Fetch and analyze Florida school enrollment, assessment, and graduation data from the Florida Department of Education (FLDOE) in R or Python. Nearly 3 million students across 67 county districts in the Sunshine State – 18 years of enrollment (2008-2025), assessments (2019-2025), and graduation rates (2016-2024).
Part of the njschooldata family.
Full documentation – all 30 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
library(flschooldata)
library(dplyr)
library(tidyr)
library(ggplot2)
theme_set(theme_minimal(base_size = 14))
enr <- fetch_enr_multi(2015:2024, use_cache = TRUE)
enr_2024 <- fetch_enr(2024, use_cache = TRUE)1. Hispanic enrollment surpassed White for the first time
A demographic milestone: Hispanic student enrollment has overtaken White enrollment in Florida, reflecting broader population shifts.
crossover <- enr |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("hispanic", "white")) |>
select(end_year, subgroup, n_students) |>
pivot_wider(names_from = subgroup, values_from = n_students)
crossover
stopifnot(nrow(crossover) > 0)
#> end_year white hispanic
#> 1 2015 1108227 846425
#> 2 2016 1101823 879982
#> 3 2017 1089439 912300
#> 4 2018 1077811 937352
#> 5 2019 1063838 963338
#> 6 2020 1054580 986005
#> 7 2021 1016772 974588
#> 8 2022 1021630 1003659
#> 9 2023 1014065 1043390
#> 10 2024 988822 1066935
Hispanic-White crossover
2. Just over half of Florida third graders read on grade level
In 2024, 55% of Florida 3rd graders scored at Level 3 or above on the FAST ELA Reading assessment.
assess_2024 <- fetch_assessment(2024, "ela", grade = 3, level = "district",
tidy = FALSE, use_cache = TRUE)
state_g3 <- assess_2024 |>
filter(is_state) |>
select(end_year, subject, grade, n_tested, pct_proficient,
pct_level_1, pct_level_2, pct_level_3, pct_level_4, pct_level_5)
state_g3
stopifnot(nrow(state_g3) > 0)
#> end_year subject grade n_tested pct_proficient pct_level_1 pct_level_2
#> 1 2024 ELA 03 216473 55 22 22
#> pct_level_3 pct_level_4 pct_level_5
#> 1 23 19 13
Florida 3rd Grade ELA
3. Multiracial students are the fastest-growing demographic
While Hispanic students are the largest group, multiracial student enrollment has grown at the highest rate percentage-wise since 2015 (34.9% growth, compared to 26.1% for Hispanic students and 15.3% for Asian students).
demo_trend <- enr |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("hispanic", "white", "black", "asian", "multiracial")) |>
select(end_year, subgroup, n_students) |>
group_by(subgroup) |>
mutate(pct_change = round((n_students / first(n_students) - 1) * 100, 1)) |>
ungroup()
demo_2024 <- demo_trend |>
filter(end_year == 2024) |>
arrange(desc(pct_change))
demo_2024
stopifnot(nrow(demo_2024) > 0)
#> end_year subgroup n_students pct_change
#> 1 2024 multiracial 119907 34.9
#> 2 2024 hispanic 1066935 26.1
#> 3 2024 asian 81688 15.3
#> 4 2024 black 599867 -4.2
#> 5 2024 white 988822 -10.8
Demographic growth trends
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2008-2025 |
fetch_enr() / fetch_enr_multi()
|
State, district, school. Race, gender |
| Assessments | 2019, 2022-2025 |
fetch_assessment() / fetch_assessment_multi()
|
ELA grades 3-10, Math grades 3-8. District, school |
| Graduation | 2016-2024 | fetch_graduation() |
State, district, school. 4-year cohort rates |
| Directory | Current | fetch_directory() |
All schools. Principal, address, phone, charter status, geo-coordinates |
| 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/flschooldata")
library(flschooldata)
library(dplyr)
# === Enrollment Data ===
# Fetch one year
enr_2025 <- fetch_enr(2025)
# Fetch multiple years
enr_multi <- fetch_enr_multi(2020:2025)
# State totals
enr_2025 %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL")
# District breakdown
enr_2025 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students))
