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


In the previous chapter, we covered how to integrate data from various data sources. However, simply collecting data is not enough; you also have to ensure the quality of the collected data. If the quality of data used is insufficient, the results of the analysis may be misleading due to biased samples or missing values. Moreover, if the collected data is not well structured and shaped, you may find it hard to correlate and investigate the data. Therefore, data preprocessing and preparation is an essential task that you must perform prior to data analysis.

Those of you familiar with how SQL operates may already understand how to use databases to process data. For example, SQL allows users to add new records with the insert operation, modify data with the update operation, and remove records with the delete operation. However, we do not need to move collected data back to the database; R already provides more powerful and convenient preprocessing functions and packages. In this...