North Carolina educates 1.5 million students across 115 traditional districts and 219 charter schools – and the demographic, geographic, and sector shifts hiding in the data are dramatic.
Part of the njschooldata family of state education data packages.
Full documentation – all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
1. Charter schools grew from 26,000 to 144,000 students in 18 years
North Carolina’s charter sector has exploded. In 2006, 99 charter schools served 26,000 students. By 2024, 219 charters serve 144,000 – nearly 10% of all students.
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
charter_summary <- enr_2024 %>%
filter(is_campus, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
group_by(is_charter) %>%
summarize(
n_schools = n(),
students = sum(n_students, na.rm = TRUE),
.groups = "drop"
)
state_total <- enr_2024 %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
pull(n_students)
charter_summary <- charter_summary %>%
mutate(pct = round(students / state_total * 100, 1))
stopifnot(nrow(charter_summary) == 2)
charter_summary
#> # A tibble: 2 x 4
#> is_charter n_schools students pct
#> <lgl> <int> <dbl> <dbl>
#> 1 FALSE 2483 1364278 90.5
#> 2 TRUE 219 143916 9.5
Charter enrollment
2. Durham is nearly a 50/50 Black-Hispanic district
In 2018 Durham was 46% Black and 32% Hispanic. By 2024 the gap closed to 39% Black and 38% Hispanic. The crossover is imminent.
enr_durham <- fetch_enr_multi(c(2018, 2020, 2024), use_cache = TRUE)
durham_demographics <- enr_durham %>%
filter(is_district, district_id == "320", grade_level == "TOTAL",
subgroup %in% c("white", "black", "hispanic", "asian")) %>%
group_by(end_year) %>%
mutate(
total = sum(n_students),
pct = round(n_students / total * 100, 1)
) %>%
ungroup() %>%
select(end_year, subgroup, n_students, pct)
stopifnot(nrow(durham_demographics) > 0)
durham_demographics
#> # A tibble: 12 x 4
#> end_year subgroup n_students pct
#> <dbl> <chr> <dbl> <dbl>
#> 1 2018 asian 753 2.3
#> 2 2018 black 14976 46.3
#> 3 2018 hispanic 10295 31.8
#> 4 2018 white 6314 19.5
#> 5 2020 asian 691 2.2
#> 6 2020 black 13924 43.6
#> 7 2020 hispanic 10943 34.3
#> 8 2020 white 6345 19.9
#> 9 2024 asian 701 2.4
#> 10 2024 black 11339 39.2
#> 11 2024 hispanic 11090 38.4
#> 12 2024 white 5770 20.0
Durham demographics
3. Rural eastern NC lost 25% of its students since 2015
Eight tobacco belt counties (Edgecombe, Halifax, Hertford, Northampton, Bertie, Martin, Washington, Tyrrell) dropped from 21,596 to 16,219 students – a 25% decline in under a decade.
