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


The unsupervised machine learning method focuses on revealing the hidden structure of unlabeled data. A key difference between unsupervised learning and supervised learning is that the latter method employs labeled data as learners. Therefore, one can evaluate the model based on known answers. In contrast, one cannot evaluate unsupervised learning as it does not have any known answers. Mostly, unsupervised learning focuses on two main areas: clustering and dimension reduction.

Clustering is a technique used to group similar objects (close in terms of distance) together in the same group (cluster). Clustering analysis does not use any label information, but simply uses the similarity between data features to group them into clusters.

Dimension reduction is a technique that focuses on removing irrelevant and redundant data to reduce the computational cost and avoid overfitting; you can reduce the features into a smaller subset without a significant loss of information. Dimension...