# Miami-Dade County (district 13)
enr_2025 %>%
filter(district_id == "13", subgroup == "total_enrollment")
# === Assessment Data ===
# Fetch 2024 ELA Grade 3 district data
assess_2024 <- fetch_assessment(2024, "ela", grade = 3)
# Fetch all grades
assess_all_grades <- fetch_assessment(2024, "ela")
# Fetch Math data
math_2024 <- fetch_assessment(2024, "math")
# State proficiency
assess_2024 %>%
filter(is_state) %>%
select(subject, grade, pct_proficient)
# Check available years
get_available_assessment_years()Python
import pyflschooldata as fl
# === Enrollment Data ===
# Check available years
years = fl.get_available_years()
print(f"Data available: {years['min_year']}-{years['max_year']}")
# Fetch one year
df = fl.fetch_enr(2025)
# Fetch multiple years
df_multi = fl.fetch_enr_multi([2020, 2021, 2022, 2023, 2024, 2025])
# State totals
state_total = df[(df['is_state'] == True) &
(df['subgroup'] == 'total_enrollment') &
(df['grade_level'] == 'TOTAL')]
# District breakdown
districts = df[(df['is_district'] == True) &
(df['subgroup'] == 'total_enrollment') &
(df['grade_level'] == 'TOTAL')].sort_values('n_students', ascending=False)
# === Assessment Data ===
# Fetch 2024 ELA Grade 3 data
assess = fl.fetch_assessment(2024, subject='ela', grade=3)
# State proficiency
state_assess = assess[assess['is_state'] == True]
print(state_assess[['subject', 'grade', 'pct_proficient']])
# Check available assessment years
assess_years = fl.get_available_assessment_years()
print(f"Assessment data: {assess_years['years']}")Explore More
Full analysis with 30 stories across 2 vignettes:
- Enrollment trends – 15 stories
- Assessment analysis – 15 stories
- Function reference
Data Notes
Source: Florida Department of Education – PK-12 Data Publications
Enrollment Data:
| Years | Source | Notes |
|---|---|---|
| 2014-2025 | FLDOE Membership Files | Full school-level demographic data |
| 2008-2013 | FLDOE FTE Files | District-level totals only |
Assessment Data:
| Years | Assessment | Notes |
|---|---|---|
| 2023-2025 | FAST (Florida Assessment of Student Thinking) | ELA Grades 3-10, Math Grades 3-8 |
| 2019, 2022 | FSA (Florida Standards Assessments) | No 2020/2021 due to COVID-19 |
Graduation Data: 2016-2024, 4-year cohort graduation rates at state, district, and school levels.
What’s included:
- Levels: State, district (67 county + special), school (~4,500)
- Demographics: White, Black, Hispanic, Asian, American Indian, Pacific Islander, Two or More Races
- Gender: Male, Female
- Grade levels: Pre-K through 12
Florida ID system:
- 67 county districts: Codes 01-67 (one per county)
- Special districts: Codes 71-75 (lab schools, FL School for Deaf/Blind)
- School numbers: 4-digit codes
-
Combined ID:
DD-SSSSformat (e.g., “13-0021” for a Miami-Dade school)
Caveats:
- Pre-2014 data has limited demographics (FTE files only)
- Charter school identification not directly available in membership files
- ESE, ELL, and free/reduced lunch in separate files (not yet integrated)
- Assessment results may be suppressed for small n-sizes to protect student privacy
Deeper Dive
4. Florida is America’s fourth-largest school system
Florida public schools serve nearly 3 million students, trailing only California, Texas, and New York.
enr <- fetch_enr_multi(2015:2024, 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))
state_totals
stopifnot(nrow(state_totals) > 0)
#> end_year n_students change pct_change
#> 1 2015 2750108 NA NA
#> 2 2016 2785286 35178 1.28
#> 3 2017 2810249 24963 0.90
#> 4 2018 2826290 16041 0.57
#> 5 2019 2840029 13739 0.49
#> 6 2020 2852303 12274 0.43
#> 7 2021 2784931 -67372 -2.36
#> 8 2022 2826573 41642 1.50
#> 9 2023 2864292 37719 1.33
#> 10 2024 2865908 1616 0.06
Florida enrollment trend
5. Miami-Dade is larger than most states
Miami-Dade County Public Schools, with 340,000+ students, is the fourth-largest school district in America.
