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


Most R users will agree that data frames provide a flexible and expressive structure for tabular data. While data frames are effective for small datasets, they are not ideal to use when processing data that is larger than a Gigabyte in size. Additionally, it is not easy to summarize data within the data frame itself; we need to load an additional package, such as plyr or reshape2, to perform advanced aggregation. Therefore, we would like to introduce how to use data.table and dplyr to perform descriptive statistics.

We first illustrate what these two packages do:

  • data.table: This is an extension of data.frame; it provides the ability to quickly aggregate and process large datasets. Additionally, it provides a much more readable and less confusing syntax compared to data frames.

  • dplyr: This provides users with SQL-like functions so that we can quickly aggregate and summarize data from various sources.

These two packages can help users quickly and easily generate descriptive statistics...