This repository contains data and code associated with the manuscript Analysis of Fixture Efficiency and Behavioral Factors of Indoor Residential Water Use of Single-Family Households. The repository is organized into data files and code files as detailed below.

## Data Files

1. City Shapefile: Located in the folder `city_shapefile`
   - Contains metropolitan statistical area (MSA) shapefiles 

2. Weather Data: Located in the folder `weather_data` and subfolder `PRISM`
   - Contains raster weather data from PRISM (Parameter-elevation Regressions on Independent Slopes Model)
   - Processed (tabular format) weather data are available in `weather_vars_process.csv`. Each weather variable in this file represents the average of pixels for each day within each city

3. Household Data: `num_unique_hhs.csv`
   - Contains the number of unique households within each city

4. Clustering Results: `clustering_results.csv`
   - Contains results and agreements of clustering of households within each of the three household categories using both kmeans and functional kmeans algorithms

5. Detailed Clustering Results: `household_cluster_wi_cities.csv`
   - Contains the detailed results of clustering (only agreements between the two methods used) within each city

6. End Use Characteristics: `end_use_charac.csv`
   - Contains the mean average and standard error of each end use characteristic (i.e., flowrate, frequency, and duration). Data is organized within each household category

7. Statistical Test Results: Folder `statistical_test_results_and_descriptive_stats`
   - Contains detailed results from Chi-square tests of independence (for categorical variables)
   - Contains detailed results from Kruskal-Wallis tests (for continuous variables)
   - Includes descriptive statistics for both categorical and continuous variables across household categories
   - Used to answer research question one in the manuscript

8. Linear Modeling Results: Folder `linear_modeling_results`
   - Contains detailed results of GEE (Generalized Estimating Equation) models
   - Contains detailed results of ME (Mixed Effects) models
   - Includes parameter estimates and level of uncertainty
   - Includes model diagnostics (e.g., variance inflation factors) for each model described in the manuscript

## Code Files

### R Scripts

1. ME.R
   - Runs the mixed effects models for research questions two and three
   - Note: The dataframes used to run this code are not included due to privacy reasons. Refer to the Data Availability Statement in the manuscript for details

2. GEE.R
   - Runs the generalized estimating equation models for research questions two and three
   - Note: The dataframes used to run this code are not included due to privacy reasons. Refer to the Data Availability Statement in the manuscript for details

### Python Jupyter Notebooks

1. Prism_&_ACS_data.ipynb
   - Used to access and/or process the PRISM and US Census data

2. Clustering.ipynb
   - Conducts the kmeans and functional kmeans clustering
   - Visualizes clustering results

3. Figures.ipynb
   - Produces some of the figures used in the manuscript and supplementary information (SI) section

4. Statistical_tests.ipynb
   - Visualizes the statistical tests results used to answer research question one

5. ME_models_visuals.ipynb
   - Visualizes the results of the Mixed Effects models for the research questions two and three

6. GEE_models_visual.ipynb
   - Visualizes the results of the Generalized Estimating Equation models for the research questions two and three