Fetch and analyze North Dakota school enrollment and graduation data from the North Dakota Department of Public Instruction in R or Python.
Part of the njschooldata family.
Full documentation — all 16 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
library(ndschooldata)
library(dplyr)
library(tidyr)
library(ggplot2)
theme_set(theme_minimal(base_size = 14))
enr <- tryCatch(
fetch_enr_multi(2008:2024, use_cache = TRUE),
error = function(e) {
warning("Failed to fetch enrollment data: ", e$message)
stop(e)
}
)1. Graduation rates dropped 7 points from their peak
The statewide graduation rate peaked at 89% in 2020 and has declined to 82% in four years.
grad_multi <- tryCatch(
fetch_graduation_multi(2013:2024, use_cache = TRUE),
error = function(e) {
warning("Failed to fetch graduation data: ", e$message)
stop(e)
}
)
grad_trend <- grad_multi %>%
filter(is_state, subgroup == "all") %>%
select(end_year, grad_rate, cohort_count, graduate_count)
stopifnot(nrow(grad_trend) > 0)
grad_trend
#> end_year grad_rate cohort_count graduate_count
#> 1 2013 0.872 7567 6598
#> 2 2014 0.869 7603 6609
#> 3 2015 0.863 7635 6589
#> 4 2016 0.873 7661 6687
#> 5 2017 0.870 7572 6588
#> 6 2018 0.880 7399 6512
#> 7 2019 0.883 7626 6730
#> 8 2020 0.890 7486 6660
#> 9 2021 0.870 7843 6825
#> 10 2022 0.843 8092 6823
#> 11 2023 0.827 8294 6863
#> 12 2024 0.824 8681 7154
Graduation trend
2. West Fargo doubled in size since 2008
The Fargo suburb is one of the fastest-growing districts in the country.
growth_districts <- enr %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("West Fargo|Fargo|Bismarck|Williston|Minot", district_name)) %>%
mutate(district_name = trimws(gsub(" Public Schools| School District| Basin| [0-9]+$", "", district_name))) %>%
filter(district_name %in% c("Fargo", "West Fargo", "Bismarck", "Williston", "Minot"))
stopifnot(nrow(growth_districts) > 0)
# Normalize to 2008 baseline
growth_indexed <- growth_districts %>%
group_by(district_name) %>%
mutate(baseline = n_students[end_year == min(end_year)],
index = n_students / baseline * 100) %>%
ungroup()
growth_indexed %>%
filter(end_year %in% c(2008, 2024)) %>%
select(district_name, end_year, n_students, index)
#> district_name end_year n_students index
#> 1 Bismarck 2008 10638 100.0000
#> 2 Fargo 2008 10493 100.0000
#> 3 West Fargo 2008 6179 100.0000
#> 4 Minot 2008 6243 100.0000
#> 5 Williston 2008 2110 100.0000
#> 6 Bismarck 2024 13732 129.0844
#> 7 Fargo 2024 11319 107.8719
#> 8 West Fargo 2024 12676 205.1465
#> 9 Minot 2024 7510 120.2947
#> 10 Williston 2024 5198 246.3507+105% growth since 2008. West Fargo went from 6,179 to 12,676 students.
Growth chart
3. Native American graduation rates lag state average
Native American students face a 19-point graduation gap compared to the state average.
grad_2024 <- tryCatch(
fetch_graduation(2024, use_cache = TRUE),
error = function(e) {
warning("Failed to fetch 2024 graduation data: ", e$message)
stop(e)
}
)
# Compare subgroups at state level
grad_subgroups <- grad_2024 %>%
filter(is_state, subgroup %in% c("all", "native_american", "white", "low_income")) %>%
select(subgroup, grad_rate, cohort_count, graduate_count) %>%
arrange(desc(grad_rate))
stopifnot(nrow(grad_subgroups) > 0)
grad_subgroups
#> subgroup grad_rate cohort_count graduate_count
#> 1 white 0.875 6420 5620
#> 2 all 0.824 8681 7154
#> 3 low_income 0.676 2302 1556
#> 4 native_american 0.634 939 595Native American students graduate at 63% compared to 82% overall. A 19-point gap that demands attention.
Native American graduation
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 2008-2024 |
fetch_enr() / fetch_enr_multi()
|
State, district. Grade level (K-12) |
| Assessments | — | — | Not yet available |
| Graduation | 2013-2024 |
fetch_graduation() / fetch_graduation_multi()
|
State, district, school. Race, gender, FRPL, SpEd, LEP |
| Directory | 2014-2024 |
fetch_directory() / fetch_district_directory()
|
School and district. Names, contacts, addresses, 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 DATA-CATEGORY-TAXONOMY.md for what each category covers.
