1.4 million students. 580+ districts. 21 counties. 25+ years of data from the New Jersey Department of Education, in one clean R and Python interface.
Full documentation – all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
1. Charter enrollment grew 15% since 2020
New Jersey’s charter sector added 8,400+ students from 2020 to 2026, growing from 55,600 to 64,000 - and it kept growing in 2026 even as statewide enrollment fell.
library(njschooldata)
library(ggplot2)
library(dplyr)
library(scales)
years <- 2020:2026
enr_all <- purrr::map_df(years, ~{
tryCatch(
fetch_enr(.x, tidy = TRUE),
error = function(e) {
warning(paste("Year", .x, "failed:", conditionMessage(e)))
NULL
}
)
})
charter_trend <- enr_all %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(sector = ifelse(is_charter, "Charter", "Traditional")) %>%
group_by(end_year, sector) %>%
summarize(n_students = sum(n_students, na.rm = TRUE), .groups = "drop")
stopifnot(nrow(charter_trend) > 0)
charter_trend
#> # A tibble: 14 x 3
#> end_year sector n_students
#> <dbl> <chr> <dbl>
#> 1 2020 Charter 55604.
#> 2 2020 Traditional 1320225.
#> 3 2021 Charter 57480
#> 4 2021 Traditional 1304920
#> 5 2022 Charter 58776.
#> 6 2022 Traditional 1302140.
#> 7 2023 Charter 58568.
#> 8 2023 Traditional 1313352.
#> 9 2024 Charter 61295
#> 10 2024 Traditional 1318693
#> 11 2025 Charter 63810.
#> 12 2025 Traditional 1317372.
#> 13 2026 Charter 64037
#> 14 2026 Traditional 1293412.
Charter Growth
2. White students dropped below 37%
White students went from 42% to under 37% of NJ public school enrollment since 2020. NJ public schools are now decisively majority-minority.
# State-level summary aggregated from district totals for time-series consistency
state_summary <- enr_all %>%
filter(is_district) %>%
group_by(end_year, subgroup, grade_level) %>%
summarize(n_students = sum(n_students, na.rm = TRUE), .groups = "drop")
state_totals <- state_summary %>%
filter(subgroup == "total_enrollment") %>%
select(end_year, grade_level, total = n_students)
state_summary <- state_summary %>%
left_join(state_totals, by = c("end_year", "grade_level")) %>%
mutate(pct = n_students / total)
demo <- state_summary %>%
filter(subgroup %in% c("white", "hispanic", "black", "asian"),
grade_level == "TOTAL") %>%
mutate(subgroup = factor(subgroup, levels = c("white", "hispanic", "black", "asian")))
stopifnot(nrow(demo) > 0)
demo %>% select(end_year, subgroup, pct) %>%
mutate(pct = round(pct * 100, 1)) %>%
tidyr::pivot_wider(names_from = subgroup, values_from = pct)
#> # A tibble: 7 x 5
#> end_year white hispanic black asian
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2020 42 30.3 14.6 10.3
#> 2 2021 40.6 31.1 14.9 10.4
#> 3 2022 39.6 32.1 14.8 10.3
#> 4 2023 38.5 33.2 14.6 10.3
#> 5 2024 37.6 34.1 14.4 10.3
#> 6 2025 36.7 35.0 14.3 10.3
#> 7 2026 36.5 35.2 14.1 10.4
Demographic Shift
3. Kindergarten rebounded from COVID
New Jersey lost 9% of kindergartners during COVID. K enrollment recovered by 2024 but slipped again in 2026, while Pre-K kept surging past pre-pandemic levels.
