washr
Publication Toolkit for Water, Sanitation and Hygiene (WASH) Data
A toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management.
README
---
output: github_document
always_allow_html: true
editor_options:
markdown:
wrap: 72
chunk_output_type: console
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
message = FALSE,
warning = FALSE,
fig.retina = 2,
fig.align = 'center'
)
```
# {{{packagename}}}
<!-- badges: start -->
[](https://creativecommons.org/licenses/by/4.0/)
<!-- badges: end -->
The goal of {{{packagename}}} is to ...
## Installation
You can install the development version of {{{packagename}}} from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("openwashdata/{{{packagename}}}")
```
```{r}
## Run the following code in console if you don't have the packages
## install.packages(c("dplyr", "knitr", "readr", "stringr", "gt", "kableExtra"))
library(dplyr)
library(knitr)
library(readr)
library(stringr)
library(gt)
library(kableExtra)
```
Alternatively, you can download the individual datasets as a CSV or XLSX
file from the table below.
1. Click Download CSV. A window opens that displays the CSV in
your browser.
2. Right-click anywhere inside the window and select "Save Page As...".
3. Save the file in a folder of your choice.
```{r, echo=FALSE, message=FALSE, warning=FALSE}
extdata_path <- "https://github.com/openwashdata/{{{packagename}}}/raw/main/inst/extdata/"
read_csv("data-raw/dictionary.csv") |>
distinct(file_name) |>
dplyr::mutate(file_name = str_remove(file_name, ".rda")) |>
dplyr::rename(dataset = file_name) |>
mutate(
CSV = paste0("[Download CSV](", extdata_path, dataset, ".csv)"),
XLSX = paste0("[Download XLSX](", extdata_path, dataset, ".xlsx)")
) |>
knitr::kable()
```
## Data
The package provides access to ...
```{r}
library({{{packagename}}})
```
### {{{dataname}}}
The dataset `{{{dataname}}}` contains data about ... It has
`r nrow({{{dataname}}})` observations and `r ncol({{{dataname}}})`
variables
```{r}
{{{dataname}}} |>
head(3) |>
gt::gt() |>
gt::as_raw_html()
```
For an overview of the variable names, see the following table.
```{r echo=FALSE, message=FALSE, warning=FALSE}
readr::read_csv("data-raw/dictionary.csv") |>
dplyr::filter(file_name == "{{{dataname}}}.rda") |>
dplyr::select(variable_name:description) |>
knitr::kable() |>
kableExtra::kable_styling("striped") |>
kableExtra::scroll_box(height = "200px")
```
## Example
```{r}
library({{{packagename}}})
# Provide some example code here
```
## License
Data are available as
[CC-BY](https://github.com/openwashdata/%7B%7B%7Bpackagename%7D%7D%7D/blob/main/LICENSE.md).
## Citation
Please cite this package using:
```{r}
citation("{{{packagename}}}")
```
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0.1 |
rolling linux/jammy R-4.5 | washr_1.0.1.tar.gz |
50.0 KiB |
1.0.1 |
rolling linux/noble R-4.5 | washr_1.0.1.tar.gz |
49.9 KiB |
1.0.1 |
rolling source/ R- | washr_1.0.1.tar.gz |
21.3 KiB |
1.0.1 |
latest linux/jammy R-4.5 | washr_1.0.1.tar.gz |
50.0 KiB |
1.0.1 |
latest linux/noble R-4.5 | washr_1.0.1.tar.gz |
49.9 KiB |
1.0.1 |
latest source/ R- | washr_1.0.1.tar.gz |
21.3 KiB |
1.0.1 |
2026-04-26 source/ R- | washr_1.0.1.tar.gz |
21.3 KiB |
1.0.1 |
2026-04-23 source/ R- | washr_1.0.1.tar.gz |
21.3 KiB |
1.0.1 |
2026-04-09 windows/windows R-4.5 | washr_1.0.1.zip |
53.9 KiB |
1.0.1 |
2025-04-20 source/ R- | washr_1.0.1.tar.gz |
21.3 KiB |