Oregon enrolls 547,424 students across 210 school districts and 1,423 schools. A decade of growth erased by a pandemic, a kindergarten class that never bounced back, and a rural district that more than doubled while everyone else shrank—there are stories hiding in these numbers.
Part of the njschooldata family.
Full documentation — all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
1. Oregon’s enrollment peaked in 2020, then COVID hit
The state added students for a decade, then lost nearly 22,000 in a single year during the pandemic.
enr <- fetch_enr_multi(2010:2024, use_cache = TRUE)
state_totals <- enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
select(end_year, n_students) |>
mutate(change = n_students - lag(n_students),
pct_change = round(change / lag(n_students) * 100, 2))
stopifnot(nrow(state_totals) > 0)
state_totals
#> end_year n_students change pct_change
#> 1 2010 561696 NA NA
#> 2 2011 561696 0 0.00
#> 3 2012 561328 -368 -0.07
#> 4 2013 560946 -382 -0.07
#> 5 2014 562262 1316 0.23
#> 6 2015 570857 8595 1.53
#> 7 2016 576407 5550 0.97
#> 8 2017 578947 2540 0.44
#> 9 2018 580684 1737 0.30
#> 10 2019 581730 1046 0.18
#> 11 2020 582661 931 0.16
#> 12 2021 560917 -21744 -3.73
#> 13 2022 553012 -7905 -1.41
#> 14 2023 552380 -632 -0.11
#> 15 2024 547424 -4956 -0.90
Oregon statewide enrollment trends
2. The COVID kindergarten collapse hasn’t recovered
Kindergarten enrollment dropped 14.6% from 2020 to 2021 and remains below pre-COVID levels, signaling smaller cohorts for years to come.
covid_grades <- enr |>
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "06", "09"),
end_year %in% 2019:2024) |>
select(end_year, grade_level, n_students) |>
tidyr::pivot_wider(names_from = grade_level, values_from = n_students)
stopifnot(nrow(covid_grades) > 0)
covid_grades
#> # A tibble: 6 x 5
#> end_year K `01` `06` `09`
#> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2019 42004 42941 46655 45383
#> 2 2020 42322 42987 47024 45430
#> 3 2021 36151 40342 44012 46115
#> 4 2022 37816 38583 42274 46429
#> 5 2023 37026 40181 41907 46727
#> 6 2024 35644 38406 41670 44871
Oregon kindergarten enrollment
3. Baker SD 5J more than doubled enrollment since 2010
Baker SD 5J grew from 2,056 to 4,829 students—a 135% increase—making it Oregon’s fastest-growing district by percentage.
baker <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_id == "1894") |>
select(end_year, district_name, n_students)
stopifnot(nrow(baker) > 0)
baker
#> end_year district_name n_students
#> 1 2010 Baker SD 5J 2056
#> 2 2011 Baker SD 5J 2056
#> 3 2012 Baker SD 5J 2089
#> 4 2013 Baker SD 5J 2074
#> 5 2014 Baker SD 5J 2164
#> 6 2015 Baker SD 5J 2398
#> 7 2016 Baker SD 5J 2620
#> 8 2017 Baker SD 5J 2944
#> 9 2018 Baker SD 5J 3329
#> 10 2019 Baker SD 5J 3832
#> 11 2020 Baker SD 5J 4089
#> 12 2021 Baker SD 5J 4669
#> 13 2022 Baker SD 5J 4527
#> 14 2023 Baker SD 5J 4453
#> 15 2024 Baker SD 5J 4829
Baker SD 5J enrollment growth
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2010-2024 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race (2016+) |
| Assessments | — | — | Not yet available |
| Graduation | — | — | Not yet available |
| Directory | Current | fetch_directory() |
Schools, districts, programs. Name, type, city |
| 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("devtools")
devtools::install_github("almartin82/orschooldata")
library(orschooldata)
library(dplyr)
# Get 2024 enrollment data (2023-24 school year)
enr <- fetch_enr(2024)
# Statewide total
enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
pull(n_students)
# Top 10 districts
enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
arrange(desc(n_students)) |>
select(district_name, n_students) |>
head(10)
# Get multiple years
enr_multi <- fetch_enr_multi(2020:2024)Python
import pyorschooldata as or_
# Get 2024 enrollment data (2023-24 school year)
enr = or_.fetch_enr(2024)
# Statewide total
state_total = enr[
(enr['is_state']) &
(enr['subgroup'] == 'total_enrollment') &
(enr['grade_level'] == 'TOTAL')
]['n_students'].values[0]
print(state_total)
# Top 10 districts
top_districts = (
enr[
(enr['is_district']) &
(enr['subgroup'] == 'total_enrollment') &
(enr['grade_level'] == 'TOTAL')
]
.sort_values('n_students', ascending=False)
[['district_name', 'n_students']]
.head(10)
)
# Get multiple years
enr_multi = or_.fetch_enr_multi([2020, 2021, 2022, 2023, 2024])Explore More
Full analysis with 15 stories: - 15 Insights from Oregon School Enrollment Data — 15 stories - Getting Started with orschooldata - Function reference
Data Notes
Data Source
Oregon Department of Education Fall Membership Reports
Census Day
Fall Membership data is collected on the first school day in October (Census Day). This snapshot represents enrollment at a single point in time.