k_trend <- state_summary %>%
filter(subgroup == "total_enrollment",
grade_level %in% c("PK", "K", "01", "06", "12")) %>%
mutate(grade_label = case_when(
grade_level == "PK" ~ "Pre-K",
grade_level == "K" ~ "Kindergarten",
grade_level == "01" ~ "Grade 1",
grade_level == "06" ~ "Grade 6",
grade_level == "12" ~ "Grade 12",
TRUE ~ grade_level
))
stopifnot(nrow(k_trend) > 0)
k_trend %>%
filter(grade_level %in% c("K", "PK")) %>%
select(end_year, grade_label, n_students)
#> # A tibble: 14 x 3
#> end_year grade_label n_students
#> <dbl> <chr> <dbl>
#> 1 2020 Kindergarten 90818
#> 2 2020 Pre-K 45013
#> 3 2021 Kindergarten 82604
#> 4 2021 Pre-K 56396
#> 5 2022 Kindergarten 86202
#> 6 2022 Pre-K 65350
#> 7 2023 Kindergarten 85873
#> 8 2023 Pre-K 71615
#> 9 2024 Kindergarten 90783
#> 10 2024 Pre-K 83463
#> 11 2025 Kindergarten 89428
#> 12 2025 Pre-K 87231
#> 13 2026 Kindergarten 86554
#> 14 2026 Pre-K 88063
COVID Kindergarten
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2000-2026 | fetch_enr() |
State, county, district, school. Race, gender, FRPL, LEP, migrant |
| Assessments | 2004-2024 |
fetch_parcc() / fetch_njask() / fetch_njgpa()
|
NJSLA, PARCC, NJASK, HSPA, GEPA. ELA, Math, Science |
| Graduation | 2011-2024 |
fetch_grad_rate() / fetch_grad_count()
|
4-yr and 6-yr ACGR. District and school level |
| Directory | Current |
get_school_directory() / get_district_directory()
|
Names, IDs, addresses, school type |
| Per-Pupil Spending | – | – | Not yet available |
| Accountability | 2018+ |
fetch_essa_status() / fetch_essa_progress()
|
CSI/TSI lists, ESSA indicators |
| Chronic Absence | 2017-2024 |
fetch_absence() / fetch_chronic_absenteeism() / fetch_days_absent()
|
By grade, by demographic. Cross-state standard via fetch_absence()
|
| EL Progress | 2022-2024 | fetch_access() |
WIDA ACCESS for ELLs |
| Special Ed | 2024+ | fetch_sped() |
Classification rates by disability category |
| Discipline | Available |
fetch_disciplinary_removals() / fetch_violence_vandalism_hib()
|
Suspensions, expulsions, HIB incidents |
| Staff | Available |
fetch_staff_demographics() / fetch_teacher_experience()
|
Demographics, experience, ratios |
| College-Going | Available |
fetch_postsecondary() / fetch_sat_participation() / fetch_ap_participation()
|
Postsecondary enrollment, SAT, AP |
| Courses | Available |
fetch_math_course_enrollment() / fetch_cs_enrollment()
|
Math, science, CS, arts, world languages |
| CTE | Available |
fetch_cte_participation() / fetch_industry_credentials()
|
Career pathways, credentials, apprenticeships |
See the full data category taxonomy
Quick Start
R
# Install from GitHub
remotes::install_github("almartin82/njschooldata")
library(njschooldata)
# Enrollment data
enr_2026 <- fetch_enr(2026, tidy = TRUE)
# Assessment data
math_g4 <- fetch_parcc(2024, grade_or_subj = 4, subj = 'math')
# Graduation rates
grate <- fetch_grad_rate(2024)
# School directory
schools <- get_school_directory()Explore More
Full analysis with 15 stories:
- NJ Enrollment Insights – 15 stories covering charter growth, demographics, COVID recovery, poverty gaps, and more
- Getting Started – installation, data fetching, and tidy format guide
- Function reference
Data Notes
Source: New Jersey Department of Education – all data comes directly from NJ DOE, not federal sources.
Available years: Enrollment data from 2000-2026. Tidy format (2020+) provides consistent structure with district, charter, and school-level records. Assessments from 2004-2024 span four different testing systems (GEPA, NJASK, PARCC, NJSLA).
Suppression rules: NJ DOE suppresses counts below 10 in some data types. Enrollment data uses half-day weighting for programs like pre-K, which can produce non-integer counts.
Census Day: NJ enrollment counts are based on October 15 enrollment (ASSA reporting).
Known caveats: - 2020+ enrollment data includes state-level rows but the vignette aggregates from district-level for time-series consistency - Charter schools appear as separate “districts” in the data - Pre-2020 and post-2020 data formats differ significantly
Deeper Dive
4. New Jersey educates 1.4 million students
New Jersey has one of the largest public school systems in the country. Enrollment recovered from the COVID dip to a 2025 peak, then fell about 1.7% in 2026 - the first real decline in years.
state_total <- state_summary %>%
filter(subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(state_total) > 0)
state_total
#> # A tibble: 7 x 6
#> end_year subgroup grade_level n_students total pct
#> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2020 total_enrollment TOTAL 1375828. 1375828. 1
#> 2 2021 total_enrollment TOTAL 1362400 1362400 1
#> 3 2022 total_enrollment TOTAL 1360916 1360916 1
#> 4 2023 total_enrollment TOTAL 1371921 1371921 1
#> 5 2024 total_enrollment TOTAL 1379988 1379988 1
#> 6 2025 total_enrollment TOTAL 1381182 1381182 1
#> 7 2026 total_enrollment TOTAL 1357450. 1357450. 1
Statewide Enrollment
5. Hispanic students hit 35% and rising
Hispanic enrollment surged from 30% to 35% of all NJ students since 2020, one of the fastest demographic shifts in state history.
