possible_ids: Find possible unique identifies of data frame

In randrescastaneda/joyn: Tool for Diagnosis of Tables Joins and Complementary Join Features

Find possible unique identifies of data frame

Description

Identify possible combinations of variables that uniquely identifying dt

Usage

possible_ids(
  dt,
  vars = NULL,
  exclude = NULL,
  include = NULL,
  exclude_classes = NULL,
  include_classes = NULL,
  verbose = getOption("possible_ids.verbose", default = FALSE),
  min_combination_size = 1,
  max_combination_size = 5,
  max_processing_time = 60,
  max_numb_possible_ids = 100,
  get_all = FALSE
)

Arguments

dt	data frame
vars	character: A vector of variable names to consider for identifying unique combinations.
exclude	character: Names of variables to exclude from analysis
include	character: Name of variable to be included, that might belong to the group excluded in the exclude
exclude_classes	character: classes to exclude from analysis (e.g., "numeric", "integer", "date")
include_classes	character: classes to include in the analysis (e.g., "numeric", "integer", "date")
verbose	logical: If FALSE no message will be displayed. Default is TRUE
min_combination_size	numeric: Min number of combinations. Default is 1, so all combinations.
max_combination_size	numeric. Max number of combinations. Default is 5. If there is a combinations of identifiers larger than max_combination_size, they won't be found
max_processing_time	numeric: Max time to process in seconds. After that, it returns what it found.
max_numb_possible_ids	numeric: Max number of possible IDs to find. See details.
get_all	logical: get all possible combinations based on the parameters above.

Value

list with possible identifiers

Number of possible IDs

The number of possible IDs in a dataframe could be very large. This is why, possible_ids() makes use of heuristics to return something useful without wasting the time of the user. In addition, we provide multiple parameter so that the user can fine tune their search for possible IDs easily and quickly.

Say for instance that you have a dataframe with 10 variables. Testing every possible pair of variables will give you 90 possible unique identifiers for this dataframe. If you want to test all the possible IDs, you will have to test more 5000 combinations. If the dataframe has many rows, it may take a while.

Examples

library(data.table)
x4 = data.table(id1 = c(1, 1, 2, 3, 3),
                id2 = c(1, 1, 2, 3, 4),
                t   = c(1L, 2L, 1L, 2L, NA_integer_),
                x   = c(16, 12, NA, NA, 15))
possible_ids(x4)

randrescastaneda/joyn documentation built on Dec. 20, 2024, 6:51 a.m.