This function works like ys_add_factors() with the difference that
the original columns become factors (retaining the original column names)
and the original columns are retained with a suffix. You can think of this
as a more convenient form of ys_add_factors(..., .suffix = "").
ys_factors(data, spec, ..., .keep_values = TRUE, .suffix = "_v")the data set to modify
a yspec object
unquoted column names for modification; passing nothing through
... will signal for all columns to be considered for factors
logical; if TRUE, value columns will be retained with
a .suffix.
a suffix to be added to original columns (holding values).
The original data frame is returned with columns converted to factors and (possibly) additional columns storing values.
Factor conversion will only take place on source columns that aren't
already factors. That is, if a column in data is already a factor, it
will be ignored. This means the function can be called multiple times on
the same input data, but once a column is converted to factor, it will
cannot be converted again in subsequent calls.
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
spec <- ys_help$spec()
data <- ys_help$data()
data <- ys_factors(data, spec)
head(data, 5)
#> C NUM ID SUBJ TIME SEQ CMT EVID AMT DV AGE WT
#> 1 <NA> 1 1 1 0.00 observation 1 dose 5 0.000 28.03 55.16
#> 2 <NA> 2 1 1 0.61 dose 2 observation NA 61.005 28.03 55.16
#> 3 <NA> 3 1 1 1.15 dose 2 observation NA 90.976 28.03 55.16
#> 4 <NA> 4 1 1 1.73 dose 2 observation NA 122.210 28.03 55.16
#> 5 <NA> 5 1 1 2.15 dose 2 observation NA 126.090 28.03 55.16
#> CRCL ALB BMI AAG SCR AST ALT HT CP TAFD TAD LDOS
#> 1 114.45 4.4 21.67 106.36 1.14 11.88 12.66 159.55 normal 0.00 0.00 5
#> 2 114.45 4.4 21.67 106.36 1.14 11.88 12.66 159.55 normal 0.61 0.61 5
#> 3 114.45 4.4 21.67 106.36 1.14 11.88 12.66 159.55 normal 1.15 1.15 5
#> 4 114.45 4.4 21.67 106.36 1.14 11.88 12.66 159.55 normal 1.73 1.73 5
#> 5 114.45 4.4 21.67 106.36 1.14 11.88 12.66 159.55 normal 2.15 2.15 5
#> MDV BLQ PHASE STUDY RF C_v SEQ_v EVID_v CP_v MDV_v BLQ_v
#> 1 missing above QL 1 SAD Normal NA 0 1 0 1 0
#> 2 non-missing above QL 1 SAD Normal NA 1 0 0 0 0
#> 3 non-missing above QL 1 SAD Normal NA 1 0 0 0 0
#> 4 non-missing above QL 1 SAD Normal NA 1 0 0 0 0
#> 5 non-missing above QL 1 SAD Normal NA 1 0 0 0 0
#> PHASE_v STUDY_v RF_v
#> 1 1 1 norm
#> 2 1 1 norm
#> 3 1 1 norm
#> 4 1 1 norm
#> 5 1 1 norm
spec$EVID
#> name value
#> col EVID
#> type numeric
#> short event ID
#> value 0 : observation
#> 1 : dose
count(data, EVID, EVID_v)
#> EVID EVID_v n
#> 1 observation 0 3210
#> 2 dose 1 1150