Utah has one of the youngest populations in America and keeps adding students while most states shrink–but dig into the district-level data and the story gets complicated fast. Salt Lake City is hemorrhaging enrollment, charter schools serve nearly 1 in 4 students, and Hispanic enrollment has surged 46% in a decade.
Part of the state schooldata project, extending the original njschooldata package to all 50 states.
Full documentation – all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
library(utschooldata)
library(dplyr)
library(tidyr)
library(ggplot2)
theme_set(theme_minimal(base_size = 14))
# Get available years dynamically
available_years <- get_available_years()
min_year <- min(available_years)
max_year <- max(available_years)
enr <- fetch_enr_multi(available_years, use_cache = TRUE)1. Salt Lake City district lost 17% of students since 2019
While suburban districts boom, Salt Lake City School District has declined from over 22,000 to under 19,000 students–a loss driven by housing costs and demographic shifts in the urban core.
salt_lake <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
district_name == "Salt Lake District") |>
select(end_year, district_name, n_students) |>
mutate(change = n_students - lag(n_students),
pct_change = round(change / lag(n_students) * 100, 1))
salt_lake
#> # A tibble: 7 x 5
#> end_year district_name n_students change pct_change
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 2019 Salt Lake District 22401 NA NA
#> 2 2020 Salt Lake District 22017 -384 -1.7
#> 3 2021 Salt Lake District 20536 -1481 -6.7
#> 4 2022 Salt Lake District 19833 -703 -3.4
#> 5 2023 Salt Lake District 19449 -384 -1.9
#> 6 2024 Salt Lake District 18966 -483 -2.5
#> 7 2025 Salt Lake District 18535 -431 -2.3
Salt Lake decline
2. Charter schools serve a growing share of Utah students
Utah has a robust charter school sector, with nearly 25% of public school students attending charter schools–one of the highest rates in the nation.
enr_latest <- fetch_enr(max_year, use_cache = TRUE)
state_total <- enr_latest |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
pull(n_students)
charter_total <- enr_latest |>
filter(is_charter, subgroup == "total_enrollment", grade_level == "TOTAL") |>
summarize(charter_total = sum(n_students, na.rm = TRUE)) |>
pull(charter_total)
charter_summary <- tibble(
sector = c("All Public Schools", "Charter Schools"),
enrollment = c(state_total, charter_total),
pct = c(100, round(charter_total / state_total * 100, 1))
)
charter_summary
#> # A tibble: 2 x 3
#> sector enrollment pct
#> <chr> <dbl> <dbl>
#> 1 All Public Schools 667789 100
#> 2 Charter Schools 163710 24.5
Charters
3. Hispanic enrollment grew 46% since 2014
Utah’s Hispanic student population has grown significantly faster than overall enrollment, adding nearly 45,000 students in just over a decade.
hispanic <- enr |>
filter(is_state, grade_level == "TOTAL", subgroup == "hispanic") |>
select(end_year, n_students) |>
mutate(change = n_students - lag(n_students),
pct_change = round(change / lag(n_students) * 100, 1))
hispanic
#> # A tibble: 12 x 4
#> end_year n_students change pct_change
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2014 97388 NA NA
#> 2 2015 101390 4002 4.1
#> 3 2016 104457 3067 3
#> 4 2017 108074 3617 3.5
#> 5 2018 110931 2857 2.6
#> 6 2019 113945 3014 2.7
#> 7 2020 117486 3541 3.1
#> 8 2021 119393 1907 1.6
#> 9 2022 126467 7074 5.9
#> 10 2023 131954 5487 4.3
#> 11 2024 132110 156 0.1
#> 12 2025 142267 10157 7.7
Hispanic growth
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2014-2026 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race, gender, FRPL, SpEd, LEP |
| Assessments | – | – | Not yet available |
| Graduation | – | – | Not yet available |
| Directory | Current | fetch_directory() |
District, school. Principal, superintendent, address, phone |
| 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/utschooldata")
library(utschooldata)
library(dplyr)
# Get 2025 enrollment data
enr <- fetch_enr(2025, use_cache = TRUE)
# View state totals
enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
select(end_year, n_students)
#> # A tibble: 1 x 2
#> end_year n_students
#> <dbl> <dbl>
#> 1 2025 667789Python
import pyutschooldata as ut
# Fetch 2025 data
enr = ut.fetch_enr(2025)
# Statewide total
total = enr[(enr['is_state']) &
