Installation
Install from GitHub:
# install.packages("remotes")
remotes::install_github("almartin82/ilschooldata")Quick Example
Fetch the most recent year of Illinois enrollment data:
library(ilschooldata)
library(dplyr)
# Fetch 2025 enrollment data (2024-25 school year)
enr <- fetch_enr(2025, use_cache = TRUE)
head(enr)## # A tibble: 6 × 18
## end_year rcdts type district_id district_name school_id school_name city
## <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 2025 65-000-0… Stat… 65-000-0000 NA -80- NA NA
## 2 2025 01-009-2… Dist… 01-009-2620 A-C Central … -26- NA Ashl…
## 3 2025 01-009-2… Scho… 01-009-2620 A-C Central … -26- A-C Centra… Chan…
## 4 2025 01-009-2… Scho… 01-009-2620 A-C Central … -26- A-C Centra… Ashl…
## 5 2025 01-009-2… Scho… 01-009-2620 A-C Central … -26- A-C Centra… Ashl…
## 6 2025 33-048-2… Dist… 33-048-2760 Abingdon-Avo… -26- NA Abin…
## # ℹ 10 more variables: county <chr>, grade_level <chr>, subgroup <chr>,
## # n_students <dbl>, pct <dbl>, is_state <lgl>, is_district <lgl>,
## # is_school <lgl>, is_charter <lgl>, aggregation_flag <chr>Understanding the Data
The data is returned in tidy (long) format by default:
- Each row is one subgroup for one school/district/state
-
subgroupidentifies the demographic group (e.g., “total_enrollment”, “white”, “hispanic”, “econ_disadv”) -
grade_levelshows the grade (“TOTAL”, “K”, “01”, “02”, etc.) -
n_studentsis the enrollment count -
pctis the percentage of total enrollment
## # A tibble: 10 × 5
## end_year type subgroup grade_level n_students
## <int> <chr> <chr> <chr> <dbl>
## 1 2025 Statewide total_enrollment TOTAL 1848560
## 2 2025 Statewide white TOTAL 818912
## 3 2025 Statewide black TOTAL 301315
## 4 2025 Statewide hispanic TOTAL 528688
## 5 2025 Statewide asian TOTAL 105368
## 6 2025 Statewide native_american TOTAL 3697
## 7 2025 Statewide pacific_islander TOTAL 1849
## 8 2025 Statewide multiracial TOTAL 83185
## 9 2025 Statewide special_ed TOTAL 375258
## 10 2025 Statewide lep TOTAL 323498Filtering by Level
Use the aggregation flags to filter data:
# State totals
state <- enr %>% filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL")
state %>% select(end_year, n_students)## # A tibble: 1 × 2
## end_year n_students
## <int> <dbl>
## 1 2025 1848560
# All districts
districts <- enr %>% filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL")
nrow(districts)## [1] 864
# All schools
schools <- enr %>% filter(is_school, subgroup == "total_enrollment", grade_level == "TOTAL")
nrow(schools)## [1] 3825Simple Analysis: Top 10 Districts
enr %>%
filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
arrange(desc(n_students)) %>%
select(district_name, city, n_students) %>%
head(10)## # A tibble: 10 × 3
## district_name city n_students
## <chr> <chr> <dbl>
## 1 Chicago Public Schools District 299 Chicago 323047
## 2 SD U-46 Elgin 33525
## 3 Rockford SD 205 Rockford 28162
## 4 Indian Prairie CUSD 204 Aurora 25932
## 5 Plainfield SD 202 Plainfield 24411
## 6 CUSD 300 Algonquin 20392
## 7 CUSD 308 Oswego 16601
## 8 Naperville CUSD 203 Naperville 15899
## 9 Schaumburg CCSD 54 Schaumburg 15266
## 10 Valley View CUSD 365U Romeoville 14529Wide Format
If you prefer wide format (one column per demographic), set
tidy = FALSE:
enr_wide <- fetch_enr(2025, tidy = FALSE, use_cache = TRUE)
enr_wide %>%
filter(type == "Statewide") %>%
select(end_year, row_total, white, black, hispanic, asian, econ_disadv)## # A tibble: 1 × 7
## end_year row_total white black hispanic asian econ_disadv
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2025 1848560 818912 301315 528688 105368 918734Historical Data
Fetch multiple years to analyze trends:
# Fetch 5 years of data
years <- 2021:2025
all_enr <- purrr::map_df(years, ~fetch_enr(.x, use_cache = TRUE))
# State enrollment trend
all_enr %>%
filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") %>%
select(end_year, n_students)## # A tibble: 5 × 2
## end_year n_students
## <int> <dbl>
## 1 2021 1887316
## 2 2022 1869325
## 3 2023 1857790
## 4 2024 1851290
## 5 2025 1848560Next Steps
- See
vignette("diagnostic-plots")for visualization examples - See
vignette("cohort-survival-forecasts")for cohort analysis methodology - Use
?fetch_enrfor full function documentation
Session Info
## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dplyr_1.2.0 ilschooldata_0.1.0
##
## loaded via a namespace (and not attached):
## [1] vctrs_0.7.1 cli_3.6.5 knitr_1.51 rlang_1.1.7
## [5] xfun_0.56 purrr_1.2.1 generics_0.1.4 textshaping_1.0.5
## [9] jsonlite_2.0.0 glue_1.8.0 htmltools_0.5.9 ragg_1.5.1
## [13] sass_0.4.10 rappdirs_0.3.4 rmarkdown_2.30 tibble_3.3.1
## [17] evaluate_1.0.5 jquerylib_0.1.4 fastmap_1.2.0 yaml_2.3.12
## [21] lifecycle_1.0.5 compiler_4.5.2 codetools_0.2-20 fs_1.6.7
## [25] pkgconfig_2.0.3 systemfonts_1.3.2 digest_0.6.39 R6_2.6.1
## [29] utf8_1.2.6 tidyselect_1.2.1 pillar_1.11.1 magrittr_2.0.4
## [33] bslib_0.10.0 withr_3.0.2 tools_4.5.2 pkgdown_2.2.0
## [37] cachem_1.1.0 desc_1.4.3