Available Years
| Era | Years | Format | Notes |
|---|---|---|---|
| Era 1 | 2010-2014 | .xls | Older Excel format |
| Era 2 | 2015-2024 | .xlsx | Modern format |
15 years across ~210 districts and ~1,423 schools.
What’s Included
- Levels: State, district, and campus
- Grade levels: K, 01-12, TOTAL
- Race/ethnicity: white, hispanic, asian, black, multiracial, native_american, pacific_islander (available 2016-2024)
Deeper Dive
4. Portland Public Schools lost 4,700 students since 2019
Oregon’s largest district peaked at 48,677 students in 2019 and has declined every year since, dropping to 90% of its peak by 2024.
portland <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_id == "2180") |>
select(end_year, district_name, n_students) |>
mutate(pct_of_peak = round(n_students / max(n_students) * 100, 1))
stopifnot(nrow(portland) > 0)
portland
#> end_year district_name n_students pct_of_peak
#> 1 2010 Portland SD 1J 45678 93.8
#> 2 2011 Portland SD 1J 45678 93.8
#> 3 2012 Portland SD 1J 45718 93.9
#> 4 2013 Portland SD 1J 46190 94.9
#> 5 2014 Portland SD 1J 46549 95.6
#> 6 2015 Portland SD 1J 47647 97.9
#> 7 2016 Portland SD 1J 48383 99.4
#> 8 2017 Portland SD 1J 48198 99.0
#> 9 2018 Portland SD 1J 48650 99.9
#> 10 2019 Portland SD 1J 48677 100.0
#> 11 2020 Portland SD 1J 48559 99.8
#> 12 2021 Portland SD 1J 46924 96.4
#> 13 2022 Portland SD 1J 45123 92.7
#> 14 2023 Portland SD 1J 44681 91.8
#> 15 2024 Portland SD 1J 43979 90.3
Top Oregon districts
5. High school grades are larger than elementary
Grade 9 enrollment exceeds kindergarten by several thousand students, reflecting the pandemic’s lasting impact on younger cohorts.
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
grade_comparison <- enr_2024 |>
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "02", "03", "09", "10", "11", "12")) |>
select(grade_level, n_students) |>
arrange(grade_level)
stopifnot(nrow(grade_comparison) > 0)
grade_comparison
#> grade_level n_students
#> 1 01 38406
#> 2 02 40577
#> 3 03 40026
#> 4 09 44871
#> 5 10 46733
#> 6 11 45424
#> 7 12 46162
#> 8 K 356446. 86 districts have fewer than 500 students
Rural Oregon is vast, and many small districts serve tiny populations spread across large geographic areas.
district_sizes <- enr_2024 |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
mutate(size_bucket = case_when(
n_students < 500 ~ "Small (<500)",
n_students < 2000 ~ "Medium (500-2K)",
n_students < 10000 ~ "Large (2K-10K)",
TRUE ~ "Very Large (10K+)"
)) |>
count(size_bucket)
stopifnot(nrow(district_sizes) > 0)
district_sizes
#> size_bucket n
#> 1 Large (2K-10K) 55
#> 2 Medium (500-2K) 59
#> 3 Small (<500) 86
#> 4 Very Large (10K+) 10
Oregon districts by size
7. Oregon’s Hispanic student share rose from 22.5% to 25.8% in eight years
While total enrollment fell after 2020, Hispanic enrollment grew to 141,060 students—now more than one in four Oregon students.
