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

Managing data with a data.table

The two major advantages of a data.table as compared to a data.frame are the speed and clearer syntax of the former. Similar to a data.frame, we can perform operations to slice and subset a data.table. Here, we introduce some operations that you can perform on data.table.

Getting ready

Ensure that you completed the Enhancing a data.frame with a data.table recipe to load and as both data.frame and data.table into your R environment.

How to do it…

Perform the following steps to perform data manipulation on data.table:

  1. First, use the head function to view the first three rows:

    > head(purchase.dt[1:3])
                      Time Action         User     Product
    1: 2015-07-01 00:00:01   view   U129297265 P0023468384
    2: 2015-07-01 00:00:03   view   U321001337 P0018926456
    3: 2015-07-01 00:00:05   view U10070718237 P0000063593
    > head(purchase[1:3])
                     Time Action         User
    1 2015-07-01 00:00:01   view   U129297265...