# dqcontinuous: Data quality check of continuous variables In StatMeasures: Easy Data Manipulation, Data Quality and Statistical Checks

## Description

Takes in a data, and returns summary of continuous variables

## Usage

 `1` ```dqcontinuous(data) ```

## Arguments

 `data` a data.frame or data.table

## Details

It is of utmost importance to know the distribution of continuous variables in the data. `dqcontinuous` produces an output which tells - continuous variable, non-missing values, missing values, percentage missing, minumum, average, maximum, standard deviation, variance, common percentiles from 1 to 99, and number of outliers for each continuous variable.

The function tags all integer and numeric variables as continuous, and produces output for them; if you think there are some variables which are integer or numeric in the data but they don't represent a continuous variable, change their type to an appropriate class.

`dqcontinuous` uses the same criteria to identify outliers as the one used for box plots. All values that are greater than 75th percentile value + 1.5 times the inter quartile range or lesser than 25th percentile value - 1.5 times the inter quartile range, are tagged as outliers.

This function works for both 'data.frame and 'data.table' but returns a 'data.frame' only.

## Value

a data.frame which contains the non-missing values, missing values, percentage of missing values, mimimum, mean, maximum, standard deviation, variance, percentiles and count of outliers of all integer and numeric variables

## Author(s)

Akash Jain

`dqcategorical`, `dqdate`, `contents`
 ```1 2 3 4 5 6``` ```# A 'data.frame' df <- data.frame(x = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), y = c(22, NA, 66, 12, 78, 34, 590, 97, 56, 37)) # Generate a data quality report of continuous variables summaryContinuous <- dqcontinuous(data = df) ```