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

Performing data filtering activities


This is a bit of a recap recipe. In the workflow proposed here, we will sum up the tricks and knowledge gained throughout the book in order to perform a data-filtering activity.

Data filtering includes all the activities performed on a dataset to make it ready for further analysis.

Isn't it the same as data cleansing?

Well, in a sense… yes. However, not exactly the same, since data filtering usually refers to some specific techniques and not to others, while data cleansing can be considered a more comprehensive concept.

That said, here we will make tests for our data frame, performing subsequent filtering activities and reporting about these activities. The following diagram shows the flow:

As you can see in the diagram, we will:

  • Look for duplicated values and remove them

  • Substitute by simulation missing values, as explained in the preceding recipe

  • Interpolate incoherent values, which are multiple values for a given attribute

  • Remove outliers, as seen in the preceding...