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waschoolpiracema

License: CC BY 4.0 R-CMD-check DOI

The goal of waschoolpiracema is to describe the profile of schools from the basic education system in the municipality of Piracema (Minas Gerais, Brazil). Moreover, the data compare the characteristics of schools, with a special concern to WASH, pre- (2020), peri- (2021) and post-COVID-19 pandemic (2022) to evaluate to what extend schools in Piracema made progress in providing WASH since the beginning of the COVID-19 pandemic.

Installation

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

# install.packages("devtools")
devtools::install_github("openwashdata/waschoolpiracema")
## 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
waschoolpiracema Download CSV Download XLSX

Data

The municipality of Piracema is located in the southeast region of Brazil, in the state of Minas Gerais. Piracema is a small size city adding up to 6,700 inhabitants (IBGE, 2023). Among all Brazilian territories, it ranks as the 3,734Âş smallest municipality out of 5,570 and in Minas Gerais it ranks as the 492Âş out of 853 (IBGE, 2023).

Piracema is located approximately 120 km away from the capital of its state (Belo Horizonte), and it is inaccessible by public transportation (IBGE, 2023). Piracema will be the study area for the next phase of the research (collection of primary data).

library(waschoolpiracema)

waschoolpiracema

The dataset waschoolpiracema contains data about the water supply, the sewage disposal, the waste collection and the sanitary equipment of the schools in Piracema. It also provides information about gender, race and education levels of the school’s students. It has 21 observations and 36 variables

waschoolpiracema |> 
  head(3) |> 
  gt::gt() |>
  gt::as_raw_html()
year sch_id admin loc drink_water public_water borehole_water well_water surface_water no_water sewage_rede_publica sewage_fossa_septica waste_servico_coleta waste_queima waste_enterra waste_destino_final_publico waste_descarta_outra_area sanitary sanitary_ei sanitary_pne sanitary_funcionarios sanitary_chuveiro qt_mat_bas pc_girl pc_boy pc_white pc_brown pc_black pc_indian pc_asian pc_nd pc_cre pc_pre pc_prim_1 pc_prim_2 pc_sec
2020 31035386 state urban TRUE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE FALSE 678 51.91740 48.08260 45.13274 41.445428 10.324484 0.2949853 0.2949853 2.507375 0 0 22.56637 39.67552 32.15339
2020 31039918 municipal rural TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE TRUE TRUE 14 50.00000 50.00000 42.85714 7.142857 0.000000 0.0000000 0.0000000 50.000000 0 0 100.00000 0.00000 0.00000
2020 31039926 municipal rural TRUE FALSE TRUE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE 127 59.05512 40.94488 45.66929 25.984252 2.362205 0.0000000 1.5748031 24.409449 0 0 100.00000 0.00000 0.00000

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

variable_name variable_type description
year double Year of Survey
sch_id double Numerical code of the school
admin factor The administration of the school is federal (1); state (2); municipal (3), or private (4). Federal, state, and municipal schools are considered public.
loc factor The school is located in an urban (1) or rural area (2)
drink_water logical The school provides drinking water with quality suitable for human consumption (i.e., ingestion, preparation, and production of food) according to the Brazilian national water quality standards (former Portaria nº 2.914/2011 now Portaria de Consolidação nº5/2017)
public_water logical The water in the school is supplied by a public network .
borehole_water logical The water in the school is supplied by a borehole
well_water logical The water in the school is supplied by a cacimba, cistern, or well
surface_water logical The water in the school is supplied by surface water source
no_water logical There is no water supply in the school
sewage_rede_publica logical The school dispose their sewage into a public sewerage system
sewage_fossa_septica logical The school dispose their sewage into septic tank
waste_servico_coleta logical The solid waste in the school is regularly collected by the public cleaning service
waste_queima logical The solid waste in the school is disposed in an area licensed by environmental agencies, intended to receive solid waste in a planned manner (e.g., landfills)
waste_enterra logical The solid waste in the school is burned or incinerated
waste_destino_final_publico logical The solid waste in the school is buried
waste_descarta_outra_area logical The solid waste in the school is disposed in another area (none of the other options)
sanitary logical The school is equipped with sanitary facilities for personal hygiene/physiological needs
sanitary_ei logical The school is equipped with sanitary facilities for children 0 to 5 years old
sanitary_pne logical The school is equipped with disability-friendly sanitary facilities following the national guidelines (ABNT - NBR 9050)
sanitary_funcionarios logical The school is equipped with sanitary facilities for personal hygiene/physiological needs exclusively for staff
sanitary_chuveiro logical The school is equipped with sanitary facilities or changing room or washing room with appropriate equipment (shower) for bathing, exclusively for students
qt_mat_bas double Total number of students per school
pc_girl double Percentage of girls per school
pc_boy double Percentage of boys per school
pc_white double Percentage of students that are classified or self-identified as white race/skin color per school
pc_brown double Percentage of students that are classified or self-identified as black race/skin color per school
pc_black double Percentage of students that are classified or self-identified as brown race/skin color per school
pc_indian double Percentage of students that are classified or self-identified as Asian race/skin color per school
pc_asian double Percentage of students that are classified or self-identified as indigenous race/skin color per school
pc_nd double Percentage of students that did not declared race/skin color per school
pc_cre double Percentage of students in daycare (0 - 3 years old)
pc_pre double Percentage of students in preschool (4 - 5 years old)
pc_prim_1 double Percentage of students in primary education first cycle (6 - 10 years old)
pc_prim_2 double Percentage of students in primary education second cycle (11 - 14 years old)
pc_sec double Percentage of students in secondary education (15 - 18 years old)

