README.Rmd

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knitr::opts_chunk$set(
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# unhcrwash

<!-- 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/)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14185117.svg)](https://zenodo.org/doi/10.5281/zenodo.14185117)
<!-- badges: end -->

The goal of unhcrwash is to make available data on WASH indicators in refugee camps and settlements.

## Installation

You can install the development version of unhcrwash from
[GitHub](https://github.com/) with:

``` r
# install.packages("devtools")
devtools::install_github("openwashdata/unhcrwash")
```

```{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.

```{r, echo=FALSE, message=FALSE, warning=FALSE}

extdata_path <- "https://github.com/openwashdata/unhcrwash/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 WASH indicators in refugee camps and settlements 

```{r}
library(unhcrwash)
```

### unhcrwash

The dataset `unhcrwash` contains data about WASH indicators in refugee camps and settlements.
It has `r nrow(unhcrwash)` observations and `r ncol(unhcrwash)` variables

```{r}
unhcrwash |> 
  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 == "unhcrwash.rda") |>
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling("striped") |> 
  kableExtra::scroll_box(height = "200px")
```


## Example

```{r}
library(unhcrwash)

# Average Water Availability by Country 
unhcrwash |> dplyr::group_by(country) |> 
  dplyr::summarise(avg_water_avail = mean(liters_per_person_per_day, na.rm = TRUE)) |> 
  dplyr::arrange(desc(avg_water_avail)) |> 
  head(5) |> 
  gt::gt() |> 
  gt::as_raw_html()
```

```{r}
library(ggplot2)
# Toilet availability
unhcrwash |> 
  dplyr::mutate(year = lubridate::year(as.Date(start_date))) |>  # Extract year as whole number
  dplyr::filter(!is.na(year) & !is.na(persons_per_toilet)) |>    # Remove missing values
  dplyr::group_by(year) |> 
  dplyr::summarise(avg_persons_per_toilet = mean(persons_per_toilet, na.rm = TRUE)) |> 
  ggplot2::ggplot(aes(x = year, y = avg_persons_per_toilet)) +
  ggplot2::geom_line() +
  ggplot2::labs(title = "Average Persons per Toilet in Refugee Camps",
                x = "Year",
                y = "Average persons per toilet") +
  ggplot2::theme_minimal()
```

```{r}
# Countries with highest refugee populations
unhcrwash |> 
  dplyr::group_by(country) |> 
  dplyr::summarise(total_population = sum(refugee_pop, na.rm = TRUE)) |> 
  dplyr::arrange(desc(total_population)) |> 
  head(5) |> 
  gt::gt() |> 
  gt::as_raw_html()
```


## License

Data are available as
[CC-BY](https://github.com/openwashdata/unhcrwash/blob/main/LICENSE.md).

## Citation

Please cite this package using:

```{r}
citation("unhcrwash")
```