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feat(peers): cog_find_peers uses per-year population

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Adds optional 'year' argument (defaults to most recent observed year for
the target). Filters and ranks candidates by gov_population_yearly.population
at that year. Returned column renamed population_acs -> population.
Cohort year attached as attr(x, 'cohort_year').

Adds .resolve_cohort_year() helper. Updates test assertions to use
'population' column name. Regenerates man/cog_find_peers.Rd.
This commit is contained in:
jared
2026-04-29 17:26:32 -04:00
parent 807ed35cb7
commit 54dd40a61d
3 changed files with 80 additions and 40 deletions
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R/peers.R
+66 -28
View File
@@ -2,31 +2,33 @@
#' Find peer governments by similarity criteria
#'
#' Selects peer governments from `canonical_fips_xwalk` by combinations of
#' government type, state, and population range. Peers are ordered by
#' `|log(pop_ratio)|` ascending (closest to the target's population first).
#' Selects peer governments by combinations of government type, state, and
#' population range at a chosen `year`. Peers are ordered by `|log(pop_ratio)|`
#' ascending (closest to the target's population first).
#'
#' @param target_govid Character scalar — `canonical_govid` of the target.
#' @param year Integer scalar. Cohort vintage. When `NULL` (default), uses the
#' most recent year for which the target has an observed population in
#' `gov_population_yearly`.
#' @param same_type If `TRUE` (default) restrict peers to the target's
#' `govs_type`.
#' @param same_state If `TRUE` restrict peers to the target's `fips_state`.
#' Default `FALSE`.
#' @param pop_range Length-2 numeric vector giving lower/upper bounds.
#' @param is_ratio If `TRUE` (default) `pop_range` is multiplied by the
#' target's `population_acs` to produce absolute bounds. If `FALSE`,
#' target's population at `year` to produce absolute bounds. If `FALSE`,
#' `pop_range` is interpreted as absolute population counts.
#' @param pop_year Reserved for future use (selecting ACS vintage). Currently
#' the corpus has a single snapshot so this argument has no effect.
#' @param max_peers Integer cap on the number of peers returned.
#' @return Tibble with columns `canonical_govid`, `gov_name`, `fips_state`,
#' `population_acs`, `pop_ratio`, `rank`.
#' `population`, `pop_ratio`, `rank`. The cohort year is attached as
#' `attr(x, "cohort_year")`.
#' @export
cog_find_peers <- function(target_govid,
year = NULL,
same_type = TRUE,
same_state = FALSE,
pop_range = c(0.7, 1.3),
is_ratio = TRUE,
pop_year = NULL,
max_peers = 10L) {
if (!is.character(target_govid) || length(target_govid) != 1L) {
cli::cli_abort("`target_govid` must be a length-1 character string.")
@@ -35,58 +37,94 @@ cog_find_peers <- function(target_govid,
pop_range[1] >= pop_range[2]) {
cli::cli_abort("`pop_range` must be a length-2 numeric with lo < hi.")
}
if (!is.null(year) &&
(!(is.numeric(year) || is.integer(year)) || length(year) != 1L)) {
cli::cli_abort("`year` must be NULL or a length-1 integer.")
}
con <- .ensure_session()
target_sql <- sprintf(
"SELECT canonical_govid, gov_name, govs_type, fips_state, population_acs
# Confirm target exists in the xwalk and pull govs_type / fips_state.
meta_sql <- sprintf(
"SELECT canonical_govid, gov_name, govs_type, fips_state
FROM canonical_fips_xwalk
WHERE canonical_govid = %s",
.sql_lit_chr(target_govid)
)
target <- DBI::dbGetQuery(con, target_sql)
if (nrow(target) == 0L) {
meta <- DBI::dbGetQuery(con, meta_sql)
if (nrow(meta) == 0L) {
cli::cli_abort(c(
"govid {target_govid} not found in corpus.",
i = "v0.1 covers types 0-3 only (state/county/city/township); see vignette('coverage-scope')."
))
}
if (is.na(target$population_acs) || target$population_acs <= 0) {
cli::cli_abort("Target {target_govid} has missing or non-positive population; cannot build pop_ratio band.")
cohort_year <- .resolve_cohort_year(con, target_govid, year)
pop_sql <- sprintf(
"SELECT population FROM gov_population_yearly
WHERE canonical_govid = %s AND year = %d",
.sql_lit_chr(target_govid), as.integer(cohort_year)
)
target_pop <- DBI::dbGetQuery(con, pop_sql)$population
if (length(target_pop) == 0L || is.na(target_pop) || target_pop <= 0) {
cli::cli_abort(c(
"Target {target_govid} has no observed population in {cohort_year}.",
i = "Use a year for which population is observed; see gov_population_yearly."
