Estimate Totals and Proportions for All Combinations of Grouping Variables (OGD Format) in Structural Survey

se_total_prop_ogd estimates totals and proportions for each combination of grouping variables using se_total_prop, returning results in a format compatible with Open Government Data (OGD) standards. along with stratification and weighting.

Usage

se_total_prop_ogd(data, ..., strata, weight, alpha = 0.05)

Arguments

data

A data frame or tibble containing the survey data.

...

Grouping variables (unquoted or programmatic) to compute combinations of totals and proportions.

strata

Stratification variable (unquoted or programmatic). Defaults to "zone" if omitted.

weight

Sampling weight variable (unquoted or programmatic).

alpha

Significance level for confidence intervals. Default is 0.05 (for 95% CI).

Value

A tibble with totals and proportions for all combinations of the specified grouping variables. The output includes confidence intervals and handles missing values by representing them as "Total".

See also

se_total_prop, se_ogd_wrapper, se_total_ogd, se_prop_ogd

Examples

# With unquoted variables
se_total_prop_ogd(nhanes, gender, birth_country, strata = strata, weight = weights)
#> # A tibble: 11 × 11
#>    gender birth_country   occ      total ci_total ci_l_total ci_u_total     prop
#>    <fct>  <fct>         <int>      <dbl>    <dbl>      <dbl>      <dbl>    <dbl>
#>  1 Total  Total          9971 316481044. 6370681. 310110363. 322851725.  1   e+0
#>  2 Female Total          5079 161922446. 5479833. 156442613. 167402279.  5.12e-1
#>  3 Male   Total          4892 154558598. 5507576. 149051022. 160066174.  4.88e-1
#>  4 Total  Missing           2     29693.   42060.    -12367.     71753.  9.38e-5
#>  5 Total  Other          2236  47811833. 2236143.  45575690.  50047977.  1.51e-1
#>  6 Total  US             7733 268639517. 6668850. 261970667. 275308367.  8.49e-1
#>  7 Female Missing           2     29693.   42060.    -12367.     71753.  9.38e-5
#>  8 Female Other          1168  23914531. 1584444.  22330087.  25498975.  7.56e-2
#>  9 Female US             3909 137978222. 5460390. 132517832. 143438611.  4.36e-1
#> 10 Male   Other          1068  23897302. 1743278.  22154024.  25640580.  7.55e-2
#> 11 Male   US             3824 130661296. 5426191. 125235105. 136087486.  4.13e-1
#> # ℹ 3 more variables: ci_prop <dbl>, ci_l_prop <dbl>, ci_u_prop <dbl>

# Programmatic usage
vars <- c("gender", "birth_country")
wt <- "weights"
se_total_prop_ogd(nhanes, !!!rlang::syms(vars), strata = strata, weight = !!rlang::sym(wt))
#> # A tibble: 11 × 11
#>    gender birth_country   occ      total ci_total ci_l_total ci_u_total     prop
#>    <fct>  <fct>         <int>      <dbl>    <dbl>      <dbl>      <dbl>    <dbl>
#>  1 Total  Total          9971 316481044. 6370681. 310110363. 322851725.  1   e+0
#>  2 Female Total          5079 161922446. 5479833. 156442613. 167402279.  5.12e-1
#>  3 Male   Total          4892 154558598. 5507576. 149051022. 160066174.  4.88e-1
#>  4 Total  Missing           2     29693.   42060.    -12367.     71753.  9.38e-5
#>  5 Total  Other          2236  47811833. 2236143.  45575690.  50047977.  1.51e-1
#>  6 Total  US             7733 268639517. 6668850. 261970667. 275308367.  8.49e-1
#>  7 Female Missing           2     29693.   42060.    -12367.     71753.  9.38e-5
#>  8 Female Other          1168  23914531. 1584444.  22330087.  25498975.  7.56e-2
#>  9 Female US             3909 137978222. 5460390. 132517832. 143438611.  4.36e-1
#> 10 Male   Other          1068  23897302. 1743278.  22154024.  25640580.  7.55e-2
#> 11 Male   US             3824 130661296. 5426191. 125235105. 136087486.  4.13e-1
#> # ℹ 3 more variables: ci_prop <dbl>, ci_l_prop <dbl>, ci_u_prop <dbl>