Fetch and analyze Idaho school enrollment data from the Idaho State Department of Education in R or Python. 30 years of grade-level enrollment across 115+ districts and 32 charter schools.
Part of the njschooldata family.
Full documentation — all 15 stories with interactive charts, getting-started guide, and complete function reference.
Highlights
library(idschooldata)
library(ggplot2)
library(dplyr)
library(scales)
theme_readme <- function() {
theme_minimal(base_size = 14) +
theme(
plot.title = element_text(face = "bold", size = 16),
plot.subtitle = element_text(color = "gray40"),
panel.grid.minor = element_blank(),
legend.position = "bottom"
)
}
colors <- c("total" = "#2C3E50", "growth" = "#27AE60", "decline" = "#E74C3C",
"district1" = "#3498DB", "district2" = "#F39C12", "district3" = "#9B59B6",
"district4" = "#1ABC9C", "district5" = "#E67E22")
# Get available years
years <- get_available_years()
if (is.list(years)) {
max_year <- years$max_year
min_year <- years$min_year
} else {
max_year <- max(years)
min_year <- min(years)
}
# Fetch data for various time spans
enr_all <- fetch_enr_multi(2002:max_year, use_cache = TRUE)
enr_decade <- fetch_enr_multi((max_year - 14):max_year, use_cache = TRUE)
enr_recent <- fetch_enr_multi((max_year - 9):max_year, use_cache = TRUE)
enr_current <- fetch_enr(max_year, use_cache = TRUE)1. Idaho’s enrollment grew 28% in 24 years
Idaho is one of the fastest-growing states in America. Public school enrollment has surged since 2002 while many states are shrinking.
state_trend <- enr_all %>%
filter(is_state, grade_level == "TOTAL", subgroup == "total_enrollment")
stopifnot(nrow(state_trend) > 0)
# Compute growth dynamically
first_yr <- state_trend %>% filter(end_year == min(end_year))
last_yr <- state_trend %>% filter(end_year == max(end_year))
growth <- last_yr$n_students - first_yr$n_students
pct_growth <- round((last_yr$n_students / first_yr$n_students - 1) * 100, 1)
yr_span <- max(state_trend$end_year) - min(state_trend$end_year)
state_trend %>% select(end_year, n_students)
#> end_year n_students
#> 1 2002 246184
#> 2 2003 248660
#> ...
#> 24 2025 318067
#> 25 2026 314097
cat("Growth:", format(growth, big.mark = ","), "students (",
pct_growth, "%) over", yr_span, "years\n")
#> Growth: 67,913 students ( 27.6 %) over 24 years
ggplot(state_trend, aes(x = end_year, y = n_students)) +
geom_line(linewidth = 1.5, color = colors["total"]) +
geom_point(size = 3, color = colors["total"]) +
scale_y_continuous(labels = comma, limits = c(0, NA)) +
labs(title = "Idaho Public School Enrollment",
subtitle = paste0("Added ", format(growth, big.mark = ","),
" students since 2002 (+", pct_growth, "%)"),
x = "School Year", y = "Students") +
theme_readme()
State growth
2. Boise proper declined while suburbs exploded
Boise Independent School District has declined to 22,000 students. Meanwhile, neighboring West Ada has grown from 20,000 to nearly 38,000. The growth is all in the suburbs.
boise_westada <- enr_all %>%
filter(is_district,
grepl("^BOISE INDEPENDENT DISTRICT|^WEST ADA DISTRICT", district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(boise_westada) > 0)
boise_westada %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2002 BOISE INDEPENDENT DISTRICT 25553
#> 2 2002 WEST ADA DISTRICT 21474
#> ...