hispanic <- state_summary %>%
filter(subgroup == "hispanic", grade_level == "TOTAL")
stopifnot(nrow(hispanic) > 0)
hispanic %>% select(end_year, n_students, pct)
#> # A tibble: 7 x 3
#> end_year n_students pct
#> <dbl> <dbl> <dbl>
#> 1 2020 417042. 0.303
#> 2 2021 424170. 0.311
#> 3 2022 437187 0.321
#> 4 2023 455576. 0.332
#> 5 2024 470906 0.341
#> 6 2025 483504. 0.350
#> 7 2026 477187 0.352
Hispanic Growth
6. The Big Three: Newark, Jersey City, and Paterson
New Jersey’s three largest traditional districts educate over 90,000 students combined - nearly 7% of the state.
big_three_names <- c("Newark Public School District",
"Jersey City Public Schools",
"Paterson Public School District")
big_three_trend <- enr_all %>%
filter(is_district, !is_charter,
district_name %in% big_three_names,
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(big_three_trend) > 0)
big_three_trend %>% select(end_year, district_name, n_students)
#> # A tibble: 18 x 3
#> end_year district_name n_students
#> <dbl> <chr> <dbl>
#> 1 2021 Newark Public School District 40085
#> 2 2021 Jersey City Public Schools 26541
#> 3 2021 Paterson Public School District 25657
#> 4 2022 Newark Public School District 40607
#> 5 2022 Jersey City Public Schools 26890
#> 6 2022 Paterson Public School District 24495
#> 7 2023 Newark Public School District 41430
#> 8 2023 Jersey City Public Schools 26418
#> 9 2023 Paterson Public School District 26067
#> 10 2024 Newark Public School District 42600
#> 11 2024 Jersey City Public Schools 26023
#> 12 2024 Paterson Public School District 24090
#> 13 2025 Newark Public School District 43980
#> 14 2025 Jersey City Public Schools 25692
#> 15 2025 Paterson Public School District 23609
#> 16 2026 Newark Public School District 43216
#> 17 2026 Jersey City Public Schools 25307
#> 18 2026 Paterson Public School District 21849
Big Three
7. Free/reduced lunch ranges from 98% to under 5%
Urban districts like Passaic City (92%) have nearly all students on free/reduced lunch while affluent suburbs like Millburn (under 2%) have almost none - a stark measure of NJ’s wealth divide.
enr_current <- fetch_enr(2026, tidy = TRUE)
frl <- enr_current %>%
filter(is_district, !is_charter,
subgroup == "free_reduced_lunch", grade_level == "TOTAL",
!is.na(pct), n_students >= 100) %>%
arrange(desc(pct)) %>%
head(15) %>%
mutate(district_label = reorder(district_name, pct))
stopifnot(nrow(frl) > 0)
frl %>% select(district_name, n_students, pct)
#> # A tibble: 15 x 3
#> district_name n_students pct
#> <chr> <dbl> <dbl>
#> 1 Kipp: Cooper Norcross, A New Jersey Nonprofit Corporation 2132. 0.98
#> 2 Mastery Schools Of Camden, Inc. 2788. 0.95
#> 3 Camden Prep, Inc. 1425. 0.937
#> 4 Passaic City School District 10132. 0.918
#> 5 Lakewood Township School District 3381. 0.898
#> 6 Woodlynne School District 343 0.875
#> 7 Union City School District 10409. 0.867
#> 8 Seaside Heights School District 127. 0.847
#> 9 Atlantic City School District 5104. 0.839
#> 10 New Brunswick School District 6929. 0.838
#> 11 Wildwood City School District 629. 0.831
#> 12 Elizabeth Public Schools 22034. 0.825
#> 13 West New York School District 6070. 0.821
#> 14 Long Branch Public School District 4069. 0.819
#> 15 Bridgeton City School District 5013. 0.799
FRL Distribution
8. English learners approach 45% in some districts
ELL students make up nearly 45% in Lakewood and Plainfield but under 1% in most suburban districts - a concentration driven by immigration patterns.
