Getting Started with laschooldata • laschooldata

Introduction

The laschooldata package provides easy access to Louisiana K-12 public school enrollment data from the Louisiana Department of Education (LDOE). The data comes from the Multi Stats reports published each October.

Installation

# Install from GitHub
devtools::install_github("almartin82/laschooldata")

Quick Start

library(laschooldata)
library(dplyr)

# Check available years
get_available_years()
#> $min_year
#> [1] 2019
#> 
#> $max_year
#> [1] 2026
#> 
#> $description
#> [1] "Louisiana enrollment data from LDOE Multi Stats files. Available years: 2019-2026. Earlier years (2007-2018) may use different formats and URLs."
# Returns: min_year = 2019, max_year = 2024

Fetching Enrollment Data

Single Year

Use fetch_enr() to download enrollment data for a single school year:

# Get 2024 data (2023-24 school year)
enr_2024 <- fetch_enr(2024, use_cache = TRUE)
#> Using cached data for 2024

# By default, returns tidy (long) format
head(enr_2024)
#> # A tibble: 6 × 14
#>   end_year district_id campus_id district_name     campus_name type  grade_level
#>      <int> <chr>       <chr>     <chr>             <chr>       <chr> <chr>      
#> 1     2024 NA          NA        State of Louisia… NA          State TOTAL      
#> 2     2024 000         NA        State of Louisia… NA          Dist… TOTAL      
#> 3     2024 001         NA        Acadia Parish     NA          Dist… TOTAL      
#> 4     2024 002         NA        Allen Parish      NA          Dist… TOTAL      
#> 5     2024 003         NA        Ascension Parish  NA          Dist… TOTAL      
#> 6     2024 004         NA        Assumption Parish NA          Dist… TOTAL      
#> # ℹ 7 more variables: subgroup <chr>, n_students <dbl>, pct <dbl>,
#> #   aggregation_flag <chr>, is_state <lgl>, is_district <lgl>, is_campus <lgl>

The tidy format includes: - end_year: School year end (e.g., 2024 = 2023-24) - type: “State”, “District”, or “Campus” - district_id: 3-digit parish code - campus_id: Site code (NA for state/district rows) - district_name: Parish name - campus_name: School name - subgroup: Demographic group (e.g., “total_enrollment”, “white”, “male”) - grade_level: Grade level (e.g., “TOTAL”, “K”, “01”) - n_students: Student count - is_state, is_district, is_campus: Aggregation level flags

Wide Format

For wide format (one row per entity), use tidy = FALSE:

enr_wide <- fetch_enr(2024, tidy = FALSE, use_cache = TRUE)
#> Downloading LDOE enrollment data for 2024 ...
#>   Downloading from: https://www.louisianabelieves.com/docs/default-source/data-management/oct-2024-multi-stats-(total-by-site-and-school-system)_web.xlsx
#>   Trying alternate URL: https://www.louisianabelieves.com/docs/default-source/data-management/oct-2024-multi-stats-(total-by-site-and-school-system)_web.xlsx
#>   Trying alternate URL: https://www.louisianabelieves.com/docs/default-source/data-management/oct-2024-multi-stats-(total-by-site-and-school-system).xlsx
#>   Reading Excel file...
#>   Reading LEA sheet: Total by School System
#> New names:
#> Reading site sheet: Total by Site
#> New names:
#> • `` -> `...1`
#> • `` -> `...2`
#> • `` -> `...3`
#> • `` -> `...4`
#> • `` -> `...5`
#> • `` -> `...6`
#> • `` -> `...7`
#> • `` -> `...8`
#> • `` -> `...9`
#> • `` -> `...10`
#> • `` -> `...11`
#> • `` -> `...12`
#> • `` -> `...13`
#> • `` -> `...14`
#> • `` -> `...15`
#> • `` -> `...16`
#> • `` -> `...17`
#> • `` -> `...18`
#> • `` -> `...19`
#> • `` -> `...20`
#> • `` -> `...21`
#> • `` -> `...22`
#> • `` -> `...23`
#> • `` -> `...24`
#> • `` -> `...25`
#> • `` -> `...26`
#> • `` -> `...27`
#> • `` -> `...28`
#> • `` -> `...29`
#> • `` -> `...30`
#> • `` -> `...31`
#> • `` -> `...32`
#> • `` -> `...33`
#> • `` -> `...34`
#> • `` -> `...35`

# Wide format has columns for each subgroup
names(enr_wide)
#>  [1] "end_year"         "type"             "district_id"      "campus_id"       
#>  [5] "district_name"    "campus_name"      "row_total"        "white"           
#>  [9] "black"            "hispanic"         "asian"            "pacific_islander"
#> [13] "native_american"  "multiracial"      "minority"         "female"          
#> [17] "pct_female"       "male"             "pct_male"         "lep"             
#> [21] "pct_lep"          "fep"              "pct_fep"          "econ_disadv"     
#> [25] "pct_econ_disadv"  "grade_infant"     "grade_preschool"  "grade_pk"        
#> [29] "grade_k"          "grade_01"         "grade_02"         "grade_03"        
#> [33] "grade_04"         "grade_05"         "grade_06"         "grade_07"        
#> [37] "grade_08"         "grade_09"         "grade_t9"         "grade_10"        
#> [41] "grade_11"         "grade_12"         "grade_extension"
# Includes: row_total, white, black, hispanic, asian, male, female, etc.