enr_multi <- fetch_enr_multi(c(2015, 2024), use_cache = TRUE)
eastern_rural <- c("Edgecombe", "Halifax", "Hertford", "Northampton",
"Bertie", "Martin", "Washington", "Tyrrell")
eastern_data <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(is_eastern = grepl(paste(eastern_rural, collapse = "|"), district_name)) %>%
filter(is_eastern) %>%
group_by(end_year) %>%
summarize(total = sum(n_students, na.rm = TRUE), .groups = "drop") %>%
mutate(pct_change = round((total / first(total) - 1) * 100, 1))
stopifnot(nrow(eastern_data) == 2)
eastern_data
#> # A tibble: 2 x 3
#> end_year total pct_change
#> <dbl> <dbl> <dbl>
#> 1 2015 21596 0
#> 2 2024 16219 -24.9
Eastern NC decline
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2006-2024 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race, gender, FRPL, SpEd, LEP |
| Assessments | 2014-2024 |
fetch_assessment() / fetch_assessment_multi()
|
EOG (3-8) + EOC. State, district, school. All ESSA subgroups |
| Directory | Current | fetch_directory() |
School names, addresses, types |
| Graduation | — | — | Not yet available |
| Per-Pupil Spending | — | — | Not yet available |
| Accountability | — | — | Not yet available |
| Chronic Absence | — | — | Not yet available |
| EL Progress | — | — | Not yet available |
| Special Ed | — | — | Not yet available |
See the full data category taxonomy
Quick Start
R
# install.packages("remotes")
remotes::install_github("almartin82/ncschooldata")
library(ncschooldata)
library(dplyr)
# Fetch one year
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
# Fetch multiple years
enr_recent <- fetch_enr_multi(c(2018, 2019, 2020, 2021, 2024), use_cache = TRUE)
# State totals
enr_2024 %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL")
# District breakdown
enr_2024 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students))
# Demographics
enr_2024 %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "black", "hispanic"))Python
import pyncschooldata as nc
# Fetch one year
enr_2024 = nc.fetch_enr(2024)
# Fetch multiple years
enr_recent = nc.fetch_enr_multi([2018, 2019, 2020, 2021, 2024])
# State totals
state_total = enr_2024[
(enr_2024['is_state'] == True) &
(enr_2024['subgroup'] == 'total_enrollment') &
(enr_2024['grade_level'] == 'TOTAL')
]
# District breakdown
districts = enr_2024[
(enr_2024['is_district'] == True) &
(enr_2024['subgroup'] == 'total_enrollment') &
(enr_2024['grade_level'] == 'TOTAL')
].sort_values('n_students', ascending=False)Explore More
Full analysis with 15 stories: - Enrollment trends – 15 stories - Function reference
Data Notes
Data Source
NC Department of Public Instruction Statistical Profile: apps.schools.nc.gov
Census Day
Enrollment counts are based on the first school month (typically late September/early October). The official reporting day varies by year but is generally the 20th school day.
Suppression Rules
- Counts fewer than 10 students are suppressed at the school level for privacy
- Suppressed values appear as
NAin the data - State and district totals include all students (no suppression at aggregate levels)
- When analyzing small schools or subgroups, expect some missing values
Data Quality Notes
- 2006-2010: Total enrollment only; no demographic breakdowns
- 2018+: Full demographic breakdowns available (12-13 subgroups)
- 2022-2023: Not cached; NC DPI API requires session authentication
-
Charter schools: Counted as campuses, not districts; use
is_charterflag - Special populations: May overlap (e.g., a student can be both LEP and economically disadvantaged)
Known Limitations
- Pre-K enrollment may be incomplete (not all programs report to DPI)
- Individual grade-level data (K, 01-08) at state level may have duplicates; use
grade_level == "TOTAL"for reliable state/district totals - Historical data before 2006 uses different reporting formats
- Some charter schools have opened/closed mid-year affecting comparisons
Deeper Dive
4. North Carolina’s 1.5 million students peaked in 2019 – COVID erased a decade of growth
NC grew steadily from 1.39M to 1.54M, then lost 66,000 students by 2021. Recovery is underway but the state is still 27K below its 2019 peak.
enr <- fetch_enr_multi(c(2006, 2010, 2015, 2020, 2024), use_cache = TRUE)
statewide <- enr %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
select(end_year, n_students)
stopifnot(nrow(statewide) > 0)
statewide
#> # A tibble: 5 x 2
#> end_year n_students
#> <dbl> <dbl>
#> 1 2006 1390168
#> 2 2010 1440212
#> 3 2015 1502009
#> 4 2020 1525592
#> 5 2024 1508194
NC statewide enrollment
5. Wake County: 160,000 students and bigger than many states
Wake County Schools is the largest district in NC, with nearly 160,000 students. Charlotte-Mecklenburg follows at 140,000.
