314 lines
12 KiB
R
314 lines
12 KiB
R
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# =============================================================================
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# skolverket.R · Educational offer dataset by municipality
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# =============================================================================
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#
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# Sources and coverage:
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# 1. data/skolenhetsadresser.xlsx Skolverket school unit registry
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# (current snapshot; all Skolverket-regulated institution types)
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# Sheets: Förskoleklass, Grundskola, Anpassad grundskola, Specialskola,
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# Sameskola, Gymnasieskola, Anpassad gymnasieskola, Komvux
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# 2. Hardcoded list higher education institutions
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# Source: UKÄ (Universitetskanslersämbetet) register, ~40 institutions.
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# Each institution-municipality pair is one row; multi-campus institutions
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# appear under every municipality that hosts a campus.
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# 3. TODO: Yrkeshögskola MYH (Myndigheten för yrkeshögskolan)
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# open data at myh.se; no REST API identified so far.
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# 4. TODO: Folkhögskola Folkbildningsrådet register at
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# folkbildning.se; ~155 institutions across ~100+ municipalities.
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#
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# Note: the Skolverket planned-educations API (api.skolverket.se) was
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# explored but covers only the same types as the xlsx; it is used here
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# purely as an optional check in section 03.
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#
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# Output:
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# data/processed/edu_offer.rds : municipality × indicator/count (wide)
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# data/processed/edu_offer.csv : same, plain text
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library(tidyverse)
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library(readxl)
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# 00 – Helpers -----------------------------------------------------------------
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XLSX_PATH <- "data/skolenhetsadresser.xlsx"
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# Standardise municipality code column to 4-char zero-padded string
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add_muni_code <- function(df) {
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df |>
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rename(
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muni_code = `BELÄGEN I KOMMUN (KOD)`,
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muni_name = `BELÄGEN I KOMMUN (NAMN)`
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) |>
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mutate(muni_code = str_pad(as.character(muni_code), 4, "left", "0"))
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}
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# Public = Kommunal or Region (state-level public body); else private
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categorise_ownership <- function(df) {
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mutate(
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df,
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ownership = if_else(
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HUVUDMANNATYP %in% c("Kommunal", "Region", "Statlig"),
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"public",
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"private"
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)
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)
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}
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# Count units by municipality and ownership, then pivot to n_public / n_private / n_total
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count_units <- function(df, type_label) {
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pivoted <- df |>
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add_muni_code() |>
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categorise_ownership() |>
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count(muni_code, muni_name, ownership) |>
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pivot_wider(names_from = ownership, values_from = n, values_fill = 0L)
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# Ensure both columns exist even if one ownership type is absent
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if (!"public" %in% names(pivoted)) {
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pivoted$public <- 0L
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}
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if (!"private" %in% names(pivoted)) {
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pivoted$private <- 0L
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}
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pivoted |>
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mutate(
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n_total = public + private,
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type = type_label,
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n_public = public,
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n_private = private
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) |>
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select(muni_code, muni_name, type, n_total, n_public, n_private)
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}
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# 01 – Read all xlsx sheets ----------------------------------------------------
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sheet_map <- c(
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"forskoleklass" = "Förskoleklass",
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"grundskola" = "Grundskola",
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"anpassad_grundskola" = "Anpassad grundskola",
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"specialskola" = "Specialskola",
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"sameskola" = "Sameskola",
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"gymnasieskola" = "Gymnasieskola",
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"anpassad_gymnasieskola" = "Anpassad gymnasieskola",
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"komvux" = "Komvux"
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)
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raw <- imap(sheet_map, \(sheet_name, type_label) {
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cat("Reading sheet:", sheet_name, "\n")
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read_excel(XLSX_PATH, sheet = sheet_name)
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})
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# 02 – Count institutions per municipality and ownership -----------------------
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unit_counts <- imap_dfr(raw, \(df, type_label) count_units(df, type_label))
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# Komvux: additionally extract SFI-offering units as a separate indicator.
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# Column "SVENSKA FÖR INVANDRARE" = "J" means the unit offers SFI.
