Build the feature matrix from various sources according to a specification object

The build() function allows to build a machine learning data set from a specification object as provided by adam_spec() (with or without data already attached).

Usage

build(spec, join = dplyr::full_join, rm = FALSE)

Arguments

spec: a specification object as provided by adam_spec() (either spec or path has to be provided)
join: either function to join data sets (e.g. dplyr::full_join() or a character (vector) giving the names of the data sets containing the .ids to keep (e.g. join = c('adxb', 'adlb')). Defaults to dplyr::full_join, which is equivalent to 'adsl' (if included) according to CDISC standards.
rm: boolean. defaults to FALSE. if TRUE, a repeated measurement feature matrix with an additional .rmtime column is prepared.

Value

build() returns a wide data set with one row per subject and standardized column names for the subject id (.id) and the treatment variable (.trt), if it is provided in the spec object. Objects with additional information on the data are provided in the attributes of the returned object.

attr("source"): a tibble giving file path and md5 checksums of the source data sets.

attr("dict"): a tibble with the following columns

param: original parameter name in the source data
column: column name of the variable in the returned data. column is derived from param by transforming it into a valid file name and possibly adding a time extension, if multiple time points are considered for a particular parameter.
label: parameter label
source: source id provided by the specification object. If created with adam_spec(), this is the name of the domain.
type: ADaM data type of the source data (adsl, bds or occds)
unit: parameter unit (if applicable)
time: measurement time point (if applicable)
spec_id: name of the corresponding spec entry (if applicable)

Details

Missing values in variables from occurrence data sets are interpreted as 'absence of event', whereas NAs in adsl and bds data are considered to be true missing values. For missing values in occds data after joining with other data sets, missing values are replace by 0 for numerics, an additional level 'none' is introduced for for factors.

Authors

Maike Ahrens (ahrensmaike), Sebastian Voss (svoss09)

Examples

feat <- build(martini_spec)
attr(feat, "dict")
#> # A tibble: 26 × 7
#>    param   label                               source type  column spec_id unit 
#>    <chr>   <chr>                               <chr>  <chr> <chr>  <chr>   <chr>
#>  1 .id     Unique Subject Identifier           adsl   adsl  .id    adsl    NA   
#>  2 .trt    Actual Treatment for Period 01      adsl   adsl  .trt   adsl    NA   
#>  3 AGE     Age                                 adsl   adsl  AGE    adsl    NA   
#>  4 SEX     Sex                                 adsl   adsl  SEX    adsl    NA   
#>  5 RACE    Race                                adsl   adsl  RACE   adsl    NA   
#>  6 CALCIUM Calcium (mg/dL) in Serum            adlb   bds   CALCI… adlb    mg/dL
#>  7 CREAT   Creatinine (mg/dL) in Serum         adlb   bds   CREAT  adlb    mg/dL
#>  8 GGT     Gamma Glutamyl Transferase (U/L) i… adlb   bds   GGT    adlb    U/L  
#>  9 HB      Hemoglobin (g/dL) in Blood          adlb   bds   HB     adlb    g/dL 
#> 10 HCT     Hematocrit (%) in Blood - Calculat… adlb   bds   HCT    adlb    %    
#> # ℹ 16 more rows