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
top_districts <- enr_2024 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students)) %>%
head(10) %>%
select(district_name, n_students)
stopifnot(nrow(top_districts) == 10)
top_districts
#> # A tibble: 10 x 2
#> district_name n_students
#> <chr> <dbl>
#> 1 Wake County Schools 159675
#> 2 Charlotte-Mecklenburg Schools 140415
#> 3 Guilford County Schools 65879
#> 4 Winston Salem / Forsyth County Schools 50842
#> 5 Cumberland County Schools 47871
#> 6 Union County Public Schools 41378
#> 7 Johnston County Public Schools 36745
#> 8 Cabarrus County Schools 34919
#> 9 Durham Public Schools 30783
#> 10 Gaston County Schools 29714
Top NC districts
6. White students dropped from 47% to 43% while Hispanic enrollment surged
Between 2018 and 2024, white enrollment fell by 106,000 students while Hispanic enrollment grew by 57,000. North Carolina is becoming a majority-minority school system.
enr_demo <- fetch_enr_multi(c(2018, 2019, 2020, 2021, 2024), use_cache = TRUE)
demographics <- enr_demo %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "black", "hispanic", "asian")) %>%
select(end_year, subgroup, n_students) %>%
mutate(subgroup = factor(subgroup,
levels = c("white", "black", "hispanic", "asian"),
labels = c("White", "Black", "Hispanic", "Asian")))
stopifnot(nrow(demographics) > 0)
demographics
#> # A tibble: 20 x 3
#> end_year subgroup n_students
#> <dbl> <fct> <dbl>
#> 1 2018 Asian 51706
#> 2 2018 Black 393618
#> 3 2018 Hispanic 271304
#> 4 2018 White 749498
#> 5 2019 Asian 54349
#> 6 2019 Black 394222
#> 7 2019 Hispanic 282328
#> 8 2019 White 746767
#> 9 2020 Asian 55582
#> 10 2020 Black 382563
#> 11 2020 Hispanic 292782
#> 12 2020 White 722929
#> 13 2021 Asian 56197
#> 14 2021 Black 373647
#> 15 2021 Hispanic 285867
#> 16 2021 White 687491
#> 17 2024 Asian 64402
#> 18 2024 Black 369522
#> 19 2024 Hispanic 328041
#> 20 2024 White 643051
NC demographics
7. Charlotte-Mecklenburg lost 10,000 students during COVID then partially recovered
CMS dropped from 147,600 in 2019 to 137,600 in 2021 – a staggering 10,000-student loss. By 2024 it has clawed back to 140,400 but remains 7,200 below its peak.
enr_cms <- fetch_enr_multi(c(2018, 2019, 2020, 2021, 2024), use_cache = TRUE)
cms_trend <- enr_cms %>%
filter(is_district, grepl("Charlotte-Mecklenburg", district_name),
subgroup == "total_enrollment", grade_level == "TOTAL") %>%
select(end_year, district_name, n_students) %>%
mutate(change = n_students - lag(n_students))
stopifnot(nrow(cms_trend) > 0)
cms_trend
#> # A tibble: 5 x 4
#> end_year district_name n_students change
#> <dbl> <chr> <dbl> <dbl>
#> 1 2018 Charlotte-Mecklenburg Schools 146693 NA
#> 2 2019 Charlotte-Mecklenburg Schools 147639 946
#> 3 2020 Charlotte-Mecklenburg Schools 146255 -1384
#> 4 2021 Charlotte-Mecklenburg Schools 137578 -8677
#> 5 2024 Charlotte-Mecklenburg Schools 140415 2837
CMS enrollment
8. Half of NC students are economically disadvantaged
757,944 students – 50.3% of the state – qualify as economically disadvantaged. This is the first year econ_disadv data is available in the package.