(enr['grade_level'] == 'TOTAL') &
(enr['subgroup'] == 'total_enrollment')]['n_students'].sum()
print(f"{total:,} students")
# 667,789 students
# Check available years
years = ut.get_available_years()
print(f"Data available: {min(years)}-{max(years)}")
# Data available: 2014-2026Explore More
Full analysis with 15 stories: - Enrollment trends – 15 stories - Function reference
Data Notes
Data Source
Data is downloaded from the Utah State Board of Education (USBE) Data and Statistics portal:
- Primary URL: https://schools.utah.gov/datastatistics/reports
-
File Pattern:
{YEAR}FallEnrollmentGradeLevelDemographics.xlsx - Collection Date: October 1 of each school year (Fall Enrollment count)
Years Available
| Era | Years | Format | Notes |
|---|---|---|---|
| Current | 2014-2026 | Excel (.xlsx) | Fall Enrollment by Grade Level and Demographics |
Earliest available year: 2014 Most recent available year: 2026 Total years of data: 13 years
Aggregation Levels
- State: Total Utah enrollment
- District (LEA): Local Education Agency totals (aggregated from schools)
- Campus (School): Individual school enrollment
Demographics Available
| Category | Fields |
|---|---|
| Race/Ethnicity | White, Black, Hispanic, Asian, Native American, Pacific Islander, Multiracial |
| Gender | Male, Female |
| Special Populations | Economically Disadvantaged, English Learners (ELL/LEP), Special Education |
| Grade Levels | Pre-K, K, 1-12 |
Known Caveats
- Data Suppression: Small cell sizes may be suppressed for privacy (typically N < 10)
- Charter Schools: Charter schools are included and can be identified where the charter_flag column is available
- Historical Changes: District boundaries and school configurations may change year to year
- October 1 Count: All enrollment figures represent the official October 1 count date
Deeper Dive
4. Utah’s enrollment continues to grow
Utah has one of the youngest populations in the nation and continues to see steady enrollment growth, unlike many states that saw declines after COVID.
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))
state_totals
#> # A tibble: 13 x 4
#> end_year n_students change pct_change
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2014 612088 NA NA
#> 2 2015 621748 9660 1.58
#> 3 2016 633461 11713 1.88
#> 4 2017 644004 10543 1.66
#> 5 2018 651796 7792 1.21
#> 6 2019 658952 7156 1.1
#> 7 2020 666858 7906 1.2
#> 8 2021 665306 -1552 -0.23
#> 9 2022 674351 9045 1.36
#> 10 2023 674650 299 0.04
#> 11 2024 672662 -1988 -0.29
#> 12 2025 667789 -4873 -0.72
Statewide enrollment
5. Granite and Alpine are Utah’s enrollment giants
Utah’s two largest districts–Granite and Alpine–each serve well over 57,000 students, but their trajectories differ. Salt Lake City has seen declines while suburban districts grow.
large_districts <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Granite|Alpine|Davis|Jordan|Canyons|Salt Lake City", district_name, ignore.case = TRUE)) |>
select(end_year, district_name, n_students)
large_districts |>
filter(end_year == max(end_year)) |>
arrange(desc(n_students))
#> # A tibble: 6 x 3
#> end_year district_name n_students
#> <dbl> <chr> <dbl>
#> 1 2025 Alpine District 84757
#> 2 2025 Davis District 69602
#> 3 2025 Jordan District 57083
#> 4 2025 Granite District 57038
#> 5 2025 Canyons District 32289
#> 6 2025 Salt Lake District 18535
Top districts
6. Utah’s student body is diversifying
While Utah remains less diverse than national averages, Hispanic enrollment has grown substantially over the past decade, now representing 21% of students.
demographics <- enr_latest |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("hispanic", "white", "black", "asian", "native_american", "pacific_islander", "multiracial")) |>
mutate(pct = round(pct * 100, 1)) |>
select(subgroup, n_students, pct) |>
arrange(desc(n_students))
demographics
#> # A tibble: 7 x 3
#> subgroup n_students pct
#> <chr> <dbl> <dbl>
#> 1 white 463352 69.4
#> 2 hispanic 142267 21.3
#> 3 multiracial 25478 3.8
#> 4 asian 11184 1.7
#> 5 pacific_islander 10844 1.6
#> 6 black 8827 1.3
#> 7 native_american 5837 0.9
Demographics
7. Pacific Islander students are a unique Utah story
Utah has one of the highest concentrations of Pacific Islander students in the nation, reflecting the state’s significant Polynesian community, particularly in Salt Lake County.
