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Home > Data > Data Science > R for Data Science Cookbook (n)
R for Data Science Cookbook (n)
R for Data Science Cookbook (n)

R for Data Science Cookbook (n): Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques

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Profile Icon Yu-Wei, Chiu (David Chiu)
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R for Data Science Cookbook (n)

Chapter 2. Data Extracting, Transforming, and Loading

This chapter covers the following topics:

  • Downloading open data
  • Reading and writing CSV files
  • Scanning text files
  • Working with Excel files
  • Reading data from databases
  • Scraping web data
  • Accessing Facebook data
  • Working with twitteR

Introduction

Before using data to answer critical business questions, the most important thing is to prepare it. Data is normally archived in files, and using Excel or text editors allows it to be easily obtained. However, data can be located in a range of different sources, such as databases, websites, and various file formats. Being able to import data from these sources is crucial.

There are four main types of data. Data recorded in text format is the simplest. As some users require storing data in a structured format, files with a .tab or .csv extension can be used to arrange data in a fixed number of columns. For many years, Excel has had a leading role in the field of data processing, and this software uses the .xls and .xlsx formats. Knowing how to read and manipulate data from databases is another crucial skill. Moreover, as most data is not stored in a database, one must know how to use the web scraping technique to obtain data from the Internet. As part of this chapter, we introduce...

Downloading open data

Before conducting any data analysis, an essential step is to collect high-quality, meaningful data. One important data source is open data, which is selected, organized, and freely available to the public. Most open data is published online in either text format or as APIs. Here, we introduce how to download the text format of an open data file with the download.file function.

Getting ready

In this recipe, you need to prepare your environment with R installed and a computer that can access the Internet.

How to do it…

Please perform the following steps to download open data from the Internet:

  1. First, visit the http://finance.yahoo.com/q/hp?s=%5EGSPC+Historical+Prices link to view the historical price of the S&P 500 in Yahoo Finance:
    How to do it…

    Figure 1: Historical price of S&P 500

  2. Scroll down to the bottom of the page, right-click and copy the link in Download to Spreadsheet (the link should appear similar to http://real-chart.finance.yahoo.com/table.csv?s=%5EGSPC&d...

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...

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...

Working with Excel files

Excel is another popular tool used to store and analyze data. Of course, one can convert Excel files to CSV files or other text formats by using Excel. Alternatively, to simplify the process, you can use install and load the xlsx package to read and process Excel data in R.

Getting ready

In this recipe, you need to prepare your environment with R installed and a computer that can access the Internet.

How to do it…

Please perform the following steps to read Excel documents:

  1. First, install and load the xlsx package:
    > install.packages("xlsx")
> library(xlsx)
  2. Access www.data.worldbank.org/topic/economy-and-growth to find world economy indicator data in Excel:
    How to do it…

    Figure 6: World economy indicator

  3. Download world economy indicator data from the following URL using download.file:
    > download.file("http://api.worldbank.org/v2/en/topic/3?downloadformat=excel", "worldbank.xls", mode="wb")
  4. Examine the downloaded file with Excel (or...

Reading data from databases

As R reads data into memory, it is perfect for processing and analyzing small datasets. However, as an enterprise accumulates much more data than individuals in their daily lives, database documents are becoming more common for the purpose of storing and analyzing bigger data. To access databases with R, one can use RJDBC, RODBC, or RMySQL as the communications bridge. In this section, we will demonstrate how to use RJDBC to connect data stored in the database.

Getting ready

In this section, we need to prepare a MySQL environment first. If you have a MySQL environment installed on your machine (Windows), you can inspect server status from MySQL Notifier. If the local server is running, the server status should prompt localhost (Online), as shown in the following screenshot:

Getting ready

Figure 8: MySQL Notifier

Once we have our database server online, we need to validate whether we are authorized to access the database with a given username and password by using any database...

Introduction

Before using data to answer critical business questions, the most important thing is to prepare it. Data is normally archived in files, and using Excel or text editors allows it to be easily obtained. However, data can be located in a range of different sources, such as databases, websites, and various file formats. Being able to import data from these sources is crucial.

There are four main types of data. Data recorded in text format is the simplest. As some users require storing data in a structured format, files with a .tab or .csv extension can be used to arrange data in a fixed number of columns. For many years, Excel has had a leading role in the field of data processing, and this software uses the .xls and .xlsx formats. Knowing how to read and manipulate data from databases is another crucial skill. Moreover, as most data is not stored in a database, one must know how to use the web scraping technique to obtain data from the Internet. As part of this chapter, we introduce...

Downloading open data

Before conducting any data analysis, an essential step is to collect high-quality, meaningful data. One important data source is open data, which is selected, organized, and freely available to the public. Most open data is published online in either text format or as APIs. Here, we introduce how to download the text format of an open data file with the download.file function.

