wsabrazil

This package contains information related to wastewater management practices and household infrastructure in the Metropolitan Region of Belém, Brazil. It includes variables such as sector codes, metropolitan region names, municipality codes, and names, as well as data on the location type, living conditions, average income, and household amenities. The data provides insights into wastewater disposal habits, water supply sources, bathroom facilities, and sanitation infrastructure in Brazilian households, contributing to understanding environmental sustainability and infrastructure development efforts. “Atlas of the 2010 Demographic Census - PGI” (n.d.)

Based on the data, it appears for example that the municipality of Marituba exhibits poorer housing conditions compared to others. In this dataset, housing conditions are represented numerically, with ‘1’ indicating correct housing conditions and ‘0’ indicating poor housing conditions. The location map displays the Metropolitan Region of Belém.

Installation

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

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

Alternatively, you can download the dataset as a CSV or XLSX file from the table below.

dataset	CSV	XLSX
wsabrazil	Download CSV	Download XLSX

Data

The dataset includes observations of wastewater management practices and household infrastructure (access to water and sanitation services) across various regions in Brazil.

The data was collected in 2010 and the package provides access to one single dataset.

library(wsabrazil)

The wsabrazil dataset has 19 variables and 1965 observations. For an overview of the variable names, see the following table.

wsabrazil

variable_name	variable_type	description
sector_code	double	sector code
MR_name	character	metropolitan region name
municipality_code	integer	municipality code
municipality_name	character	municipality name
sector_situation	character	location type: urban or rural
sector_type	integer	living conditions: 1 stands for correct housing conditions, 0 stands for poor housing conditions
avg_income	integer	average nominal monthly income of permanent private households
total_households	integer	number of permanent private households
piped_water	integer	number of permanent private households with water supply by piped network
well_spring_water	integer	number of permanent private households with water supply by property’s well or spring
stored_rainwater	integer	number of permanent private households with water supply by stored rainwater
other_water_source	integer	number of permanent private households with water supply by other source
private_bathroom	integer	number of permanent private households with private bathroom or toilet
bathroom_sewerage	integer	number of permanent private households with private bathroom or toilet & sanitation via sewerage or drainage network
bathroom_septic_tank	integer	number of permanent private households with private bathroom or toilet & sanitation via septic tank
bathroom_cesspit	integer	number of permanent private households with private bathroom or toilet & sanitation via cesspit
bathroom_ditch	integer	number of permanent private households with private bathroom or toilet & wastewater discharged into ditch
bathroom_waterbodies	integer	number of permanent private households with private bathroom or toilet & wastewater discharged into water bodies (river, lake or sea)
bathroom_other	integer	number of permanent private households with private bathroom or toilet & wastewater discharged into other outlet

Example

From the dataset, we can explore the distribution of water sources in the Metropolitan Region of Belém. We create here a horizontal bar plot to visualize the frequency of different water sources available, utilizing variables such as piped water or stored rainwater. We observe from the resulting plot (see Figure below) that the majority of private households are supplied by piped water. Interestingly, none of the households store rainwater. This is possibly due to factors such as local climate patterns and infrastructure limitations.

library(dplyr)
library(ggplot2)
library(wsabrazil)

# Reshape the data into long format
data_long <- wsabrazil |> 
  tidyr::pivot_longer(cols = piped_water:other_water_source, 
                      names_to = "water_source", 
                      values_to = "frequency")
  
# Create a horizontal bar plot of water source types
plot <- ggplot(data_long, aes(x = frequency, y = reorder(water_source, frequency))) +
  geom_col(fill = "#3399FF") +
  # labs(x = "Frequency", y = "Water Source", 
       title = "Water Supply in Belém", 
       caption = "") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", color = "#333333"))

plot + scale_x_continuous(labels = scales::number_format())

License

Data are available as CC-BY.

Citation

To cite this package, please use:

citation("wsabrazil")
#> To cite package 'wsabrazil' in publications use:
#> 
#>   Götschmann M, Santos L (2024). "wsabrazil: Wastewater management and
#>   household infrastructure in Brazil - Demographic Census 2010."
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{gotschmann_etall:2024,
#>     title = {wsabrazil: Wastewater management and household infrastructure in Brazil - Demographic Census 2010},
#>     author = {Margaux Götschmann and Lais Santos},
#>     year = {2024},
#>     abstract = {This dataset about wastewater management and household infrastructure from various Brazilian regions provides insights into wastewater disposal habits, water sources, bathroom facilities, and sanitation infrastructure.},
#>     version = {0.0.0.9000},
#>   }

References

“Atlas of the 2010 Demographic Census - PGI.” n.d. https://censo2010.ibge.gov.br/apps/atlas/#/home/.