README.md
In elipousson/bcps: Baltimore City Public Schools

bcpss

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 that offers a broader range of Baltimore-specific datasets and functions for working with that data.

You can install the development version from GitHub with:

# install.packages("remotes")
remotes::install_github("elipousson/bcpss")

library(bcpss)
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.3     ✔ readr     2.1.4
#> ✔ forcats   1.0.0     ✔ stringr   1.5.1
#> ✔ ggplot2   3.4.4     ✔ tibble    3.2.1
#> ✔ lubridate 1.9.3     ✔ tidyr     1.3.0
#> ✔ purrr     1.0.2     
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
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?

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)

| school_number | school_name | total_enrollment | |--------------:|:-------------------------------|-----------------:| | 105 | Moravia Park Elementary School | 763 | | 242 | Northwood Elementary School | 590 | | 164 | Arundel Elementary School | 523 | | 83 | William Paca Elementary School | 509 | | 206 | Furley Elementary School | 495 |

Five largest BCPSS elementary schools by total enrollment

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.

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.

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:

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:

# 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

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"
  )

educationdata: Retrieve data from the Urban Institute's Education Data API as a data.frame for easy analysis.
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: 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