Getting ready

In this recipe, you need to prepare your environment with R installed and a computer that can access the Internet.

How to do it…

Please perform the following steps to download open data from the Internet:

  1. First, visit the http://finance.yahoo.com/q/hp?s=%5EGSPC+Historical+Prices link to view the historical price of the S&P 500 in Yahoo Finance:

    Figure 1: Historical price of S&P 500

  2. Scroll down to the bottom of the page, right-click and copy the link in Download to Spreadsheet (the link should appear similar to http://real-chart.finance.yahoo.com/table.csv?s=%5EGSPC&d=6&e...

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...
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Page 1 of 10

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Key benefits

  • • Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages
  • • Understand how to apply useful data analysis techniques in R for real-world applications
  • • An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis

Description

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.

Who is this book for?

This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages.

What you will learn

  • • Get to know the functional characteristics of R language
  • • Extract, transform, and load data from heterogeneous sources
  • • Understand how easily R can confront probability and statistics problems
  • • Get simple R instructions to quickly organize and manipulate large datasets
  • • Create professional data visualizations and interactive reports
  • • Predict user purchase behavior by adopting a classification approach
  • • Implement data mining techniques to discover items that are frequently purchased together
  • • Group similar text documents by using various clustering methods

Product Details

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Publication date : Jul 29, 2016
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781784392048
Category :
Data
Languages :
R
Concepts :
Data Science
Tools :
ggplot

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Publication date : Jul 29, 2016
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781784392048
Category :
Data
Languages :
R
Concepts :
Data Science
Tools :
ggplot

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Table of Contents

13 Chapters
1. Functions in R Chevron down icon Chevron up icon
1. Functions in R
Introduction
Creating R functions
Matching arguments
Understanding environments
Working with lexical scoping
Understanding closure
Performing lazy evaluation
Creating infix operators
Using the replacement function
Handling errors in a function
The debugging function
2. Data Extracting, Transforming, and Loading Chevron down icon Chevron up icon
2. Data Extracting, Transforming, and Loading
Introduction
Downloading open data
Reading and writing CSV files
Scanning text files
Working with Excel files
Reading data from databases
Scraping web data
Accessing Facebook data
Working with twitteR
3. Data Preprocessing and Preparation Chevron down icon Chevron up icon
3. Data Preprocessing and Preparation
Introduction
Renaming the data variable
Converting data types
Working with the date format
Adding new records
Filtering data
Dropping data
Merging data
Sorting data
Reshaping data
Detecting missing data
Imputing missing data
4. Data Manipulation Chevron down icon Chevron up icon
4. Data Manipulation
Introduction
Enhancing a data.frame with a data.table
Managing data with a data.table
Performing fast aggregation with a data.table
Merging large datasets with a data.table
Subsetting and slicing data with dplyr
Sampling data with dplyr
Selecting columns with dplyr
Chaining operations in dplyr
Arranging rows with dplyr
Eliminating duplicated rows with dplyr
Adding new columns with dplyr
Summarizing data with dplyr
Merging data with dplyr
5. Visualizing Data with ggplot2 Chevron down icon Chevron up icon
5. Visualizing Data with ggplot2
Introduction
Creating basic plots with ggplot2
Changing aesthetics mapping
Introducing geometric objects
Performing transformations
Adjusting scales
Faceting
Adjusting themes
Combining plots
Creating maps
6. Making Interactive Reports Chevron down icon Chevron up icon
6. Making Interactive Reports
Introduction
Creating R Markdown reports
Learning the markdown syntax
Embedding R code chunks
Creating interactive graphics with ggvis
Understanding basic syntax and grammar
Controlling axes and legends
Using scales
Adding interactivity to a ggvis plot
Creating an R Shiny document
Publishing an R Shiny report
7. Simulation from Probability Distributions Chevron down icon Chevron up icon
7. Simulation from Probability Distributions
Introduction
Generating random samples
Understanding uniform distributions
Generating binomial random variates
Generating Poisson random variates
Sampling from a normal distribution
Sampling from a chi-squared distribution
Understanding Student's t-distribution
Sampling from a dataset
Simulating the stochastic process
8. Statistical Inference in R Chevron down icon Chevron up icon
8. Statistical Inference in R
Introduction
Getting confidence intervals
Performing Z-tests
Performing student's T-tests
Conducting exact binomial tests
Performing Kolmogorov-Smirnov tests
Working with the Pearson's chi-squared tests
Understanding the Wilcoxon Rank Sum and Signed Rank tests
Conducting one-way ANOVA
Performing two-way ANOVA
9. Rule and Pattern Mining with R Chevron down icon Chevron up icon
9. Rule and Pattern Mining with R
Introduction
Transforming data into transactions
Displaying transactions and associations
Mining associations with the Apriori rule
Pruning redundant rules
Visualizing association rules
Mining frequent itemsets with Eclat
Creating transactions with temporal information
Mining frequent sequential patterns with cSPADE
10. Time Series Mining with R Chevron down icon Chevron up icon
10. Time Series Mining with R
Introduction
Creating time series data
Plotting a time series object
Decomposing time series
Smoothing time series
Forecasting time series
Selecting an ARIMA model
Creating an ARIMA model
Forecasting with an ARIMA model
Predicting stock prices with an ARIMA model
11. Supervised Machine Learning Chevron down icon Chevron up icon
11. Supervised Machine Learning
Introduction
Fitting a linear regression model with lm
Summarizing linear model fits
Using linear regression to predict unknown values
Measuring the performance of the regression model
Performing a multiple regression analysis
Selecting the best-fitted regression model with stepwise regression
Applying the Gaussian model for generalized linear regression
Performing a logistic regression analysis
Building a classification model with recursive partitioning trees
Visualizing a recursive partitioning tree
Measuring model performance with a confusion matrix
Measuring prediction performance using ROCR
12. Unsupervised Machine Learning Chevron down icon Chevron up icon
12. Unsupervised Machine Learning
Introduction
Clustering data with hierarchical clustering
Cutting tree into clusters
Clustering data with the k-means method
Clustering data with the density-based method
Extracting silhouette information from clustering
Comparing clustering methods
Recognizing digits using the density-based clustering method
Grouping similar text documents with k-means clustering methods
Performing dimension reduction with Principal Component Analysis (PCA)
Determining the number of principal components using a scree plot
Determining the number of principal components using the Kaiser method
Visualizing multivariate data using a biplot
Index Chevron down icon Chevron up icon
Index

