19cStates

Analyzes nineteenth-century Western state making.

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Getting Started

1. Clone this depository.
2. Download data folder from Dataverse.
3. Place the data folder in directory root: 19cStates/data/
4. Create virtual conda environment with requirements.txt
5. Run all cells in each notebook, consecutively.

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To Do

1. src py files in parallel to notebooks
2. put results into doc
3. write it up

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Project Organization

Below is a map of the directory with annotations for general orientation.

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├── LICENSE                <- Open source! 
├── README.md              <- The top-level README for developers using this project.
├── data
│   ├── interim            <- Intermediate data that has been transformed.
│   ├── processed          <- The final, canonical data sets for modeling.
│   └── raw                <- The original, immutable data dump.
│
├── models                 <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks              <- Annotated jupyter notebooks for interactive replication. Follow the order of filename.
│   ├── 00_cr_congresses   <- creates state-year intnat congresses df
│   ├── 01_cr_journals     <- creates west-year stats journals df
│   ├── 02_cr_19cStates    <- creates 19cStates df, including congresses, journals, and time series
│   ├── 03_an_19cStates    <- analyzes 19cStates df: factor + panel regression analyses
│   └── 04_viz_19cStates   <- visualizes trends in indicators 
│
├── references             <- Codebooks, data dictionaries, manuals, and all other explanatory materials.
│
├── reports                <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures            <- Generated graphics and figures to be used in reporting
│
├── requirements.txt       <- Gen virtual computing env with this file

Forthcoming:

├── setup.py               <- makes project pip installable (pip install -e .) so src can be imported
│
└── src                    <- Source code for use in this project. * FORTHCOMING!
    ├── __init__.py        <- Makes src a Python module
    │
    ├── data               <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    ├── features           <- Scripts to turn raw data into features for modeling
    │   └── build_features.py
    │
    ├── models             <- Scripts to train models and then use trained models to make predictions
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── visualize.py

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Project based on the cookiecutter data science project template.