UN-Water Global Analysis and Assessment of Sanitation and Drinking-water • glaas

UN-Water Global Analysis and Assessment of Sanitation and Drinking-water

GLAAS provides policy and decision-makers at all levels with reliable, easily accessible, comprehensive data on water, sanitation and hygiene (WASH) systems, including on governance, monitoring, human resources and finance. GLAAS monitors elements of WASH systems that are required to sustain and extend WASH services and systems to all, and especially to the most vulnerable population groups.

Installation

You can install the development version of glaas from GitHub with:

devtools::install_github("openwashdata/glaas", dependencies = TRUE)

Download as CSV Files

If you prefer to work with the data outside of R, you can download individual datasets as CSV files.

Right-click on the “Download CSV” link for the dataset you want.
Select “Save Link As” or “Download Linked File” .
Choose where you’d like to save the file on your computer.

dataset	CSV
glaas	Download CSV

Data

library(glaas)

glaas

The dataset glaas has 263033 observations and 18 variables

get(glaas) |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()

GrandParentText	ParentText	IndText_HL	LocText	Time	IsComparable_2013	IsComparable_2016	IsComparable_2018	IsComparable_2021	IsComparable_2024	Dim1ValText	Dim2ValText	Dim3ValText	Dim4ValText	Dim5ValText	Dim6ValText	ValText	DataType
Finance	Domestic absorption	Absorption of domestic capital commitments (estimated %)	Brazil	2016	TRUE	TRUE	TRUE	TRUE	TRUE	Drinking-water	Urban	NA	NA	NA	NA	Between 50 to 75%	Text
Finance	Domestic absorption	Absorption of domestic capital commitments (estimated %)	Brazil	2013	TRUE	TRUE	TRUE	TRUE	TRUE	Drinking-water	Urban	NA	NA	NA	NA	Less than 50%	Text
Finance	Domestic absorption	Absorption of domestic capital commitments (estimated %)	Barbados	2021	FALSE	TRUE	TRUE	TRUE	FALSE	Sanitation	Rural	NA	NA	NA	NA	No response	Text

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

variable_name	variable_type	description
GrandParentText	character	The high-level category or domain of the data, such as Finance, Governance, or Monitoring.
ParentText	character	The specific subcategory or topic within the high-level category, such as Domestic absorption or External funding absorption.
IndText_HL	character	The detailed description or indicator text for the high-level category, providing context or specific details about the data.
LocText	character	The location or country associated with the data, such as Brazil or Barbados.
Time	numeric	The year in which the data was recorded or the time period of the data.
IsComparable_2013	logical	A boolean value indicating whether the data is comparable to the data from 2013.
IsComparable_2016	logical	A boolean value indicating whether the data is comparable to the data from 2016.
IsComparable_2018	logical	A boolean value indicating whether the data is comparable to the data from 2018.
IsComparable_2021	logical	A boolean value indicating whether the data is comparable to the data from 2021.
IsComparable_2024	logical	A boolean value indicating whether the data is comparable to the data from 2024.
Dim1ValText	character	The first dimension value text, representing categories such as Drinking-water or Sanitation.
Dim2ValText	character	The second dimension value text, representing categories such as Urban or Rural.
Dim3ValText	character	The third dimension value text, representing categories such as National* or Address.
Dim4ValText	character	The fourth dimension value text, representing categories such as Behaviour change improvement initiatives or Standards or regulations.
Dim5ValText	character	The fifth dimension value text, representing categories such as Themes or Quality.
Dim6ValText	logical	The sixth dimension value text, representing categories such as Sufficiency or Treated.
ValText	character	The value text, providing specific details or descriptions about the data, such as Between 50 to 75% or No response.
DataType	character	The data type of the column, indicating whether the data is Text or Decimal.

License

Data are available as CC-BY.

Citation

#> To cite package 'glaas' in publications use:
#> 
#>   Massari N (2025). "glaas: UN-Water Global Analysis and Assessment of
#>   Sanitation and Drinking-water." doi:10.5281/zenodo.15497462
#>   <https://doi.org/10.5281/zenodo.15497462>,
#>   <https://github.com/openwashdata/glaas>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{massari:2025,
#>     title = {glaas: UN-Water Global Analysis and Assessment of Sanitation and Drinking-water},
#>     author = {Nicolo Massari},
#>     year = {2025},
#>     doi = {10.5281/zenodo.15497462},
#>     url = {https://github.com/openwashdata/glaas},
#>     abstract = {GLAAS provides policy and decision-makers at all levels with reliable, easily accessible, comprehensive data on water, sanitation and hygiene (WASH) systems, including on governance, monitoring, human resources and finance. GLAAS monitors elements of WASH systems that are required to sustain and extend WASH services and systems to all, and especially to the most vulnerable population groups.},
#>     version = {0.1.3},
#>   }