Map categorical factor levels to a data.frame

map_cat_data(data, meta_data)

Arguments

data

A data.frame of the pk data

meta_data

A data.frame of meta data

Examples

# Dictionary
meta_data_501
#>    Name Type                      Label                    Unit Min Max
#> 1    ID   id                    Subject                    <NA>  NA  NA
#> 2   OCC  occ                   Occasion                    <NA>  NA  NA
#> 3  TIME time                       Time                       h  NA  NA
#> 4   TAD  tad            Time After Dose                       h  NA  NA
#> 5    DV   dv              Concentration                    mg/L  NA  NA
#> 6  EVID evid           Event Identifier                    <NA>  NA  NA
#> 7   MDV  mdv Missing Dependent Variable                    <NA>  NA  NA
#> 8   AMT  amt                     Amount                      mg  NA  NA
#> 9  RATE rate                       Rate                    mg/h  NA  NA
#> 10   WT  cov                     Weight                      kg  NA  NA
#> 11  AGE  cov                        Age                     yrs  NA  NA
#> 12  SEX  cat                        Sex "0":"Male"|"1":"Female"  NA  NA

# Data
head(data_501)
#>   ID OCC TIME TAD    DV EVID MDV  AMT RATE   WT AGE SEX
#> 1  1   1    0  -5  0.00    1   1 1000  200 58.4  51   1
#> 2  1   1    1  -4  6.47    0   0    0    0 58.4  51   1
#> 3  1   1    5   0 23.96    0   0    0    0 58.4  51   1
#> 4  1   1   12   7 17.81    0   0    0    0 58.4  51   1
#> 5  1   1   24  19  5.63    0   0    0    0 58.4  51   1
#> 6  2   1    0  -5  0.00    1   1 1000  200 79.3  53   1

# Map data
head(map_cat_data(data_501, meta_data_501))
#>   ID OCC TIME TAD    DV EVID MDV  AMT RATE   WT AGE    SEX
#> 1  1   1    0  -5  0.00    1   1 1000  200 58.4  51 Female
#> 2  1   1    1  -4  6.47    0   0    0    0 58.4  51 Female
#> 3  1   1    5   0 23.96    0   0    0    0 58.4  51 Female
#> 4  1   1   12   7 17.81    0   0    0    0 58.4  51 Female
#> 5  1   1   24  19  5.63    0   0    0    0 58.4  51 Female
#> 6  2   1    0  -5  0.00    1   1 1000  200 79.3  53 Female