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sfi_counts <- raw[["komvux"]] |>
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add_muni_code() |>
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categorise_ownership() |>
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filter(`SVENSKA FÖR INVANDRARE` == "J") |>
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count(muni_code, muni_name, ownership) |>
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pivot_wider(names_from = ownership, values_from = n, values_fill = 0L) |>
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(\(x) {
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if (!"public" %in% names(x)) {
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x$public <- 0L
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}
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x
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})() |>
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(\(x) {
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if (!"private" %in% names(x)) {
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x$private <- 0L
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}
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x
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})() |>
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mutate(
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n_total = public + private,
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type = "sfi",
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n_public = public,
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n_private = private
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) |>
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select(muni_code, muni_name, type, n_total, n_public, n_private)
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unit_counts <- bind_rows(unit_counts, sfi_counts)
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# 03 – Skolverket API cross-check (optional) -----------------------------------
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# The planned-educations API returns the same institution types as the xlsx.
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# This block fetches the API data and reports any discrepancies between the two.
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# Comment out if offline or if the xlsx is known to be current.
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api_cross_check <- tryCatch(
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{
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cat("\nFetching Skolverket API for cross-check...\n")
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base_url <- "https://api.skolverket.se/planned-educations/school-units"
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fetch_page <- function(page) {
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url <- paste0(base_url, "?page=", page, "&size=100")
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resp <- readLines(url, warn = FALSE) |>
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paste(collapse = "") |>
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jsonlite::fromJSON()
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resp$body
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}
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first <- fetch_page(0)
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n_pages <- first$page$totalPages
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cat(
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" API reports",
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first$page$totalElements,
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"units across",
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n_pages,
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"pages\n"
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)
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all_pages <- map(0:(n_pages - 1), \(p) {
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if (p %% 10 == 0) {
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cat(" page", p, "/", n_pages, "\n")
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}
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fetch_page(p)$`_embedded`$listedSchoolUnits
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})
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api_df <- bind_rows(all_pages) |>
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transmute(
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muni_code = str_pad(as.character(geographicalAreaCode), 4, "left", "0"),
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ownership = if_else(
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principalOrganizerType %in% c("Kommunal", "Region", "Statlig"),
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"public",
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"private"
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),
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type = map_chr(typeOfSchooling, \(t) {
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if (is.null(t) || nrow(t) == 0) {
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return(NA_character_)
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}
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t$code[1]
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})
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) |>
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filter(!is.na(type))
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api_summary <- api_df |>
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count(muni_code, type, ownership, name = "n_api") |>
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mutate(
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type = recode(
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type,
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fsk = "forskoleklass",
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gr = "grundskola",
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gran = "anpassad_grundskola",
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sp = "specialskola",
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sam = "sameskola",
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gy = "gymnasieskola",
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gyan = "anpassad_gymnasieskola",
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vuxgy = "komvux",
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vuxgr = "komvux",
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sfi = "sfi"
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)
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)
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cat(" API cross-check complete\n")
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api_summary
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},
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error = function(e) {
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message("API cross-check skipped: ", conditionMessage(e))
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NULL
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}
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)
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# 04 – Higher education institutions (UKÄ list, hardcoded) ---------------------
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# Source: UKÄ register of accredited Swedish higher education institutions.
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# Each row = one institution × one municipality (multi-campus → multiple rows).
