Book Image

Data Analysis with R

Book Image

Data Analysis with R

Overview of this book

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it’s easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.
Table of Contents (20 chapters)
Data Analysis with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Summary


This chapter began with a discussion of relation databases. You've learned that the DBI package defines a standard interface on which various database drivers build upon. You then learned how to query these types of databases, and load the results in R.

Next, you gained an appreciation for JSON and XML (right?!), and how to approach the import of data from these formats. We then put our chops to the test by wielding data provided to us by two different web service APIs.

I stealthily snuck in some fancy new R constructs in this chapter. For example, prior to this chapter, we've never explicitly worked with lists before.

Finally, you've learned about how to look for information beyond which this chapter can provide, and some other places that we can get data to play around with.

In the next chapter, we won't be talking about how to load data from different sources—we'll be talking about how to deal with disorderly data that is already loaded.