pi_districts <- enr_latest |>
filter(is_district, grade_level == "TOTAL", subgroup == "pacific_islander") |>
filter(n_students > 100) |>
mutate(pct = round(pct * 100, 2)) |>
select(district_name, n_students, pct) |>
arrange(desc(pct)) |>
head(10)
pi_districts
#> # A tibble: 10 x 3
#> district_name n_students pct
#> <chr> <dbl> <dbl>
#> 1 Mana Academy Charter School 185 61.5
#> 2 Wallace Stegner Academy 143 6.6
#> 3 Salt Lake District 919 4.96
#> 4 Granite District 2092 3.67
#> 5 Provo District 432 3.21
#> 6 Logan City District 135 2.67
#> 7 Jordan District 1272 2.23
#> 8 Tooele District 349 2.23
#> 9 Alpine District 1287 1.52
#> 10 Davis District 1025 1.47
Pacific Islander
8. Utah County is the growth engine
Provo, Alpine, and Nebo districts in Utah County are seeing consistent growth as young families settle along the I-15 corridor south of Salt Lake.
utah_county <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Alpine|Provo|Nebo", district_name, ignore.case = TRUE)) |>
group_by(district_name) |>
arrange(end_year) |>
summarize(
first_year = first(n_students),
last_year = last(n_students),
pct_change = round((last_year / first_year - 1) * 100, 1),
.groups = "drop"
) |>
arrange(desc(pct_change))
stopifnot(nrow(utah_county) > 0)
utah_county
#> # A tibble: 3 x 4
#> district_name first_year last_year pct_change
#> <chr> <dbl> <dbl> <dbl>
#> 1 Nebo District 33117 42946 29.7
#> 2 Alpine District 79748 84757 6.3
#> 3 Provo District 16165 13463 -16.7
Growth chart
9. Rural districts face decline
While the Wasatch Front booms, rural districts in southern and eastern Utah face enrollment pressure as families move to urban centers for jobs and services.
rural <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Carbon|Emery|Grand|San Juan|Millard", district_name, ignore.case = TRUE)) |>
group_by(district_name) |>
filter(n() >= 5) |>
arrange(end_year) |>
summarize(
first_year = first(n_students),
last_year = last(n_students),
pct_change = round((last_year / first_year - 1) * 100, 1),
.groups = "drop"
) |>
arrange(pct_change)
stopifnot(nrow(rural) > 0)
rural
#> # A tibble: 5 x 4
#> district_name first_year last_year pct_change
#> <chr> <dbl> <dbl> <dbl>
#> 1 Grand District 1520 1371 -9.8
#> 2 Emery District 2181 1986 -8.9
#> 3 Carbon District 3484 3186 -8.6
#> 4 San Juan District 2876 2768 -3.8
#> 5 Millard District 2916 3064 5.1
Regional chart
10. Washington County is Utah’s fastest-growing region
The St. George area (Washington County School District) has exploded with growth as retirees and remote workers flock to southern Utah.
washington <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Washington", district_name, ignore.case = TRUE)) |>
select(end_year, district_name, n_students) |>
mutate(change = n_students - lag(n_students),
pct_change = round(change / lag(n_students) * 100, 1))
washington
#> # A tibble: 14 x 5
#> end_year district_name n_students change pct_change
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 2019 Washington District 31074 NA NA
#> 2 2020 Washington District 33884 2810 9
#> 3 2021 Washington District 35346 1462 4.3
#> 4 2022 Washington District 36453 1107 3.1
#> 5 2023 Washington District 36623 170 0.5
#> 6 2024 Washington District 36753 130 0.4
#> 7 2025 Washington District 36006 -747 -2
Washington County
11. Kindergarten enrollment dipped during COVID but recovered
Unlike many states, Utah saw kindergarten enrollment bounce back relatively quickly after COVID disruptions, though it has since declined further.
