madshapR (version 2.0.0)

as_dataset: Validate and coerce any object as a dataset

Description

Checks if an object is a valid dataset and returns it with the appropriate madshapR::class attribute. This function mainly helps validate inputs within other functions of the package but could be used separately to check if a dataset is valid.

Usage

as_dataset(object, col_id = NULL)

Value

A data frame with madshapR::class 'dataset'.

Arguments

object

A potential dataset object to be coerced.

col_id

An optional character string specifying the name(s) or position(s) of the column(s) used as identifiers.

Details

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.

Examples

{

# use madshapR_examples provided by the package
library(dplyr)
library(fabR)

###### Example 1: A dataset can have an id column specified as an attribute. 
dataset <- as_dataset(madshapR_examples$`dataset_example`, col_id = "part_id")
print(attributes(dataset)$`madshapR::col_id`)
glimpse(dataset)

###### Example 2: Any data frame can be a dataset by definition.
dataset <- tibble(iris %>% add_index("my_index"))
dataset <- as_dataset(dataset, "my_index")
print(attributes(dataset)$`madshapR::col_id`)

}