Value labeling and filtering based on variable relationship
Source:R/choices_labeled.R
value_choices.Rd
Wrapper on choices_labeled to label variable values basing on other variable values.
Usage
value_choices(data, var_choices, var_label = NULL, subset = NULL, sep = " - ")
# S3 method for character
value_choices(data, var_choices, var_label = NULL, subset = NULL, sep = " - ")
# S3 method for data.frame
value_choices(data, var_choices, var_label = NULL, subset = NULL, sep = " - ")
Arguments
- data
(
data.frame
,character
) Ifdata.frame
, then data to extract labels from. Ifcharacter
, then name of the dataset to extract data from once available.- var_choices
(
character
orNULL
) vector with choices column names.- var_label
(
character
) vector with labels column names.- subset
(
character
orfunction
) Ifcharacter
, vector with values to subset. Iffunction
, then this function is used to determine the possible columns (e.g. all factor columns). In this case, the function must take only single argument "data" and return a character vector.See examples for more details.
- sep
(
character
) separator used in case of multiple column names.
Examples
ADRS <- teal.transform::rADRS
value_choices(ADRS, "PARAMCD", "PARAM", subset = c("BESRSPI", "INVET"))
#> number of choices: 2
#>
#> BESRSPI: Best Confirmed Overall Response by Investigator
#> INVET: Investigator End Of Induction Response
#>
value_choices(ADRS, c("PARAMCD", "ARMCD"), c("PARAM", "ARM"))
#> number of choices: 9
#>
#> BESRSPI - ARM A: Best Confirmed Overall Response by Investigator - A: Drug X
#> INVET - ARM A: Investigator End Of Induction Response - A: Drug X
#> OVRINV - ARM A: Overall Response by Investigator - by visit - A: Drug X
#> BESRSPI - ARM C: Best Confirmed Overall Response by Investigator - C: Combination
#> INVET - ARM C: Investigator End Of Induction Response - C: Combination
#> OVRINV - ARM C: Overall Response by Investigator - by visit - C: Combination
#> BESRSPI - ARM B: Best Confirmed Overall Response by Investigator - B: Placebo
#> INVET - ARM B: Investigator End Of Induction Response - B: Placebo
#> OVRINV - ARM B: Overall Response by Investigator - by visit - B: Placebo
#>
value_choices(ADRS, c("PARAMCD", "ARMCD"), c("PARAM", "ARM"),
subset = c("BESRSPI - ARM A", "INVET - ARM A", "OVRINV - ARM A")
)
#> number of choices: 3
#>
#> BESRSPI - ARM A: Best Confirmed Overall Response by Investigator - A: Drug X
#> INVET - ARM A: Investigator End Of Induction Response - A: Drug X
#> OVRINV - ARM A: Overall Response by Investigator - by visit - A: Drug X
#>
value_choices(ADRS, c("PARAMCD", "ARMCD"), c("PARAM", "ARM"), sep = " --- ")
#> number of choices: 9
#>
#> BESRSPI --- ARM A: Best Confirmed Overall Response by Investigator --- A: Drug X
#> INVET --- ARM A: Investigator End Of Induction Response --- A: Drug X
#> OVRINV --- ARM A: Overall Response by Investigator - by visit --- A: Drug X
#> BESRSPI --- ARM C: Best Confirmed Overall Response by Investigator --- C: Combination
#> INVET --- ARM C: Investigator End Of Induction Response --- C: Combination
#> OVRINV --- ARM C: Overall Response by Investigator - by visit --- C: Combination
#> BESRSPI --- ARM B: Best Confirmed Overall Response by Investigator --- B: Placebo
#> INVET --- ARM B: Investigator End Of Induction Response --- B: Placebo
#> OVRINV --- ARM B: Overall Response by Investigator - by visit --- B: Placebo
#>
# delayed version
value_choices("ADRS", c("PARAMCD", "ARMCD"), c("PARAM", "ARM"))
#> value_choices with delayed data: ADRS
#> $ data
#> [1] "ADRS"
#> $ var_choices
#> [1] "PARAMCD" "ARMCD"
#> $ var_label
#> [1] "PARAM" "ARM"
#> $ subset
#> NULL
#> $ sep
#> [1] " - "
# functional subset
value_choices(ADRS, "PARAMCD", "PARAM", subset = function(data) {
levels(data$PARAMCD)[1:2]
})
#> number of choices: 2
#>
#> BESRSPI: Best Confirmed Overall Response by Investigator
#> INVET: Investigator End Of Induction Response
#>