covid_grades <- enr |>
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "05", "09")) |>
select(end_year, grade_level, n_students) |>
pivot_wider(names_from = grade_level, values_from = n_students)
covid_grades
#> # A tibble: 12 x 5
#> end_year K `01` `05` `09`
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2014 50363 51424 48499 45721
#> 2 2015 48859 51431 49181 46699
#> 3 2016 48327 50322 49563 47616
#> 4 2017 48242 49981 51455 48522
#> 5 2018 47605 49812 53389 50125
#> 6 2019 49081 49081 53465 51044
#> 7 2020 48789 50699 52766 51908
#> 8 2021 46874 49242 51542 53340
#> 9 2022 48744 49624 51764 55245
#> 10 2023 46655 50346 50921 55330
#> 11 2024 45217 48138 52547 54351
#> 12 2025 44776 46313 51677 53658
COVID grades
12. High school enrollment is surging
As larger elementary cohorts from the 2010s move through the system, Utah high schools are seeing significant enrollment growth–up 25% since 2014.
hs_trend <- enr |>
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("09", "10", "11", "12")) |>
group_by(end_year) |>
summarize(hs_total = sum(n_students, na.rm = TRUE), .groups = "drop") |>
mutate(change = hs_total - lag(hs_total),
pct_change = round(change / lag(hs_total) * 100, 1))
hs_trend
#> # A tibble: 12 x 4
#> end_year hs_total change pct_change
#> <dbl> <dbl> <dbl> <dbl>
#> 1 2014 173049 NA NA
#> 2 2015 178071 5022 2.9
#> 3 2016 183492 5421 3
#> 4 2017 187727 4235 2.3
#> 5 2018 192340 4613 2.5
#> 6 2019 196008 3668 1.9
#> 7 2020 200437 4429 2.3
#> 8 2021 205808 5371 2.7
#> 9 2022 210817 5009 2.4
#> 10 2023 214148 3331 1.6
#> 11 2024 216094 1946 0.9
#> 12 2025 216526 432 0.2
High school
13. English learner population nearly doubled
The number of English learners (ELL/LEP) in Utah schools has grown from 34,000 to over 61,000 since 2014–an 79% increase.
ell_trend <- enr |>
filter(is_state, grade_level == "TOTAL", subgroup == "lep") |>
select(end_year, n_students, pct) |>
mutate(pct = round(pct * 100, 1),
change = n_students - lag(n_students),
pct_change = round(change / lag(n_students) * 100, 1))
ell_trend
#> # A tibble: 12 x 5
#> end_year n_students pct change pct_change
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2014 34394 5.6 NA NA
#> 2 2015 37033 6 2639 7.7
#> 3 2016 38414 6.1 1381 3.7
#> 4 2017 39662 6.2 1248 3.2
#> 5 2018 43763 6.7 4101 10.3
#> 6 2019 49374 7.5 5611 12.8
#> 7 2020 53234 8 3860 7.8
#> 8 2021 52788 7.9 -446 -0.8
#> 9 2022 55546 8.2 2758 5.2
#> 10 2023 59176 8.8 3630 6.5
#> 11 2024 59147 8.8 -29 0
#> 12 2025 61481 9.2 2334 3.9
ELL growth
14. Nearly 1 in 3 students are economically disadvantaged
About 29% of Utah students qualify as economically disadvantaged–lower than the national average but still representing nearly 194,000 students.
special_pops <- enr_latest |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("econ_disadv", "lep", "special_ed")) |>
mutate(pct = round(pct * 100, 1)) |>
select(subgroup, n_students, pct) |>
arrange(desc(n_students))
special_pops
#> # A tibble: 3 x 3
#> subgroup n_students pct
#> <chr> <dbl> <dbl>
#> 1 econ_disadv 193572 29
#> 2 special_ed 88462 13.2
#> 3 lep 61481 9.2
Special populations
15. Elementary enrollment is declining while high school grows
Utah kindergarten enrollment dropped 11% since its 2014 peak, even as high school grades are at record levels. This points to future enrollment declines.
grades <- enr_latest |>
filter(is_state, subgroup == "total_enrollment") |>
filter(!grade_level %in% c("TOTAL")) |>
select(grade_level, n_students) |>
arrange(desc(n_students))
grades
#> # A tibble: 14 x 2
#> grade_level n_students
#> <chr> <dbl>
#> 1 11 54834
#> 2 10 54609
#> 3 09 53658
#> 4 12 53425
#> 5 08 53017
#> 6 06 52775
#> 7 07 51808
#> 8 05 51677
#> 9 04 51285
#> 10 03 51101
#> 11 02 48511
#> 12 01 46313
#> 13 K 44776
#> 14 PK 16008
Grade distribution