Extract All Metadata for Variables

Returns a summary of all metadata fields for one or more variables in a survey design object or data frame. Useful for auditing metadata state or building codebooks.

Usage

extract_metadata(x, ..., fill = NULL)

Arguments

x

A survey design object or data.frame.

...

<data-masked> Variable names (bare, unquoted). If empty, all variables are included.

fill

NULL (default) or "include". NULL omits variables that have no metadata in any field; "include" returns all variables regardless.

Value

A named list. Each entry is a named list with keys: variable_label, value_labels, question_preface, note, universe, missing_codes, transformations.

See also

Other metadata: extract_missing_codes(), extract_question_preface(), extract_universe(), extract_val_labels(), extract_var_label(), extract_var_note(), infer_question_prefaces(), set_missing_codes(), set_question_preface(), set_universe(), set_val_labels(), set_var_label(), set_var_note(), survey_metadata(), survey_weighting_history()

Examples

d <- as_survey(nhanes_2017, ids = sdmvpsu, weights = wtint2yr,
               strata = sdmvstra, nest = TRUE)
d <- set_universe(d, ridageyr = "All participants 0+")
extract_metadata(d, ridageyr)
#> $ridageyr
#> $ridageyr$variable_label
#> [1] "Age in years at screening"
#> 
#> $ridageyr$value_labels
#> NULL
#> 
#> $ridageyr$question_preface
#> NULL
#> 
#> $ridageyr$note
#> NULL
#> 
#> $ridageyr$universe
#> [1] "All participants 0+"
#> 
#> $ridageyr$missing_codes
#> NULL
#> 
#> $ridageyr$transformations
#> list()
#> 
#> 
extract_metadata(d, fill = "include")
#> $seqn
#> $seqn$variable_label
#> [1] "Respondent sequence number"
#> 
#> $seqn$value_labels
#> NULL
#> 
#> $seqn$question_preface
#> NULL
#> 
#> $seqn$note
#> NULL
#> 
#> $seqn$universe
#> NULL
#> 
#> $seqn$missing_codes
#> NULL
#> 
#> $seqn$transformations
#> list()
#> 
#> 
#> $sdmvpsu
#> $sdmvpsu$variable_label
#> [1] "Masked variance pseudo-PSU"
#> 
#> $sdmvpsu$value_labels
#> NULL
#> 
#> $sdmvpsu$question_preface
#> NULL
#> 
#> $sdmvpsu$note
#> NULL
#> 
#> $sdmvpsu$universe
#> NULL
#> 
#> $sdmvpsu$missing_codes
#> NULL
#> 
#> $sdmvpsu$transformations
#> list()
#> 
#> 
#> $sdmvstra
#> $sdmvstra$variable_label
#> [1] "Masked variance pseudo-stratum"
#> 
#> $sdmvstra$value_labels
#> NULL
#> 
#> $sdmvstra$question_preface
#> NULL
#> 
#> $sdmvstra$note
#> NULL
#> 
#> $sdmvstra$universe
#> NULL
#> 
#> $sdmvstra$missing_codes
#> NULL
#> 
#> $sdmvstra$transformations
#> list()
#> 
#> 
#> $wtmec2yr
#> $wtmec2yr$variable_label
#> [1] "Full sample 2 year MEC exam weight"
#> 
#> $wtmec2yr$value_labels
#> NULL
#> 
#> $wtmec2yr$question_preface
#> NULL
#> 
#> $wtmec2yr$note
#> NULL
#> 
#> $wtmec2yr$universe
#> NULL
#> 
#> $wtmec2yr$missing_codes
#> NULL
#> 
#> $wtmec2yr$transformations
#> list()
#> 
#> 
#> $wtint2yr
#> $wtint2yr$variable_label
#> [1] "Full sample 2 year interview weight"
#> 
#> $wtint2yr$value_labels
#> NULL
#> 
#> $wtint2yr$question_preface
