Enrollment and demographic data
The Baltimore City Public School System provides data on
total enrollment and selected demographic characteristics by grade and
grade range for all schools. The data included in this package is
imported from the original Excel file. You can see the import process by
looking at the bcpss_data.R
on the bcpss GitHub repo.
For this article, I am using the dplyr and ggplot2 packages from the tidyverse family and the sf package which required for the mapbaltimore package I use in the mapping section.
library(bcpss)
library(dplyr)
library(ggplot2)
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
# Set a theme for the example plots
theme_set(theme_light(base_size = 13))The enrollment and demographic data is available in both a wide and
long format. Both data sets are not 100% ‘tidy’ because they include
data for individual grades (grade) and grade ranges
(grade_range). Both of these columns are factors ordered
from lowest grade to highest (or lowest to highest starting grade for
grade ranges).
The grades in the original data from BCPS included one value (“93”)
where I am not sure what the corresponding category is. Grade ranges are
overlapping, for example, including “PK to K” and “PK to 5”. The
total_enrollment column always indicates the total number
of students at the school in the grade or grade range.
The data includes demographic characteristics including non-Hispanic
racial categories, Hispanic identity, English learners
(percent_el), students with disabilities
(percent_swd), and Direct Certification
(percent_direct_certification). Direct certification is a
relatively new category that is used in the same way that data on
student participation in free and reduced-price meal (FRM) programs have
been used as a proxy for student economic disadvantage.
Options for wide and long format data
The wide format (enrollment_demographics_SY1920) is most
useful for comparing demographic characteristics to one another. For
example, see this scatter plot showing positive association between the
share of English language learners and share of Hispanic students.
enrollment_demographics_SY1920 |>
filter(grade_range == "All Grades") |>
ggplot(aes(x = percent_hispanic, y = percent_el)) +
geom_point() +
geom_smooth() +
scale_x_continuous(labels = scales::percent) +
scale_y_continuous(labels = scales::percent)
#> `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
In the long format, the value of these 12 characteristics are found
in the share column which shows the percent share of
students in the grade or grade range belonging to the group indicated by
variable and label columns.
# Look at enrollment and demographic data in a long format
glimpse(enrollment_demographics_SY1920_long)
#> Rows: 26,916
#> Columns: 10
#> $ school_number <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ school_name <chr> "All Baltimore City Schools", "All Baltimore City Sch…
#> $ management_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ grade_band <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ grade <fct> PK, PK, PK, PK, PK, PK, PK, PK, PK, PK, PK, PK, K, K,…
#> $ grade_range <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
#> $ total_enrollment <dbl> 4283, 4283, 4283, 4283, 4283, 4283, 4283, 4283, 4283,…
#> $ variable <chr> "percent_males", "percent_females", "percent_direct_c…
#> $ share <dbl> 0.499, 0.501, 0.551, 0.059, 0.109, 0.757, 0.073, 0.13…
#> $ label <chr> "% Males", "% Females", "% Direct Certification", "% …Summary rows for all city schools
Both data sets include summary rows that aggregate data across all
Baltimore City Schools by grade and grade range. These summary rows use
“0” as the school_number and “All Baltimore City Schools”
as the school_name. The following plot shows how to filter
the data to make a simple Cleveland dot plot. The data on percent
non-Hispanic Pacific Islander, Multi-racial, and Asian are all excluded
from the citywide summary data.
enrollment_demographics_allschools <- enrollment_demographics_SY1920_long |>
filter(
grade_range == "All Grades",
school_name == "All Baltimore City Schools"
)
enrollment_demographics_allschools |>
ggplot() +
geom_point(aes(y = label, x = share), size = 3, color = "darkblue") +
scale_x_continuous(labels = scales::percent)
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_point()`).
Plotting data for a single school
You can find a list of city school names and school numbers by taking a look at the corresponding columns in the data.
