If you work for The Policy Lab and have access to final_data_one_line_per_individual.csv, then make all should recreate all of the results after you setup the python and R environments as described below. We do not share this file on github because it contains potentially personally identifying information.
However, if you want to go beyond inspecting our code and would like to play with some data (just not the ZCTA level data), you can do so by editing 020_effects_on_vaccinations.Rmd to ignore final_data_one_line_per_individual.csv and instead to use dat_indiv.csv which is built from aggregated counts. If you have trouble doing so, feel free to leave an Issue here and someone from The Policy Lab should respond.
To run the code in this repository, you will need Python 3.8+ and R 4.0.2+ pre-installed.
In Python, we use poetry to manage our dependencies. In R, we use renv.
To install these dependency managers, you can run (assumes Mac or Linux; see tools' documentation for other operating systems):
curl -sSL https://install.python-poetry.org | python3 -
Rscript -e 'if(!requireNamespace("remotes")){install.packages("remotes");remotes::install_github("rstudio/renv")} else {remotes::install_github("rstudio/renv")}'Once these are installed, you can install all dependencies with
poetry install
Rscript -e 'renv::restore()'Alternatively, the included Dockerfile will create a working environment with all these dependencies already installed.
In order to run some of this code, you will also need a Census API Key. Once you have the key, copy the .env.sample file to .env and fill in your API key in the specified location.
Our code can generally be divided into four sections:
- Random sampling strategy
- Preparation of data for analysis
- Actual analysis
- Tests
The first two take place in Jupyter notebooks using Python (src/notebooks/100_sampling and src/notebooks/200_analysis). The last takes place in R at src/R.
To recreate our week-over-week sampling strategy, run the notebooks in src/notebooks/100_sampling. These may be accessed through a Jupyter notebook
which you can start by running poetry run jupyter notebook.
To recreate our data preparation, run the notebooks in src/notebooks/200_analysis. These may be accessed through a Jupyter notebook
which you can start by running poetry run jupyter notebook.
There is a little bit of prework to convert the output of the Python scripts to that expected by the R scripts. This is automatically run by make all.
To make the report and all associated figures (each in their own file src/R/400_analysis/output). These are all automatically run by make all.
See the Makefile for the dependencies between files.
Several tests have been written in the tests folder. They may be run with poetry run py.test.
MIT