The goal of this package is to facilite the use and analysis of data form the WHO/UNICEF Joint Monitoring Programme for Water and Sanitation. It provides a tidy snapshot of the JMP WASH household, WASH in schools and WASH in health care facilities data that is normally available in Excel sheets on https://washdata.org. The excel sheets filenames and date downloaded are stored in the jmpwashdata::jmp_files data frame as a reference. The last download for jmpwashdata version 0.1.4 took place on 2023-01-15.
The goal is to keep the package up to date with changes on the JMP website and eventually to automate this process. If data are out of data with the main JMP website, please feel free to post an issue so we can rebuild it: https://github.com/WASHNote/jmpwashdata/issues
Please support the development and maintenance of this package. The simplest way to do this is to provide us with attribution.
citation(package = "jmpwashdata")
#>
#> To cite package 'jmpwashdata' in publications use:
#>
#> Nicolas Dickinson (2021). jmpwashdata: WHO/UNICEF Joint Monitoring
#> Programme Water and Sanitation Data. R package version 0.1.4.
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {jmpwashdata: WHO/UNICEF Joint Monitoring Programme Water and Sanitation Data},
#> author = {Nicolas Dickinson},
#> year = {2021},
#> note = {R package version 0.1.4},
#> }
#>
#> ATTENTION: This citation information has been auto-generated from the
#> package DESCRIPTION file and may need manual editing, see
#> 'help("citation")'.The easiest way to install this is by using devtools. You may install devtools as follows:
install.packages("devtools")Simply run the following code.
devtools::install_github("WASHNote/jmpwashdata")You cannot yet install from CRAN. The package will be submitted to CRAN as soon as the documentation has been completed. Rather. you must build it from source and the easiest way to do this is with devtools.
For those interested in contributing to the development of the package, you may also clone the repository and open it in RStudio.
- v.0.1.4 February 2023 Update of data and cleanup of some of the extraction messages.
- v.0.1.3 November 2021 Addition of extraction of regional and world school and healthcare facility datasets.
- v.0.1.2 October 2021 Update of data files to include the new world and region files and changes in other files and to add more error handling. Includes now the data summary sheets found in the inequality files parsed to be in a cleaner long format.
- v.0.1.1 July 2021 New published data files extracted with the 2019 and 2020 data sets from JMP Excel sheets.
- v.0.1.0 June 2021 Extraction of 2017 JMP files.
- Complete codebook of all jmp datasets and of the package
- Complete labeling of all of the datasets
- Complete how-to documentation and several case studies to demonstrate use
- Add WASH in Schools and WASH in Health Care Facilities country files.
- Add use cases on combining with other data sets (national monitoring data, country TrackFin studies, etc.)
- Add tests for data extraction and validation to cross validate country files against world files and different sheets against one another (as an extraction test and internal validation of the data sets).
- Add helper functions to transform world and regional data between the original wide format and a long format.
- Standardize the (long) data format used by datasets in the package.
- Automate rebuilds using file hashes and sampling and a periodic poll of the JMP website
- Post article on “Enhancing the use and quality of official statistics using open source”
- Python wrapper library for easy inclusion in Python projects
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