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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.

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wsabrazil

License: CC BY 4.0 R-CMD-check

This package contains information related to wastewater management practices and household infrastructure in 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 most of the municipalities exhibits poor housing conditions. In this dataset, housing conditions are represented numerically, with ‘1’ indicating correct housing conditions and ‘0’ indicating poor housing conditions. The location map displays all Brazilian municipalities from which data has been collected.

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 package provides access to one single dataset.

library(wsabrazil)

The wsabrazil dataset has 33 variables and 192896 observations. For an overview of the variable names, see the following table.

wsabrazil
variable_name variable_type description
sector_code double Sector code
great_region_name character Name of the great region
FU_code integer Federation unit code
FU_name character Federation unit name
meso_code integer Mesoregion code
meso_name character Mesoregion name
micro_code integer Microregion code
micro_name character Microregion name
MR_code integer Metropolitan region code
MR_name character Metropolitan region name
municipality_code integer Municipality code
municipality_name character Municipality name
district_code integer District code
district_name character District name
subdistrict_code integer Subdistrict code
subdistrict_name character Subdistrict name
neighb_code integer Neighborhood code
neighb_name character Neighborhood 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

Examples

1. Housing conditions across municipalities

The location map displayed above was created as follows:

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

shapefile <- st_read("man/gadm41_BRA_2.json")
merged_data <- merge(shapefile, wsabrazil, by.x = "CC_2", by.y = "municipality_code")

# Plot the choropleth map
ggplot() +
  geom_sf(data = merged_data, aes(fill = as.factor(sector_type))) +
  scale_fill_manual(name = "sector_type", values = c("0" = "#E69F00", "1" = "#0072B2"),
                    labels = c("0" = "poor", "1" = "correct")) +
  labs(title = "Housing conditions across municipalities") +
  theme(plot.title = element_text(hjust = 0.5, face = "bold", color = "#333333", size = 24),
        legend.title = element_text(face = "bold", color = "#333333", size = 16),
        legend.text = element_text(color = "#333333", size = 16))

2. Water supply in Brazil

From the dataset, we can also explore the distribution of water sources in the whole country. 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, almost 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)
library(tidyr)

data_long_summary <- wsabrazil |> 
  pivot_longer(cols = piped_water:other_water_source, 
                      names_to = "water_source", 
                      values_to = "frequency") |> 
  group_by(water_source) |> 
  summarise(total_frequency = sum(frequency, na.rm = TRUE)) |> 
  arrange(total_frequency)

# Create a horizontal bar plot of water source types
plot <- ggplot(data_long_summary, aes(x = total_frequency, y = reorder(water_source, total_frequency))) +
  geom_col(fill = "#0072B2") +
  labs(x = "Frequency", y = "Water Source", 
       title = "Water supply in Brazil", 
       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."
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{gotschmann_etall:2024,
#>     title = {wsabrazil: Wastewater management and household infrastructure in Brazil},
#>     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/.

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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.

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