# Show first five school names
unique(enrollment_demographics_SY1920$school_name)[1:5]
#> [1] "All Baltimore City Schools"
#> [2] "Steuart Hill Academic Academy"
#> [3] "Cecil Elementary School"
#> [4] "City Springs Elementary/Middle School"
#> [5] "James McHenry Elementary/Middle School"
# Show first five school numbers
unique(enrollment_demographics_SY1920$school_number)[1:5]
#> [1] 0 4 7 8 10To look at enrollment or demographic characteristics for a school by
grade and not grade range, you can simply filter out all missing values
in the grade column. For example, see this bar chart
showing enrollment by grade at James McHenry Elementary/Middle School in
southwest Baltimore.
enrollment_demographics_SY1920_long |>
filter(
school_name == "James McHenry Elementary/Middle School",
!is.na(grade)
) |>
ggplot() +
geom_col(aes(x = grade, y = total_enrollment),
fill = "darkred"
)
You can also use the summary data we looked at earlier to compare an
individual school to city schools as a whole. This example reuses the
enrollment_demographics_allschools object created in the
prior section.
enrollment_demographics_SY1920_long |>
filter(
school_name == "James McHenry Elementary/Middle School",
grade_range == "All Grades"
) |>
bind_rows(enrollment_demographics_allschools) |>
ggplot(aes(y = label, x = share, color = school_name), size = 3, alpha = 0.8) +
geom_point() +
scale_x_continuous(labels = scales::percent) +
scale_color_viridis_d() +
theme(legend.position = "bottom")
#> Warning: Removed 11 rows containing missing values or values outside the scale range
#> (`geom_point()`).
Mapping enrollment and demographic data
If you are interested in connecting the enrollment and demographic
data to the spatial data included with this package, you should be aware
that the spatial data refers to schools and school numbers as programs
and program numbers. Here is an example of how to join the school or
program locations (bcps_programs_SY2021) to the enrollment
data using the dplyr left_join function and then mapping
schools by total enrollment. This example uses the Baltimore City
boundary data from my mapbaltimore
package so you must have that package installed in order to
reproduce this map.
enrollment_demographics_SY1920_allgrades <- enrollment_demographics_SY1920 |>
# Filter to rows with data on all grades
filter(grade_range == "All Grades") |>
# Select school_number and total_enrollment variables
select(school_number, total_enrollment)
bcps_programs_SY2021 |>
# Filter to elementary, elementary/middle, middle, and high schools
filter(category %in% c("E", "EM", "M", "H")) |>
# Join enrollment data to program location data
left_join(enrollment_demographics_SY1920_allgrades,
by = c("program_number" = "school_number")
) |>
ggplot() +
geom_sf(aes(size = total_enrollment, color = category), alpha = 0.6) +
# Add outline of city boundaries using mapbaltimore package
geom_sf(data = mapbaltimore::baltimore_city, fill = NA, color = "gray80") +
scale_color_viridis_d()Enrollment by year with MSDE data
This package also includes enrollment data by year from the Maryland
State Department of Education (MSDE) covering the period from 2009 to
2019. Enrollment data for each year is created in the fall of the school
year so data from 2009 is relevant for the 2009-2010 school year. To
access data on all schools filter the school_number to 0
and filter to grade ranges by using is.na(grade) as this
example illustrates:
enrollment_msde_SY0919 |>
filter(school_number == 0, is.na(grade)) |>
ggplot(aes(
x = school_year,
y = enrolled_count,
color = grade_range
)) +
geom_point() +
geom_line(aes(group = grade_range)) +
scale_y_log10()
Plotting enrollment data for a single school is similar to the approach outlined in the previous section but users should note that the school names used by MSDE do differ slightly from the school names used by BCPSS.
enrollment_msde_SY0919 |>
filter(
school_name == "Cecil Elementary",
!is.na(grade),
!is.na(enrolled_count)
) |>
ggplot(aes(
x = school_year,
y = enrolled_count,
color = grade
)) +
geom_point() +
geom_line(aes(group = grade)) +
facet_wrap(~grade)