Book Image

RStudio for R Statistical Computing Cookbook

By : Andrea Cirillo
Book Image

RStudio for R Statistical Computing Cookbook

By: Andrea Cirillo

Overview of this book

The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications.
Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Detecting and removing missing values


Missing values are values that should have been recorded but, for some reason, weren't actually recorded. Those values are different, from values without meaning, represented in R with NaN (not a number).

Most of us understood missing values due to circumstances such as the following one:

> x <- c(1,2,3,NA,4)
> mean(x)
[1] NA

"Oh come on, I know you can do it. Just ignore that useless NA" was probably your reaction, or at least it was mine.

Fortunately, R comes packed with good functions for missing value detection and handling.

In this recipe and the following one, we will see two opposite approaches to missing value handling:

  • Removing missing values

  • Simulating missing values by interpolation

I have to warn you that removing missing values can be considered right in a really small number of cases, since it compromises the integrity of your data sources and can greatly reduce the reliability of your results.

Nevertheless, if you are strongly willing...