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

Creating time series data

To begin time series analysis, we need to create a time series object from a numeric vector or matrix. In this recipe, we introduce how to create a time series object from the financial report of Taiwan Semiconductor (2330.TW) with the ts function.

Getting ready

Download the tw2330_finance.csv dataset from the following GitHub link:

How to do it…

Please perform the following steps to create time series data:

  1. First, read Taiwan Semiconductor's financial report into an R session:

    > tw2330 = read.csv('tw2330_finance.csv', header=TRUE)
    > str(tw2330)
    'data.frame': 32 obs. of  5 variables:
     $ Time            : Factor w/ 32 levels "2008Q1","2008Q2",..: 1 2 3 4 5 6 7 8 9 10 ...
     $ Total.Income    : int  875 881 930 646 395 742 899 921 922 1050 ...
     $ Gross.Sales     : num  382 402 431 202 74.8 343 429 447 442 519 ...
     $ Operating.Income: num  291 304 329 120 12.1 251 320 336 341 405 ...
     $ EPS ...