Examples

# Load necessary libraries
library(waschoolpiracema)
library(ggplot2)
library(dplyr)
library(tidyr)

# Load the dataset
load("data/waschoolpiracema.rda")

# Convert admin to a factor with descriptive labels
waschoolpiracema$admin <- factor(waschoolpiracema$admin, levels = c(1, 2, 3, 4),
                     labels = c("Federal", "State", "Municipal", "Private"))
# Create the plot
ggplot(waschoolpiracema, aes(x = qt_mat_bas, y = pc_girl, color = as.factor(admin))) +
  geom_point() +
  labs(title = "Percentage of Girls vs Total Number of Students per School",
       x = "Total Number of Students",
       y = "Percentage of Girls",
       color = "Administration Type") +
  theme_minimal()

# Summarize the data to get average percentages per year
summary_data <- waschoolpiracema %>%
  group_by(year) %>%
  summarise(avg_pc_girl = mean(pc_girl, na.rm = TRUE),
            avg_pc_boy = mean(pc_boy, na.rm = TRUE))

# Create the plot
ggplot(summary_data, aes(x = year)) +
  geom_line(aes(y = avg_pc_girl, color = "Girls")) +
  geom_line(aes(y = avg_pc_boy, color = "Boys")) +
  labs(title = "Average Percentage of Girls and Boys over the Years",
       x = "Year",
       y = "Average Percentage",
       color = "Gender") +
  theme_minimal()

# List of columns related to sanitary, sewage, and waste facilities
sanitary_sewage_waste_cols <- c(
  "sanitary", "sanitary_ei", "sanitary_pne", "sanitary_funcionarios", "sanitary_chuveiro",
  "sewage_rede_publica", "sewage_fossa_septica",
  "waste_servico_coleta", "waste_queima", "waste_enterra", "waste_destino_final_publico", "waste_descarta_outra_area"
)

# Convert relevant columns to integers
waschoolpiracema[sanitary_sewage_waste_cols] <- lapply(waschoolpiracema[sanitary_sewage_waste_cols], function(x) as.integer(x))

# Summarize the waschoolpiracema data to get the count and percentage of schools with facilities per year
summary_data <- waschoolpiracema %>%
  group_by(year) %>%
  summarise(across(all_of(sanitary_sewage_waste_cols), ~ mean(.x, na.rm = TRUE))) %>%
  pivot_longer(cols = sanitary_sewage_waste_cols, names_to = "facility", values_to = "percentage")

# Create the plot
ggplot(summary_data, aes(x = factor(year), y = percentage, fill = facility)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Percentage of Schools with Sanitary, Sewage, and Waste Facilities by Year",
       x = "Year",
       y = "Percentage of Schools",
       fill = "Facility Type") +
  theme_minimal() +
  scale_y_continuous(labels = scales::percent)