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
top_10 <- enr_2024 |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
arrange(desc(n_students)) |>
head(10) |>
select(district_name, n_students)
top_10
stopifnot(nrow(top_10) > 0)
#> district_name n_students
#> 1 MIAMI-DADE 337610
#> 2 BROWARD 251397
#> 3 HILLSBOROUGH 224144
#> 4 ORANGE 207695
#> 5 PALM BEACH 191390
#> 6 DUVAL 129083
#> 7 POLK 115990
#> 8 LEE 99952
#> 9 PINELLAS 90969
#> 10 PASCO 85808
Florida top 10 districts
6. COVID barely dented Florida enrollment
While other states saw sharp declines, Florida’s enrollment dipped only briefly in 2021 and has since rebounded.
covid <- enr |>
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year >= 2019) |>
select(end_year, n_students) |>
mutate(change = n_students - lag(n_students))
covid
stopifnot(nrow(covid) > 0)
#> end_year n_students change
#> 1 2019 2840029 NA
#> 2 2020 2852303 12274
#> 3 2021 2784931 -67372
#> 4 2022 2826573 41642
#> 5 2023 2864292 37719
#> 6 2024 2865908 16167. Hispanic students are the plurality
Hispanic students now comprise over 35% of Florida enrollment, surpassing white students.
demographics <- enr_2024 |>
filter(is_state, grade_level == "TOTAL",
subgroup %in% c("hispanic", "white", "black", "asian", "multiracial")) |>
mutate(pct = round(pct * 100, 1)) |>
select(subgroup, n_students, pct) |>
arrange(desc(n_students))
demographics
stopifnot(nrow(demographics) > 0)
#> subgroup n_students pct
#> 1 hispanic 1066935 37.2
#> 2 white 988822 34.5
#> 3 black 599867 20.9
#> 4 multiracial 119907 4.2
#> 5 asian 81688 2.9
Florida student demographics
8. Central Florida is the growth engine
Orange, Osceola, and Polk counties have been among the fastest-growing, driven by the Orlando metro boom.
central_fl <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Orange|Osceola|Polk", district_name, ignore.case = TRUE),
end_year %in% c(2015, 2024)) |>
group_by(district_name) |>
summarize(
y2015 = n_students[end_year == 2015],
y2024 = n_students[end_year == 2024],
pct_change = round((y2024 / y2015 - 1) * 100, 1),
.groups = "drop"
) |>
arrange(desc(pct_change))
central_fl
stopifnot(nrow(central_fl) > 0)
#> # A tibble: 3 x 4
#> district_name y2015 y2024 pct_change
#> <chr> <dbl> <dbl> <dbl>
#> 1 OSCEOLA 59276 74278 25.3
#> 2 POLK 99650 115990 16.4
#> 3 ORANGE 191640 207695 8.49. Broward and Miami-Dade are declining
South Florida’s two largest districts have been losing students as families relocate.
south_fl <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Broward|Miami-Dade", district_name, ignore.case = TRUE),
end_year >= 2018) |>
select(end_year, district_name, n_students) |>
pivot_wider(names_from = district_name, values_from = n_students)
south_fl
stopifnot(nrow(south_fl) > 0)
#> end_year BROWARD MIAMI-DADE
#> 1 2018 271951 354767
#> 2 2019 270961 350372
#> 3 2020 269163 347239
#> 4 2021 260224 334858
#> 5 2022 256027 329483
#> 6 2023 254728 335831
#> 7 2024 251397 337610
South Florida enrollment decline
10. Florida Virtual School is a district unto itself
Florida Virtual School (FLVS) serves tens of thousands of students statewide.
virtual <- enr_2024 |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Virtual|FLVS", district_name, ignore.case = TRUE)) |>
select(district_name, n_students)
virtual
stopifnot(nrow(virtual) > 0)
#> district_name n_students
#> 1 FL VIRTUAL 887411. Kindergarten is the leading indicator
Florida kindergarten enrollment has been relatively stable, unlike states with sharp K declines.
k_trend <- enr |>
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "05", "09"),
end_year >= 2019) |>
select(end_year, grade_level, n_students) |>
pivot_wider(names_from = grade_level, values_from = n_students)
k_trend
stopifnot(nrow(k_trend) > 0)
#> end_year K 01 05 09
#> 1 2019 200437 206545 222947 221023
#> 2 2020 202460 207143 217518 223805
#> 3 2021 186147 200215 207750 227753
#> 4 2022 199099 200741 217770 231499
#> 5 2023 197925 209516 209060 237920
#> 6 2024 195032 204402 205135 23250612. Black students are 21% of enrollment
Florida has a significant Black student population, concentrated in South Florida and the Jacksonville region.
black_enr <- enr_2024 |>
filter(is_state, grade_level == "TOTAL", subgroup == "black") |>
select(subgroup, n_students, pct)
black_enr
stopifnot(nrow(black_enr) > 0)
#> subgroup n_students pct
#> 1 black 599867 0.209311313. Florida’s county-based system is unique
Florida organizes schools by county, with 67 county districts (plus a few specialty districts). This structure differs from most states.
