Fetch and analyze Nebraska school enrollment data from the Nebraska Department of Education – 365,467 students across 420+ districts, with 22 years of data (2003-2024).
Part of the njschooldata family.
Full documentation – all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
1. Hispanic enrollment has grown 138% since 2005
Nebraska’s demographic transformation: Hispanic students went from 32,000 to 77,000 in two decades.
enr <- fetch_enr_multi(c(2005, 2010, 2015, 2020, 2024), use_cache = TRUE)
demographics <- enr %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "hispanic", "black", "asian")) %>%
select(end_year, subgroup, n_students)
stopifnot(nrow(demographics) > 0)
demographics %>%
pivot_wider(names_from = subgroup, values_from = n_students)
#> end_year white black hispanic asian
#> 1 2005 260334 22523 32373 5966
#> 2 2010 251265 25340 44171 7445
#> 3 2015 244283 22498 57665 8780
#> 4 2020 245206 23716 67707 10590
#> 5 2024 232394 23100 76933 11061Hispanic students: 32,373 to 76,933 (+138%). White students declined by 27,940.
2. Gretna grew 132% since 2010 – Nebraska’s fastest-growing district
From 2,921 to 6,788 students, this Omaha-area suburb has more than doubled enrollment.
enr_multi <- fetch_enr_multi(2010:2024, use_cache = TRUE)
gretna <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name == "GRETNA PUBLIC SCHOOLS") %>%
select(end_year, district_name, n_students)
stopifnot(nrow(gretna) > 0)
gretna %>%
filter(end_year %in% c(2010, 2014, 2018, 2022, 2024))
#> end_year district_name n_students
#> 1 2010 GRETNA PUBLIC SCHOOLS 2921
#> 2 2014 GRETNA PUBLIC SCHOOLS 3688
#> 3 2018 GRETNA PUBLIC SCHOOLS 5090
#> 4 2022 GRETNA PUBLIC SCHOOLS 6339
#> 5 2024 GRETNA PUBLIC SCHOOLS 6788+3,867 students (+132%) in 14 years. Gretna passed Kearney to become a top-8 district.
3. Lexington is 77% Hispanic – a meatpacking transformation
Tyson Foods turned this central Nebraska town from majority-white to majority-Hispanic. The percentage has held steady around 75-77% since 2010.
lexington <- enr_multi %>%
filter(is_district, grepl("LEXINGTON", district_name, ignore.case = TRUE),
grade_level == "TOTAL",
subgroup %in% c("white", "hispanic", "total_enrollment")) %>%
select(end_year, subgroup, n_students) %>%
pivot_wider(names_from = subgroup, values_from = n_students) %>%
mutate(hispanic_pct = round(hispanic / total_enrollment * 100, 1))
stopifnot(nrow(lexington) > 0)
lexington %>%
filter(end_year %in% c(2010, 2015, 2020, 2024))
#> end_year total_enrollment white hispanic hispanic_pct
#> 1 2010 2804 485 2146 76.5
#> 2 2015 2995 484 2214 73.9
#> 3 2020 3169 444 2368 74.7
#> 4 2024 3229 400 2474 76.677% Hispanic and growing. Lexington’s enrollment grew 15% since 2010 while its white student population shrank.
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2003-2024 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race, gender |
| Assessments | — | — | Not yet available |
| Graduation | — | — | Not yet available |
| Directory | current | fetch_directory() |
Superintendents, principals, admin |
| 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 for what each category covers.
Quick Start
R
# install.packages("remotes")
remotes::install_github("almartin82/neschooldata")
library(neschooldata)
library(dplyr)
# Fetch one year
enr_2024 <- fetch_enr(2024)
# Fetch multiple years
enr_recent <- fetch_enr_multi(2019:2024)
# 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 by race
enr_2024 %>%
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "hispanic", "black", "asian"))Python
import pyneschooldata as ne
# Fetch 2024 data (2023-24 school year)
enr = ne.fetch_enr(2024)
# Statewide total
total = enr[enr['is_state'] & (enr['grade_level'] == 'TOTAL') & (enr['subgroup'] == 'total_enrollment')]['n_students'].sum()
print(f"{total:,} students")
#> 365,467 students
# Get multiple years
enr_multi = ne.fetch_enr_multi([2020, 2021, 2022, 2023, 2024])
# Check available years
years = ne.get_available_years()
print(f"Data available: {years['min_year']}-{years['max_year']}")
#> Data available: 2003-2024Explore More
Full analysis with 15 stories: - 15 Insights from Nebraska School Enrollment Data – 15 stories - Function reference
Data Notes
Data Source
Nebraska Department of Education: education.ne.gov
Available Years
| Years | Source | Notes |
|---|---|---|
| 2018-2024 | NDE CSV | Modern format with full demographics |
| 2011-2017 | NDE TXT | Full race/ethnicity breakdown |
| 2003-2010 | NDE TXT | Legacy format (Asian/Pacific Islander combined) |
What’s included
- Levels: State, district (~423), school (~1,215)
- Demographics: White, Black, Hispanic, Asian, Native American, Pacific Islander, Two or More Races
- Gender: Male, Female
- Grade levels: PK-12 plus totals
Census Day
Enrollment counts are collected on the last Friday in September each year (Nebraska’s “Count Day”). This is the official date for state funding purposes.
