Data Analysis with R - Second Edition: A comprehensive guide to manipulating, analyzing, and visualizing data in R

by Tony Fischetti

Overview

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.
Starting with the basics of R and statistical reasoning, this book 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 with packages like Rcpp, ggplot2, and dplyr. 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.

Product Details

ISBN-13:	9781788397339
Publisher:	Packt Publishing
Publication date:	03/28/2018
Sold by:	Barnes & Noble
Format:	eBook
Pages:	570
File size:	26 MB
Note:	This product may take a few minutes to download.

About the Author

Tony Fischetti is a data scientist at College Factual, where he gets to use R everyday to build personalized rankings and recommender systems. He graduated in cognitive science from Rensselaer Polytechnic Institute, and his thesis was strongly focused on using statistics to study visual short-term memory.
Tony enjoys writing and contributing to open source software, blogging at onthelambda, writing about himself in third person, and sharing his knowledge using simple, approachable language and engaging examples.
The more traditionally exciting of his daily activities include listening to records, playing the guitar and bass (poorly), weight training, and helping others.

Table of Contents

Table of Contents

1. RefresheR
2. The Shape of Data
3. Describing Relationships
4. Probability
5. Using Data to Reason about the World
6. Testing Hypotheses
7. Bayesian Methods
8. The Bootstrap
9. Predicting Continuous Variables
10. Predicting Categorical Variables
11. Predicting Changes with Time
12. Sources Of Data
13. Dealing with Missing Data
14. Dealing with Messy Data
15. Dealing with Large Data
16. Working with Popular R Packages
17. Reproducibility and Best Practices