jared
cc4d21ec82
cog_find_peers selects peers from canonical_fips_xwalk by same-type,
same-state, and population-range (ratio or absolute) criteria,
ordered by |log(pop_ratio)| ascending.
cog_peer_compare accepts the find_peers result (or a plain character
vector of govids), pulls spending for target + peers via cog_spending,
and appends summary rows (summary_p25/p50/p75) so the whole result
can be faceted by `role` in a single ggplot call. Summary rows honor
per_capita + adjust_to_year by picking the right value column.
target_rank reports the target's rank among target+peers at max(years).
Provenance is rewritten with verb = cog_peer_compare and peer_count.
Also: globalVariables('.data') in zzz.R to silence R CMD check on
tidy-eval pronouns.
Tests: 21 new / 120 total pass. devtools::check() 0E/0W/2N.
45 lines
1.3 KiB
R
45 lines
1.3 KiB
R
% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/peers.R
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\name{cog_peer_compare}
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\alias{cog_peer_compare}
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\title{Compare a target government against a peer set}
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\usage{
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cog_peer_compare(
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target_govid,
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peers,
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category,
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years,
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per_capita = TRUE,
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adjust_to_year = NULL
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)
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}
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\arguments{
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\item{target_govid}{Character scalar.}
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\item{peers}{A tibble from [cog_find_peers()] or a character vector of
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`canonical_govid`s.}
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\item{category}{Character scalar or vector.}
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\item{years}{Integer vector.}
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\item{per_capita}{Default `TRUE` — peer compare usually normalizes by
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population.}
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\item{adjust_to_year}{Integer base year for CPI-U conversion or `NULL`.}
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}
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\value{
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Tibble matching [cog_spending()]'s columns, plus a `role`
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column taking values `"target"`, `"peer"`, `"summary_p25"`,
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`"summary_p50"`, or `"summary_p75"`, and `target_rank` (target's rank
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among target+peers at `max(years)`, NA for other rows). Provenance
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attribute reports `verb = "cog_peer_compare"` and `peer_count`.
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}
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\description{
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Pulls spending for the target plus a peer set (either a
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[cog_find_peers()] result or a character vector of `canonical_govid`) and
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appends peer-distribution summary rows (`summary_p25`, `summary_p50`,
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`summary_p75`) so the result can be faceted by `role` in a single ggplot
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call.
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}
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