ell <- enr_current %>%
filter(is_district, !is_charter,
subgroup == "lep", grade_level == "TOTAL",
!is.na(pct), n_students >= 50) %>%
arrange(desc(pct)) %>%
head(15) %>%
mutate(district_label = reorder(district_name, pct))
stopifnot(nrow(ell) > 0)
ell %>% select(district_name, n_students, pct)
#> # A tibble: 15 x 3
#> district_name n_students pct
#> <chr> <dbl> <dbl>
#> 1 Lakewood Township School District 1691. 0.449
#> 2 Plainfield Public School District 4153. 0.436
#> 3 Dover Public School District 1375. 0.434
#> 4 Irvington Public School District 3315. 0.421
#> 5 Elizabeth Public Schools 11057. 0.414
#> 6 New Brunswick School District 3365. 0.407
#> 7 Paterson Public School District 8849. 0.405
#> 8 Trenton Public School District 5795. 0.403
#> 9 Red Bank Borough Public School District 453. 0.396
#> 10 Union City School District 4694. 0.391
#> 11 Perth Amboy Public School District 3820. 0.383
#> 12 Passaic City School District 4084. 0.37
#> 13 Bridgeton City School District 2309. 0.368
#> 14 Bound Brook School District 650. 0.343
#> 15 Palisades Park School District 560. 0.331
ELL Concentration
9. Top 10 districts serve 15% of all students
Just 10 out of nearly 580 districts educate about 1 in 7 NJ students. Newark alone has 43,000.
top_10 <- enr_current %>%
filter(is_district, !is_charter,
subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students)) %>%
head(10) %>%
mutate(district_label = reorder(district_name, n_students))
stopifnot(nrow(top_10) > 0)
top_10 %>% select(district_name, n_students)
#> # A tibble: 10 x 2
#> district_name n_students
#> <chr> <dbl>
#> 1 Newark Public School District 43216
#> 2 Elizabeth Public Schools 26708
#> 3 Jersey City Public Schools 25307
#> 4 Paterson Public School District 21849
#> 5 Edison Township School District 16191
#> 6 Trenton Public School District 14380.
#> 7 Toms River Regional School District 13925
#> 8 Woodbridge Township School District 13425
#> 9 Hamilton Township Public School District 12112
#> 10 Union City School District 12006
Top 10 Districts
10. Multiracial students: fastest-growing category
Multiracial students grew 46% since 2020 - from 2.4% to 3.5% of enrollment - making it the fastest-growing racial category in NJ.
multi <- state_summary %>%
filter(subgroup == "multiracial", grade_level == "TOTAL")
stopifnot(nrow(multi) > 0)
multi %>% select(end_year, n_students, pct)
#> # A tibble: 7 x 3
#> end_year n_students pct
#> <dbl> <dbl> <dbl>
#> 1 2020 32622 0.0237
#> 2 2021 34518 0.0253
#> 3 2022 37474 0.0275
#> 4 2023 40934. 0.0298
#> 5 2024 43436. 0.0315
#> 6 2025 45246. 0.0327
#> 7 2026 47160 0.0347
Multiracial Growth
11. Pre-K nearly doubled since 2020
NJ’s Pre-K enrollment surged from 45,000 to 88,000 since 2020 - nearly doubling, fueled by the state’s expanding universal pre-K program.
prek <- state_summary %>%
filter(subgroup == "total_enrollment", grade_level == "PK")
stopifnot(nrow(prek) > 0)
prek %>% select(end_year, n_students)
#> # A tibble: 7 x 2
#> end_year n_students
#> <dbl> <dbl>
#> 1 2020 45013
#> 2 2021 56396
#> 3 2022 65350
#> 4 2023 71615
#> 5 2024 83463
#> 6 2025 87231
#> 7 2026 88063
Pre-K Surge
12. Bergen County has more students than several US states
With 130,000+ students, Bergen County alone has a larger public school system than entire states like Wyoming, Vermont, and North Dakota.
county_enr <- enr_current %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
group_by(county_name) %>%
summarize(n_students = sum(n_students, na.rm = TRUE),
n_districts = n(), .groups = "drop") %>%
filter(county_name != "Charters") %>%
arrange(desc(n_students)) %>%
head(15) %>%
mutate(county_label = reorder(county_name, n_students))
stopifnot(nrow(county_enr) > 0)
county_enr
#> # A tibble: 15 x 4
#> county_name n_students n_districts county_label
#> <chr> <dbl> <int> <fct>
#> 1 Bergen 130172. 76 Bergen
#> 2 Essex 125813 23 Essex
#> 3 Middlesex 121810. 25 Middlesex
#> 4 Union 95696. 23 Union
#> 5 Monmouth 87674 51 Monmouth
#> 6 Hudson 80614 13 Hudson
#> 7 Camden 77633 39 Camden
#> 8 Passaic 73151 20 Passaic
#> 9 Morris 72082 40 Morris
#> 10 Burlington 68970 39 Burlington
#> 11 Ocean 63832. 28 Ocean
#> 12 Mercer 58960 12 Mercer
#> 13 Somerset 48696. 19 Somerset
#> 14 Gloucester 46076. 28 Gloucester
#> 15 Atlantic 40586 24 Atlantic
County Enrollment
13. Most NJ districts are tiny
Half of NJ’s 580 districts have fewer than 1,200 students. The median district is smaller than a single large high school.
