uscogdata/man/cog_peer_compare.Rd

% 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("primary", "direct", "total"),
  coverage = c("all", "census", "consistent")
)
}
\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}{`"primary"` (default), `"direct"`, or
`"total"` -- see [cog_spending()] for the three concepts. `"total"` is
refused here because combining Total across peer sets counts
intergovernmental transfers twice; `"primary"` and `"direct"` combine
safely.}

\item{coverage}{How to handle the Census of Governments survey cycle,
  which is a **complete census only in years ending in 2 and 7** -- every
  other year is a sample, and the sample varies enormously (on the bundled
  fixture, Wisconsin's 608-city universe reports 597 governments in FY2012
  and 112 in FY2019).

  * `"all"` (default) -- every unit that reported that year. Unchanged
    behaviour, so existing code keeps working.
  * `"census"` -- census years only. Aborts if the requested range holds
    none, rather than silently returning nothing.
  * `"consistent"` -- only units reporting in *every* requested year, giving
    a balanced panel.

  Regardless of mode, `provenance$coverage` always carries per-year
  `n_units_reporting`, `n_units_expected` and `is_census_year`, and
  `provenance$coverage_mode` records the mode. `is_census_year` is a
  statement about the **survey calendar**, never a claim of completeness:
  FY1967 is a census year in which only 97 of Wisconsin's 608 cities
  report. `n_units_reporting` is the number that tells the truth.

  The comparison target is exempt from `"consistent"` balancing -- it is the
  subject of the comparison, not a member of the cohort -- and the
  `summary_*` quantiles are computed AFTER the filter, so they describe the
  cohort actually returned. `n_units_reporting` counts peers only, against
  the cohort size: "3 of your 15 peers reported in FY2019".}
}
\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`.

  **The `summary_*` rows are per-category quantiles: they are not additive.**
  Each one is computed **within each `(year, spend_subtype,
  category)` cell** across the peer set, so a `summary_p50` row is *the
  median peer's value in that one category*, not *the value of the median
  peer's total*. The median peer for Police and the median peer for Fire
  are usually different governments, so summing `summary_*` rows across
  categories does not give any peer's total and misstates the band it
  appears to describe — measured at −32.7% to +251.0% across 24 years on
  one cohort, with a sign flip at FY2012.

  Facet by `role` **and** `category` (the documented use, and what the
  rows are built for). For a genuine "median peer's total spending" line,
  sum each peer's own categories first and take the quantile of those
  per-government totals:

  ```r
  library(dplyr)
  cmp |>
    filter(role %in% c("target", "peer")) |>
    group_by(year, role, canonical_govid) |>
    summarise(total = sum(amt_per_capita_real, na.rm = TRUE), .groups = "drop") |>
    filter(role == "peer") |>
    group_by(year) |>
    summarise(p50 = quantile(total, 0.5, na.rm = TRUE))
  ```
}
\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. Those summary rows are quantiles **within each category**, not
quantiles of each peer's total — see the `@return` section before summing
them.
}
\section{Reading `coverage`}{

`provenance$coverage` reports `n_units_reporting` against
`n_units_expected` per year. **`n_units_reporting` is category-conditional:
it counts cohort members with rows for the category you asked for, not
cohort members collected that year.** A government that was surveyed and
genuinely spends nothing in that category is indistinguishable here from one
that was never surveyed.

The ratio is therefore **not a response rate** and must not be used as one.
In FY2022 — a complete census year — Georgia reports 393 of 567 cities for
`category = "Police"`; the 174-city gap is overwhelmingly cities that
contract policing to the county sheriff, not non-response.

The comparison that *is* valid is the same category across a census year
(ending in 2 or 7) and a sample year, where the real-zero component is
roughly constant and the difference reflects the survey cycle. `is_census_year`
marks which is which.
}