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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 -->

[![License: CC BY
4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](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

VersionRepositoryFileSize
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

Dependencies (latest)

Imports

Suggests