit.

In building the Zipf’s Law project we’ll follow a widely-used template for organizing small and medium-sized data analysis projects (Noble 2009). The project will live in a directory called zipf, which will also be a Git repository stored on GitHub (Chapter 6). The following is an abbreviated version of the project directory tree as it appears towards the end of the book:

zipf/
├── .gitignore
├── CITATION.md
├── CONDUCT.md
├── CONTRIBUTING.md
├── Makefile
├── bin
│   ├── book_summary.sh
│   ├── collate.py
│   ├── countwords.py
│   └── ...
├── data
│   ├── dracula.txt
│   ├── frankenstein.txt
│   └── ...
├── docs
│   └── ...
├── results
│   ├── collated.csv
│   ├── dracula.csv
│   ├── dracula.png
│   └── ...
└── ...

The full, final directory tree is documented in Appendix D.

### 1.1.1 Standard information

Our project will contain a few standard files that should be present in every research software project, open source or otherwise:

• README includes basic information on our project. We’ll create it in Chapter 7, and extend it in Chapter 14.

• LICENSE is the project’s license. We’ll add it in Section 8.4.

• CONTRIBUTING explains how to contribute to the project. We’ll add it in Section 8.11.

• CONDUCT is the project’s Code of Conduct. We’ll add it in Section 8.3.

• CITATION explains how to cite the software. We’ll add it in Section 14.7.

Some projects also include a CONTRIBUTORS or AUTHORS file that lists everyone who has contributed to the project, while others include that information in the README (we do this in Chapter 7) or make it a section in CITATION. These files are often called boilerplate, meaning they are copied without change from one use to the next.

### 1.1.2 Organizing project content

Following Noble (2009), the directories in the repository’s root are organized according to purpose:

• Runnable programs go in bin/ (an old Unix abbreviation for “binary”, meaning “not text”). This will include both shell scripts, e.g. book_summary.sh developed in Chapter 4, and Python programs, e.g. countwords.py, developed in Chapter 5.

• Raw data goes in data/ and is never modified after being stored. You’ll set up this directory, and its contents in Section 1.2.

• Results are put in results/. This includes cleaned-up data, figures, and everything else created using what’s in bin and data. In this project, we’ll describe exactly how bin and data are used with Makefile created in Chapter 9.

• Finally, documentation and manuscripts go in docs/. In this project docs will contain automatically generated documentation for the Python package, created in Section 14.6.2.

This structure works well for many computational research projects and we encourage its use beyond just this book. We will add some more folders and files not directly addressed by Noble (2009) when we talk about testing (Chapter 11), provenance (Chapter 13), and packaging (Chapter 14).

The data files used in the book are archived at an online repository called Figshare (which we discuss in detail in Section 13.1.2) and can be accessed at:

https://doi.org/10.6084/m9.figshare.13040516

We can download a zip file containing the data files by clicking “download all” at this URL and then unzipping the contents into a new zipf/data directory (also called a folder) that follows the project structure described above. Here’s how things look once we’re done:

zipf/
└── data
├── dracula.txt
├── frankenstein.txt
├── jane_eyre.txt
├── moby_dick.txt
├── sense_and_sensibility.txt
├── sherlock_holmes.txt
└── time_machine.txt

## 1.3 Installing the Software

In order to conduct our analysis we need to install the following software:

1. A Bash shell
2. Git version control
3. A text editor
4. Python 3 (via the Anaconda distribution)
5. GNU Make

Comprehensive software installation instructions for Windows, Mac, and Linux operating systems (with video tutorials) are maintained by The Carpentries as part of their workshop website template at:

https://carpentries.github.io/workshop-template/#setup

We can follow those instructions to install the Bash shell, Git, a text editor and Anaconda. We recommend Anaconda as the method for installing Python, as it includes Conda as well as many of the packages we’ll use in this book.

You can check if Make is already on your computer by typing make -v into the Bash shell. If it is not, you can install it as follows:

• Linux (Debian/Ubuntu): Install it from the Bash shell using sudo apt-get install make.
• Mac: Install Xcode (via the App Store).
• Windows: Follow the installation instructions maintained by the Master of Data Science program at the University of British Columbia.

conda in the Shell on Windows

If you are using Windows and the conda command isn’t available at the Bash shell, you’ll need to open the Anaconda Prompt program (via the Windows start menu) and run the command conda init bash (this only needs to be done once). After that, your shell will be configured to use conda going forward.

Software Versions

Throughout the book, we’ll be showing you examples of the output you can expect to see. This output is derived from running a Mac with: Git version 2.29.2, Python version 3.7.6, GNU bash version 3.2.57(1)-release (x86_64-apple-darwin19), GNU Make 3.81, and conda 4.9.2. In some cases, what you see printed to the screen may differ slightly based on software version. We’ll help you understand how to interpret the output so you can keep working and troubleshoot regardless of software version.

## 1.4 Summary

Now that our project structure is set up, our data is downloaded, and our software is installed, we are ready to start our analysis.

## 1.5 Exercises

• Put runnable code in a bin/ directory.
• Put raw/original data in a data/ directory and never modify it.
• Put results in a results/ directory. This includes cleaned-up data and figures (i.e. everything created using what’s in bin and data).
• Put documentation and manuscripts in a docs/ directory.