This function downloads tidy education finance data using data from the NCES F-33 Survey, Census Bureau Small Area Income Poverty Estimates (SAIPE), and community data from the ACS 5-Year Estimates.
Usage
get_finance_data(
yr = "2022",
geo = "all",
dataset_type = "skinny",
cpi_adj = "none",
refresh = FALSE,
quiet = FALSE
)Arguments
- yr
A string specifying the year(s) to retrieve. Can be a single year ("2022"), a range ("2020:2022"), or "all" for all available years.
- geo
A string specifying the geographic scope. Can be "all" for all states, a single state code ("KY"), or a comma-separated list of state codes ("IN,KY,OH,TN"). Default is FALSE, which uses cached data if available.
- dataset_type
A string specifying whether to download the "skinny" (default) or "full" dataset. The skinny version excludes detailed expenditure data for faster downloads.
- cpi_adj
A string specifying the CPI adjustment baseline year. Can be "none" (default) for no adjustment, or a year between 2012-2022 to use as the baseline year. When a year is specified (e.g., "2022"), revenue, expenditure, and economic variables are adjusted to that school year's dollars using CPI averaged over the months of the school year (e.g., "2022" uses the 2021-22 school year CPI). When cpi_adj is set to a value other than "none", a new column "cpi_adj_index" will be added to the output showing the adjustment index used for each row.
- refresh
A logical value indicating whether to force a refresh of the cached data.
- quiet
A logical value indicating whether to suppress download progress messages. Default is FALSE.
Examples
if (FALSE) { # \dontrun{
# get data for all states for 2022
df <- get_finance_data(yr = "2022", geo = "all")
# get data for Kentucky for 2020-2022
ky_data <- get_finance_data(yr = "2020:2022", geo = "KY")
# get data for multiple states for all available years
regional_data <- get_finance_data(yr = "all", geo = "IN,KY,OH,TN")
# get full dataset with detailed expenditure data
full_data <- get_finance_data(yr = "2022", geo = "KY", dataset_type = "full")
# use with pipe
library(dplyr)
get_finance_data(yr = "2022", geo = "KY") |>
select(district_name, rev_total, exp_curr_total) |>
arrange(desc(rev_total))
# get data adjusted to 2015 dollars
adjusted_data <- get_finance_data(yr = "2020:2022", geo = "KY", cpi_adj = "2015")
} # }