))
}
if (isTRUE(is_ratio)) {
lo <- target$population_acs * pop_range[1]
hi <- target$population_acs * pop_range[2]
lo <- target_pop * pop_range[1]
hi <- target_pop * pop_range[2]
} else {
lo <- pop_range[1]; hi <- pop_range[2]
}
preds <- c(
sprintf("canonical_govid != %s", .sql_lit_chr(target_govid)),
sprintf("population_acs BETWEEN %.6f AND %.6f", lo, hi)
sprintf("p.canonical_govid != %s", .sql_lit_chr(target_govid)),
sprintf("p.year = %d", as.integer(cohort_year)),
sprintf("p.population BETWEEN %.6f AND %.6f", lo, hi)
)
if (isTRUE(same_type)) preds <- c(preds, sprintf("govs_type = %d", target$govs_type))
if (isTRUE(same_state)) preds <- c(preds, sprintf("fips_state = %s", .sql_lit_chr(target$fips_state)))
if (isTRUE(same_type)) preds <- c(preds, sprintf("x.govs_type = %d", meta$govs_type))
if (isTRUE(same_state)) preds <- c(preds, sprintf("x.fips_state = %s", .sql_lit_chr(meta$fips_state)))
peers_sql <- sprintf(
"SELECT canonical_govid, gov_name, fips_state, population_acs,
population_acs / %.6f AS pop_ratio
FROM canonical_fips_xwalk
"SELECT p.canonical_govid, x.gov_name, x.fips_state, p.population,
p.population / %.6f AS pop_ratio
FROM gov_population_yearly p
JOIN canonical_fips_xwalk x USING (canonical_govid)
WHERE %s
ORDER BY ABS(LN(CAST(population_acs AS DOUBLE) / %.6f))
ORDER BY ABS(LN(CAST(p.population AS DOUBLE) / %.6f))
LIMIT %d",
target$population_acs,
target_pop,
paste(preds, collapse = " AND "),
target$population_acs,
target_pop,
as.integer(max_peers)
)
peers <- tibble::as_tibble(DBI::dbGetQuery(con, peers_sql))
if (nrow(peers) > 0L) peers$rank <- seq_len(nrow(peers))
else peers$rank <- integer(0)
peers$rank <- if (nrow(peers) > 0L) seq_len(nrow(peers)) else integer(0)
attr(peers, "cohort_year") <- as.integer(cohort_year)
peers
}
#' @noRd
.resolve_cohort_year <- function(con, target_govid, year) {
if (!is.null(year)) return(as.integer(year))
sql <- sprintf(
"SELECT MAX(year) AS y FROM gov_population_yearly
WHERE canonical_govid = %s",
.sql_lit_chr(target_govid)
)
y <- DBI::dbGetQuery(con, sql)$y
if (length(y) == 0L || is.na(y)) {
cli::cli_abort(
"Target {target_govid} has no observed population in any year."
)
}
as.integer(y)
}
#' Compare a target government against a peer set
#'
#' Pulls spending for the target plus a peer set (either a
man/cog_find_peers.Rd
+11 -9
View File
@@ -6,17 +6,21 @@
\usage{
cog_find_peers(
target_govid,
year = NULL,
same_type = TRUE,
same_state = FALSE,
pop_range = c(0.7, 1.3),
is_ratio = TRUE,
pop_year = NULL,
max_peers = 10L
)
}
\arguments{
\item{target_govid}{Character scalar — `canonical_govid` of the target.}
\item{year}{Integer scalar. Cohort vintage. When `NULL` (default), uses the
most recent year for which the target has an observed population in
`gov_population_yearly`.}
\item{same_type}{If `TRUE` (default) restrict peers to the target's
`govs_type`.}
@@ -26,20 +30,18 @@ Default `FALSE`.}
\item{pop_range}{Length-2 numeric vector giving lower/upper bounds.}
\item{is_ratio}{If `TRUE` (default) `pop_range` is multiplied by the
target's `population_acs` to produce absolute bounds. If `FALSE`,
target's population at `year` to produce absolute bounds. If `FALSE`,
`pop_range` is interpreted as absolute population counts.}
\item{pop_year}{Reserved for future use (selecting ACS vintage). Currently
the corpus has a single snapshot so this argument has no effect.}
\item{max_peers}{Integer cap on the number of peers returned.}
}
\value{
Tibble with columns `canonical_govid`, `gov_name`, `fips_state`,
`population_acs`, `pop_ratio`, `rank`.
`population`, `pop_ratio`, `rank`. The cohort year is attached as
`attr(x, "cohort_year")`.
}
\description{
Selects peer governments from `canonical_fips_xwalk` by combinations of
government type, state, and population range. Peers are ordered by
`|log(pop_ratio)|` ascending (closest to the target's population first).
Selects peer governments by combinations of government type, state, and
population range at a chosen `year`. Peers are ordered by `|log(pop_ratio)|`
ascending (closest to the target's population first).
}
tests/testthat/test-peers.R
+3 -3
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@@ -3,7 +3,7 @@ test_that("cog_find_peers returns same-type peers in the default pop band", {
peers <- cog_find_peers("101006006") # Broward County
expect_s3_class(peers, "tbl_df")
expected_cols <- c("canonical_govid", "gov_name", "fips_state",
"population_acs", "pop_ratio", "rank")
"population", "pop_ratio", "rank")
expect_true(all(expected_cols %in% names(peers)))
expect_true(all(peers$pop_ratio >= 0.7 & peers$pop_ratio <= 1.3))
expect_false("101006006" %in% peers$canonical_govid)
@@ -22,8 +22,8 @@ test_that("cog_find_peers absolute pop range works", {
peers <- cog_find_peers("101006006",
pop_range = c(1.5e6, 2.5e6),
is_ratio = FALSE, max_peers = 20L)
expect_true(all(peers$population_acs >= 1.5e6 &
peers$population_acs <= 2.5e6))
expect_true(all(peers$population >= 1.5e6 &
peers$population <= 2.5e6))
})
test_that("cog_find_peers errors cleanly on unknown govid", {