% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{acs_pums_wy}
\alias{acs_pums_wy}
\title{ACS PUMS 2022 1-Year: Wyoming Persons}
\format{
A data frame with 5,962 rows and 96 variables.
Columns \code{pwgtp1} through \code{pwgtp80} are the 80 successive difference
replicate weights for variance estimation; the remaining 16 variables are:
\itemize{
\item \code{puma}: Public Use Microdata Area code. Use as the cluster ID (PSU)
for variance estimation.
\item \code{st}: State FIPS code (all 56 = Wyoming).
\item \code{pwgtp}: Person weight. Represents the number of people in the
Wyoming population that this record represents.
\item \code{agep}: Age (0--99 years).
\item \code{sex}: Sex (1 = male, 2 = female).
\item \code{rac1p}: Recoded detailed race (1 = White alone, 2 = Black or African
American alone, 3 = American Indian alone, 6 = Asian alone,
9 = Two or more races).
\item \code{hisp}: Recoded Hispanic origin (01 = Not Spanish/Hispanic/Latino;
02--24 = specific Hispanic origin).
\item \code{schl}: Educational attainment (24 categories: 01 = no schooling,
16 = regular high school diploma, 21 = bachelor's degree,
24 = doctorate degree).
\item \code{esr}: Employment status recode (1 = civilian employed at work,
2 = civilian employed with job but not at work, 3 = unemployed,
4 = Armed Forces at work, 5 = Armed Forces not at work,
6 = Not in labor force).
\item \code{pincp}: Total person income in the past 12 months (dollars, signed;
negative values indicate a net loss). Multiply by \code{adjinc / 1e6} to
adjust to constant dollars.
\item \code{wagp}: Wages or salary income in the past 12 months (dollars).
\code{NA} if not applicable.
\item \code{hicov}: Health insurance coverage (1 = with health insurance,
2 = without health insurance).
\item \code{dis}: Disability recode (1 = with a disability, 2 = without a
disability).
\item \code{povpip}: Income-to-poverty ratio (0--501; 501 means 501\% or more).
\item \code{wkhp}: Usual hours worked per week in the past 12 months. \code{NA} if
not in the labor force.
\item \code{adjinc}: Adjustment factor for income and earnings. Divide by
1,000,000 and multiply income variables to convert to 2022 constant
dollars.
}
}
\source{
U.S. Census Bureau. 2022 ACS 1-Year PUMS.
\url{https://www.census.gov/programs-surveys/acs/microdata/access.html}
}
\usage{
acs_pums_wy
}
\description{
All person records from the 2022 American Community Survey (ACS) 1-Year
Public Use Microdata Sample (PUMS) for Wyoming (state FIPS 56). Wyoming is
the least-populous U.S. state, making this the smallest state-level PUMS
file — ideal for fast tests and examples.
}
\details{
\strong{Survey design:} Successive difference replication (SDR). Use
\code{as_survey_replicate()} with all 80 replicate weights:

\if{html}{\out{<div class="sourceCode r">}}\preformatted{svy <- as_survey_replicate(
  acs_pums_wy,
  weights    = pwgtp,
  repweights = pwgtp1:pwgtp80,
  type       = "successive-difference"
)
}\if{html}{\out{</div>}}

\strong{Income adjustment:} Income variables (\code{pincp}, \code{wagp}) are in survey-year
dollars. Multiply by \code{adjinc / 1e6} to convert to 2022 inflation-adjusted
dollars before comparing across ACS years.

\strong{Metadata:}
The ACS PUMS source is a plain CSV with no embedded labels. Columns in
\code{acs_pums_wy} carry no \code{"label"}, \code{"labels"}, or \code{"question_preface"}
attributes. Variable descriptions are documented here in \code{?acs_pums_wy} and
in \code{data-raw/README.md}. Use \code{set_var_label()} and
\code{set_val_labels()} to attach labels manually before analysis if needed.
}
\examples{
# Wyoming population represented
sum(acs_pums_wy$pwgtp)

# Age distribution
hist(acs_pums_wy$agep, main = "Age distribution, Wyoming 2022",
     xlab = "Age")

# Confirm 80 replicate weights are present
sum(grepl("^pwgtp[0-9]", names(acs_pums_wy)))
}
\keyword{datasets}
