Creates a data frame with labeled columns of various types, suitable for
demonstrating r_data_types and label_map.
Value
A data frame with n rows and 7 labeled columns:
- id
Integer sequence (Patient Identifier)
- boolean
Integer 1/2 (Binary Indicator)
- logical
Character "F"/"T" (Logical Status)
- f_real
Uniform random values (Random Uniform Value)
- float
Normal random values (Random Normal Value)
- char
Character "male"/"female" (Gender)
- factor
Factor C1-C5 (Category Group)
Examples
# Create and inspect labeled data
dta <- sample_data(n = 20)
str(dta)
#> 'data.frame': 20 obs. of 7 variables:
#> $ id : int 1 2 3 4 5 6 7 8 9 10 ...
#> ..- attr(*, "label")= chr "Patient Identifier"
#> $ boolean: int 1 2 1 2 1 1 1 2 1 1 ...
#> ..- attr(*, "label")= chr "Binary Indicator"
#> $ logical: chr "F" "T" "F" "T" ...
#> ..- attr(*, "label")= chr "Logical Status"
#> $ f_real : num 0.567 0.672 0.303 0.371 0.567 ...
#> ..- attr(*, "label")= chr "Random Uniform Value"
#> $ float : num -1.222 -2.454 -1.489 -0.432 -0.943 ...
#> ..- attr(*, "label")= chr "Random Normal Value"
#> $ char : chr "female" "female" "male" "male" ...
#> ..- attr(*, "label")= chr "Gender"
#> $ factor : Factor w/ 5 levels "C1","C2","C3",..: 3 5 5 2 3 2 2 3 5 4 ...
#> ..- attr(*, "label")= chr "Category Group"
label_map(dta)
#> key label
#> id id Patient Identifier
#> boolean boolean Binary Indicator
#> logical logical Logical Status
#> f_real f_real Random Uniform Value
#> float float Random Normal Value
#> char char Gender
#> factor factor Category Group
# Full workflow: generate, convert types, extract labels
dta <- sample_data(n = 100)
dta_clean <- r_data_types(dta, skip_vars = "id")
lmap <- label_map(dta_clean)
print(lmap)
#> key label
#> id id Patient Identifier
#> boolean boolean Binary Indicator
#> logical logical Logical Status
#> f_real f_real Random Uniform Value
#> float float Random Normal Value
#> char char Gender
#> factor factor Category Group