Add factors to data set based on spec information

ys_add_factors(
  .data,
  .spec,
  ...,
  .all = TRUE,
  .suffix = getOption("ys.fct.suffix", "_f")
)

yspec_add_factors(
  .data,
  .spec,
  ...,
  .all = TRUE,
  .suffix = getOption("ys.fct.suffix", "_f")
)

ys_make_factor(values, x, strict = TRUE)

yspec_make_factor(values, x, strict = TRUE)

Arguments

.data	the data set to modify
.spec	a yspec object
...	unquoted column names for modification
.all	if `TRUE` then any column with a `values` attribute or where the `make_factor` field evaluates to `TRUE` will be added as a factor
.suffix	used to make the column name for the factors
values	a vector of values to convert to a factor
x	a ycol object
strict	if `FALSE`, then an factor will be returned for any `values` type

Details

Note that `.suffix` can be chosen using option `ys.fct.suffix`.

Examples


spec <- load_spec_ex()

ys_make_factor(c(1,0,1,1,1,0), spec$SEX)
#> [1] female male   female female female male  
#> Levels: male female

data <- data.frame(SEX = c(1,1,1,1,0,0,1,1), STUDY= c(202,100))

head(ys_add_factors(data, spec, SEX, STUDY))
#>   SEX STUDY  SEX_f
#> 1   1   202 female
#> 2   1   100 female
#> 3   1   202 female
#> 4   1   100 female
#> 5   0   202   male
#> 6   0   100   male
#>                                                                                                   STUDY_f
#> 1 The first phase 2 study conducted only at sites 1, 2, 3 when the formulation was still oral liquid only
#> 2                                                                                 The first phase 1 study
#> 3 The first phase 2 study conducted only at sites 1, 2, 3 when the formulation was still oral liquid only
#> 4                                                                                 The first phase 1 study
#> 5 The first phase 2 study conducted only at sites 1, 2, 3 when the formulation was still oral liquid only
#> 6                                                                                 The first phase 1 study

data <- ys_help$data()
spec <- ys_help$spec()

head(ys_add_factors(data, spec))
#>    C NUM ID SUBJ TIME SEQ CMT EVID AMT      DV   AGE    WT   CRCL ALB   BMI
#> 1 NA   1  1    1 0.00   0   1    1   5   0.000 28.03 55.16 114.45 4.4 21.67
#> 2 NA   2  1    1 0.61   1   2    0  NA  61.005 28.03 55.16 114.45 4.4 21.67
#> 3 NA   3  1    1 1.15   1   2    0  NA  90.976 28.03 55.16 114.45 4.4 21.67
#> 4 NA   4  1    1 1.73   1   2    0  NA 122.210 28.03 55.16 114.45 4.4 21.67
#> 5 NA   5  1    1 2.15   1   2    0  NA 126.090 28.03 55.16 114.45 4.4 21.67
#> 6 NA   6  1    1 3.19   1   2    0  NA  84.682 28.03 55.16 114.45 4.4 21.67
#>      AAG  SCR   AST   ALT     HT CP TAFD  TAD LDOS MDV BLQ PHASE STUDY   RF
#> 1 106.36 1.14 11.88 12.66 159.55  0 0.00 0.00    5   1   0     1     1 norm
#> 2 106.36 1.14 11.88 12.66 159.55  0 0.61 0.61    5   0   0     1     1 norm
#> 3 106.36 1.14 11.88 12.66 159.55  0 1.15 1.15    5   0   0     1     1 norm
#> 4 106.36 1.14 11.88 12.66 159.55  0 1.73 1.73    5   0   0     1     1 norm
#> 5 106.36 1.14 11.88 12.66 159.55  0 2.15 2.15    5   0   0     1     1 norm
#> 6 106.36 1.14 11.88 12.66 159.55  0 3.19 3.19    5   0   0     1     1 norm
#>    C_f       SEQ_f EVID_f   CP_f MDV_f   BLQ_f PHASE_f STUDY_f   RF_f
#> 1 <NA> observation      1 normal     1 below Q       1     SAD Normal
#> 2 <NA>        dose      0 normal     0 below Q       1     SAD Normal
#> 3 <NA>        dose      0 normal     0 below Q       1     SAD Normal
#> 4 <NA>        dose      0 normal     0 below Q       1     SAD Normal
#> 5 <NA>        dose      0 normal     0 below Q       1     SAD Normal
#> 6 <NA>        dose      0 normal     0 below Q       1     SAD Normal