README.Rmd

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output: github_document
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```{r, include = FALSE}
knitr::opts_chunk$set(
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  fig.path = "man/figures/README-",
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)

library(tidyverse)
```

# gdho

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The goal of `gdho` is to provide a detailed list of global humanitarian
organizations complied by [Humanitarian
Outcomes](https://www.humanitarianoutcomes.org/projects/gdho) which
contains basic information such as organization website and headquarter
location, as well as, operational information such as annual
expenditure. The original database is updated annually and this package
version uses data retrieved on September 20, 2023.

[![Humanitarian Organization Headquarters
Concentration](man/figures/world_map-1.png)](https://openwashdata.github.io/gdho/articles/examples.html)

## Installation

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

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

Alternatively, you can download the individual datasets as CSV or XLSX
file from the table below.

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

extdata_path <- "https://github.com/openwashdata/gdho/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 2 datasets `gdho` and `gdho_full`. They
are essentially the same data where the former is a concise version that
removes detailed country columns (200+ columns) about whether this
country has the humanitarian organization. Therefore here we only
describe the dataset `gdho`.

> All the organisations included the database have responded to
> humanitarian needs in at least one emergency context, individually or
> in partnership with other organisations, even if their stated mission
> is not strictly humanitarian. Not included are NGOs devoted to
> development, human rights, or political causes, or that do not work in
> emergency settings.

```{r}
library(gdho)
```

The `gdho` data set has `r {ncol(gdho)}` variables and `r {nrow(gdho)}`
observations.

```{r message=FALSE, warning=FALSE}
gdho |>
  head() |>
  gt::gt() |>
  gt::as_raw_html()
```

For an overview of the variable names, see the following table.

```{r, echo=FALSE}
readr::read_csv("data-raw/dictionary.csv") |>
  dplyr::filter(file_name == "gdho.rda") |>
  dplyr::select(variable_name:description) |> 
  knitr::kable() |> 
  kableExtra::kable_styling() |> 
  kableExtra::scroll_box(height = "400px")
```

## Example

The humanitarian organizations are categorized into 5 types: INGO
(International NGO), NNGO (National NGO), UN (United Nation), Red
Cross/Crescent, and NA (Not Available). Most of the organizations fall
into the NNGO type. For different types of organizations, how is their
organization reach distribute?

```{r}
ggplot(data = gdho) +
  geom_bar(aes(x = type, fill=`international_or_national`)) +
  labs(title = "Organization type distribution", fill = "organization reach")
```

Throughout the years, how do different types of humanitarian
organizations increase?

```{r}
count_by_year <- gdho |>
  filter(!is.na(year_founded)) |>
  group_by(year_founded, type) |>
  summarise(count = n())
ggplot(data = count_by_year) +
  geom_line(aes(x = year_founded, y = count, color = type)) +
  labs(title = "Temporal trend of founding humanitarian organizations", color = "organization type")
```

# License

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

# Citation

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

## Related References

[1] [GDHO project
description](https://www.humanitarianoutcomes.org/projects/gdho)