Quick Start
R
# install.packages("remotes")
remotes::install_github("almartin82/ndschooldata")
library(ndschooldata)
library(dplyr)
# Fetch one year
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
# Fetch multiple years
enr_recent <- fetch_enr_multi(2019:2024, use_cache = TRUE)
# 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))
# Check available years
get_available_years()
# Fetch graduation rates
grad_2024 <- fetch_graduation(2024, use_cache = TRUE)
# State graduation rate
grad_2024 %>%
filter(is_state, subgroup == "all") %>%
select(grad_rate, cohort_count, graduate_count)
# District graduation rates
grad_2024 %>%
filter(is_district, subgroup == "all") %>%
arrange(desc(grad_rate)) %>%
select(district_name, grad_rate, cohort_count)
# Graduation rate by subgroup (state level)
grad_2024 %>%
filter(is_state, subgroup %in% c("all", "male", "female", "native_american", "white")) %>%
select(subgroup, grad_rate, cohort_count)Python
import pyndschooldata as nd
# Fetch one year
enr_2024 = nd.fetch_enr(2024)
# Fetch multiple years
enr_recent = nd.fetch_enr_multi([2019, 2020, 2021, 2022, 2023, 2024])
# State totals
state_totals = enr_2024[
(enr_2024['is_state'] == True) &
(enr_2024['subgroup'] == 'total_enrollment') &
(enr_2024['grade_level'] == 'TOTAL')
]
# District breakdown
district_totals = enr_2024[
(enr_2024['is_district'] == True) &
(enr_2024['subgroup'] == 'total_enrollment') &
(enr_2024['grade_level'] == 'TOTAL')
].sort_values('n_students', ascending=False)
# Check available years
years = nd.get_available_years()
print(f"Data available from {years['min_year']} to {years['max_year']}")Explore More
- Full documentation — 16 stories with charts
- Enrollment vignette — 16 stories
- Function reference
Data Notes
Data Sources
- Enrollment: North Dakota DPI (nd.gov/dpi/data)
- Graduation: ND Insights (insights.nd.gov)
Data Availability
| Data Type | Years | Source | Notes |
|---|---|---|---|
| Enrollment | 2008-2024 | NDDPI | District-level enrollment by grade (K-12) |
| Graduation | 2013-2024 | ND Insights | 4-year cohort graduation rates (state, district, school) |
| Directory | 2014-2024 | ND Insights | School and district names, contacts, addresses |
Suppression Rules
- Graduation data: Cohorts with fewer than 10 students are suppressed (marked as * or empty)
- Enrollment data: No suppression in main enrollment files
What’s Included
Enrollment data: - Levels: State, district (~167) - Grade levels: K-12 plus totals - Demographics: Limited (not in main file; available via insights.nd.gov)
Graduation rate data: - Levels: State, district (~167), school (~450) - Years: 2013-2024 (12 years) - Cohort type: 4-year adjusted cohort graduation rate (ACGR) - Subgroups: All, male, female, white, Black, Hispanic, Asian American, Native American, English Learner, IEP, Low Income, and more
Deeper Dive
4. The oil boom reshaped North Dakota schools
Enrollment grew 23% from 2008 to 2024 as the Bakken brought families to the state.
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 2008 94052
#> 2 2009 93406
#> 3 2010 93715
#> 4 2011 94729
#> 5 2012 95778
#> ...
#> 17 2024 115767From 94,000 to 116,000 students in 16 years. The boom changed everything.
Statewide enrollment
5. Bismarck leads the state in enrollment
The capital city edges out West Fargo and Fargo as the state’s largest district.
enr_2024 <- tryCatch(
fetch_enr(2024, use_cache = TRUE),
error = function(e) {
warning("Failed to fetch 2024 enrollment data: ", e$message)
stop(e)
}
)
top_districts <- enr_2024 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students)) %>%
head(10) %>%
select(district_name, n_students) %>%
mutate(district_name = gsub(" Public Schools| School District", "", district_name))
stopifnot(nrow(top_districts) > 0)
top_districts
#> district_name n_students
#> 1 Bismarck 1 13732
#> 2 West Fargo 6 12676
#> 3 Fargo 1 11319
#> 4 Minot 1 7510
#> 5 Grand Forks 1 7428
#> 6 Williston Basin 7 5198
#> 7 Mandan 1 4368
#> 8 Dickinson 1 3977
#> 9 McKenzie Co 1 2105
#> 10 Jamestown 1 2080Bismarck: 13,732 students. The capital city leads the state, with West Fargo close behind at 12,676.
