### Description
This repository includes R code to run all of the analysis for the paper:
Ante-Testard PA, Rerolle F, Nguyen AT, Ashraf S, Parvez SM, Naser AM, Benmarhnia T, Rahman M, Luby SP, Benjamin-Chung J, Arnold BF. **WASH interventions and child diarrhea at the interface of climate and socioeconomic position in Bangladesh.** *Nature Communications*. (2024) https://doi.org/10.1038/s41467-024-45624-1
This work was funded by the Bill & Melinda Gates Foundation (OPPGD759) and the National Institute of Allery and Infectious Diseases (R01-AI166671).
If you have any questions about the files in this repository, please contact Pearl Ante-Testard at UCSF (pearl.ante@ucsf.edu).
### Systems Requirement
All analyses were running using R software version 4.2.1 on Mac OSX Ventura using the RStudio IDE (https://www.rstudio.com).
`> sessionInfo()`
`R version 4.2.1 (2022-06-23)`
`Platform: x86_64-apple-darwin17.0 (64-bit)`
`Running under: macOS Ventura 13.4.1`
### Installation Guide
You can download and install R from CRAN: https://cran.r-project.org
You can download and install RStudio from their website: https://www.rstudio.com
All R packages required to run the analyses are sourced in the file `0-config.R`.
The installation time should be < 10 minutes total on a typical desktop computer.
### Instructions for Use
To run the analyses:
1. Clone the GitHub repository in your computer.
2. Create subdirectories named: `1-data/0-untouched`,`1-data/0-gadm`,`1-data/0-terraclim`, `1-data/0-terraclim/ppt`, `1-data/0-terraclim/tmax`, `1-data/0-terraclim/soil`, `1-data/0-worldpop` and `1-data/2-final`.
3. Create subdirectories to save the output for each analysis: effect modification analysis (`2-initial-analysis/output`), spatial analysis (`3-secondary-analysis/output`) and supplementary analysis (`4-supplementary/output`).
4. Save the public raw datasets of the WASH Benefits Bangladesh trial (https://osf.io/wvyn4/) in the subdirectory `1-data/0-untouched`.
5. Join and format datasets directly using scripts in the `1-dm` folder. Save formatted datasets in `1-data/2-final`.
6. The Bangladesh geospatial data can be downloaded from:
GADM shapefiles: https://gadm.org/ (save to `1-data/0-gadm`)
WorldPop population and wealth layers with GPS: https://www.worldpop.org/ (save to `1-data/0-worldpop`)
Precipitation data with GPS used to create the heavy rainfall variable: `https://www.gloh2o.org/mswep/` (The formatted dataset in the OSF does not include GPS. However, the script on how to create the heavy rainfall indicator is available `4-supplementary/R/6_heavy_rain_ind.R)`.
TerraClimate data: https://www.climatologylab.org/terraclimate.html (save to `1-data/0-terraclim`). The formatted TerraClimate datasets are not included since they include the GPS. However, the script on how to create the TerraClimate indicators are available `4-supplementary/R/4_read_terraclim_data.R`)
TerraClimate precipitation: `1-data/0-terraclim/ppt`
TerraClimate maximum temperature: `1-data/0-terraclim/tmax`
TerraClimate soil moisture: `1-data/0-terraclim/soil`
N.B. The GPS of the study clusters are not available. Our IRB has deemed that sharing this information could potentially compromise participant privacy. However, this limitation only applies to the spatial analysis; the scripts are accessible, excluding the geographic location data.