#> 49 2026 BOISE INDEPENDENT DISTRICT 21717
#> 50 2026 WEST ADA DISTRICT 37919
ggplot(boise_westada, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2.5) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["district1"], colors["district2"])) +
labs(title = "Boise Flat, West Ada Exploding",
subtitle = "Suburban growth outpacing the urban core",
x = "School Year", y = "Students", color = "") +
theme_readme()
Boise vs West Ada
3. COVID barely slowed Idaho
Most states lost 3-5% enrollment during the pandemic. Idaho dipped briefly in 2020, then resumed growing. Families moved TO Idaho during the pandemic, offsetting any losses.
covid_years <- enr_all %>%
filter(is_state, grade_level == "TOTAL", subgroup == "total_enrollment",
end_year >= 2018)
stopifnot(nrow(covid_years) > 0)
covid_years %>% select(end_year, n_students)
#> end_year n_students
#> 1 2018 302689
#> 2 2019 307228
#> 3 2020 311991
#> 4 2021 310653
#> 5 2022 316159
#> 6 2023 318979
#> 7 2024 318660
#> 8 2025 318067
#> 9 2026 314097
ggplot(covid_years, aes(x = end_year, y = n_students)) +
geom_line(linewidth = 1.5, color = colors["total"]) +
geom_point(size = 3, color = colors["total"]) +
geom_vline(xintercept = 2021, linetype = "dashed", color = "red", alpha = 0.5) +
annotate("text", x = 2021.3, y = max(covid_years$n_students, na.rm = TRUE) * 0.98,
label = "COVID", hjust = 0, color = "red") +
scale_y_continuous(labels = comma, limits = c(0, NA)) +
labs(title = "Idaho Barely Slowed During COVID",
subtitle = "Brief dip before resuming growth (2020-21 marked)",
x = "School Year", y = "Students") +
theme_readme()
COVID resilience
Data Taxonomy
| Category | Years | Function | Details |
|---|---|---|---|
| Enrollment | 1995-2026 |
fetch_enr() / fetch_enr_multi()
|
Grade-level (PK-12) by district/charter. Subgroups: total_enrollment. Entity flags: is_state, is_district, is_campus, is_charter. |
| Assessments | — | — | Not yet available |
| Graduation | — | — | Not yet available |
| Directory | 2011-2026 | fetch_directory() |
Building-level. District, charter status, grades served, school level, enrollment. |
| 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
Installation
# install.packages("remotes")
remotes::install_github("almartin82/idschooldata")R
library(idschooldata)
library(dplyr)
# Fetch one year
enr_2026 <- fetch_enr(2026)
# Fetch multiple years
enr_recent <- fetch_enr_multi(2020:2026)
# Fetch all available years
enr_all <- fetch_enr_multi(1996:2026)
# State totals
enr_2026 %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL")
# District breakdown
enr_2026 %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students))
# Grade-level breakdown
enr_2026 %>%
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "02", "03", "04", "05")) %>%
select(grade_level, n_students)Python
import pyidschooldata as id_
# Check available years
years = id_.get_available_years()
print(f"Data available from {years['min_year']} to {years['max_year']}")
# Fetch one year
enr_2026 = id_.fetch_enr(2026)
# Fetch multiple years
enr_recent = id_.fetch_enr_multi([2020, 2021, 2022, 2023, 2024, 2025, 2026])
# State totals
state_total = enr_2026[
(enr_2026['is_state'] == True) &
(enr_2026['subgroup'] == 'total_enrollment') &
(enr_2026['grade_level'] == 'TOTAL')
]
# District breakdown
districts = enr_2026[
(enr_2026['is_district'] == True) &
(enr_2026['subgroup'] == 'total_enrollment') &
(enr_2026['grade_level'] == 'TOTAL')
].sort_values('n_students', ascending=False)Explore More
- Full documentation — all 15 stories with interactive charts
- Function reference
Data Notes
| Years | Source | Aggregation Levels | Notes |
|---|---|---|---|
| 1996-2026 | Idaho SDE Finance Portal | State, District, Charter | Grade-level breakdowns for all years |
What’s available
- Levels: State, district (~115), charter (~32)
- Grade levels: Preschool through Grade 12
- Years: 30 years of historical data
- Suppression: Idaho SDE does not suppress enrollment counts in this dataset
ID System
Idaho uses a 3-digit district numbering system: - District ID: 3 digits (e.g., 001 for Boise) - Charter IDs: Higher numbers in the 400+ series
Demographics Note
Race/ethnicity and special population data (ELL, Special Ed) are available through the Idaho Report Card but not included in the main enrollment file. This package focuses on the comprehensive grade-level enrollment data from the SDE Finance Portal.