Deeper Dive
4. Nebraska added 39,000 students between 2005 and 2024
Two decades of steady growth – though the pandemic peak in 2020 was actually higher than 2024.
enr <- fetch_enr_multi(c(2005, 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
#> end_year n_students
#> 1 2005 326083
#> 2 2010 333835
#> 3 2015 349925
#> 4 2020 366966
#> 5 2024 365467+39,384 students (+12%) in 19 years. Growth stalled slightly after the 2020 pandemic peak.
5. Omaha and Lincoln enroll a quarter of all Nebraska students
The two cities anchor the state’s education system – but they are far from half.
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(8) %>%
select(district_name, n_students)
stopifnot(nrow(top_districts) > 0)
top_districts
#> district_name n_students
#> 1 OMAHA PUBLIC SCHOOLS 51693
#> 2 LINCOLN PUBLIC SCHOOLS 41654
#> 3 MILLARD PUBLIC SCHOOLS 23300
#> 4 PAPILLION LA VISTA COMMUNITY SCHOOLS 12039
#> 5 ELKHORN PUBLIC SCHOOLS 11455
#> 6 GRAND ISLAND PUBLIC SCHOOLS 10070
#> 7 BELLEVUE PUBLIC SCHOOLS 9444
#> 8 GRETNA PUBLIC SCHOOLS 6788Omaha + Lincoln: 93,347 students. That’s 25.5% of the state in two districts.
6. Elkhorn doubled enrollment since 2010
This Omaha suburb grew from 5,300 to 11,500 students in 14 years – the fastest growth in metro Omaha.
enr_multi <- fetch_enr_multi(2010:2024, use_cache = TRUE)
suburban <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name %in% c("ELKHORN PUBLIC SCHOOLS", "MILLARD PUBLIC SCHOOLS")) %>%
select(end_year, district_name, n_students)
# Papillion changed names in 2017; combine under one label
papillion <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("PAPILLION", district_name)) %>%
mutate(district_name = "PAPILLION LA VISTA COMMUNITY SCHOOLS") %>%
select(end_year, district_name, n_students)
suburban <- bind_rows(suburban, papillion)
stopifnot(nrow(suburban) > 0)
suburban %>%
filter(end_year %in% c(2010, 2015, 2020, 2024)) %>%
pivot_wider(names_from = end_year, values_from = n_students)
#> district_name `2010` `2015` `2020` `2024`
#> 1 ELKHORN PUBLIC SCHOOLS 5306 7553 10322 11455
#> 2 MILLARD PUBLIC SCHOOLS 22647 23702 24038 23300
#> 3 PAPILLION LA VISTA COMMUNITY SCHOOLS 9797 11401 12190 12039+116% growth for Elkhorn since 2010. Millard and Papillion were essentially flat.
7. 43 fewer tiny districts since 2010
Districts with fewer than 200 students dropped from 198 to 155 – consolidation in action.
regional <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
filter(end_year %in% c(2010, 2024)) %>%
mutate(size = case_when(
n_students >= 10000 ~ "Large (10,000+)",
n_students >= 1000 ~ "Medium (1,000-9,999)",
n_students >= 200 ~ "Small (200-999)",
TRUE ~ "Tiny (<200)"
)) %>%
group_by(end_year, size) %>%
summarize(n_districts = n(), total_students = sum(n_students), .groups = "drop")
stopifnot(nrow(regional) > 0)
regional %>%
pivot_wider(names_from = end_year, values_from = c(n_districts, total_students))
#> size n_districts_2010 n_districts_2024 total_students_2010 total_students_2024
#> 1 Large (10,000+) 3 6 106250 150211
#> 2 Medium (1,000-9,999) 38 38 113409 104607
#> 3 Small (200-999) 224 224 95267 95214
#> 4 Tiny (<200) 198 155 18909 1543543 fewer tiny districts since 2010. Large districts doubled from 3 to 6, absorbing most of the growth.