Multiple Years

Use fetch_enr_multi() for multiple years:

enr_multi <- fetch_enr_multi(2022:2024, use_cache = TRUE)
#> Fetching 2022 ...
#> Using cached data for 2022
#> Fetching 2023 ...
#> Using cached data for 2023
#> Fetching 2024 ...
#> Using cached data for 2024

# Track state enrollment trends
enr_multi |>
  filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
  select(end_year, n_students)
#> # A tibble: 3 × 2
#>   end_year n_students
#>      <int>      <dbl>
#> 1     2022     685606
#> 2     2023     681176
#> 3     2024     676751

Common Analysis Examples

State-Level Summary

enr_2024 |>
  filter(is_state, grade_level == "TOTAL") |>
  select(subgroup, n_students) |>
  arrange(desc(n_students))
#> # A tibble: 14 × 2
#>    subgroup         n_students
#>    <chr>                 <dbl>
#>  1 total_enrollment     676751
#>  2 fep                  640883
#>  3 econ_disadv          474402
#>  4 minority             401486
#>  5 male                 346497
#>  6 female               330254
#>  7 black                282521
#>  8 white                275265
#>  9 hispanic              77836
#> 10 lep                   35868
#> 11 multiracial           26225
#> 12 asian                 10745
#> 13 native_american        3666
#> 14 pacific_islander        493

District Enrollment Ranking

enr_2024 |>
  filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
  arrange(desc(n_students)) |>
  select(district_name, n_students) |>
  head(10)
#> # A tibble: 10 × 2
#>    district_name           n_students
#>    <chr>                        <dbl>
#>  1 State of Louisiana          676751
#>  2 Jefferson Parish             47702
#>  3 East Baton Rouge Parish      39932
#>  4 St. Tammany Parish           36384
#>  5 Caddo Parish                 32614
#>  6 Lafayette Parish             29877
#>  7 Calcasieu Parish             28623
#>  8 Livingston Parish            26852
#>  9 Ascension Parish             24076
#> 10 Bossier Parish               22447

Grade-Level Distribution

enr_2024 |>
  filter(is_state, subgroup == "total_enrollment", grade_level != "TOTAL") |>
  select(grade_level, n_students) |>
  arrange(grade_level)
#> # A tibble: 18 × 2
#>    grade_level n_students
#>    <chr>            <dbl>
#>  1 01               50106
#>  2 02               50649
#>  3 03               51503
#>  4 04               49820
#>  5 05               48905
#>  6 06               48855
#>  7 07               48717
#>  8 08               48909
#>  9 09               48486
#> 10 10               50991
#> 11 11               47493
#> 12 12               45213
#> 13 EXT                 78
#> 14 INF                297
#> 15 K                48084
#> 16 PK               26152
#> 17 PS                6494
#> 18 T9                5999

Racial Demographics by District

# Get racial demographics for top 5 districts
top_districts <- enr_2024 |>
  filter(is_district, subgroup == "total_enrollment", grade_level == "TOTAL") |>
  arrange(desc(n_students)) |>
  head(5) |>
  pull(district_id)

enr_2024 |>
  filter(is_district, district_id %in% top_districts, grade_level == "TOTAL") |>
  filter(subgroup %in% c("white", "black", "hispanic", "asian")) |>
  tidyr::pivot_wider(
    id_cols = district_name,
    names_from = subgroup,
    values_from = n_students
  )
#> # A tibble: 5 × 5
#>   district_name            white  black hispanic asian
#>   <chr>                    <dbl>  <dbl>    <dbl> <dbl>
#> 1 State of Louisiana      275265 282521    77836 10745
#> 2 Caddo Parish              8072  21002     1938   459
#> 3 East Baton Rouge Parish   4376  27194     6104  1465
#> 4 Jefferson Parish          9860  15559    18705  2240
#> 5 St. Tammany Parish       22037   7896     4175   483

