Washington educates 1.1 million students across 295 school districts, from the tech corridors of Seattle to the wheat fields of the Palouse. This package gives you 16 years of enrollment data (2010-2025) for every school, district, and the state – race, gender, grade level, special populations, and more – via the OSPI data.wa.gov API.
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
library(waschooldata)
library(dplyr)
library(tidyr)
enr <- fetch_enr_multi(2010:2025, use_cache = TRUE)1. White students dropped from 64% to 48% as Hispanic enrollment nearly doubled
The state’s demographics reflect Pacific Rim immigration and a growing Hispanic population that rose from 167K to 295K students.
demographics <- enr |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("white", "hispanic", "asian", "black", "multiracial"),
end_year %in% c(2010, 2015, 2020, 2025)) |>
select(end_year, subgroup, n_students, pct) |>
mutate(pct = round(pct * 100, 1))
stopifnot(nrow(demographics) > 0)
demographics |>
pivot_wider(names_from = end_year, values_from = c(n_students, pct))
#> # A tibble: 5 x 9
#> subgroup n_students_2010 n_students_2015 n_students_2020 n_students_2025
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 white 657143 615697 601749 526102
#> 2 black 56515 48578 50251 53176
#> 3 hispanic 167426 235730 273842 294985
#> 4 asian 80375 77981 91377 100676
#> 5 multiracial 35867 81757 101807 101068
#> # i 4 more variables: pct_2010 <dbl>, pct_2015 <dbl>, pct_2020 <dbl>,
#> # pct_2025 <dbl>
Demographics
2. The kindergarten cliff is real: K enrollment down 14% from 2020 peak
Washington’s kindergarten enrollment dropped during COVID and hasn’t recovered, signaling smaller cohorts ahead.
k_trend <- enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "K") |>
select(end_year, n_students) |>
mutate(pct_of_peak = round(n_students / max(n_students) * 100, 1))
stopifnot(nrow(k_trend) > 0)
k_trend
#> end_year n_students pct_of_peak
#> 1 2010 73735 88.9
#> 2 2011 75364 90.9
#> 3 2012 77896 93.9
#> 4 2013 80426 97.0
#> 5 2014 81286 98.0
#> 6 2015 81348 98.1
#> 7 2016 79874 96.3
#> 8 2017 81151 97.8
#> 9 2018 81428 98.2
#> 10 2019 82130 99.0
#> 11 2020 82947 100.0
#> 12 2021 70977 85.6
#> 13 2022 78640 94.8
#> 14 2023 78406 94.5
#> 15 2024 76359 92.1
#> 16 2025 71443 86.1
Kindergarten enrollment
3. Foster care enrollment dropped 53% from 2019 peak
Washington tracks foster care enrollment at every school, revealing the population peaked at 7,573 in 2019 and has since dropped to 3,560.
