Click here to view the data story

Information Visualisation: Gender Pay Gap

Jupyter Book visualising the gender pay gap for software developers, using the Stack Overflow Annual Developer Survey data. For more details regarding the datasets source and the cleaning process, see the Dataset and preprocessing page.

Table of Contents

• Information Visualisation: Gender Pay Gap
• Table of Contents
• Getting started
• Structure
• Usage
• Deploy
• Dataset cleaning scripts
• Authors

Getting started

git clone git@github.com:p-adema/info-vis.git
cd info-vis
pip3 install -r requirements.txt

Additionally, install conda to setup a local environment.

• MacOS installation instructions
• Windows installation instructions
• Linux installation instructions

In case you don't have jupyter installed locally, simply run pip3 install jupyter.

Structure

.
├── LICENSE             # Code license
├── README.md           # General project information and instructions
├── _build              # Jupyter build that will be deployed
├── _config.yml         # Jupyter config file
├── _toc.yml            # Jupyter Table of Contents config file
├── data                # Contains dataset including cleaning scripts
├── docs                # Files to be published on GitHub Pages
├── notebooks           # Contains all notebook graphs used for the data story
├── requirements.txt    # Project dependencies
├── scripts             # Additional scripts used for deployment or cleaning
└── static              # Images, css and js files to be published on GitHub Pages

Usage

Run jupyter notebook inside the root of this repository. All notebooks that go into the data story should be created inside the notebooks directory. In order to keep a maintainable and flexible structure, create one notebook per plot.

The main data story notebook is ./notebooks/story.ipynb. In between texts, you can include the results from other plots, simply by adding a new code cell with the following contents %run example_plot.ipynb. Only the result of the last cell inside example_plot.ipynb will be rendered.

Deploy

In order to deploy to GitHub pages, you can run the following command in a terminal: ./scripts/deploy.sh. This will build the project, notebooks and adds metadata to the story.ipynb in order to hide the input cells.

Dataset cleaning scripts

Click here to have a look at the scripts that have been used to clean the datasets prior to the data story development phase.

Authors

• Peter Adema
• Aize van Basten Batenburg
• Wim Berkelmans
• Kim Koomen