Top districts
6. Kindergarten enrollment dropped 10% from its peak
The enrollment wave from the oil boom is aging out. Kindergarten peaked at 9,620 in 2020 and fell to 8,636 by 2024.
grade_levels <- enr %>%
filter(is_state, subgroup == "total_enrollment") %>%
mutate(level = case_when(
grade_level %in% c("K", "01", "02", "03", "04", "05") ~ "Elementary (K-5)",
grade_level %in% c("06", "07", "08") ~ "Middle (6-8)",
grade_level %in% c("09", "10", "11", "12") ~ "High School (9-12)",
TRUE ~ NA_character_
)) %>%
filter(!is.na(level)) %>%
group_by(end_year, level) %>%
summarize(total = sum(n_students, na.rm = TRUE), .groups = "drop")
stopifnot(nrow(grade_levels) > 0)
grade_levels %>% filter(end_year %in% c(2008, 2019, 2024)) %>% arrange(end_year, level)
#> end_year level total
#> 1 2008 Elementary (K-5) 40768
#> 2 2008 High School (9-12) 31492
#> 3 2008 Middle (6-8) 21792
#> 4 2019 Elementary (K-5) 53721
#> 5 2019 High School (9-12) 31430
#> 6 2019 Middle (6-8) 25691
#> 7 2024 Elementary (K-5) 54642
#> 8 2024 High School (9-12) 34556
#> 9 2024 Middle (6-8) 26569
Demographics chart
7. 35 districts have under 100 students
Tiny rural schools define the North Dakota landscape.
size_dist <- enr_2024 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(size_category = case_when(
n_students < 100 ~ "Under 100",
n_students < 500 ~ "100-499",
n_students < 1000 ~ "500-999",
n_students < 5000 ~ "1,000-4,999",
TRUE ~ "5,000+"
)) %>%
mutate(size_category = factor(size_category,
levels = c("Under 100", "100-499", "500-999",
"1,000-4,999", "5,000+"))) %>%
count(size_category)
stopifnot(nrow(size_dist) > 0)
size_dist
#> size_category n
#> 1 Under 100 35
#> 2 100-499 98
#> 3 500-999 20
#> 4 1,000-4,999 8
#> 5 5,000+ 635 districts with fewer than 100 students. That’s 21% of all 167 districts.
District size
8. COVID barely dented North Dakota enrollment (-0.7%)
Unlike other states, ND saw only a small pandemic drop.
covid_years <- enr %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL",
end_year %in% 2018:2024) %>%
select(end_year, n_students) %>%
mutate(change = n_students - lag(n_students),
pct_change = round(change / lag(n_students) * 100, 1))
stopifnot(nrow(covid_years) > 0)
covid_years
#> end_year n_students change pct_change
#> 1 2018 108945 NA NA
#> 2 2019 110842 1897 1.7
#> 3 2020 112858 2016 1.8
#> 4 2021 112045 -813 -0.7
#> 5 2022 113858 1813 1.6
#> 6 2023 115385 1527 1.3
#> 7 2024 115767 382 0.3Only -0.7% in 2021. Most states lost 3-5%. Rural schools stayed open.
COVID chart
9. Oil counties vs. traditional farming areas
The Bakken oil formation transformed Williams and McKenzie counties while agricultural areas stayed flat.
oil_districts <- enr %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Williston|Watford|Tioga|Alexander|Dickinson|Mandan", district_name)) %>%
mutate(region = case_when(
grepl("Williston|Watford|Tioga|Alexander", district_name) ~ "Oil Counties",
TRUE ~ "Traditional"
)) %>%
group_by(end_year, region) %>%
summarize(total = sum(n_students, na.rm = TRUE), .groups = "drop")
stopifnot(nrow(oil_districts) > 0)
oil_districts %>% filter(end_year %in% c(2008, 2015, 2024))
#> end_year region total
#> 1 2008 Oil Counties 2412
#> 2 2008 Traditional 5629
#> 3 2015 Oil Counties 4035
#> 4 2015 Traditional 6879
#> 5 2024 Oil Counties 6052
#> 6 2024 Traditional 8345
Oil counties
10. Bismarck: steady growth as state capital
While West Fargo doubled in size, Bismarck’s enrollment has grown steadily without the volatility.
