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https://github.com/elipousson/bcpss
R package to access data for Baltimore City Public School System (BCPSS)
https://github.com/elipousson/bcpss
baltimore r r-package
Last synced: 27 days ago
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R package to access data for Baltimore City Public School System (BCPSS)
- Host: GitHub
- URL: https://github.com/elipousson/bcpss
- Owner: elipousson
- License: cc0-1.0
- Created: 2021-02-12T22:03:43.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2024-01-30T18:42:48.000Z (9 months ago)
- Last Synced: 2024-03-26T16:11:30.437Z (8 months ago)
- Topics: baltimore, r, r-package
- Language: R
- Homepage: https://elipousson.github.io/bcpss/
- Size: 18.1 MB
- Stars: 3
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE.md
- Citation: CITATION.cff
Awesome Lists containing this project
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "90%"
)
```
# bcpss
[](https://lifecycle.r-lib.org/articles/stages.html#stable) [](https://opensource.org/licenses/MIT) [](https://www.repostatus.org/#active)
The goal of bcpss is to make data from the Baltimore City Public School system more consistent and accessible to R users. This package may pair well with the [mapbaltimore package](https://github.com/elipousson/mapbaltimore) that offers a broader range of Baltimore-specific datasets and functions for working with that data.
## Installation
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("remotes")
remotes::install_github("elipousson/bcpss")
```
## Example
```{r setup}
library(bcpss)
library(tidyverse)
theme_set(theme_minimal())
```
Currently, this package includes datasets that include school and grade-level enrollment and demographic data, the published results from a parent survey, and the published results from a combined student and educator survey completed in 2019. This data can be used to answer questions, such as, what are the elementary schools with the greatest total student enrollment?
```{r enrollment_demographics}
top_5_es <- enrollment_demographics_SY1920 |>
filter(
grade_range == "All Grades",
grade_band == "E"
) |>
select(school_number, school_name, total_enrollment) |>
top_n(5, total_enrollment) |>
arrange(desc(total_enrollment))
top_5_es_caption <- "Five largest BCPSS elementary schools by total enrollment"
knitr::kable(top_5_es, caption = top_5_es_caption)
```
Both the enrollment/demographic data and the parent survey are available in both a wide and long format.
The package also includes spatial data for elementary school attendance zones and program locations for the 2020-2021 school year.
```{r bcps_es_zones}
bcps_es_zones_SY2021 |>
ggplot() +
geom_sf(aes(fill = zone_name)) +
scale_fill_viridis_d() +
guides(fill = "none") +
labs(title = "BCPSS Elementary School Attendance Zones")
```
These two sources can be used in combinations by joining the `program_number` in the spatial data with the equivalent `school_number` used in the survey and demographic data.
```{r top_5_es_map}
top_5_es_map <- bcps_programs_SY2021 |>
left_join(top_5_es, by = c("program_number" = "school_number")) |>
filter(!is.na(total_enrollment)) |>
ggplot() +
geom_sf(data = bcps_es_zones_SY2021, fill = NA, color = "darkblue") +
geom_sf(aes(color = school_name)) +
geom_sf_label(aes(label = program_name_short, fill = school_name), color = "white") +
scale_fill_viridis_d(end = 0.85) +
guides(fill = "none", color = "none") +
labs(title = top_5_es_caption)
top_5_es_map
```
The `bcpss_enrollment` data is a subset of the statewide data available through the `{marylandedu}` package (a tidied version of data downloads available from the Maryland State Department of Education).
Using the `marylandedu::md_nces_directory` data, you can summarise enrollment by year and grade span:
```{r}
baltimore_nces_directory <- marylandedu::md_nces_directory |>
select(year, lss_name, school_number, grade_span)
bcpss_enrollment_summary <- bcpss_enrollment |>
dplyr::filter(
school_number != 0,
race == "All",
grade_range == "All Grades"
) |>
left_join(
baltimore_nces_directory,
by = join_by(lss_name, year, school_number)
) |>
summarise(
n_schools = n_distinct(school_number),
enrolled_count_mean = mean(enrolled_count, na.rm = TRUE),
enrolled_count_total = sum(enrolled_count, na.rm = TRUE),
.by = c(lss_name, year, grade_span)
) |>
filter(
# Exclude missing and uncommon grade span values
!is.na(grade_span),
!(grade_span %in% c("EMH", "MH"))
)
```
The summary data can be plottted:
```{r}
# Create a convenience plotting function
bcpss_enrollment_summary_plot <- function(data = NULL, mapping = aes(), ...) {
ggplot(data = data, mapping = mapping) +
geom_point() +
geom_line() +
labs(
x = "Year",
...,
color = "Grade span",
caption = paste0(
"Note that schools with ",
knitr::combine_words(c("missing", "EMH", "MH")),
" grade spans are excluded.\nData: Maryland State Department of Education."
)
) +
scale_y_continuous(labels = scales::label_number()) +
scale_color_viridis_d(end = 0.85)
}
bcpss_enrollment_summary |>
bcpss_enrollment_summary_plot(
aes(x = year, y = enrolled_count_mean, color = grade_span),
y = "Average enrollment",
title = "Average Baltimore City public school enrollment by grade span, 2003-2023"
)
```
Note that this summary is incomplete without the accompanying total enrollment count showing the shift from elementary to elementary middle schools
```{r}
bcpss_enrollment_summary |>
bcpss_enrollment_summary_plot(
aes(x = year, y = enrolled_count_total, color = grade_span),
y = "Total enrollment",
title = "Total Baltimore City public school enrollment by grade span, 2003-2023"
)
```
## Related projects
### U.S. Education data
- [educationdata](https://github.com/UrbanInstitute/education-data-package-r): Retrieve data from the Urban Institute\'s [Education Data API](https://educationdata.urban.org/) as a `data.frame` for easy analysis.
- [EdSurvey](https://www.air.org/project/nces-data-r-project-edsurvey): EdSurvey is an R statistical package designed for the analysis of national and international education data from the National Center for Education Statistics (NCES).
- [edbuildr](https://github.com/EdBuild/edbuildr): Import EdBuild's master dataset of school district finance, student demographics, and community economic indicators for every school district in the United States.
- [Elementary School Operating Status + NCES 2019-2020 School District Boundaries](https://github.com/hrbrmstr/2021-esos-nces)
### Baltimore City data
- [mapbaltimore](https://elipousson.github.io/mapbaltimore/)
- [baltimoredata](https://elipousson.github.io/baltimoredata/)
- [mapmaryland](https://elipousson.github.io/mapmaryland/)
### Other local area education data
- [CPSenrollpack](https://github.com/cymack/CPSenrollpack): "R package of enrollment data for Chicago Public High Schools, 2006-07 to 2018-19"