Deeper Dive
4. West Ada is Idaho’s school giant
West Ada School District in suburban Boise (Meridian/Eagle area) serves nearly 38,000 students - 75% larger than Boise itself. It’s the largest district in the state and still growing.
top_10 <- enr_current %>%
filter(is_district, grade_level == "TOTAL", subgroup == "total_enrollment") %>%
arrange(desc(n_students)) %>%
head(10)
stopifnot(nrow(top_10) > 0)
# Compute ratio dynamically
west_ada <- top_10$n_students[1]
boise <- top_10$n_students[top_10$district_name == "BOISE INDEPENDENT DISTRICT"]
ratio_pct <- round((west_ada / boise - 1) * 100)
top_10 %>% select(district_name, n_students)
#> district_name n_students
#> 1 WEST ADA DISTRICT 37919
#> 2 BOISE INDEPENDENT DISTRICT 21717
#> 3 BONNEVILLE JOINT DISTRICT 13511
#> 4 NAMPA SCHOOL DISTRICT 12473
#> 5 POCATELLO DISTRICT 11437
#> 6 VALLIVUE SCHOOL DISTRICT 10700
#> 7 IDAHO FALLS DISTRICT 9751
#> 8 COEUR D ALENE DISTRICT 9680
#> 9 TWIN FALLS DISTRICT 8774
#> 10 IDAHO HOME LEARNING ACADEMY CHARTER 7504
ggplot(top_10, aes(x = reorder(district_name, n_students), y = n_students)) +
geom_col(fill = colors["total"]) +
coord_flip() +
scale_y_continuous(labels = comma) +
labs(title = "Idaho's Largest School Districts",
subtitle = paste0("West Ada serves ", ratio_pct, "% more students than Boise"),
x = "", y = "Students") +
theme_readme()
West Ada giant
5. The Treasure Valley boom
Eagle, Kuna, Star, and Middleton are among the fastest-growing communities in America. These districts around Boise have doubled or tripled enrollment in 15 years as families flee California.
treasure_valley <- c("WEST ADA DISTRICT", "KUNA JOINT DISTRICT", "MIDDLETON DISTRICT")
tv_trend <- enr_decade %>%
filter(is_district,
grepl(paste(treasure_valley, collapse = "|"), district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(tv_trend) > 0)
tv_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2012 WEST ADA DISTRICT 35188
#> 2 2012 KUNA JOINT DISTRICT 4983
#> 3 2012 MIDDLETON DISTRICT 3219
#> ...
#> 43 2026 WEST ADA DISTRICT 37919
#> 44 2026 KUNA JOINT DISTRICT 5699
#> 45 2026 MIDDLETON DISTRICT 4401
ggplot(tv_trend, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2.5) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["district1"], colors["district2"], colors["district3"])) +
labs(title = "Treasure Valley Suburban Growth",
subtitle = "West Ada, Kuna, and Middleton growing rapidly",
x = "School Year", y = "Students", color = "") +
theme_readme()
Treasure Valley boom
6. Kindergarten enrollment follows national trends
Like most states, Idaho’s kindergarten enrollment has declined in recent years as birth rates drop statewide. The decline appears to have stabilized since 2021.
k_trend <- enr_decade %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "K")
stopifnot(nrow(k_trend) > 0)
k_trend %>% select(end_year, n_students)
#> end_year n_students
#> 1 2012 23038
#> 2 2013 23765
#> ...
