Book Image

R for Data Science Cookbook (n)

By : Yu-Wei, Chiu (David Chiu)
Book Image

R for Data Science Cookbook (n)

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis.
Table of Contents (19 chapters)
R for Data Science Cookbook
About the Author
About the Reviewer


R is the mainstream programming language of choice for data scientists. According to polls conducted by KDnuggets, a leading data analysis website, R ranked as the most popular language for analytics, data mining, and data science in the three most recent surveys (2012 to 2014). For many data scientists, R is more than just a programming language because the software also provides an interactive environment that can perform all types of data analysis.

R has many advantages in data manipulation and analysis, and the three most well-known are as follows:

  • Open Source and free: Using SAS or SPSS requires the purchase of a usage license. One can use R for free, allowing users to easily learn how to implement statistical algorithms from the source code of each function.

  • Powerful data analysis functions: R is famous in the data science community. Many biologists, statisticians, and programmers have wrapped their models into R packages before distributing these packages worldwide through CRAN (Comprehensive R Archive Network). This allows any user to start their analysis project by downloading and installing an R package from CRAN.

  • Easy to use: As R is a self-explanatory, high-level language, programming in R is fairly easy. R users only need to know how to use the R functions and how each parameter works through its powerful documentation. We can easily conduct high-level data analysis without having knowledge of the complex underlying mathematics.

R users will most likely agree that these advantages make complicated data analysis easier and more approachable. Notably, R also allows us to take the role of just a basic user or a developer. For an R user, we only need to know how a function works without requiring detailed knowledge of how it is implemented. Similarly to SPSS, we can perform various types of data analysis through R's interactive shell. On the other hand, as an R developer, we can write their function to create a new model, or they can even wrap implemented functions into a package.

Instead of explaining how to write an R program from scratch, the aim of this book is to cover how to become a developer in R. The main purpose of this chapter is to show users how to define their function to accelerate the analysis procedure. Starting with creating a function, this chapter covers the environment of R, and it explains how to create matching arguments. There is also content on how to perform functional programming in R, how to create advanced functions, such as infix operator and replacement, and how to handle errors and debug functions.