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(3 Ratings)
5 star 33.3%
4 star 66.7%
3 star 0%
2 star 0%
1 star 0%
Rahul Raoniar Mar 09, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Finally a book for engineering students. After searching 20+ books for Data science with R, finally got it. This book covers almost everything that an engineering student needs during master's or PhD. It covers almost every aspect of data processing like adding, dropping, filtering, merging, shorting and reshaping data set with fantastic libraries like dplyr, data.table and many more. It covers almost every statistical inference and testing techniques such as t-test, ANOVA, Various regression models (Linear, Multiple, Logistic, Step-wise regression etc), Distributions ( Normal, Poison, Binomial etc). This book also covers time series analysis with ARIMA and machine learning for both supervised and unsupervised with k-mean and other clustering techniques. It has one dedicated chapter on plotting functions like ggplot2, ggvis and mapping with spacial data, which provides awesome plots. This book is for all students and data scientist professional those want to explore the statistical characteristics of data sets. This book covers almost every part of data processing using R. The main advantage of this book is that after every code examples it provides detailed description of methodology involved in code. Like for Chi-Square test we can do it in few lines in R, this book provides the step wise code and at the end of the code it describes the theory and equation involved for calculation, which provides a great help for better understanding the underlying concepts.As a PhD scholar in Transportation Engineering, I can say, this is the best book available in market so far for data science with R.
Amazon Verified review Amazon
Sudipta Chakraborty Oct 06, 2017
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Today i received it from amazon as expected. Thank you amazon. This book is really good and pretty much helpful for those who are seeking a career in data science or data analytics domain. As of now i am giving 4 star once i done with this book i will be reviewing once again. Thanks
Amazon Verified review Amazon
Prashant Sharma Nov 07, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Its a good book. Must have for all R programmers
Amazon Verified review Amazon

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Machine Learning Engineering with Python
Machine Learning Engineering with Python
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Aug 2023 462 pages
Full star icon 4.6 (37)
eBook
eBook
  • eBook eBook R$80
  • Paperback Paperback R$278.99
R$80 R$222.99
R$278.99
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About the author

Profile icon Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu)
Yu-Wei, Chiu (David Chiu) is the founder of LargitData (www.LargitData.com), a startup company that mainly focuses on providing big data and machine learning products. He has previously worked for Trend Micro as a software engineer, where he was responsible for building big data platforms for business intelligence and customer relationship management systems. In addition to being a start-up entrepreneur and data scientist, he specializes in using Spark and Hadoop to process big data and apply data mining techniques for data analysis. Yu-Wei is also a professional lecturer and has delivered lectures on big data and machine learning in R and Python, and given tech talks at a variety of conferences. In 2015, Yu-Wei wrote Machine Learning with R Cookbook, Packt Publishing. In 2013, Yu-Wei reviewed Bioinformatics with R Cookbook, Packt Publishing. For more information, please visit his personal website at www.ywchiu.com. **********************************Acknowledgement************************************** I have immense gratitude for my family and friends for supporting and encouraging me to complete this book. I would like to sincerely thank my mother, Ming-Yang Huang (Miranda Huang); my mentor, Man-Kwan Shan; the proofreader of this book, Brendan Fisher; Members of LargitData; Data Science Program (DSP); and other friends who have offered their support.
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