hispanic_share <- enr |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("total_enrollment", "hispanic")) |>
select(end_year, subgroup, n_students) |>
tidyr::pivot_wider(names_from = subgroup, values_from = n_students) |>
filter(!is.na(hispanic)) |>
mutate(hispanic_pct = round(hispanic / total_enrollment * 100, 1))
stopifnot(nrow(hispanic_share) > 0)
hispanic_share
#> # A tibble: 9 x 4
#> end_year total_enrollment hispanic hispanic_pct
#> <int> <dbl> <dbl> <dbl>
#> 1 2016 576407 129410 22.5
#> 2 2017 578947 131089 22.6
#> 3 2018 580684 133822 23
#> 4 2019 581730 136186 23.4
#> 5 2020 582661 138273 23.7
#> 6 2021 560917 137101 24.4
#> 7 2022 553012 138112 25
#> 8 2023 552380 139928 25.3
#> 9 2024 547424 141060 25.8
Hispanic student share trend
8. Woodburn is 86% Hispanic—Oregon’s most concentrated district
Woodburn SD 103, south of Portland, has the highest Hispanic student share of any sizeable Oregon district at 86.1%.
hisp_districts <- enr_2024 |>
filter(is_district, grade_level == "TOTAL",
subgroup %in% c("total_enrollment", "hispanic")) |>
select(district_name, subgroup, n_students) |>
tidyr::pivot_wider(names_from = subgroup, values_from = n_students) |>
mutate(hispanic_pct = round(hispanic / total_enrollment * 100, 1)) |>
filter(total_enrollment >= 5000) |>
arrange(desc(hispanic_pct))
stopifnot(nrow(hisp_districts) > 0)
hisp_districts
#> # A tibble: 27 x 4
#> district_name total_enrollment hispanic hispanic_pct
#> <chr> <dbl> <dbl> <dbl>
#> 1 Woodburn SD 103 5242 4514 86.1
#> 2 Hermiston SD 8 5419 3256 60.1
#> 3 Forest Grove SD 15 5809 3369 58
#> 4 Salem-Keizer SD 24J 38787 18050 46.5
#> 5 Reynolds SD 7 9613 4229 44
#> 6 Hillsboro SD 1J 18716 7725 41.3
#> 7 McMinnville SD 40 6419 2436 37.9
#> 8 Gresham-Barlow SD 10J 11371 3768 33.1
#> 9 Centennial SD 28J 5485 1755 32
#> 10 Tigard-Tualatin SD 23J 11620 3452 29.7
#> # i 17 more rows9. Portland SD 1J is Oregon’s largest district at 43,979 students
Portland leads Salem-Keizer by over 5,000 students, though both have declined since their pre-COVID peaks.
largest <- enr_2024 |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
arrange(desc(n_students)) |>
select(district_name, n_students) |>
head(10)
stopifnot(nrow(largest) > 0)
largest
#> district_name n_students
#> 1 Portland SD 1J 43979
#> 2 Salem-Keizer SD 24J 38787
#> 3 Beaverton SD 48J 37988
#> 4 Hillsboro SD 1J 18716
#> 5 Bend-LaPine Administrative SD 1 17075
#> 6 North Clackamas SD 12 16874
#> 7 Eugene SD 4J 16318
#> 8 Medford SD 549C 13750
#> 9 Tigard-Tualatin SD 23J 11620
#> 10 Gresham-Barlow SD 10J 1137110. White students dropped from 63.4% to 58.4% of Oregon enrollment since 2016
Oregon is diversifying rapidly. White enrollment fell by 46,000 students while Hispanic and multiracial populations grew.
race_trend <- enr |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "hispanic", "multiracial", "asian", "black")) |>
select(end_year, subgroup, n_students) |>
filter(!is.na(n_students))
stopifnot(nrow(race_trend) > 0)
race_wide <- race_trend |>
tidyr::pivot_wider(names_from = subgroup, values_from = n_students)
race_wide
#> # A tibble: 9 x 6
#> end_year asian black hispanic white multiracial
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2016 22726 13744 129410 365593 32597
#> 2 2017 23067 13654 131089 364581 34200
#> 3 2018 23324 13509 133822 362396 35677
#> 4 2019 23267 13301 136186 360197 37136
#> 5 2020 23208 13176 138273 358257 38306
#> 6 2021 22733 13021 137101 338528 38629
#> 7 2022 22145 12731 138112 329994 39219
#> 8 2023 22181 12982 139928 326100 40024
#> 9 2024 22288 13114 141060 319798 40294
Race/ethnicity enrollment trends
11. 15 years of data reveal long-term shifts
Oregon’s enrollment data spans from 2010 to 2024, capturing the Great Recession recovery, pre-pandemic growth, COVID disruption, and early recovery.
