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

Scanning text files

In previous recipes, we introduced how to use read.table and read.csv to load data into an R session. However, read.table and read.csv only work if the number of columns is fixed and the data size is small. To be more flexible in data processing, we will demonstrate how to use the scan function to read data from the file.

Getting ready

In this recipe, you need to have completed the previous recipes and have snp500.csv downloaded in the current directory.

How to do it…

Please perform the following steps to scan data from the CSV file:

  1. First, you can use the scan function to read data from snp500.csv:

    > stock_data3 <- scan('snp500.csv',sep=',', what=list(Date = '', Open = 0, High = 0, Low = 0,Close = 0, Volume = 0, Adj_Close = 0),  skip=1, fill=T)
    Read 16481 records
  2. You can then examine loaded data with mode and str:

    > mode(stock_data3)
    [1] "list"
    > str(stock_data3)
    List of 7
     $ Date     : chr [1:16481] "2015-07-02" "2015-07-01" "2015-06-30" "2015-06-29" ...
     $ Open...