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


After completing data analysis, it is important to document the research results and share the findings with others. The most common methods involve documenting results through text, slides, or web pages. However, these formats are generally limited to only sharing the results and don't often include the research process. As a result, other researchers cannot reproduce the research, making it hard to fully understand how the author conducted the analysis. Without knowing how to replicate the research, the authenticity of the study may be questioned.

R scripts can be used to perform data analysis and generate figures, and it is possible to create a reproducible report by copying all the code and images into a document. However, as this is quite labor intensive, there is a risk of making errors. A better solution is to automate the documentation process. This allows the user to dynamically generate a report, in any format, which records both scripts and the analysis results.