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.
acs_pums_wyA data frame with 5,962 rows and 96 variables.
Columns pwgtp1 through pwgtp80 are the 80 successive difference
replicate weights for variance estimation; the remaining 16 variables are:
puma: Public Use Microdata Area code. Use as the cluster ID (PSU)
for variance estimation.
st: State FIPS code (all 56 = Wyoming).
pwgtp: Person weight. Represents the number of people in the
Wyoming population that this record represents.
agep: Age (0--99 years).
sex: Sex (1 = male, 2 = female).
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).
hisp: Recoded Hispanic origin (01 = Not Spanish/Hispanic/Latino;
02--24 = specific Hispanic origin).
schl: Educational attainment (24 categories: 01 = no schooling,
16 = regular high school diploma, 21 = bachelor's degree,
24 = doctorate degree).
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).
pincp: Total person income in the past 12 months (dollars, signed;
negative values indicate a net loss). Multiply by adjinc / 1e6 to
adjust to constant dollars.
wagp: Wages or salary income in the past 12 months (dollars).
NA if not applicable.
hicov: Health insurance coverage (1 = with health insurance,
2 = without health insurance).
dis: Disability recode (1 = with a disability, 2 = without a
disability).
povpip: Income-to-poverty ratio (0--501; 501 means 501% or more).
wkhp: Usual hours worked per week in the past 12 months. NA if
not in the labor force.
adjinc: Adjustment factor for income and earnings. Divide by
1,000,000 and multiply income variables to convert to 2022 constant
dollars.
Survey design: Successive difference replication (SDR). Use
as_survey_replicate() with all 80 replicate weights:
svy <- as_survey_replicate(
acs_pums_wy,
weights = pwgtp,
repweights = pwgtp1:pwgtp80,
type = "successive-difference"
)
Income adjustment: Income variables (pincp, wagp) are in survey-year
dollars. Multiply by adjinc / 1e6 to convert to 2022 inflation-adjusted
dollars before comparing across ACS years.
Metadata:
The ACS PUMS source is a plain CSV with no embedded labels. Columns in
acs_pums_wy carry no "label", "labels", or "question_preface"
attributes. Variable descriptions are documented here in ?acs_pums_wy and
in data-raw/README.md. Use set_var_label() and
set_val_labels() to attach labels manually before analysis if needed.
# 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)))
Run the code above in your browser using DataLab