README.md

waschoolpiracema

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 3,734º smallest municipality out of 5,570 and in Minas Gerais it ranks as the 492º 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},
#>   }