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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.

Installation

You can install the development version from GitHub with:

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

Example 

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"
  )

U.S. Education data

Baltimore City data

Other local area education data

  • CPSenrollpack: “R package of enrollment data for Chicago Public High Schools, 2006-07 to 2018-19”