foster_trend <- enr |>
filter(is_state, grade_level == "TOTAL", subgroup == "foster_care") |>
select(end_year, n_students, pct) |>
mutate(pct = round(pct * 100, 2))
stopifnot(nrow(foster_trend) > 0)
foster_trend
#> end_year n_students pct
#> 1 2010 6670 0.64
#> 2 2011 6715 0.64
#> 3 2012 6124 0.58
#> 4 2013 5627 0.53
#> 5 2014 5452 0.51
#> 6 2015 5268 0.48
#> 7 2016 5224 0.47
#> 8 2017 5873 0.53
#> 9 2018 6739 0.60
#> 10 2019 7573 0.67
#> 11 2020 6812 0.59
#> 12 2021 5598 0.51
#> 13 2022 4903 0.45
#> 14 2023 4112 0.37
#> 15 2024 3317 0.30
#> 16 2025 3560 0.32
Foster care trend
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2010-2025 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race, gender, FRPL, SpEd, LEP, foster care, homeless, migrant |
| Assessments | – | – | Not yet available |
| Graduation | – | – | Not yet available |
| Directory | 2010-2025 | fetch_directory() |
State, district, school. Address, phone, admin, ESD, county, school type |
| 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("devtools")
devtools::install_github("almartin82/waschooldata")
library(waschooldata)
library(dplyr)
# Get 2025 enrollment data (2024-25 school year)
enr <- fetch_enr(2025)
# Statewide total
enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
select(end_year, n_students)
# Top 5 districts
enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
arrange(desc(n_students)) |>
select(district_name, n_students) |>
head(5)Python
import pywaschooldata as wa
# Fetch 2025 data (2024-25 school year)
enr = wa.fetch_enr(2025)
# Statewide total
total = enr[(enr['is_state'] == True) &
(enr['subgroup'] == 'total_enrollment') &
(enr['grade_level'] == 'TOTAL')]['n_students'].sum()
print(f"{total:,} students")
# Get multiple years
enr_multi = wa.fetch_enr_multi([2020, 2021, 2022, 2023, 2024, 2025])
# Check available years
years = wa.get_available_years()
print(f"Data available: {years['min_year']}-{years['max_year']}")Explore More
Full analysis with 15 stories covering statewide trends, regional patterns, and demographic shifts:
- Enrollment trends – 15 stories
- Function reference
Data Notes
Data Source
All data comes directly from the Washington Office of Superintendent of Public Instruction (OSPI) via the Washington State Report Card and data.wa.gov.
Data Availability
| Era | Years | Source |
|---|---|---|
| Report Card | 2015-2025 | data.wa.gov (full detail) |
| Student Enrollment | 2010-2014 | data.wa.gov (fewer ID columns) |
16 years total across ~2,400 schools and 295 districts.
Suppression Rules
Washington suppresses student counts where: - Counts are less than 10 students (for privacy) - Subgroup percentages that could identify individuals
Census Day
Enrollment data is collected on Census Day (typically the first week of October). This is a snapshot of enrollment at that point in the school year.
Known Caveats
- The 2010-2014 “Student Enrollment” datasets use slightly different column formats than the 2015+ “Report Card Enrollment” datasets; this package normalizes them to a consistent schema
- ESD (Educational Service District) information is available for recent years but may be missing for older data (2010-2014)
- Some district names have changed over time; the package uses current names
- Charter schools were not legal in Washington until 2012, so charter data begins in 2015
Deeper Dive
4. Washington added 70,000 students over 16 years before COVID reversed the trend
The Evergreen State grew enrollment steadily from 1.03 million to nearly 1.15 million before the pandemic wiped out years of gains.
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 1034935 NA NA
#> 2 2011 1045333 10398 1.00
#> 3 2012 1051404 6071 0.58
#> 4 2013 1058680 7276 0.69
#> 5 2014 1068234 9554 0.90
#> 6 2015 1086314 18080 1.69
#> 7 2016 1100849 14535 1.34
#> 8 2017 1115820 14971 1.36
#> 9 2018 1130714 14894 1.33
#> 10 2019 1137367 6653 0.59
#> 11 2020 1146882 9515 0.84
#> 12 2021 1093331 -53551 -4.67
#> 13 2022 1091343 -1988 -0.18
#> 14 2023 1096695 5352 0.49
#> 15 2024 1100059 3364 0.31
#> 16 2025 1105384 5325 0.48
Statewide enrollment
5. Seattle peaked at 56,000 students then lost 9% in five years
Washington’s largest district peaked in 2020 and has lost nearly 5,000 students since, even as tech industry growth transformed the region.
