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Home > Programming > Programming Language > R Programming By Example
R Programming By Example
R Programming By Example

R Programming By Example: Practical, hands-on projects to help you get started with R

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R Programming By Example

Understanding Votes with Descriptive Statistics

This chapter shows how to perform a descriptive statistics analysis to get a general idea about the data we're dealing with, which is usually the first step in data analysis projects and is a basic ability for data analysts in general. We will learn how to clean and transform data, summarize data in a useful way, find specific observations, create various kinds of plots that provide intuition for the data, use correlations to understand relations among numerical variables, use principal components to find optimal variable combinations, and put everything together into code that is reusable, understandable, and easily modifiable.

Since this is a book about programming with R and not about doing statistics with R, our focus will be on the programming side of things, not the statistical side. Keep that in mind while reading it...

This chapter's required packages

During this chapter we will make use of the following R packages. If you don't already have them installed, you can look into Appendix, Required Packages for instructions on how do so.

Package

Used for

ggplot2

High-quality graphs

viridis

Color palette for graphs

corrplot

Correlation plots

ggbiplot

Principal components plots

progress

Show progress for iterations

The Brexit votes example

In June 2016, a referendum was held in the United Kingdom (UK) to decide whether or not to remain part of the European Union (EU). 72% of registered voters took part, and of those, 51.2% voted to leave the EU. In February 2017, Martin Rosenbaum, freedom of information specialist at BBC News, published the article, Local Voting Figures Shed New Light on EU Referendum (http://www.bbc.co.uk/news/uk-politics-38762034). He obtained data from 1,070 electoral wards (the smallest administrative division for electoral purposes in the UK), with numbers for Leave and Remain votes in each ward.

Martin Rosenbaum calculated some statistical associations between the proportion of Leave votes in a ward and some of its social, economic, and demographic characteristics by making use of the most recent UK census, which was conducted in 2011. He used his data for a university...

Cleaning and setting up the data

Setting up the data for this example is straightforward. We will load the data, correctly label missing values, and create some new variables for our analysis. Before we start, make sure the data.csv file is in the same directory as the code you're working with, and that your working directory is properly setup. If you don't know how to do so, setting up your working directory is quite easy, you simply call the setwd() function passing the directory you want to use as such. For example, setwd(/home/user/examples/) would use the /home/user/examples directory to look for files, and save files to.

If you don’t know how to do so, setting up your working directory is quite easy, you simply call the setwd() function passing the directory you want to use as such. For example, setwd(/home/user/examples/) would use the /home/user/examples...

Summarizing the data into a data frame

To get a summary of the data, we may execute summary(data) and see the relevant summaries for each type of variable. The summary is tailored for each column's data type. As you can see, numerical variables such as ID and NVotes get a quantile summary, while factor (categorical) variables get a count for each different category, such as AreaType and RegionName. If there are many categories, the summary will show the categories that appear the most and group the rest into a (Other) group, as we can see at the bottom of RegionName.

summary(data)
#>       ID        RegionName              NVotes          Leave
#> Min.   :  1    Length: 1070        Min.   : 1039    Min.   : 287
#> 1st Qu.: 268   Class : character   1st Qu.: 4252    1st Qu.: 1698
#> Median : 536   Mode  : character   Median : 5746    Median : 2874
#> Mean   : 536...

Getting intuition with graphs and correlations

Now that we have some clean data to work with, we will create lots of plots to build intuition about the data. In this chapter, we will work with plots that are easy to create and are used for exploratory purposes. In Chapter 4, Simulating Sales Data and Working with Databases, we will look into publication ready plots that are a little more verbose to create.

Visualizing variable distributions

Our first plot is a simple one and shows the proportion of votes by each RegionName. As you can see in the plot shown below, the London, North West, and West Midlands regions account for around 55 percent of the observations in the data.

Vote Proportion by Region

To create the plot, we...

Creating a new dataset with what we've learned

What we have learned so far in this chapter is that age, education, and ethnicity are important factors in understanding the way people voted in the Brexit Referendum. Younger people with higher education levels are related with votes in favor of remaining in the EU. Older white people are related with votes in favor of leaving the EU. We can now use this knowledge to make a more succinct data set that incorporates this knowledge. First we add relevant variables, and then we remove non-relevant variables.

Our new relevant variables are two groups of age (adults below and above 45), two groups of ethnicity (whites and non-whites), and two groups of education (high and low education levels):

data$Age_18to44 <- (
    data$Age_18to19 +
    data$Age_20to24 +
    data$Age_25to29 +
    data$Age_30to44
)
data$Age_45plus <- (
 ...