econ_data <- enr_2024 %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("total_enrollment", "econ_disadv")) %>%
select(subgroup, n_students) %>%
mutate(pct = round(n_students / max(n_students) * 100, 1))
stopifnot(nrow(econ_data) == 2)
econ_data
#> # A tibble: 2 x 3
#> subgroup n_students pct
#> <chr> <dbl> <dbl>
#> 1 total_enrollment 1508194 100
#> 2 econ_disadv 757944 50.3
Econ disadvantaged
9. English Learners grew 42% in six years
From 118,569 in 2018 to 168,383 in 2024, NC schools added nearly 50,000 English Learners. LEP students now make up 11.2% of total enrollment.
enr_lep <- fetch_enr_multi(c(2018, 2019, 2020, 2021, 2024), use_cache = TRUE)
lep_trend <- enr_lep %>%
filter(is_state, grade_level == "TOTAL", subgroup == "lep") %>%
select(end_year, n_students) %>%
mutate(pct_change = round((n_students / first(n_students) - 1) * 100, 1))
stopifnot(nrow(lep_trend) > 0)
lep_trend
#> # A tibble: 5 x 3
#> end_year n_students pct_change
#> <dbl> <dbl> <dbl>
#> 1 2018 118569 0
#> 2 2019 127843 7.8
#> 3 2020 126240 6.5
#> 4 2021 131322 10.8
#> 5 2024 168383 42
English Learners trend
10. Wake County grew 33% since 2006 but may have peaked
Wake County added nearly 40,000 students from 2006 to 2020, then dipped slightly by 2024. After years of relentless growth, has the Research Triangle’s anchor district plateaued?
enr_wake <- fetch_enr_multi(c(2006, 2010, 2015, 2020, 2024), use_cache = TRUE)
wake_trend <- enr_wake %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Wake", district_name)) %>%
select(end_year, district_name, n_students) %>%
mutate(change = n_students - lag(n_students))
stopifnot(nrow(wake_trend) > 0)
wake_trend
#> # A tibble: 5 x 4
#> end_year district_name n_students change
#> <dbl> <chr> <dbl> <dbl>
#> 1 2006 Wake County Schools 120367 NA
#> 2 2010 Wake County Schools 139064 18697
#> 3 2015 Wake County Schools 153488 14424
#> 4 2020 Wake County Schools 160622 7134
#> 5 2024 Wake County Schools 159675 -947
Wake County growth
11. The coast is growing while the Piedmont shrinks
Coastal counties (New Hanover, Brunswick, Pender) gained students while Piedmont cities (Guilford, Forsyth, Alamance) lost nearly 9,000 since 2015.
enr_regional <- fetch_enr_multi(c(2015, 2024), use_cache = TRUE)
coastal <- c("New Hanover", "Brunswick", "Pender")
piedmont <- c("Guilford", "Forsyth", "Alamance")
regional <- enr_regional %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(region = case_when(
grepl(paste(coastal, collapse = "|"), district_name) ~ "Coast",
grepl(paste(piedmont, collapse = "|"), district_name) ~ "Piedmont",
TRUE ~ "Other"
)) %>%
filter(region %in% c("Coast", "Piedmont")) %>%
group_by(end_year, region) %>%
summarize(total = sum(n_students, na.rm = TRUE), .groups = "drop")
stopifnot(nrow(regional) == 4)
regional
#> # A tibble: 4 x 3
#> end_year region total
#> <dbl> <chr> <dbl>
#> 1 2015 Coast 46807
#> 2 2015 Piedmont 147754
#> 3 2024 Coast 48559
#> 4 2024 Piedmont 138801
Coast vs Piedmont
12. Union County grew 32% since 2006 on Charlotte suburban boom
Union County Public Schools went from 31,330 to 41,378 students. Weddington, Waxhaw, and Indian Trail fueled the growth, though the pace has slowed since 2015.