# List of columns related to race
race_cols <- c("pc_white", "pc_brown", "pc_black", "pc_indian", "pc_asian", "pc_nd")

# Summarize the waschoolpiracema data to get the average percentage of students per race per year
summary_data <- waschoolpiracema %>%
  group_by(year) %>%
  summarise(across(all_of(race_cols), ~ mean(.x, na.rm = TRUE))) %>%
  pivot_longer(cols = race_cols, names_to = "race", values_to = "percentage")

# Create the line plot
ggplot(summary_data, aes(x = factor(year), y = percentage, color = race, group = race)) +
  geom_line(size = 1.2) +
  geom_point(size = 3) +
  labs(title = "Evolution of Racial Composition of Students Over the Years",
       x = "Year",
       y = "Average Percentage of Students",
       color = "Race") +
  theme_bw() +  # Use a different theme for better visualization
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 12),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  ) +
  scale_color_brewer(palette = "Set1")

water_cols <- c("drink_water", "public_water", "borehole_water", "well_water", "surface_water", "no_water")

# Convert relevant columns to integers
waschoolpiracema[water_cols] <- lapply(waschoolpiracema[water_cols], function(x) as.integer((x)))

# Summarize the data to get the count and percentage of schools with each type of water supply per year
summary_data <- waschoolpiracema %>%
  group_by(year) %>%
  summarise(across(all_of(water_cols), ~ sum(.x, na.rm = TRUE))) %>%
  pivot_longer(cols = water_cols, names_to = "water_supply", values_to = "count")

# Create the stacked bar plot
ggplot(summary_data, aes(x = factor(year), y = count, fill = water_supply)) +
  geom_bar(stat = "identity") +
  labs(title = "Distribution of Water Supply Types in Schools Over the Years",
       x = "Year",
       y = "Number of Schools",
       fill = "Water Supply Type") +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 16, face = "bold"),
    axis.title = element_text(size = 14),
    axis.text = element_text(size = 12),
    legend.title = element_text(size = 14),
    legend.text = element_text(size = 12)
  ) +
  scale_fill_brewer(palette = "Set3")

Capstone Project

This dataset is shared as part of a capstone project in Data Science for openwashdata. For more information about the project and to explore further insights, please visit the project page at https://ds4owd-001.github.io/project-poaguek/ (to be public available)

This study is a sub-project of a PhD project. It is also an initial study comparing the BNSC from 2020 and 2021(#TODO: add reference). Findings will be essential for the next phase of the research, which will be the collection of primary data in schools in the municipality of Piracema through qualitative methods (interviews, on-spot observations and art-based research).

License

Data are available as CC-BY.

Citation

Please cite this package using:

citation("waschoolpiracema")
#> To cite package 'waschoolpiracema' in publications use:
#> 
#>   Tabin A, Poague K, Zhong M (2024). "waschoolpiracema: WASH in Schools
#>   in Piracema, Brazil." doi:10.5281/zenodo.12701107
#>   <https://doi.org/10.5281/zenodo.12701107>,
#>   <https://github.com/openwashdata/waschoolpiracema>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Misc{tabin_etall:2024,
#>     title = {waschoolpiracema: WASH in Schools in Piracema, Brazil},
#>     author = {Alexis Tabin and Kasandra Poague and Mian Zhong},
#>     year = {2024},
#>     doi = {10.5281/zenodo.12701107},
#>     url = {https://github.com/openwashdata/waschoolpiracema},
#>     abstract = {The main goal of this study was to describe the profile of schools from the basic education system in the municipality of Piracema (Minas Gerais, Brazil). Moreover, we also aimed to compare the characteristics of schools, with a special concern to WASH, pre- (2020), peri- (2021) and post-COVID-19 pandemic (2022) to evaluate to what extend schools in Piracema made progress in providing WASH since the beginning of the COVID-19 pandemic. This study is a sub-project of a PhD project and an initial study comparing the BNSC from 2020 and 2021 has already been conducted and published by the author of this project (for more details see references). Findings will be essential for the next phase of the research, which will be the collection of primary data in schools in the municipality of Piracema through qualitative methods (interviews, on-spot observations and art-based research).},
#>     keywords = {brazil,opendata,openwashdata,piracema,r,sanitation,wash},
#>     version = {0.0.1},
#>   }