data_dict_filter: Subset data dictionary by row values

Description

Subsets either or both the 'Variables' and 'Categories' elements of a data dictionary. Rows are conserved if their values satisfy the condition. This is a wrapper function analogous to dplyr::filter().

Usage

data_dict_filter(
  data_dict,
  filter_var = NULL,
  filter_cat = NULL,
  filter_all = NULL
)

Value

A list of data frame(s) identifying a workable data dictionary structure.

Arguments

data_dict: A list of data frame(s) representing metadata to be filtered.
filter_var: Expressions that are defined in the element 'Variables' in the data dictionary.
filter_cat: Expressions that are defined in the element 'Categories' in the data dictionary.
filter_all: Expressions that are defined both in the 'Categories' and 'Variables' in the data dictionary.

Details

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.

Examples

Run this code

{

library(dplyr)

# use madshapR_examples provided by the package
# Data dictionary where the column 'table' is added to 
# refer to the associated dataset.

data_dict <- 
  madshapR_examples$`data_dictionary_example` %>% 
  lapply(function(x) mutate(x,table = "dataset"))

###### Example 1 search and filter through a column in 'Variables' element
data_dict_f1 <- data_dict_filter(data_dict,filter_var = "name == 'gndr'")
glimpse(data_dict_f1)

###### Example 2 search and filter through a column in 'Categories' element
data_dict_f2 <- data_dict_filter(data_dict,filter_cat = "missing == TRUE")
glimpse(data_dict_f2)

###### Example 3 search and filter through a column across all elements.
# The column must exist in both 'Variables' and 'Categories' and have the
# same meaning
data_dict_f3 <- data_dict_filter(data_dict,filter_all = "table == 'dataset'")
glimpse(data_dict_f3)

}