data_quality: Performs a quality audit of a table

In MathieuMarauri/auditdata: Performs a Quality Audit of a Table

Description

This function performs a quality check on a table. The number of missing values by variable along with the quantiles for the numeric variables and a frequency table for each categorical variable can be found in the result.

Usage

data_quality(
  data,
  numeric_cutoff = -1,
  na_type = NULL,
  max_length = Inf,
  global_only = FALSE
)

Arguments

data	a data.frame.
numeric_cutoff	the minimum number of distinct values required for a numeric vector not to be coerced to a fator. -1 is the default, meaning no minimum required.
na_type	charcater vector with valus that should be considered NA. Default to NULL, no values other than regular NA are treated as NA.
max_length	the maximum number of rows in the frequency tables
global_only	logical, whether to return only the global summary

Details

The types are defined based on the types in the input table and on the value of other arguments. 'numeric_cutoff' allows numeric variables to be classified as categorical if they have less unique values than the value of 'numeric_cutoff'. Date, POSIXct and POSIXlt are the only classes treated as date.

Value

a list with a global summary, and if available, information on numeric, categorical and date variables

Examples

data(iris)
res <- data_quality(iris)
# global quality
res$global
# numerical data summary
res$numeric_output
# categorical data summary
res$categorical_output

MathieuMarauri/auditdata documentation built on March 6, 2020, 7:09 p.m.