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

Reading and writing CSV files

In the previous recipe, we downloaded the historical S&P 500 market index from Yahoo Finance. We can now read the data into an R session for further examination and manipulation. In this recipe, we demonstrate how to read a file with an R function.

Getting ready

In this recipe, you need to have followed the previous recipe by downloading the S&P 500 market index text file to the current directory.

How to do it…

Please perform the following steps to read text data from the CSV file.

  1. First, determine the current directory with getwd, and use list.files to check where the file is, as follows:

    > getwd()
    > list.files('./')
  2. You can then use the read.table function to read data by specifying the comma as the separator:

    > stock_data <- read.table('snp500.csv', sep=',' , header=TRUE)
  3. Next, filter data by selecting the first six rows with column Date, Open, High, Low, and Close:

    > subset_data <- stock_data[1:6, c("Date", "Open", "High", "Low", "Close...