enr_union <- fetch_enr_multi(c(2006, 2010, 2015, 2020, 2024), use_cache = TRUE)
union_trend <- enr_union %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Union", district_name)) %>%
select(end_year, district_name, n_students)
stopifnot(nrow(union_trend) > 0)
union_trend
#> # A tibble: 5 x 3
#> end_year district_name n_students
#> <dbl> <chr> <dbl>
#> 1 2006 Union County Public Schools 31330
#> 2 2010 Union County Public Schools 38282
#> 3 2015 Union County Public Schools 41296
#> 4 2020 Union County Public Schools 41452
#> 5 2024 Union County Public Schools 41378
Union County growth
13. Asheville-area mountain counties lost 10% of students
Seven mountain counties around Asheville (Buncombe, Henderson, Haywood, Madison, Transylvania, Yancey, Mitchell) dropped from 55,623 to 49,817 students since 2015.
enr_mountain <- fetch_enr_multi(c(2015, 2024), use_cache = TRUE)
mountain <- c("Buncombe", "Henderson", "Haywood", "Madison",
"Transylvania", "Yancey", "Mitchell")
mountain_data <- enr_mountain %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(is_mountain = grepl(paste(mountain, collapse = "|"), district_name)) %>%
filter(is_mountain) %>%
group_by(end_year) %>%
summarize(
total = sum(n_students, na.rm = TRUE),
n_districts = n(),
.groups = "drop"
)
stopifnot(nrow(mountain_data) == 2)
mountain_data
#> # A tibble: 2 x 3
#> end_year total n_districts
#> <dbl> <dbl> <int>
#> 1 2015 55623 7
#> 2 2024 49817 7
Mountain counties
14. Special education holds steady at 13-14% of enrollment
NC’s special education population has been remarkably stable at around 13-14% of total enrollment since 2018, even as overall enrollment fluctuated with COVID.
enr_sped <- fetch_enr_multi(c(2018, 2019, 2020, 2021, 2024), use_cache = TRUE)
sped_trend <- enr_sped %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("total_enrollment", "special_ed")) %>%
select(end_year, subgroup, n_students) %>%
pivot_wider(names_from = subgroup, values_from = n_students) %>%
mutate(
pct_sped = round(special_ed / total_enrollment * 100, 1),
sped_change = round((special_ed / first(special_ed) - 1) * 100, 1)
)
stopifnot(nrow(sped_trend) > 0)
sped_trend
#> # A tibble: 5 x 5
#> end_year total_enrollment special_ed pct_sped sped_change
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2018 1521108 208352 13.7 0
#> 2 2019 1535687 211629 13.8 1.6
#> 3 2020 1525592 209750 13.7 0.7
#> 4 2021 1469401 204434 13.9 -1.9
#> 5 2024 1508194 202380 13.4 -2.9
Special ed trend
15. Triangle vs Triad: diverging metro areas
The Research Triangle (Wake, Durham, Orange, Johnston, Chatham) grew from 237K to 243K while the Piedmont Triad (Guilford, Forsyth, Davidson, Randolph, Alamance) shrank from 185K to 171K since 2015.
enr_metro <- fetch_enr_multi(c(2015, 2020, 2024), use_cache = TRUE)
triangle <- c("Wake", "Durham", "Orange", "Johnston", "Chatham")
triad <- c("Guilford", "Forsyth", "Davidson", "Randolph", "Alamance")
metro_data <- enr_metro %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(region = case_when(
grepl(paste(triangle, collapse = "|"), district_name) ~ "Triangle",
grepl(paste(triad, collapse = "|"), district_name) ~ "Triad",
TRUE ~ "Other"
)) %>%
filter(region %in% c("Triangle", "Triad")) %>%
group_by(end_year, region) %>%
summarize(total = sum(n_students, na.rm = TRUE), .groups = "drop")
stopifnot(nrow(metro_data) == 6)
metro_data
#> # A tibble: 6 x 3
#> end_year region total
#> <dbl> <chr> <dbl>
#> 1 2015 Triad 184979
#> 2 2015 Triangle 236723
#> 3 2020 Triad 181208
#> 4 2020 Triangle 246320
#> 5 2024 Triad 171352
#> 6 2024 Triangle 243012
Triangle vs Triad