#> 14 2025 22544
#> 15 2026 22154
ggplot(k_trend, aes(x = end_year, y = n_students)) +
geom_line(linewidth = 1.5, color = colors["growth"]) +
geom_point(size = 3, color = colors["growth"]) +
scale_y_continuous(labels = comma) +
labs(title = "Idaho Kindergarten Enrollment",
subtitle = "Decline stabilized since 2021",
x = "School Year", y = "Students") +
theme_readme()
Kindergarten growth
7. Charter schools serve 1 in 14 Idaho students
Idaho has 32 charter schools serving nearly 23,000 students. Idaho Home Learning Academy alone accounts for over 7,500 students - a third of all charter enrollment.
charter <- enr_current %>%
filter(is_charter, grade_level == "TOTAL", subgroup == "total_enrollment")
stopifnot(nrow(charter) > 0)
charter_total <- sum(charter$n_students, na.rm = TRUE)
state_total <- enr_current %>%
filter(is_state, grade_level == "TOTAL", subgroup == "total_enrollment") %>%
pull(n_students)
charter_ratio <- round(state_total / charter_total)
charter %>%
arrange(desc(n_students)) %>%
head(10) %>%
select(district_name, n_students)
#> district_name n_students
#> 1 IDAHO HOME LEARNING ACADEMY CHARTER 7504
#> 2 INSPIRE VIRTUAL CHARTER LEA 1553
#> 3 IDAHO ARTS CHARTER 1421
#> 4 COMPAS PUBLIC CHARTER SCHOOL 1289
#> 5 NORTH STAR CHARTER 1143
#> 6 VISION CHARTER SCHOOL 757
#> 7 XAVIER CHARTER SCHOOL 675
#> 8 WHITE PINE CHARTER SCHOOL 653
#> 9 ANSER CHARTER SCHOOL 598
#> 10 COEUR D'ALENE CHARTER ACADEMY 593
charter %>%
arrange(desc(n_students)) %>%
head(10) %>%
ggplot(aes(x = reorder(district_name, n_students), y = n_students)) +
geom_col(fill = colors["district3"]) +
coord_flip() +
scale_y_continuous(labels = comma) +
labs(title = "Idaho's Largest Charter Schools",
subtitle = paste0(format(charter_total, big.mark = ","),
" students in charters (",
round(charter_total / state_total * 100, 1),
"% of state, 1 in ", charter_ratio, ")"),
x = "", y = "Students") +
theme_readme()
Charter share
8. Rural Idaho is emptying out
While Boise suburbs boom, northern and eastern Idaho are hollowing out. Wallace (Silver Valley), Salmon, and Challis have lost half their students in 20 years.
rural <- c("WALLACE DISTRICT", "SALMON DISTRICT", "CHALLIS JOINT DISTRICT")
rural_trend <- enr_all %>%
filter(is_district,
grepl(paste(rural, collapse = "|"), district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(rural_trend) > 0)
rural_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2002 CHALLIS JOINT DISTRICT 647
#> 2 2002 SALMON DISTRICT 852
#> 3 2002 WALLACE DISTRICT 579
#> ...
ggplot(rural_trend, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["decline"], colors["district2"], colors["district3"])) +
labs(title = "Rural Idaho is Emptying Out",
subtitle = "Northern mountain communities losing students",
x = "School Year", y = "Students", color = "") +
theme_readme()
Rural decline
9. High school enrollment surged 22% in 15 years
Idaho’s high schools are seeing record enrollment as the wave of growth works through the grades.
hs_trend <- enr_decade %>%
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("09", "10", "11", "12")) %>%
group_by(end_year) %>%
summarize(n_students = sum(n_students, na.rm = TRUE), .groups = "drop")
stopifnot(nrow(hs_trend) > 0)
# Compute growth dynamically
hs_first <- hs_trend$n_students[1]
hs_last <- hs_trend$n_students[nrow(hs_trend)]
hs_pct <- round((hs_last / hs_first - 1) * 100)
hs_trend
#> # A tibble: 15 x 2
#> end_year n_students
#> <dbl> <dbl>
#> 1 2012 83121
#> 2 2013 83952
#> ...