seattle <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Seattle", district_name, ignore.case = TRUE)) |>
select(end_year, district_name, n_students) |>
mutate(pct_of_peak = round(n_students / max(n_students) * 100, 1))
stopifnot(nrow(seattle) > 0)
seattle
#> end_year district_name n_students pct_of_peak
#> 1 2010 Seattle School District No. 1 47058 84.0
#> 2 2011 Seattle School District No. 1 48299 86.2
#> 3 2012 Seattle School District No. 1 49851 88.9
#> 4 2013 Seattle School District No. 1 51201 91.3
#> 5 2014 Seattle School District No. 1 52181 93.1
#> 6 2015 Seattle School District No. 1 53361 95.2
#> 7 2016 Seattle School District No. 1 53767 95.9
#> 8 2017 Seattle School District No. 1 54722 97.6
#> 9 2018 Seattle School District No. 1 55321 98.7
#> 10 2019 Seattle School District No. 1 55325 98.7
#> 11 2020 Seattle School District No. 1 56051 100.0
#> 12 2021 Seattle School District No. 1 54021 96.4
#> 13 2022 Seattle School District No. 1 51653 92.2
#> 14 2023 Seattle School District No. 1 51528 91.9
#> 15 2024 Seattle School District No. 1 50968 90.9
#> 16 2025 Seattle School District No. 1 51200 91.3
Top districts
6. Puget Sound holds 427K students – nearly 4x Eastern Washington
The I-5 corridor dominates enrollment, but Eastern Washington’s districts face different challenges.
esd_enrollment <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year == 2025, !is.na(esd_name)) |>
group_by(esd_name) |>
summarize(
districts = n(),
students = sum(n_students, na.rm = TRUE),
.groups = "drop"
) |>
arrange(desc(students))
stopifnot(nrow(esd_enrollment) > 0)
esd_enrollment
#> # A tibble: 11 x 3
#> esd_name districts students
#> <chr> <int> <dbl>
#> 1 Puget Sound Educational Service District 121 35 427222
#> 2 Northwest Educational Service District 189 35 166971
#> 3 Educational Service District 112 30 97301
#> 4 Educational Service District 101 59 94984
#> 5 Educational Service District 123 22 77778
#> 6 Capital Region ESD 113 44 75121
#> 7 Educational Service District 105 25 65229
#> 8 North Central Educational Service District 171 29 48048
#> 9 Olympic Educational Service District 114 15 46477
#> 10 Washington State Charter School Commission 15 4600
#> 11 Spokane Public Schools Charter Authorizer 2 268
Regional chart
7. Suburban districts are booming: Sumner-Bonney Lake up 23% since 2015
The fastest-growing large districts are spread across the state, from Sumner-Bonney Lake south of Seattle to Central Valley near Spokane.
growth_rates <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year %in% c(2015, 2025)) |>
select(district_id, district_name, end_year, n_students) |>
pivot_wider(names_from = end_year, values_from = n_students, names_prefix = "yr_") |>
filter(yr_2015 > 5000) |>
mutate(
change = yr_2025 - yr_2015,
pct_change = round((yr_2025 - yr_2015) / yr_2015 * 100, 1)
) |>
arrange(desc(pct_change))
stopifnot(nrow(growth_rates) > 0)
head(growth_rates, 10)
#> # A tibble: 10 x 6
#> district_id district_name yr_2015 yr_2025 change pct_change
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 100259 Sumner-Bonney Lake School Dist~ 8988 11048 2060 22.9
#> 2 100126 Lake Stevens School District 8515 10215 1700 20
#> 3 100016 Auburn School District 15722 18234 2512 16
#> 4 100183 Omak School District 5257 6071 814 15.5
#> 5 100022 Bethel School District 18678 21538 2860 15.3
#> 6 100263 Tahoma School District 8118 9286 1168 14.4
#> 7 100127 Lake Washington School District 27293 31146 3853 14.1
#> 8 100218 Richland School District 12729 14499 1770 13.9
#> 9 100039 Central Valley School District 13396 15102 1706 12.7
#> 10 100195 Pasco School District 17182 19001 1819 10.6
Growth chart
8. Spokane anchors Eastern Washington with nearly 30,000 students
Spokane Public Schools serves 29,690 students, making it the largest district east of the Cascades and the state’s second-largest overall.
