The user passes a data set to modify, a yspec object, and the names of
flags in the yspec object to create factors in the data set using
ys_factors().
ys_factors_fl(data, spec, ...)Only eligible columns will be considered for factor creation (either
a values field is present or the make_factor flag is set).
Therefore, a flag containing a general list of data columns
(potentially continuous and discrete) could be passed without
generating an error.
data <- ys_help$data()
spec <- ys_help$spec()
names(ys_flags(spec))
#> [1] "covariate" "nm" "times" "cat"
data <- ys_factors_fl(data, spec, cat)
head(data, 2)
#> C NUM ID SUBJ TIME SEQ CMT EVID AMT DV AGE WT CRCL ALB
#> 1 NA 1 1 1 0.00 observation 1 1 5 0.000 28.03 55.16 114.45 4.4
#> 2 NA 2 1 1 0.61 dose 2 0 NA 61.005 28.03 55.16 114.45 4.4
#> BMI AAG SCR AST ALT HT CP TAFD TAD LDOS MDV BLQ PHASE STUDY
#> 1 21.67 106.36 1.14 11.88 12.66 159.55 0 0.00 0.00 5 1 0 1 SAD
#> 2 21.67 106.36 1.14 11.88 12.66 159.55 0 0.61 0.61 5 0 0 1 SAD
#> RF SEQ_v STUDY_v
#> 1 norm 0 1
#> 2 norm 1 1
data <- ys_factors_fl(data, spec, covariate, nm)
head(data, 2)
#> C NUM ID SUBJ TIME SEQ CMT EVID AMT DV AGE WT CRCL ALB
#> 1 NA 1 1 1 0.00 observation 1 1 5 0.000 28.03 55.16 114.45 4.4
#> 2 NA 2 1 1 0.61 dose 2 0 NA 61.005 28.03 55.16 114.45 4.4
#> BMI AAG SCR AST ALT HT CP TAFD TAD LDOS MDV BLQ PHASE
#> 1 21.67 106.36 1.14 11.88 12.66 159.55 0 0.00 0.00 5 missing 0 1
#> 2 21.67 106.36 1.14 11.88 12.66 159.55 0 0.61 0.61 5 non-missing 0 1
#> STUDY RF SEQ_v STUDY_v MDV_v
#> 1 SAD norm 0 1 1
#> 2 SAD norm 1 1 0