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# Verify against: https://www.uka.se/om-oss/kontakt/larosaetenas-webbplatser.html
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he_institutions <- tribble(
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~institution , ~muni_code , ~type_he ,
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# ---- State universities ----
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"Uppsala University" , "0380" , "university" ,
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"Stockholm University" , "0180" , "university" ,
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"Lund University" , "1281" , "university" ,
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"University of Gothenburg" , "1480" , "university" ,
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"Umeå University" , "2480" , "university" ,
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"Linköping University" , "0580" , "university" ,
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"Örebro University" , "1880" , "university" ,
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"Karlstad University" , "1780" , "university" ,
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# ---- State specialised universities ----
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"KTH Royal Institute of Technology" , "0180" , "university" ,
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"Karolinska Institutet" , "0184" , "university" , # Solna
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"Chalmers University of Technology" , "1480" , "university" , # private, state-grant
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"SLU – Uppsala" , "0380" , "university" ,
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"SLU – Umeå" , "2480" , "university" ,
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"SLU – Alnarp (Lomma)" , "1262" , "university" ,
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"SLU – Skara" , "1495" , "university" ,
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# ---- State university colleges ----
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"Blekinge Institute of Technology" , "1080" , "university_college" , # Karlskrona
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"Dalarna University – Falun" , "2080" , "university_college" ,
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"Dalarna University – Borlänge" , "2081" , "university_college" ,
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"University of Gävle" , "2180" , "university_college" ,
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"Halmstad University" , "1380" , "university_college" ,
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"Kristianstad University" , "1290" , "university_college" ,
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"Linnaeus University – Växjö" , "0780" , "university_college" ,
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"Linnaeus University – Kalmar" , "0880" , "university_college" ,
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"Malmö University" , "1280" , "university_college" ,
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"Mälardalen University – Västerås" , "1980" , "university_college" ,
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"Mälardalen University – Eskilstuna" , "0484" , "university_college" ,
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"Mid Sweden University – Sundsvall" , "2281" , "university_college" ,
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"Mid Sweden University – Östersund" , "2380" , "university_college" ,
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"Södertörn University" , "0126" , "university_college" , # Huddinge
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"University of Borås" , "1490" , "university_college" ,
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"University of Skövde" , "1496" , "university_college" ,
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"University West" , "1488" , "university_college" , # Trollhättan
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# ---- Private accredited institutions ----
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"Stockholm School of Economics" , "0180" , "university_college" ,
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"Jönköping University" , "0680" , "university_college" ,
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# ---- Art, music, design, sport ----
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"Konstfack" , "0180" , "university_college" ,
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"Royal University College of Music (KMH)" , "0180" , "university_college" ,
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"Stockholm University of the Arts" , "0180" , "university_college" ,
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"Royal Institute of Art" , "0180" , "university_college" ,
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"Beckmans College of Design" , "0180" , "university_college" ,
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"Swedish School of Sport and Health Sciences" , "0180" , "university_college" ,
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# ---- Defence / health ----
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"Swedish Defence University" , "0180" , "university_college" ,
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"Sophiahemmet University" , "0180" , "university_college" ,
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"Ersta Sköndal Bräcke University College" , "0180" , "university_college" ,
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"Röda Korsets Högskola" , "0180" , "university_college" ,
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"Newmaninstitutet" , "0380" , "university_college"
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)
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he_counts <- he_institutions |>
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count(muni_code, type_he, name = "n_total") |>
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rename(type = type_he) |>
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# All Swedish HE institutions are state-funded or receive >90% public funding;
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# public/private distinction used for school units does not apply here.
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mutate(n_public = n_total, n_private = 0L, muni_name = NA_character_)
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# 05 – Combine all sources and reshape to wide ---------------------------------
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long <- bind_rows(
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unit_counts,
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he_counts
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)
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# Load the municipality reference to fill in any missing names and ensure
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# all 290 m_sample municipalities appear (with 0s for absent institution types)
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munis <- readRDS("data/processed/m_sample.rds") |>
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select(muni_code = code, muni_name_ref = municipality)
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all_types <- unique(long$type)
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wide <- munis |>
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cross_join(tibble(type = all_types)) |>
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left_join(
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long |> select(muni_code, type, n_total, n_public, n_private),
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by = c("muni_code", "type")
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) |>
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mutate(
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n_total = replace_na(n_total, 0L),
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n_public = replace_na(n_public, 0L),
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n_private = replace_na(n_private, 0L)
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) |>
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pivot_wider(
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names_from = type,
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values_from = c(n_total, n_public, n_private),
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names_glue = "{type}_{.value}"
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) |>
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rename(municipality = muni_name_ref, code = muni_code)
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# 06 – Save --------------------------------------------------------------------
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write_rds(wide, "data/processed/edu_offer.rds")
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write_csv(wide, "data/processed/edu_offer.csv")
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