#> 14 2025 102039
#> 15 2026 101402
cat("HS growth:", format(hs_first, big.mark = ","), "->",
format(hs_last, big.mark = ","), "(+", hs_pct, "%)\n")
#> HS growth: 83,121 -> 101,402 (+ 22 %)
ggplot(hs_trend, aes(x = end_year, y = n_students)) +
geom_line(linewidth = 1.5, color = colors["total"]) +
geom_point(size = 3, color = colors["total"]) +
scale_y_continuous(labels = comma, limits = c(0, NA)) +
labs(title = "Idaho High School Enrollment (Grades 9-12)",
subtitle = paste0("From ", format(hs_first, big.mark = ","), " to ",
format(hs_last, big.mark = ","), " (+", hs_pct, "%)"),
x = "School Year", y = "Students") +
theme_readme()
High school surge
10. The Magic Valley is holding steady
Twin Falls, Jerome, and Minidoka County in south-central Idaho have maintained steady enrollment even as some rural areas shrink. Agriculture and food processing anchor these communities.
magic_valley <- c("TWIN FALLS DISTRICT", "JEROME JOINT DISTRICT", "MINIDOKA COUNTY JOINT DISTRICT")
mv_trend <- enr_decade %>%
filter(is_district,
grepl(paste(magic_valley, collapse = "|"), district_name),
!grepl("VIRTUAL|CHARTER", district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(mv_trend) > 0)
mv_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2012 MINIDOKA COUNTY JOINT DISTRICT 4320
#> 2 2012 TWIN FALLS DISTRICT 8147
#> 3 2012 JEROME JOINT DISTRICT 3860
#> ...
ggplot(mv_trend, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2.5) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["district1"], colors["district2"], colors["district3"])) +
labs(title = "Magic Valley Districts Holding Steady",
subtitle = "Twin Falls and Jerome maintain enrollment",
x = "School Year", y = "Students", color = "") +
theme_readme()
Magic Valley
11. Elementary is the biggest cohort
Idaho’s elementary schools (K-5) serve the largest share of students. The grade distribution shows a healthy pipeline with growing younger grades - a sign of continued future growth.
grades <- enr_current %>%
filter(is_state, subgroup == "total_enrollment",
grade_level %in% c("K", "01", "02", "03", "04", "05",
"06", "07", "08", "09", "10", "11", "12"))
stopifnot(nrow(grades) > 0)
grade_order <- c("K", "01", "02", "03", "04", "05",
"06", "07", "08", "09", "10", "11", "12")
grades$grade_level <- factor(grades$grade_level, levels = grade_order)
grades %>% select(grade_level, n_students)
#> grade_level n_students
#> 1 K 22154
#> 2 01 22781
#> 3 02 23230
#> 4 03 23578
#> 5 04 23636
#> 6 05 24017
#> 7 06 24082
#> 8 07 24543
#> 9 08 24576
#> 10 09 26429
#> 11 10 26003
#> 12 11 25117
#> 13 12 23864
ggplot(grades, aes(x = grade_level, y = n_students)) +
geom_col(fill = colors["total"]) +
scale_y_continuous(labels = comma) +
labs(title = "Idaho Enrollment by Grade Level",
subtitle = "Strong elementary enrollment signals continued growth",
x = "Grade", y = "Students") +
theme_readme()
Grade distribution
12. Coeur d’Alene leads northern Idaho
In the Idaho panhandle, Coeur d’Alene School District leads with nearly 10,000 students. Post Falls and Lakeland are also growing as people relocate from Spokane and beyond.
north <- c("COEUR D ALENE DISTRICT", "POST FALLS DISTRICT", "LAKELAND DISTRICT")
north_trend <- enr_decade %>%
filter(is_district,
grepl(paste(north, collapse = "|"), district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(north_trend) > 0)
north_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2012 COEUR D ALENE DISTRICT 9903
#> 2 2012 POST FALLS DISTRICT 5133
#> 3 2012 LAKELAND DISTRICT 4061
#> ...
