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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 with:

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

dataset CSV XLSX
unhcrwash Download CSV Download XLSX

Data

The package provides access to WASH indicators in refugee camps and settlements

unhcrwash

The dataset unhcrwash contains data about WASH indicators in refugee camps and settlements. It has 6425 observations and 27 variables

unhcrwash |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()
form_id start_date end_date location_id location_name country post_emergency persons_per_handpump persons_per_tap liters_per_person_per_day non_chlorinated_0_cfu chlorinated_safe_water_quality households_with_toilet persons_per_toilet persons_per_shower persons_per_hygiene_promoter refugee_pop reporting_monthly liters_per_person_household potable_water_storage_10l protected_water_sources menstrual_hygiene_satisfaction household_toilet defecate_in_toilet access_to_soap solid_waste_disposal_access reporting_annual
54418187 2024-01-01 2024-12-31 518 Dagahaley Kenya NA NA NA NA NA NA NA NA NA NA NA NA 18.2 99 98 64 42 83 87 96 2024-05-17
54418188 2024-01-01 2024-12-31 519 Hagadera Kenya NA NA NA NA NA NA NA NA NA NA NA NA 13.4 98 97 72 39 91 75 99 2024-05-17
54418189 2024-01-01 2024-12-31 520 Ifo Kenya NA NA NA NA NA NA NA NA NA NA NA NA 15.0 99 87 63 43 89 69 95 2024-05-17

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

variable_name variable_type description
form_id double Unique Identifier for the form
date_start double Start Date for the data collection
date_end double End date for the data collection
location_id double Unique Identifier for the location of the refugee camp
location_name character Name of the location of the refugee camp
country_name character Country in which the refugee camp is located
emergency_post_emergency character If emergency is ongoing or not (Emergency or Post Emergency)
number_of_persons_per_usable_handpump_well_spring double No. of persons per usable handpump well spring
number_of_persons_per_usable_water_tap double Number of persons per unit of usable tap water source
average_number_liters_of_potable_water_available_per_person_per_day double Average number of litres of potable water available per person per day
percent_water_quality_tests_at_non_chlorinated_water_collection_locations_with_0_cfu_100ml double Percentage of water quality tests conducted at non-chlorinated water collection points that returned results with 0 colony-forming units (CFU) per 100 milliliters
percent_of_water_quality_tests_at_chlorinated_collection_locations_with_frc_in_the_range_0_2_2mg_l_and_turbidity_5ntu5 double Percentage of water quality tests at chlorinated collection points where the Free Residual Chlorine (FRC) levels were within the range of 0.2 to 2 mg per L, and turbidity was less than or equal to 5 NTU (Nephelometric Turbidity Units)
percent_households_with_household_toilet_latrine_monthly double Percentage of households that have access to a household toilet or latrine, evaluated on a monthly basis
number_of_persons_per_toilet_latrine double Number of persons per toilet latrine
number_of_persons_per_bath_shelter_shower double Number of persons per bath shower
number_of_persons_per_hygiene_promoter double Number of persons per hygiene promoter
refugee_population double Refugee Population
reporting_period_monthly_indicator double Monthly Reporting Period
average_number_l_p_d_of_potable_water_collected_at_household_level double Average water (litres) collected per household per day
percent_households_with_at_least_10_liters_person_potable_water_storage_capacity double Percentage of households with at least 10 litres of water storage capacity
percent_households_collecting_drinking_water_from_protected_treated_sources double Percentage of households collecting water from sheltered and treated sources
percent_of_women_of_reproductive_age_who_are_satisfied_with_menstrual_hygiene_management_materials_and_facilities double Percentage of women of reproductive age satisfied with facilities for management of menstrual hygiene and waste
percent_households_with_household_toilet_latrine double Percentage of households with a latrine
percent_households_reporting_defecating_in_a_toilet double Percentage of households defecating in a toilet
percent_households_with_access_to_soap double Percentage of households with access to soap
percent_households_with_access_to_solid_waste_disposal_facility double Percentage of households with access to a waste disposal facility
reporting_period_annual_indicator double Annual Reporting Period

Example

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()
country avg_water_avail
Iraq 99.06584
Democratic Republic of the Congo 43.85081
Jordan 40.07407
Ghana 38.13077
Bangladesh 35.86667
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()

# 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()
country total_population
Uganda 33730168
Ethiopia 30630168
Kenya 18034151
Chad 15437507
Sudan 12848789

License

Data are available as CC-BY.

Citation

Please cite this package using:

citation("unhcrwash")
#> To cite package 'unhcrwash' in publications use:
#> 
#>   Dubey Y (2024). "unhcrwash: WASH Data From Refugee Camps and
#>   Settlements (UNHCR)." doi:10.5281/zenodo.14185117
#>   <https://doi.org/10.5281/zenodo.14185117>,
#>   <https://github.com/openwashdata/unhcrwash>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{unhcrwashwash:2024,
#>     title = {unhcrwash: WASH Data From Refugee Camps and Settlements (UNHCR)},
#>     year = {2024},
#>     author = {Yash Dubey},
#>     doi = {10.5281/zenodo.14185117},
#>     url = {https://github.com/openwashdata/unhcrwash},
#>     abstract = {This is a dataset on WASH indicators in refugee camps and settlements. The data is collected from the UNHCR Information Management System (IRIS) and is available on the UNHCR WASH dashboard. This dataset includes data from 191 sites and 29 countries.},
#>     version = {0.1.0},
#>   }