bismarck_growth <- enr %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Bismarck|Fargo|West Fargo", district_name)) %>%
mutate(district_name = trimws(gsub(" Public Schools| School District| [0-9]+$", "", district_name))) %>%
filter(district_name %in% c("Bismarck", "Fargo", "West Fargo")) %>%
group_by(district_name) %>%
mutate(yoy_change = (n_students - lag(n_students)) / lag(n_students) * 100) %>%
ungroup() %>%
filter(!is.na(yoy_change))
stopifnot(nrow(bismarck_growth) > 0)
bismarck_growth %>%
group_by(district_name) %>%
summarize(avg_yoy = round(mean(yoy_change, na.rm = TRUE), 2),
max_yoy = round(max(yoy_change, na.rm = TRUE), 2),
min_yoy = round(min(yoy_change, na.rm = TRUE), 2))
#> district_name avg_yoy max_yoy min_yoy
#> 1 Bismarck 1.62 3.72 -1.16
#> 2 Fargo 0.48 2.39 -1.87
#> 3 West Fargo 4.61 7.78 1.33
Bismarck chart
11. Kindergarten as a leading indicator
Kindergarten enrollment predicts total enrollment 12 years later. The recent K decline signals future challenges.
k_vs_total <- enr %>%
filter(is_state, subgroup == "total_enrollment") %>%
filter(grade_level %in% c("K", "TOTAL")) %>%
select(end_year, grade_level, n_students) %>%
pivot_wider(names_from = grade_level, values_from = n_students) %>%
rename(kindergarten = K, total = TOTAL) %>%
mutate(k_pct = kindergarten / total * 100)
stopifnot(nrow(k_vs_total) > 0)
k_vs_total %>% select(end_year, kindergarten, k_pct)
#> end_year kindergarten k_pct
#> 1 2008 6729 7.154553
#> 2 2009 7214 7.723273
#> 3 2010 7470 7.970976
#> ...
#> 12 2019 9324 8.411974
#> 13 2020 9620 8.523986
#> 14 2021 8992 8.025347
#> ...
#> 17 2024 8636 7.459812
Kindergarten chart
12. Grand Forks: holding steady while others surge
Grand Forks has stabilized while Fargo grew modestly and Minot plateaued after its oil-boom surge.
gf_trend <- enr %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL",
grepl("Grand Forks|Fargo|Minot", district_name)) %>%
mutate(district_name = trimws(gsub(" Public Schools| School District| [0-9]+$", "", district_name))) %>%
filter(district_name %in% c("Grand Forks", "Fargo", "Minot")) %>%
group_by(district_name) %>%
mutate(indexed = n_students / first(n_students) * 100) %>%
ungroup()
stopifnot(nrow(gf_trend) > 0)
gf_trend %>% filter(end_year %in% c(2008, 2016, 2024)) %>% select(district_name, end_year, n_students, indexed)
#> district_name end_year n_students indexed
#> 1 Fargo 2008 10493 100.0000
#> 2 Grand Forks 2008 7192 100.0000
#> 3 Minot 2008 6243 100.0000
#> 4 Fargo 2016 11167 106.4233
#> 5 Grand Forks 2016 7264 101.0011
#> 6 Minot 2016 7529 120.5991
#> 7 Fargo 2024 11319 107.8719
#> 8 Grand Forks 2024 7428 103.2814
#> 9 Minot 2024 7510 120.2947
Grand Forks chart
13. The smallest districts are getting smaller
Rural consolidation continues as tiny districts shrink further.
# Track smallest districts over time
small_district_trend <- enr %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
group_by(end_year) %>%
summarize(
under_50 = sum(n_students < 50, na.rm = TRUE),
under_100 = sum(n_students < 100, na.rm = TRUE),
under_200 = sum(n_students < 200, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("under"), names_to = "category", values_to = "count") %>%
mutate(category = case_when(
category == "under_50" ~ "Under 50 students",
category == "under_100" ~ "Under 100 students",
category == "under_200" ~ "Under 200 students"
))
stopifnot(nrow(small_district_trend) > 0)
small_district_trend %>% filter(end_year %in% c(2008, 2016, 2024)) %>% arrange(end_year, category)
#> end_year category count
#> 1 2008 Under 100 students 57
#> 2 2008 Under 200 students 100
#> 3 2008 Under 50 students 33
#> 4 2016 Under 100 students 39
#> 5 2016 Under 200 students 78
#> 6 2016 Under 50 students 20
#> 7 2024 Under 100 students 35
#> 8 2024 Under 200 students 73
#> 9 2024 Under 50 students 13
Small districts
14. McKenzie County leads in graduation rates
Among districts with 100+ student cohorts, the oil-country districts outperform the metro areas.