#> NULL
#> 
#> $wtint2yr$note
#> NULL
#> 
#> $wtint2yr$universe
#> NULL
#> 
#> $wtint2yr$missing_codes
#> NULL
#> 
#> $wtint2yr$transformations
#> list()
#> 
#> 
#> $ridstatr
#> $ridstatr$variable_label
#> [1] "Interview/Examination status"
#> 
#> $ridstatr$value_labels
#> NULL
#> 
#> $ridstatr$question_preface
#> NULL
#> 
#> $ridstatr$note
#> NULL
#> 
#> $ridstatr$universe
#> NULL
#> 
#> $ridstatr$missing_codes
#> NULL
#> 
#> $ridstatr$transformations
#> list()
#> 
#> 
#> $riagendr
#> $riagendr$variable_label
#> [1] "Gender"
#> 
#> $riagendr$value_labels
#> NULL
#> 
#> $riagendr$question_preface
#> NULL
#> 
#> $riagendr$note
#> NULL
#> 
#> $riagendr$universe
#> NULL
#> 
#> $riagendr$missing_codes
#> NULL
#> 
#> $riagendr$transformations
#> list()
#> 
#> 
#> $ridageyr
#> $ridageyr$variable_label
#> [1] "Age in years at screening"
#> 
#> $ridageyr$value_labels
#> NULL
#> 
#> $ridageyr$question_preface
#> NULL
#> 
#> $ridageyr$note
#> NULL
#> 
#> $ridageyr$universe
#> [1] "All participants 0+"
#> 
#> $ridageyr$missing_codes
#> NULL
#> 
#> $ridageyr$transformations
#> list()
#> 
#> 
#> $ridreth3
#> $ridreth3$variable_label
#> [1] "Race/Hispanic origin w/ NH Asian"
#> 
#> $ridreth3$value_labels
#> NULL
#> 
#> $ridreth3$question_preface
#> NULL
#> 
#> $ridreth3$note
#> NULL
#> 
#> $ridreth3$universe
#> NULL
#> 
#> $ridreth3$missing_codes
#> NULL
#> 
#> $ridreth3$transformations
#> list()
#> 
#> 
#> $indfmpir
#> $indfmpir$variable_label
#> [1] "Ratio of family income to poverty"
#> 
#> $indfmpir$value_labels
#> NULL
#> 
#> $indfmpir$question_preface
#> NULL
#> 
#> $indfmpir$note
#> NULL
#> 
#> $indfmpir$universe
#> NULL
#> 
#> $indfmpir$missing_codes
#> NULL
#> 
#> $indfmpir$transformations
#> list()
#> 
#> 
#> $dmdeduc2
#> $dmdeduc2$variable_label
#> [1] "Education level - Adults 20+"
#> 
#> $dmdeduc2$value_labels
#> NULL
#> 
#> $dmdeduc2$question_preface
#> NULL
#> 
#> $dmdeduc2$note
#> NULL
#> 
#> $dmdeduc2$universe
#> NULL
#> 
#> $dmdeduc2$missing_codes
#> NULL
#> 
#> $dmdeduc2$transformations
#> list()
#> 
#> 
#> $bpxsy1
#> $bpxsy1$variable_label
#> [1] "Systolic: Blood pres (1st rdg) mm Hg"
#> 
#> $bpxsy1$value_labels
#> NULL
#> 
#> $bpxsy1$question_preface
#> NULL
#> 
#> $bpxsy1$note
#> NULL
#> 
#> $bpxsy1$universe
#> NULL
#> 
#> $bpxsy1$missing_codes
#> NULL
#> 
#> $bpxsy1$transformations
#> list()
#> 
#> 
#> $bpxdi1
#> $bpxdi1$variable_label
#> [1] "Diastolic: Blood pres (1st rdg) mm Hg"
#> 
#> $bpxdi1$value_labels
#> NULL
#> 
#> $bpxdi1$question_preface
#> NULL
#> 
#> $bpxdi1$note
#> NULL
#> 
#> $bpxdi1$universe
#> NULL
#> 
#> $bpxdi1$missing_codes
#> NULL
#> 
#> $bpxdi1$transformations
#> list()
#> 
#> 
#> $bpxpls
#> $bpxpls$variable_label
#> [1] "60 sec. pulse (30 sec. pulse * 2)"
#> 
#> $bpxpls$value_labels
#> NULL
#> 
#> $bpxpls$question_preface
#> NULL
#> 
#> $bpxpls$note
#> NULL
#> 
#> $bpxpls$universe
#> NULL
#> 
#> $bpxpls$missing_codes
#> NULL
#> 
#> $bpxpls$transformations
#> list()
#> 
#>