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

se_prop_ogd estimates survey proportions for every combination of the supplied grouping variables, using se_prop internally and returning results in a format suitable for Open Government Data (OGD). The output includes proportions for each combination of grouping variables, as well as for the overall population.

Usage

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

Arguments

data

A data frame or tibble.

...

Grouping variables (unquoted or programmatic).

strata

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

weight

Sampling weights variable (unquoted or programmatic).

alpha

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

Value

A tibble with survey proportion estimates for all combinations of grouping variables. Grouping variables are converted to factors with "Total" representing the overall group.

See also

se_total_ogd, se_mean_ogd, se_ogd_wrapper, se_prop

Examples

# Unquoted variables
se_prop_ogd(nhanes, strata = strata, weight = weights, gender, birth_country)
#> # A tibble: 11 × 7
#>    gender birth_country   occ      prop       ci       ci_l     ci_u
#>    <fct>  <fct>         <int>     <dbl>    <dbl>      <dbl>    <dbl>
#>  1 Total  Total          9971 1         0         1         1       
#>  2 Female Total          5079 0.512     0.0141    0.498     0.526   
#>  3 Male   Total          4892 0.488     0.0141    0.474     0.502   
#>  4 Total  Missing           2 0.0000938 0.000133 -0.0000390 0.000227
#>  5 Total  Other          2236 0.151     0.00779   0.143     0.159   
#>  6 Total  US             7733 0.849     0.00779   0.841     0.857   
#>  7 Female Missing           2 0.0000938 0.000133 -0.0000390 0.000227
#>  8 Female Other          1168 0.0756    0.00530   0.0703    0.0809  
#>  9 Female US             3909 0.436     0.0141    0.422     0.450   
#> 10 Male   Other          1068 0.0755    0.00572   0.0698    0.0812  
#> 11 Male   US             3824 0.413     0.0141    0.399     0.427   

# Programmatic use
wt <- "weights"
vars <- c("gender", "birth_country")
se_prop_ogd(nhanes, strata = strata, weight = !!rlang::sym(wt), !!!rlang::syms(vars))
#> # A tibble: 11 × 7
#>    gender birth_country   occ      prop       ci       ci_l     ci_u
#>    <fct>  <fct>         <int>     <dbl>    <dbl>      <dbl>    <dbl>
#>  1 Total  Total          9971 1         0         1         1       
#>  2 Female Total          5079 0.512     0.0141    0.498     0.526   
#>  3 Male   Total          4892 0.488     0.0141    0.474     0.502   
#>  4 Total  Missing           2 0.0000938 0.000133 -0.0000390 0.000227
#>  5 Total  Other          2236 0.151     0.00779   0.143     0.159   
#>  6 Total  US             7733 0.849     0.00779   0.841     0.857   
#>  7 Female Missing           2 0.0000938 0.000133 -0.0000390 0.000227
#>  8 Female Other          1168 0.0756    0.00530   0.0703    0.0809  
#>  9 Female US             3909 0.436     0.0141    0.422     0.450   
#> 10 Male   Other          1068 0.0755    0.00572   0.0698    0.0812  
#> 11 Male   US             3824 0.413     0.0141    0.399     0.427