Campaign Spending Analysis: Read This First
@eric_bickel and @ryanes
Project Leads: @eric_bickel, @ryanes
Project Description: This ProPublica repository is part of Data for Democracy. Our purpose is to collaboratively work through analytic processes that support the journalism at ProPublica. This repository in particular contains analysis of campaign spending data. Currently, contributors are focusing on cleaning the campaign spending dataset. We are always open to ideas for how to work with this dataset to make it more useful to ProPublica. Please contact @ryanes or @eric_bickel on Slack with any suggestions or questions.
New contributors should review the analysis workflow below and then read the dataset description to access the campaign spending data and review the data cleaning methods.
Reading, cleaning, and analyzing data should be done in a reproducible notebook format when possible. When submitting pull requests, please submit them from a fork of the repository and on a separate branch. Data for Democracy has an awesome set of instructions for how to do this if you need it.
If contributors are working on projects other than updating the files in the main directory, they are encouraged to keep their work in a folder that is named in a way that describes the folder's contents. Some examples might be
alternate_cleaning_python. This should make it easier for new contributors to follow what is happening and make judgements about how to organize their contributions.
Loading and Cleaning Datasets
For each analysis, data needs to be loaded and cleaned to a format that is useable for the current analysis and for future analyses.
After data has been cleaned, both the raw data and cleaned data should be uploaded to a project-specific data.world repo. Additionally, the project's readme should be updated with a summary of the cleansing process and any code associated with cleaning should be pushed to the project's GitHub repo.
Team members working in exploratory analysis work up general statistics, distributions of important variables, and hypotheses based on initial exploration of covariation.
When an analysis job is complete, a pull request to the GitHub repo should be made to be edited by collaborators of the project or a committee of assigned editors.
Team members use modeling techniques to test the hypotheses generated in the exploratory analysis phase and to quantify relationships between variables in the data. Team members may also be working to test specific hypotheses generated by ProPublica.
Algorithms used in the modeling should be vetted through open discussions with the team and through pull requests, and final model specification should be a collaborative effort using any individual findings from the discussion. The project readme should outline these specifications, and the final modeling code should be pushed to the GitHub repo.
Team members detail the findings in a reproducible report that can be immediately used by ProPublica. All sources and data used should be linked in the report, and the project readme containing all background in methodology and links to data and code.