## Dealing with Missing Data in R Template

This Dealing with Missing Data in R template shows you how to check for missing values in a dataset and count them. It also provides two ways of dealing with missing values in a dataset - either by substituting the missing values with the average, or by removing the entries which have missing values in them. Some other related topics you might be interested to explore are correlation between two variables in R, export data as csv in R, calculating data median in R, calculating data mean in R, and calculating standard deviation of data in R. You can now download the R template for free. Dealing with missing data in R is among the topics covered in detail in the 365 Data Science e-learning program.

###### Elitsa Kaloyanova

Head of Data Content## Who is it for

This is an open-access R template in r .format that will be useful for anyone who wants to work as computer scientist, data analyst and data scientist.

## How it can help you

Dealing with missing values is an essential step of data preprocessing and the data analysis process. Real data is messy and contains noise or missing values, which need to be dealt with, as some programming methods are unable to handle data which contains missing values.