data_integrity: Data integrity

In funModeling: Exploratory Data Analysis and Data Preparation Tool-Box

Data integrity

Description

A handy function to return different vectors of variable names aimed to quickly filter NA, categorical (factor / character), numerical and other types (boolean, date, posix). It also returns a vector of variables which have high cardinality. It returns an 'integrity' object, which has: 'status_now' (comes from status function), and 'results' list, following elements can be found:

vars_cat: Vector containing the categorical variables names (factor or character)

vars_num: Vector containing the numerical variables names

vars_char: Vector containing the character variables names

vars_factor: Vector containing the factor variables names

vars_other: Vector containing the other variables names (date time, posix and boolean)

vars_num_with_NA: Summary table for numerical variables with NA

vars_cat_with_NA: Summary table for categorical variables with NA

vars_cat_high_card: Summary table for high cardinality variables (where thershold = MAX_UNIQUE parameter)

vars_one_value: Vector containing the variables names with 1 unique different value

Explore the NA and high cardinality variables by doing summary(integrity_object), or a full summary by doing print(integrity_object)

Usage

data_integrity(data, MAX_UNIQUE = 35)

Arguments

data	data frame or a single vector
MAX_UNIQUE	max unique threshold to flag a categorical variable as a high cardinality one. Normally above 35 values it is needed to reduce the number of different values.

Value

An 'integrity' object.

Examples

# Example 1:
data_integrity(heart_disease)
# Example 2:
# changing the default minimum threshold to flag a variable as high cardiniality
data_integrity(data=data_country, MAX_UNIQUE=50)

funModeling documentation built on May 29, 2024, 3:24 a.m.