decade_summary <- enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year %in% c(2010, 2015, 2020, 2021, 2024)) |>
select(end_year, n_students) |>
mutate(label = case_when(
end_year == 2010 ~ "Post-recession",
end_year == 2015 ~ "Mid-decade",
end_year == 2020 ~ "Pre-COVID peak",
end_year == 2021 ~ "COVID low",
end_year == 2024 ~ "Current"
))
stopifnot(nrow(decade_summary) > 0)
decade_summary
#> end_year n_students label
#> 1 2010 561696 Post-recession
#> 2 2015 570857 Mid-decade
#> 3 2020 582661 Pre-COVID peak
#> 4 2021 560917 COVID low
#> 5 2024 547424 Current12. Beaverton vs Hillsboro: Suburban rivals
Washington County’s two largest districts show different trajectories over the past decade.
wash_county <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name %in% c("Beaverton SD 48J", "Hillsboro SD 1J")) |>
select(end_year, district_name, n_students)
stopifnot(nrow(wash_county) > 0)
wash_county |>
filter(end_year %in% c(2010, 2015, 2020, 2024))
#> end_year district_name n_students
#> 1 2010 Hillsboro SD 1J 20714
#> 2 2010 Beaverton SD 48J 37950
#> 3 2015 Hillsboro SD 1J 20884
#> 4 2015 Beaverton SD 48J 39763
#> 5 2020 Hillsboro SD 1J 20269
#> 6 2020 Beaverton SD 48J 41215
#> 7 2024 Hillsboro SD 1J 18716
#> 8 2024 Beaverton SD 48J 37988
Washington County suburban districts
13. Multiracial is Oregon’s fastest-growing racial group
Multiracial students grew from 32,597 in 2016 to 40,294 in 2024—a 23.6% increase—even as total enrollment declined.
multi <- enr |>
filter(is_state, grade_level == "TOTAL", subgroup == "multiracial") |>
select(end_year, n_students) |>
mutate(growth_from_2016 = round((n_students / first(n_students) - 1) * 100, 1))
stopifnot(nrow(multi) > 0)
multi
#> end_year n_students growth_from_2016
#> 1 2016 32597 0.0
#> 2 2017 34200 4.9
#> 3 2018 35677 9.4
#> 4 2019 37136 13.9
#> 5 2020 38306 17.5
#> 6 2021 38629 18.5
#> 7 2022 39219 20.3
#> 8 2023 40024 22.8
#> 9 2024 40294 23.6
Multiracial student growth
14. Bend-LaPine grew 8% while Portland shrank
Bend-LaPine Administrative SD 1 added over 1,200 students since 2010, bucking statewide post-COVID decline trends.
bend <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_id == "1976") |>
select(end_year, district_name, n_students)
stopifnot(nrow(bend) > 0)
bend
#> end_year district_name n_students
#> 1 2010 Bend-LaPine Administrative SD 1 15819
#> 2 2011 Bend-LaPine Administrative SD 1 15819
#> 3 2012 Bend-LaPine Administrative SD 1 16157
#> 4 2013 Bend-LaPine Administrative SD 1 16275
#> 5 2014 Bend-LaPine Administrative SD 1 16575
#> 6 2015 Bend-LaPine Administrative SD 1 17122
#> 7 2016 Bend-LaPine Administrative SD 1 17517
#> 8 2017 Bend-LaPine Administrative SD 1 17984
#> 9 2018 Bend-LaPine Administrative SD 1 18325
#> 10 2019 Bend-LaPine Administrative SD 1 18384
#> 11 2020 Bend-LaPine Administrative SD 1 18647
#> 12 2021 Bend-LaPine Administrative SD 1 17542
#> 13 2022 Bend-LaPine Administrative SD 1 17419
#> 14 2023 Bend-LaPine Administrative SD 1 17356
#> 15 2024 Bend-LaPine Administrative SD 1 17075
Bend-LaPine enrollment trend
15. Grade-by-grade snapshot reveals demographic wave
Each grade level tells a story: today’s kindergartners are tomorrow’s high schoolers.
grade_snapshot <- enr |>
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "02", "03", "04", "05", "06", "07", "08", "09", "10", "11", "12"),
end_year == 2024) |>
select(grade_level, n_students) |>
arrange(grade_level)
stopifnot(nrow(grade_snapshot) > 0)
grade_snapshot
#> grade_level n_students
#> 1 01 38406
#> 2 02 40577
#> 3 03 40026
#> 4 04 41618
#> 5 05 41454
#> 6 06 41670
#> 7 07 42008
#> 8 08 42831
#> 9 09 44871
#> 10 10 46733
#> 11 11 45424
#> 12 12 46162
#> 13 K 35644
Grade-by-grade enrollment