county_count <- enr_2024 |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
summarize(
n_districts = n_distinct(district_name),
total_students = sum(n_students, na.rm = TRUE)
)
county_count
stopifnot(nrow(county_count) > 0)
#> n_districts total_students
#> 1 76 286590814. Tampa Bay is catching up to South Florida
Hillsborough and Pinellas counties represent Florida’s other major metro area. While South Florida declines, Tampa Bay has held steady.
tampa <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Hillsborough|Pinellas|Pasco", district_name, ignore.case = TRUE)) |>
group_by(district_name) |>
summarize(
y2015 = n_students[end_year == 2015],
y2024 = n_students[end_year == 2024],
pct_change = round((y2024 / y2015 - 1) * 100, 1),
.groups = "drop"
) |>
arrange(desc(y2024))
tampa
stopifnot(nrow(tampa) > 0)
#> # A tibble: 3 x 4
#> district_name y2015 y2024 pct_change
#> <chr> <dbl> <dbl> <dbl>
#> 1 HILLSBOROUGH 207457 224144 8
#> 2 PINELLAS 103754 90969 -12.3
#> 3 PASCO 69218 85808 24
Tampa Bay enrollment
15. Jacksonville is Florida’s hidden giant
Duval County (Jacksonville) often flies under the radar, but it’s Florida’s 6th largest district with over 125,000 students.
jax <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Duval", district_name, ignore.case = TRUE)) |>
select(end_year, district_name, n_students) |>
mutate(change = n_students - lag(n_students))
jax
stopifnot(nrow(jax) > 0)
#> end_year district_name n_students change
#> 1 2015 DUVAL 128665 NA
#> 2 2016 DUVAL 129169 504
#> 3 2017 DUVAL 129414 245
#> 4 2018 DUVAL 129562 148
#> 5 2019 DUVAL 130224 662
#> 6 2020 DUVAL 130283 59
#> 7 2021 DUVAL 126802 -3481
#> 8 2022 DUVAL 128939 2137
#> 9 2023 DUVAL 129788 849
#> 10 2024 DUVAL 129083 -705
Jacksonville enrollment
16. Small rural counties face steep declines
While large metros grow, many of Florida’s smallest counties are losing students rapidly.
# Get the first and last year for comparison
first_year <- min(enr$end_year)
last_year <- max(enr$end_year)
county_change <- enr |>
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year %in% c(first_year, last_year)) |>
group_by(district_name) |>
filter(n() == 2) |> # Only counties with both years
summarize(
y_first = n_students[end_year == first_year],
y_last = n_students[end_year == last_year],
change = y_last - y_first,
pct_change = round((y_last / y_first - 1) * 100, 1),
.groups = "drop"
)
# Counties losing the most (by %)
declining <- county_change |>
filter(y_first > 1000) |> # Exclude tiny counties
arrange(pct_change) |>
head(10)
declining
stopifnot(nrow(declining) > 0)
#> district_name y_first y_last change pct_change
#> 1 GADSDEN 5837 4598 -1239 -21.2
#> 2 LIBERTY 1330 1156 -174 -13.1
#> 3 PINELLAS 103754 90969 -12785 -12.3
#> 4 LAFAYETTE 1132 1007 -125 -11.0
#> 5 MADISON 2498 2228 -270 -10.8
#> 6 JACKSON 6726 6037 -689 -10.2
#> 7 DESOTO 4658 4199 -459 -9.9
#> 8 HARDEE 5095 4598 -497 -9.8
#> 9 TAYLOR 2885 2602 -283 -9.8
#> 10 CALHOUN 2149 1942 -207 -9.6
Rural county decline
17. Miami-Dade outperforms the state average
Despite being the largest district with over 25,000 3rd graders tested, Miami-Dade’s 56% proficiency rate exceeds the state average.
large_districts <- assess_2024 |>
filter(!is_state, n_tested > 5000) |>
arrange(desc(n_tested)) |>
select(district_name, n_tested, pct_proficient) |>
head(10)
large_districts
stopifnot(nrow(large_districts) > 0)
#> district_name n_tested pct_proficient
#> 1 MIAMI-DADE 25178 56
#> 2 BROWARD 18457 57
#> 3 HILLSBOROUGH 16802 51
#> 4 ORANGE 15743 57
#> 5 PALM BEACH 14593 54
#> 6 DUVAL 10095 49
#> 7 POLK 9295 49
#> 8 LEE 7644 48
#> 9 PINELLAS 6654 62
#> 10 PASCO 6511 53
Florida large district comparison