Book Image

Data Analysis with R, Second Edition - Second Edition

Book Image

Data Analysis with R, Second Edition - Second Edition

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. 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.
Table of Contents (24 chapters)
Title Page
Copyright and Credits
Packt Upsell
Contributors
Preface
Index

Wait to optimize


Prominent computer scientist and mathematician Donald Knuth famously stated:

                                             Premature optimization is the root of all evil.

I, personally, hold that money is the root of all evil, but premature optimization is definitely up there!

Why is premature optimization so evil? Well, there are a few reasons. First, programmers can sometimes be pretty bad at identifying what the bottleneck of a program - the routine or routines that have the slowest throughput - is, and optimize the wrong parts of a program. Identification of bottlenecks can most accurately be performed by profiling your code after it's been completed in an un-optimized form.

Secondly, clever tricks and shortcuts for speeding up code often introduce subtle bugs and unexpected behavior. Now, the speedup of the code (if there is any!) must be taken in context with the time it took to complete the bug-finding and bug-fixing expedition; occasionally, a net negative amount of time...