top_grad_districts <- grad_2024 %>%
filter(is_district, subgroup == "all", cohort_count >= 100) %>%
arrange(desc(grad_rate)) %>%
head(10) %>%
select(district_name, grad_rate, cohort_count, graduate_count) %>%
mutate(district_name = gsub(" Public School.*| School District.*", "", district_name))
stopifnot(nrow(top_grad_districts) > 0)
top_grad_districts
#> district_name grad_rate cohort_count graduate_count
#> 1 McKenzie Co 1 0.858 106 91
#> 2 Mandan 1 0.853 334 285
#> 3 Bismarck 1 0.845 1057 893
#> 4 Grand Forks 1 0.828 599 496
#> 5 Devils Lake 1 0.823 130 107
#> 6 Jamestown 1 0.821 207 170
#> 7 Fargo 1 0.800 949 759
#> 8 West Fargo 6 0.799 884 706
#> 9 Wahpeton 37 0.790 119 94
#> 10 Dickinson 1 0.780 296 231
Top graduation districts
15. Cohort size has grown 15% since 2013
More students are reaching senior year as the oil boom generation ages through.
cohort_trend <- grad_multi %>%
filter(is_state, subgroup == "all") %>%
select(end_year, cohort_count, graduate_count) %>%
mutate(
non_grad = cohort_count - graduate_count,
pct_change = round((cohort_count / first(cohort_count) - 1) * 100, 1)
)
stopifnot(nrow(cohort_trend) > 0)
cohort_trend
#> end_year cohort_count graduate_count non_grad pct_change
#> 1 2013 7567 6598 969 0.0
#> 2 2014 7603 6609 994 0.5
#> 3 2015 7635 6589 1046 0.9
#> 4 2016 7661 6687 974 1.2
#> 5 2017 7572 6588 984 0.1
#> 6 2018 7399 6512 887 -2.2
#> 7 2019 7626 6730 896 0.8
#> 8 2020 7486 6660 826 -1.1
#> 9 2021 7843 6825 1018 3.6
#> 10 2022 8092 6823 1269 6.9
#> 11 2023 8294 6863 1431 9.6
#> 12 2024 8681 7154 1527 14.7
Cohort size
16. Medium-sized districts lead in graduation rates
Mid-size districts (200-999 students) outperform both small rural and large urban districts.
# Join enrollment to graduation data
# Note: enrollment uses CC-DDD format, graduation uses CCDDD format
district_size <- enr_2024 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
mutate(district_id_clean = gsub("-", "", district_id)) %>%
select(district_id_clean, enrollment = n_students)
grad_with_size <- grad_2024 %>%
filter(is_district, subgroup == "all", cohort_count >= 10) %>%
mutate(district_id_clean = district_id) %>%
left_join(district_size, by = "district_id_clean") %>%
mutate(size_category = case_when(
enrollment < 200 ~ "Small (<200)",
enrollment < 1000 ~ "Medium (200-999)",
enrollment < 5000 ~ "Large (1,000-4,999)",
TRUE ~ "Very Large (5,000+)"
)) %>%
filter(!is.na(size_category))
size_summary <- grad_with_size %>%
group_by(size_category) %>%
summarize(
n_districts = n(),
avg_grad_rate = weighted.mean(grad_rate, cohort_count, na.rm = TRUE),
total_cohort = sum(cohort_count, na.rm = TRUE),
.groups = "drop"
) %>%
mutate(size_category = factor(size_category,
levels = c("Small (<200)", "Medium (200-999)",
"Large (1,000-4,999)", "Very Large (5,000+)")))
stopifnot(nrow(size_summary) > 0)
size_summary
#> size_category n_districts avg_grad_rate total_cohort
#> 1 Small (<200) 30 0.8608376 431
#> 2 Medium (200-999) 79 0.8827524 2302
#> 3 Large (1,000-4,999) 8 0.8143729 1408
#> 4 Very Large (5,000+) 6 0.7921319 4414
Rural vs urban