Test if the object has categorical variables, typically a data frame or categorical entries in the data dictionary. This function mainly helps validate input within other functions of the package but could be used to check if a dataset or a data dictionary has categorical variables.
has_categories(...)Object that can be either a dataset or a data dictionary.
A logical.
A data dictionary contains the list of variables in a dataset and metadata
about the variables and can be associated with a dataset. A data dictionary
object is a list of data frame(s) named 'Variables' (required) and
'Categories' (if any). To be usable in any function, the data frame
'Variables' must contain at least the name column, with all unique and
non-missing entries, and the data frame 'Categories' must contain at least
the variable and name columns, with unique combination of
variable and name.
A dataset is a data table containing variables. A dataset object is a data frame and can be associated with a data dictionary. If no data dictionary is provided with a dataset, a minimum workable data dictionary will be generated as needed within relevant functions. Identifier variable(s) for indexing can be specified by the user. The id values must be non-missing and will be used in functions that require it. If no identifier variable is specified, indexing is handled automatically by the function.
{
library(dplyr)
###### Example 1: use madshapR_examples provided by the package
dataset_with_cat <- madshapR_examples$`dataset_example` %>%
mutate(prg_ever_cat = as_category(prg_ever))
has_categories(madshapR_examples$`dataset_example`)
has_categories(dataset_with_cat)
has_categories(madshapR_examples$`data_dictionary_example`)
###### Example 2: any data frame can be a dataset
has_categories(iris)
has_categories(mtcars)
}
#> [1] FALSE