spokane <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Spokane", district_name, ignore.case = TRUE),
end_year == 2025) |>
select(district_name, n_students) |>
arrange(desc(n_students))
stopifnot(nrow(spokane) > 0)
spokane
#> district_name n_students
#> 1 Spokane School District 29690
#> 2 East Valley School District (Spokane) 3655
#> 3 West Valley School District (Spokane) 3501
#> 4 Spokane International Academy 830
#> 5 Innovation Spokane Schools 235
Spokane enrollment
9. 130 districts have fewer than 500 students
Nearly 40% of Washington’s districts serve fewer than 500 students, challenging their long-term viability.
district_sizes <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year == 2025) |>
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) 81
#> 2 Medium (500-2K) 87
#> 3 Small (<500) 130
#> 4 Very Large (10K+) 32
District sizes
10. Tacoma held steady at ~30K for a decade before COVID
The state’s third-largest city maintained remarkably stable enrollment from 2010 to 2020 before losing 2,000 students.
tacoma <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Tacoma", district_name, ignore.case = TRUE)) |>
select(end_year, district_name, n_students)
stopifnot(nrow(tacoma) > 0)
tacoma
#> end_year district_name n_students
#> 1 2010 Tacoma School District 29625
#> 2 2011 Tacoma School District 29856
#> 3 2012 Tacoma School District 29704
#> 4 2013 Tacoma School District 30122
#> 5 2014 Tacoma School District 30411
#> 6 2015 Tacoma School District 30606
#> 7 2016 Tacoma School District 30554
#> 8 2017 Tacoma School District 30326
#> 9 2018 Tacoma School District 30414
#> 10 2019 Tacoma School District 30320
#> 11 2020 Tacoma School District 30406
#> 12 2021 Tacoma School District 28734
#> 13 2022 Tacoma School District 28779
#> 14 2023 Tacoma School District 28457
#> 15 2024 Tacoma School District 28353
#> 16 2025 Tacoma School District 29014
Tacoma enrollment
11. From 1.03M to 1.1M: 16 years of enrollment milestones
Washington’s enrollment data spans 2010-2025, documenting the tech boom, pandemic disruption, and demographic transformation.
decade_summary <- enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year %in% c(2010, 2015, 2019, 2021, 2025)) |>
select(end_year, n_students) |>
mutate(label = case_when(
end_year == 2010 ~ "Post-recession",
end_year == 2015 ~ "Tech boom",
end_year == 2019 ~ "Pre-COVID peak",
end_year == 2021 ~ "COVID low",
end_year == 2025 ~ "Current"
))
stopifnot(nrow(decade_summary) > 0)
decade_summary
#> end_year n_students label
#> 1 2010 1034935 Post-recession
#> 2 2015 1086314 Tech boom
#> 3 2019 1137367 Pre-COVID peak
#> 4 2021 1093331 COVID low
#> 5 2025 1105384 Current
Enrollment milestones
12. 1 in 6 Washington students now receives special education services
Special education identification rose from 13.2% to 16.4% over 16 years, with the rate accelerating since 2022.
sped_trend <- enr |>
filter(is_state, grade_level == "TOTAL", subgroup == "special_ed") |>
select(end_year, n_students, pct) |>
mutate(pct = round(pct * 100, 1))
stopifnot(nrow(sped_trend) > 0)
sped_trend
#> end_year n_students pct
#> 1 2010 136129 13.2
#> 2 2011 141583 13.5
#> 3 2012 145177 13.8
#> 4 2013 146773 13.9
#> 5 2014 148462 13.9
#> 6 2015 149314 13.7
#> 7 2016 153648 14.0
#> 8 2017 157984 14.2
#> 9 2018 163839 14.5
#> 10 2019 169270 14.9
#> 11 2020 170961 14.9
#> 12 2021 158218 14.5
#> 13 2022 161967 14.8
#> 14 2023 168599 15.4
#> 15 2024 176801 16.1
#> 16 2025 181381 16.4
Special education trends
13. Yakima Valley: where 93% of students are Hispanic and half are English learners
Central Washington’s agricultural heartland has the state’s highest concentrations of English learners and economically disadvantaged students.
