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

Online repositories


Look back to the Web Technologies Task View we talked about in the previous section. There are a tremendous amount of R packages specifically designed to import data directly from specialized sources on the web. Among these are packages to search for and retrieve the full text of academic articles in the Public Library of Science journals (rplos), to search for and download the full text of Wikipedia articles (WikipediR), to download data about Berlin from the German government (BerlinData), to interface with the Chromosome Counts Database (chromer), to download historical financial data (quantmod), and to access the information in the PubChem chemistry database (rpubchem).

These examples notwithstanding, given that there are many hundreds of immense repositories of public data, it is far too much to expect the R community to have a package specially built for every single one. Luckily, with the ability to handle many different data formats under our belt, we can just...