Compare weighted and unweighted data vs the ideal

Use evaluate_weights_y2() to evaluate given variables given weighting. For variables used in weighting, compare 1) unweighted data, 2) weighted data, 3) target weighting goals, 4) movement after weighting, and 5) difference from the target. For variables not used in weighting, can only compare 1) unweighted data, 2) weighted data, and 3) movement.

Usage

evaluate_weights_y2(
  dataset,
  ...,
  weight_var,
  nas = TRUE,
  remove_missing = FALSE
)

Arguments

dataset

A dataframe to be weighted

...

List of variables to check

weight_var

Weight variable

nas

DEFAULT = TRUE, Remove NAs from calculations

remove_missing

DEFAULT = FALSE, Before running frequencies, filter out responses that are "MISSING"

Value

A tibble with the following columns: variable, label, result_unweighted, target, result_weighted, movement, diff_from_target

Examples

municipal_data %>%
  define_target_y2(
    s_sex,
    c(
      '1' = .49,
      '2' = .5,
      '3' = .01
    )
  )
#> Error in as.character(x): Can't convert `x` <haven_labelled> to <character>.

weights_schema <- municipal_data %>%
  rake_y2(
    s_sex
  )
#> Error in rake_y2(., s_sex): Expected target(s) "target_s_sex" not found

municipal_data$trimmed_weights <- trim_weights_y2(weights_schema)
#> Error: object 'weights_schema' not found

municipal_data %>%
  evaluate_weights_y2(
    s_sex,
    weight_var = trimmed_weights
  )
#> Error in purrr::map(variables, ~combine_data(w_variable = .x, nas = nas,     dataset = dataset, weight = {        {            weight_var        }    }, remove_missing = remove_missing)): ℹ In index: 1.
#> Caused by error in `map()`:
#> ℹ In index: 1.
#> Caused by error in `dplyr::count()`:
#> ℹ In argument: `n = base::sum(trimmed_weights, na.rm = TRUE)`.
#> ℹ In group 1: `s_sex = 1`.
#> Caused by error:
#> ! object 'trimmed_weights' not found