Variable label extraction and custom selection from data — variable

Wrapper on choices_labeled to label variables basing on existing labels in data.

Usage

variable_choices(data, subset = NULL, fill = FALSE, key = NULL)

# S3 method for class 'character'
variable_choices(data, subset = NULL, fill = FALSE, key = NULL)

# S3 method for class 'data.frame'
variable_choices(data, subset = NULL, fill = TRUE, key = NULL)

Arguments

data

(data.frame or character) If data.frame, then data to extract labels from. If character, then name of the dataset to extract data from once available.

subset

(character or function) If character, then a vector of column names. If function, 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.

fill

(logical(1)) if TRUE, the function will return variable names for columns with non-existent labels; otherwise will return NA for them.

key

(character) vector with names of the variables, which are part of the primary key of the data argument.

This is an optional argument, which allows to identify variables associated with the primary key and display the appropriate icon for them in the teal.widgets::optionalSelectInput() widget.

Value

Named character vector with additional attributes or delayed_data object.

Examples

library(teal.data)

ADRS <- rADRS
variable_choices(ADRS)
#> number of choices: 65 
#> 
#> STUDYID: Study Identifier
#> USUBJID: Unique Subject Identifier
#> SUBJID: Subject Identifier for the Study
#> SITEID: Study Site Identifier
#> AGE: Age
#> AGEU: Age Units
#> SEX: Sex
#> RACE: Race
#> ETHNIC: Ethnicity
#> COUNTRY: Country
#> DTHFL: Subject Death Flag
#> INVID: Investigator Identifier
#> INVNAM: Investigator Name
#> ARM: Description of Planned Arm
#> ARMCD: Planned Arm Code
#> ACTARM: Description of Actual Arm
#> ACTARMCD: Actual Arm Code
#> TRT01P: Planned Treatment for Period 01
#> TRT01A: Actual Treatment for Period 01
#> TRT02P: Planned Treatment for Period 02
#> TRT02A: Actual Treatment for Period 02
#> REGION1: Geographic Region 1
#> STRATA1: Stratification Factor 1
#> STRATA2: Stratification Factor 2
#> BMRKR1: Continuous Level Biomarker 1
#> BMRKR2: Categorical Level Biomarker 2
#> ITTFL: Intent-To-Treat Population Flag
#> SAFFL: Safety Population Flag
#> BMEASIFL: Response Evaluable Population Flag
#> BEP01FL: Biomarker Evaluable Population Flag
#> AEWITHFL: AE Leading to Drug Withdrawal Flag
#> RANDDT: Date of Randomization
#> TRTSDTM: Datetime of First Exposure to Treatment
#> TRTEDTM: Datetime of Last Exposure to Treatment
#> TRT01SDTM: Datetime of First Exposure to Treatment in Period 01
#> TRT01EDTM: Datetime of Last Exposure in Period 01
#> TRT02SDTM: Datetime of First Exposure to Treatment in Period 02
#> TRT02EDTM: Datetime of Last Exposure to Treatment in Period 02
#> AP01SDTM: Period 01 Start Datetime
#> AP01EDTM: Period 01 End Datetime
#> AP02SDTM: Period 02 Start Datetime
#> AP02EDTM: Period 02 End Datetime
#> EOSSTT: End of Study Status
#> EOTSTT: End of Treatment Status
#> EOSDT: End of Study Date
#> EOSDY: End of Study Relative Day
#> DCSREAS: Reason for Discontinuation from Study
#> DTHDT: Date of Death
#> DTHCAUS: Cause of Death
#> DTHCAT: Cause of Death Category
#> LDDTHELD: Elapsed Days from Last Dose to Death
#> LDDTHGR1: Last Dose to Death - Days Elapsed Grp 1
#> LSTALVDT: Date Last Known Alive
#> DTHADY: Relative Day of Death
#> ADTHAUT: Autopsy Performed
#> ASEQ: Analysis Sequence Number
#> RSSEQ: Sequence Number
#> PARAM: Parameter
#> PARAMCD: Parameter Code
#> AVAL: Analysis Value
#> AVALC: Analysis Value (C)
#> ADTM: Analysis Datetime
#> ADY: Analysis Relative Day
#> AVISIT: Analysis Visit
#> AVISITN: Analysis Visit (N)
#> 
variable_choices(ADRS, subset = c("PARAM", "PARAMCD"))
#> number of choices: 2 
#> 
#> PARAM: Parameter
#> PARAMCD: Parameter Code
#> 
variable_choices(ADRS, subset = c("", "PARAM", "PARAMCD"))
#> number of choices: 3 
#> 
#> : 
#> PARAM: Parameter
#> PARAMCD: Parameter Code
#> 
variable_choices(
  ADRS,
  subset = c("", "PARAM", "PARAMCD"),
  key = default_cdisc_join_keys["ADRS", "ADRS"]
)
#> number of choices: 3 
#> 
#> : 
#> PARAM: Parameter
#> PARAMCD: Parameter Code
#> 

# delayed version
variable_choices("ADRS", subset = c("USUBJID", "STUDYID"))
#> variable_choices with delayed data: ADRS
#> $ data
#> [1] "ADRS"
#> $ subset
#> [1] "USUBJID" "STUDYID"
#> $ key
#> NULL

# functional subset (with delayed data) - return only factor variables
variable_choices("ADRS", subset = function(data) {
  idx <- vapply(data, is.factor, logical(1))
  names(data)[idx]
})
#> variable_choices with delayed data: ADRS
#> $ data
#> [1] "ADRS"
#> $ subset
#> function (data) 
#> {
#>     idx <- vapply(data, is.factor, logical(1))
#>     names(data)[idx]
#> }
#> <environment: 0x56414d9da410>
#> $ key
#> NULL