8. Omaha Public Schools enrollment held steady since 2015
Despite suburban growth, OPS barely changed – from 51,928 in 2015 to 51,693 in 2024.
omaha <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name == "OMAHA PUBLIC SCHOOLS") %>%
filter(end_year %in% c(2015, 2018, 2020, 2022, 2024)) %>%
select(end_year, n_students) %>%
mutate(change = n_students - lag(n_students))
stopifnot(nrow(omaha) > 0)
omaha
#> end_year n_students change
#> 1 2015 51928 NA
#> 2 2018 52836 908
#> 3 2020 53483 647
#> 4 2022 51626 -1857
#> 5 2024 51693 67-235 students (-0.5%) over 9 years. OPS actually peaked at 53,483 in 2020 before COVID dipped enrollment.
9. Pre-K grew by 3,768 while Kindergarten shrank by 1,589
Nebraska expanded early childhood access – Pre-K jumped from 18,493 to 22,261 since 2015.
grades <- enr_multi %>%
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("PK", "K", "01", "02", "03")) %>%
filter(end_year %in% c(2015, 2020, 2024)) %>%
select(end_year, grade_level, n_students)
stopifnot(nrow(grades) > 0)
grades %>%
pivot_wider(names_from = end_year, values_from = n_students) %>%
mutate(change_15_24 = `2024` - `2015`)
#> grade_level `2015` `2020` `2024` change_15_24
#> 1 01 26735 26111 25850 -885
#> 2 02 24853 25991 26415 1562
#> 3 03 26137 25865 26085 -52
#> 4 K 26867 26893 25278 -1589
#> 5 PK 18493 22718 22261 3768Pre-K: +3,768. Kindergarten: -1,589. Early childhood is expanding while K shrinks.
10. Grand Island is 61% Hispanic
The meatpacking industry transformed this central Nebraska city into one of the most Hispanic school districts in the Great Plains.
grand_island <- enr_2024 %>%
filter(grepl("GRAND ISLAND", district_name, ignore.case = TRUE), is_district,
grade_level == "TOTAL",
subgroup %in% c("white", "hispanic", "black", "asian")) %>%
mutate(pct = round(n_students / sum(n_students) * 100, 1)) %>%
select(subgroup, n_students, pct) %>%
arrange(desc(pct))
stopifnot(nrow(grand_island) > 0)
grand_island
#> subgroup n_students pct
#> 1 hispanic 5922 60.8
#> 2 white 3301 33.9
#> 3 black 444 4.6
#> 4 asian 79 0.861% Hispanic. Grand Island is one of the most demographically transformed districts in the Great Plains.
11. COVID hit Papillion and Omaha hardest, while Elkhorn surged 8%
Between 2019 and 2021, Elkhorn grew while every other major metro district shrank.
covid <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name %in% c("OMAHA PUBLIC SCHOOLS", "MILLARD PUBLIC SCHOOLS",
"ELKHORN PUBLIC SCHOOLS"),
end_year %in% c(2019, 2021)) %>%
select(district_name, end_year, n_students)
# Handle Papillion name change
papillion_covid <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("PAPILLION", district_name),
end_year %in% c(2019, 2021)) %>%
mutate(district_name = "PAPILLION LA VISTA COMMUNITY SCHOOLS") %>%
select(district_name, end_year, n_students)
covid <- bind_rows(covid, papillion_covid) %>%
pivot_wider(names_from = end_year, values_from = n_students) %>%
rename(y2019 = `2019`, y2021 = `2021`) %>%
mutate(pct_change = round((y2021 - y2019) / y2019 * 100, 1)) %>%
arrange(pct_change)
stopifnot(nrow(covid) > 0)
covid
#> district_name y2019 y2021 pct_change
#> 1 PAPILLION LA VISTA COMMUNITY SCHOOLS 12158 11831 -2.7
#> 2 OMAHA PUBLIC SCHOOLS 53194 51914 -2.4
#> 3 MILLARD PUBLIC SCHOOLS 24104 23633 -2.0
#> 4 ELKHORN PUBLIC SCHOOLS 9857 10642 8.0Papillion: -2.7%. Omaha: -2.4%. Elkhorn: +8.0%. Suburban flight accelerated during COVID.
12. Hispanic students are 21% of the state and growing
Nebraska’s 76,933 Hispanic students are the fastest-growing demographic group.
diversity <- enr_2024 %>%
filter(is_state, grade_level == "TOTAL") %>%
filter(subgroup %in% c("total_enrollment", "hispanic", "white", "black", "asian")) %>%
select(subgroup, n_students) %>%
mutate(pct = round(n_students / max(n_students) * 100, 1))
stopifnot(nrow(diversity) > 0)
diversity
#> subgroup n_students pct
#> 1 total_enrollment 365467 100.0
#> 2 white 232394 63.6
#> 3 black 23100 6.3
#> 4 hispanic 76933 21.1
#> 5 asian 11061 3.021% Hispanic statewide, up from 10% in 2005. The demographic shift continues.
