Files
uscogdata/man/cog_peer_compare.Rd
jared 81fd1a5279 feat: refuse expenditure_concept = total in the cross-government verbs
Owner ruling R1. Combining Census Total across governments counts
intergovernmental transfers twice, and these results land in Tableau where a
warning would be invisible -- so this is a hard error whose message names the
fix and the reason.
2026-07-27 10:14:16 -04:00

54 lines
1.8 KiB
R

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/peers.R
\name{cog_peer_compare}
\alias{cog_peer_compare}
\title{Compare a target government against a peer set}
\usage{
cog_peer_compare(
target_govid,
peers,
category,
years,
per_capita = TRUE,
adjust_to_year = NULL,
expenditure_concept = c("direct", "total")
)
}
\arguments{
\item{target_govid}{Character scalar.}
\item{peers}{A tibble from [cog_find_peers()] or a character vector of
`canonical_govid`s.}
\item{category}{Character scalar or vector.}
\item{years}{Integer vector.}
\item{per_capita}{Default `TRUE` — peer compare usually normalizes by
population.}
\item{adjust_to_year}{Integer base year for CPI-U conversion or `NULL`.}
\item{expenditure_concept}{`"direct"` (default) or `"total"`. Currently only
`"direct"` is accepted; the `"total"` option exists in [cog_spending()] for
single-government queries but cannot be used here because combining Total
across peer sets counts intergovernmental transfers twice.}
}
\value{
Tibble matching [cog_spending()]'s columns, plus a `role`
column taking values `"target"`, `"peer"`, `"summary_p25"`,
`"summary_p50"`, or `"summary_p75"`, `target_rank` (target's rank
among target+peers at `max(years)`, NA for other rows), and
`cohort_year` (the year used to build the peer cohort, read from
`attr(peers, "cohort_year")`; `NA` when `peers` was a bare character
vector). Provenance reports `verb = "cog_peer_compare"`, `peer_count`,
`cohort_year`, and `cohort_govids`.
}
\description{
Pulls spending for the target plus a peer set (either a
[cog_find_peers()] result or a character vector of `canonical_govid`) and
appends peer-distribution summary rows (`summary_p25`, `summary_p50`,
`summary_p75`) so the result can be faceted by `role` in a single ggplot
call.
}