ggplot(north_trend, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2.5) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["district1"], colors["district2"], colors["district3"])) +
labs(title = "North Idaho District Growth",
subtitle = "Coeur d'Alene, Post Falls, and Lakeland expanding",
x = "School Year", y = "Students", color = "") +
theme_readme()
North Idaho
13. Small districts dominate the landscape
Idaho has 115+ school districts, but many are tiny. Over 40 districts have fewer than 500 students. These small rural districts face unique challenges with limited resources.
district_sizes <- enr_current %>%
filter(is_district, grade_level == "TOTAL", subgroup == "total_enrollment") %>%
mutate(size_category = case_when(
n_students >= 10000 ~ "10,000+",
n_students >= 5000 ~ "5,000-9,999",
n_students >= 2000 ~ "2,000-4,999",
n_students >= 1000 ~ "1,000-1,999",
n_students >= 500 ~ "500-999",
TRUE ~ "Under 500"
)) %>%
group_by(size_category) %>%
summarize(n_districts = n(), .groups = "drop")
stopifnot(nrow(district_sizes) > 0)
district_sizes$size_category <- factor(district_sizes$size_category,
levels = c("Under 500", "500-999", "1,000-1,999", "2,000-4,999", "5,000-9,999", "10,000+"))
district_sizes
#> size_category n_districts
#> 1 Under 500 56
#> 2 500-999 26
#> 3 1,000-1,999 16
#> 4 2,000-4,999 9
#> 5 5,000-9,999 4
#> 6 10,000+ 6
ggplot(district_sizes, aes(x = size_category, y = n_districts)) +
geom_col(fill = colors["total"]) +
labs(title = "Idaho Districts by Size",
subtitle = "Many small rural districts, few large ones",
x = "District Size (Students)", y = "Number of Districts") +
theme_readme()
District size distribution
14. Nampa and Caldwell anchor Canyon County
Nampa School District and Caldwell serve Canyon County west of Boise. Both have grown with the Treasure Valley, though Nampa has seen recent declines from its 2014 peak.
canyon <- c("NAMPA SCHOOL DISTRICT", "CALDWELL DISTRICT")
canyon_trend <- enr_decade %>%
filter(is_district,
grepl(paste(canyon, collapse = "|"), district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(canyon_trend) > 0)
canyon_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2012 NAMPA SCHOOL DISTRICT 13376
#> 2 2012 CALDWELL DISTRICT 6124
#> ...
ggplot(canyon_trend, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2.5) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["district1"], colors["district2"])) +
labs(title = "Canyon County Districts",
subtitle = "Nampa and Caldwell growing with Treasure Valley",
x = "School Year", y = "Students", color = "") +
theme_readme()
Canyon County
15. Idaho Falls leads eastern Idaho
Idaho Falls School District (#91) anchors the eastern region with nearly 10,000 students. Nearby Bonneville Joint District serves the growing suburban areas with 13,500 students. Eastern Idaho grows more slowly than the Treasure Valley.
eastern <- c("IDAHO FALLS DISTRICT", "BONNEVILLE JOINT DISTRICT")
eastern_trend <- enr_decade %>%
filter(is_district,
grepl(paste(eastern, collapse = "|"), district_name),
subgroup == "total_enrollment", grade_level == "TOTAL")
stopifnot(nrow(eastern_trend) > 0)
eastern_trend %>% select(end_year, district_name, n_students)
#> end_year district_name n_students
#> 1 2012 BONNEVILLE JOINT DISTRICT 11827
#> 2 2012 IDAHO FALLS DISTRICT 9282
#> ...
ggplot(eastern_trend, aes(x = end_year, y = n_students, color = district_name)) +
geom_line(linewidth = 1.2) +
geom_point(size = 2.5) +
scale_y_continuous(labels = comma) +
scale_color_manual(values = c(colors["district1"], colors["district2"])) +
labs(title = "Eastern Idaho Districts",
subtitle = "Idaho Falls and Bonneville anchor the region",
x = "School Year", y = "Students", color = "") +
theme_readme()
Eastern Idaho