13. Lincoln is gaining on Omaha
Lincoln Public Schools added 6,740 students since 2010. The gap between the two districts narrowed from 13,775 to 10,039.
two_cities <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("OMAHA PUBLIC|LINCOLN PUBLIC", district_name, ignore.case = TRUE)) %>%
mutate(city = if_else(grepl("OMAHA", district_name, ignore.case = TRUE), "Omaha", "Lincoln")) %>%
select(end_year, city, n_students)
stopifnot(nrow(two_cities) > 0)
two_cities %>%
pivot_wider(names_from = city, values_from = n_students) %>%
filter(end_year %in% c(2010, 2015, 2020, 2024)) %>%
mutate(gap = Omaha - Lincoln)
#> end_year Omaha Lincoln gap
#> 1 2010 48689 34914 13775
#> 2 2015 51928 39034 12894
#> 3 2020 53483 42258 11225
#> 4 2024 51693 41654 10039Lincoln: +6,740 (+19%). Omaha: +3,004 (+6%). The gap narrowed by 3,736 students.
14. The I-80 corridor gained 21,000 students while the rest of the state grew too
Districts along Interstate 80 from Omaha to Kearney hold 46% of all students but captured most of the growth.
# Major I-80 districts: Omaha metro, Lincoln, Grand Island, Kearney
i80_patterns <- c("OMAHA", "LINCOLN", "MILLARD", "PAPILLION", "BELLEVUE",
"ELKHORN", "GRAND ISLAND", "KEARNEY")
i80_growth <- enr_multi %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year %in% c(2010, 2024)) %>%
rowwise() %>%
mutate(is_i80 = any(sapply(i80_patterns, function(x) grepl(x, district_name, ignore.case = TRUE)))) %>%
ungroup() %>%
group_by(end_year, is_i80) %>%
summarize(total = sum(n_students), n_districts = n(), .groups = "drop") %>%
mutate(region = if_else(is_i80, "I-80 Corridor", "Rest of State"))
stopifnot(nrow(i80_growth) > 0)
i80_growth %>%
select(end_year, region, total, n_districts) %>%
pivot_wider(names_from = end_year, values_from = c(total, n_districts))
#> region total_2010 total_2024 n_districts_2010 n_districts_2024
#> 1 Rest of State 185827 196252 439 402
#> 2 I-80 Corridor 148008 169215 24 21I-80 corridor: +21,207 (+14%). Rest of state: +10,425 (+6%). Two Nebraskas growing at different speeds.
15. Tribal districts are 90-99% Native American
UMO N HO N NATION, WINNEBAGO, and WALTHILL serve predominantly Native American populations near Nebraska’s reservations.
# Look at districts with notable Native American populations
native_districts <- enr_2024 %>%
filter(is_district, grade_level == "TOTAL",
subgroup == "native_american") %>%
arrange(desc(n_students)) %>%
head(10) %>%
select(district_name, n_students)
# Get total enrollment for these districts to calculate percentages
native_pct <- enr_2024 %>%
filter(is_district, grade_level == "TOTAL",
subgroup %in% c("total_enrollment", "native_american"),
district_name %in% native_districts$district_name) %>%
select(district_name, subgroup, n_students) %>%
pivot_wider(names_from = subgroup, values_from = n_students) %>%
mutate(pct = round(native_american / total_enrollment * 100, 1)) %>%
arrange(desc(pct)) %>%
head(8)
stopifnot(nrow(native_pct) > 0)
native_pct
#> district_name total_enrollment native_american pct
#> 1 UMO N HO N NATION PUBLIC SCHS 664 655 98.6
#> 2 WINNEBAGO PUBLIC SCHOOLS DISTRICT 17 638 598 93.7
#> 3 WALTHILL PUBLIC SCHOOLS 321 294 91.6
#> 4 ISANTI COMMUNITY SCHOOL 229 206 90.0
#> 5 NIOBRARA PUBLIC SCHOOLS 220 135 61.4
#> 6 GORDON-RUSHVILLE PUBLIC SCHS 534 88 16.5
#> 7 CHADRON PUBLIC SCHOOLS 971 87 9.0
#> 8 SO SIOUX CITY COMMUNITY SCHS 3801 107 2.8Tribal districts are 90-99% Native American. Critical funding for reservation schools.