dist_sizes <- enr_current %>%
filter(is_district, !is_charter,
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(dist_sizes) > 0)
cat("Districts:", nrow(dist_sizes), "\n")
#> Districts: 579
cat("Median:", median(dist_sizes$n_students, na.rm = TRUE), "\n")
#> Median: 1162
cat("Under 1000:", sum(dist_sizes$n_students < 1000, na.rm = TRUE), "\n")
#> Under 1000: 266
cat("Over 10000:", sum(dist_sizes$n_students > 10000, na.rm = TRUE), "\n")
#> Over 10000: 14
District Size Distribution
14. NJ’s enrollment pyramid shows the pre-K boom
The 2026 grade-level distribution reveals the pre-K surge: PK enrollment (88K) has now surpassed Kindergarten (87K), reflecting NJ’s universal pre-K push.
grade_enr <- state_summary %>%
filter(end_year == 2026, subgroup == "total_enrollment",
grade_level != "TOTAL", !is.na(grade_level)) %>%
mutate(grade_label = case_when(
grade_level == "PK" ~ "Pre-K",
grade_level == "K" ~ "K",
TRUE ~ paste("Grade", grade_level)
),
grade_order = case_when(
grade_level == "PK" ~ 0,
grade_level == "K" ~ 1,
TRUE ~ as.numeric(grade_level) + 1
)) %>%
arrange(grade_order) %>%
mutate(grade_label = factor(grade_label, levels = grade_label))
stopifnot(nrow(grade_enr) > 0)
grade_enr %>% select(grade_label, n_students)
#> # A tibble: 14 x 2
#> grade_label n_students
#> <fct> <dbl>
#> 1 Pre-K 88063
#> 2 K 86554
#> 3 Grade 01 91396
#> 4 Grade 02 93705
#> 5 Grade 03 94599
#> 6 Grade 04 97367
#> 7 Grade 05 96650
#> 8 Grade 06 97968
#> 9 Grade 07 98315
#> 10 Grade 08 99814
#> 11 Grade 09 102207
#> 12 Grade 10 102068.
#> 13 Grade 11 102865
#> 14 Grade 12 105879
Grade Pyramid
15. NJ’s poverty gap: 96 points between districts
The highest-poverty NJ districts have 98% of students on free/reduced lunch. Affluent Westfield has under 2%. This 96-point gap captures NJ’s extreme wealth inequality.
frl_all <- enr_current %>%
filter(is_district, !is_charter,
subgroup == "free_reduced_lunch", grade_level == "TOTAL",
!is.na(pct), n_students >= 100) %>%
arrange(desc(pct))
top_5 <- frl_all %>% head(5) %>% mutate(group = "Highest FRL")
bottom_5 <- frl_all %>% tail(5) %>% mutate(group = "Lowest FRL")
frl_extremes <- bind_rows(top_5, bottom_5) %>%
mutate(district_label = reorder(district_name, pct))
stopifnot(nrow(frl_extremes) > 0)
frl_extremes %>% select(district_name, n_students, pct, group)
#> # A tibble: 10 x 4
#> district_name n_students pct group
#> <chr> <dbl> <dbl> <chr>
#> 1 Kipp: Cooper Norcross, A New Jersey Nonprofit Corporation 2132. 0.98 Highest FRL
#> 2 Mastery Schools Of Camden, Inc. 2788. 0.95 Highest FRL
#> 3 Camden Prep, Inc. 1425. 0.937 Highest FRL
#> 4 Passaic City School District 10132. 0.918 Highest FRL
#> 5 Lakewood Township School District 3381. 0.898 Highest FRL
#> 6 Pequannock Township School District 100. 0.05 Lowest FRL
#> 7 Scotch Plains-Fanwood School District 269. 0.047 Lowest FRL
#> 8 Bernards Township School District 131. 0.029 Lowest FRL
#> 9 Ridgewood Public School District 129. 0.024 Lowest FRL
#> 10 Livingston Board Of Education School District 121. 0.019 Lowest FRL
Poverty Gap