Year-over-Year Comparison

enr_multi <- fetch_enr_multi(2019:2024, use_cache = TRUE)
#> Fetching 2019 ...
#> Using cached data for 2019
#> Fetching 2020 ...
#> Using cached data for 2020
#> Fetching 2021 ...
#> Using cached data for 2021
#> Fetching 2022 ...
#> Using cached data for 2022
#> Fetching 2023 ...
#> Using cached data for 2023
#> Fetching 2024 ...
#> Using cached data for 2024

state_trends <- enr_multi |>
  filter(is_state, subgroup == "total_enrollment", grade_level == "TOTAL") |>
  select(end_year, n_students) |>
  arrange(end_year) |>
  mutate(
    change = n_students - lag(n_students),
    pct_change = change / lag(n_students) * 100
  )

state_trends
#> # A tibble: 6 × 4
#>   end_year n_students change pct_change
#>      <int>      <dbl>  <dbl>      <dbl>
#> 1     2019     643986     NA     NA    
#> 2     2020     624527 -19459     -3.02 
#> 3     2021     615839  -8688     -1.39 
#> 4     2022     685606  69767     11.3  
#> 5     2023     681176  -4430     -0.646
#> 6     2024     676751  -4425     -0.650

Caching

The package caches downloaded data locally to speed up repeated access:

# View cache status
cache_status()

# Clear all cached data
clear_cache()

# Clear specific year
clear_cache(2024)

# Force fresh download (bypass cache)
enr_fresh <- fetch_enr(2024, use_cache = FALSE)

Data Structure Details

Parish Codes

Louisiana uses 3-digit parish codes for district IDs: - 001 = Acadia Parish - 017 = East Baton Rouge Parish - 026 = Jefferson Parish - 000 = State total (included in LEA data)

School Types

State: Statewide totals
District: Parish/LEA-level data
Campus: Individual school data

Subgroups Available

Demographics (counts): - total_enrollment, white, black, hispanic, asian - pacific_islander, native_american, multiracial, minority

Gender (calculated from percentages): - male, female

Special Populations (calculated from percentages): - lep (Limited English Proficiency) - fep (Fully English Proficient) - econ_disadv (Economically Disadvantaged)

Grade Levels

INF: Infants (Special Ed)
PS: Pre-School (Special Ed)
PK: Pre-K (Regular Ed)
K: Kindergarten
01-12: Grades 1-12
T9: Transitional 9th grade
EXT: Extension Academy

Data Processing Notes

Gender and Percentage Data

Gender, LEP, and Economic Disadvantage data are stored as percentages in the LDOE source files, not counts. The package converts these to counts using:

count = round(total_enrollment * percentage / 100)

Important: The source data format varies by year: - 2024+: Percentages stored as “48.8%” (with % sign) - 2019-2023: Percentages stored as decimals like 0.488

The package automatically detects and normalizes both formats to ensure consistent output across all years.

Validating Gender Data

To verify gender data is being processed correctly:

enr <- fetch_enr(2024, tidy = FALSE, use_cache = TRUE)
#> Using cached data for 2024
state <- enr[enr$type == "State", ]

# These should be approximately equal
state$row_total
#> [1] 676751
state$male + state$female
#> [1] 676751

# Percentages should be in 0-100 range
state$pct_male  # ~51%
#> [1] 51.2
state$pct_female  # ~49%
#> [1] 48.8

Troubleshooting

Common Issues

“end_year must be between 2019 and 2024” The package currently supports 2019-2024. Earlier years use different file formats.

Missing data for some districts Some smaller districts or charter schools may have suppressed data for privacy.

Male/female values seem off Gender data is stored as percentages in the source file. The package converts to counts using: count = round(total * pct / 100). Ensure pct_male and pct_female are in the 40-60% range (not 0.40-0.60).

Getting Help

# View function documentation
?fetch_enr
?get_available_years
?tidy_enr

Session Info

sessionInfo()
#> 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        laschooldata_0.1.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] jsonlite_2.0.0    compiler_4.5.2    tidyselect_1.2.1  stringr_1.6.0    
#>  [5] snakecase_0.11.1  tidyr_1.3.2       jquerylib_0.1.4   systemfonts_1.3.2
#>  [9] textshaping_1.0.5 yaml_2.3.12       fastmap_1.2.0     readxl_1.4.5     
#> [13] R6_2.6.1          generics_0.1.4    curl_7.0.0        knitr_1.51       
#> [17] htmlwidgets_1.6.4 tibble_3.3.1      janitor_2.2.1     desc_1.4.3       
#> [21] lubridate_1.9.5   bslib_0.10.0      pillar_1.11.1     rlang_1.1.7      
#> [25] utf8_1.2.6        stringi_1.8.7     cachem_1.1.0      xfun_0.56        
#> [29] fs_1.6.7          sass_0.4.10       otel_0.2.0        timechange_0.4.0 
#> [33] cli_3.6.5         withr_3.0.2       pkgdown_2.2.0     magrittr_2.0.4   
#> [37] digest_0.6.39     rappdirs_0.3.4    lifecycle_1.0.5   vctrs_0.7.1      
#> [41] evaluate_1.0.5    glue_1.8.0        cellranger_1.1.0  codetools_0.2-20 
#> [45] ragg_1.5.1        purrr_1.2.1       rmarkdown_2.30    httr_1.4.8       
#> [49] tools_4.5.2       pkgconfig_2.0.3   htmltools_0.5.9