yakima_districts <- enr |>
filter(is_district, grade_level == "TOTAL", end_year == 2025,
grepl("Yakima|Sunnyside|Toppenish|Wapato|Grandview", district_name)) |>
select(district_name, subgroup, n_students) |>
pivot_wider(names_from = subgroup, values_from = n_students) |>
mutate(
pct_hispanic = round(hispanic / total_enrollment * 100, 1),
pct_ell = round(lep / total_enrollment * 100, 1),
pct_econ_disadv = round(econ_disadv / total_enrollment * 100, 1)
) |>
select(district_name, total_enrollment, pct_hispanic, pct_ell, pct_econ_disadv) |>
arrange(desc(total_enrollment))
stopifnot(nrow(yakima_districts) > 0)
yakima_districts
#> # A tibble: 7 x 5
#> district_name total_enrollment pct_hispanic pct_ell pct_econ_disadv
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Yakima School District 15621 82.1 34.2 86.8
#> 2 Sunnyside School Distri~ 6169 92.9 33.3 87.6
#> 3 West Valley School Dist~ 5570 41.5 9.9 54.9
#> 4 Toppenish School Distri~ 3670 86.9 36.3 89.9
#> 5 Grandview School Distri~ 3586 93.4 32.7 85.5
#> 6 East Valley School Dist~ 3408 58.5 15 65.7
#> 7 Wapato School District 3225 77.2 50.6 89.9
Yakima Valley demographics
14. Ridgefield grew 84% while Vancouver lost 7% – Clark County’s diverging paths
The Portland metro spillover has reshaped Clark County, with small suburban districts booming while the urban core declines.
clark_districts <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Vancouver|Evergreen SD|Camas|Battle Ground|Ridgefield", district_name),
end_year %in% c(2015, 2020, 2025)) |>
select(district_name, end_year, n_students) |>
pivot_wider(names_from = end_year, values_from = n_students, names_prefix = "yr_") |>
mutate(
growth_2015_2025 = round((yr_2025 - yr_2015) / yr_2015 * 100, 1)
) |>
arrange(desc(growth_2015_2025))
stopifnot(nrow(clark_districts) > 0)
clark_districts
#> # A tibble: 4 x 5
#> district_name yr_2015 yr_2020 yr_2025 growth_2015_2025
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Ridgefield School District 2343 3499 4315 84.2
#> 2 Camas School District 6695 7654 7272 8.6
#> 3 Battle Ground School District 13589 13365 13080 -3.7
#> 4 Vancouver School District 23486 23404 21943 -6.6
Clark County growth
15. The Tri-Cities boom: Pasco grew 31% while Kennewick and Richland added 19% and 32%
The Tri-Cities in southeastern Washington have seen sustained population growth driven by Hanford cleanup and agricultural expansion.
tri_cities <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Richland|Kennewick|Pasco", district_name)) |>
select(end_year, district_name, n_students)
stopifnot(nrow(tri_cities) > 0)
tri_cities_wide <- tri_cities |>
filter(end_year %in% c(2010, 2015, 2020, 2025)) |>
pivot_wider(names_from = end_year, values_from = n_students, names_prefix = "yr_")
tri_cities_wide
#> # A tibble: 3 x 5
#> district_name yr_2010 yr_2015 yr_2020 yr_2025
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Kennewick School District 16085 17611 19554 19109
#> 2 Pasco School District 14473 17182 19226 19001
#> 3 Richland School District 10965 12729 14295 14499
Tri-Cities growth