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

Using optimized packages


Many of the functionalities in base R have alternative implementations available in contributed packages. Quite often, these packages offer a faster or less memory-intensive substitute for the base R equivalent. For example, in addition to adding a ton of extra functionality, the glmnet package performs regression far faster than glm, in my experience.

For faster data import, you might be able to use fread from the data.table package, or the read_* family of functions from the readr package. It is not uncommon for data import tasks that used to take several hours to take only a few minutes with these read functions.

For common data manipulation tasks, such as merging (joining), conditional selection, sorting, and so on, you will find that the data.table and dplyr packages offer incredible speed improvements. Both of these packages have a ton of users who swear by them, and the community support is solid. You'd be well advised to become proficient in one of these packages...