Building new variables with principal components

Principal Component Analysis (PCA) is a dimensionality reduction technique that is widely used in data analysis when there are many numerical variables, some of which may be correlated, and we would like to reduce the number of dimensions required to understand the data.

It can be useful to help us understand the data, since thinking in more than three dimensions can be problematic, and to accelerate algorithms that are computationally intensive, especially with large numbers of variables. With PCA, we can extract most of the information into only one or two variables constructed in a very specific way, such that they capture the most variance while having the added benefit of being uncorrelated among them by construction.

The first principal component is a linear combination of the original variables which captures the maximum...

Putting it all together into high-quality code

Now that we have the fundamentals about analyzing data with descriptive statistics, we're going to improve our code's structure and flexibility by breaking it up into functions. Even though this is common knowledge among efficient programmers, it's not a common practice among data analysts. Many data analysts would simply paste the code we have developed all together, as-is, into a single file, and run it every time they wanted to perform the analysis. We won't be adding new features to the analysis. All we'll do is reorder code into functions to encapsulate their inner-workings and communicate intention with function names (this substantially reduces the need for comments).

We'll focus on producing high-quality code that is easy to read, reuse, modify, and fix (in case of bugs). The way we actually do...

Summary

This chapter showed how to perform a qualitative analysis that is useful as a first step when doing data analysis. We showed some descriptive statistics techniques and how to implement them programmatically. With these skills, we are able to perform simple yet powerful analyses and save the results for later use. Specifically, we showed how to do basic data cleaning, how to create graphs programmatically, how to create matrix scatter plots and matrix correlations, how to perform Principal Component Analysis, and how to combine these tools to understand the data at hand. Finally, we touched on the basics of high-quality code and showed how to transform your initial data analysis code into programs that are modular, flexible, and easy to work with.

In Chapter 3, Predicting Votes with Linear Models, we'll show how to extend the current analysis with qualitative tools...

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Key benefits

  • Get a firm hold on the fundamentals of R through practical hands-on examples
  • Get started with good R programming fundamentals for data science
  • Exploit the different libraries of R to build interesting applications in R

Description

R is a high-level statistical language and is widely used among statisticians and data miners to develop analytical applications. Often, data analysis people with great analytical skills lack solid programming knowledge and are unfamiliar with the correct ways to use R. Based on the version 3.4, this book will help you develop strong fundamentals when working with R by taking you through a series of full representative examples, giving you a holistic view of R. We begin with the basic installation and configuration of the R environment. As you progress through the exercises, you'll become thoroughly acquainted with R's features and its packages. With this book, you will learn about the basic concepts of R programming, work efficiently with graphs, create publication-ready and interactive 3D graphs, and gain a better understanding of the data at hand. The detailed step-by-step instructions will enable you to get a clean set of data, produce good visualizations, and create reports for the results. It also teaches you various methods to perform code profiling and performance enhancement with good programming practices, delegation, and parallelization. By the end of this book, you will know how to efficiently work with data, create quality visualizations and reports, and develop code that is modular, expressive, and maintainable.

Who is this book for?

This books is for aspiring data science professionals or statisticians who would like to learn about the R programming language in a practical manner. Basic programming knowledge is assumed.

What you will learn

  • Discover techniques to leverage R's features, and work with packages
  • Perform a descriptive analysis and work with statistical models using R
  • Work efficiently with objects without using loops
  • Create diverse visualizations to gain better understanding of the data
  • Understand ways to produce good visualizations and create reports for the results
  • Read and write data from relational databases and REST APIs, both packaged and unpackaged
  • Improve performance by writing better code, delegating that code to a more efficient programming language, or making it parallel
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Table of Contents

11 Chapters
Introduction to R Chevron down icon Chevron up icon
Introduction to R
What R is and what it isn't
Comparing R with other software
The interpreter and the console
Tools to work efficiently with R
How to use this book
Tracking state with symbols and variables
Working with data types and data structures
Divide and conquer with functions
Complex logic with control structures
The examples in this book
Summary
Understanding Votes with Descriptive Statistics Chevron down icon Chevron up icon
Understanding Votes with Descriptive Statistics
This chapter's required packages
The Brexit votes example
Cleaning and setting up the data
Summarizing the data into a data frame
Getting intuition with graphs and correlations
Creating a new dataset with what we've learned
Building new variables with principal components
Putting it all together into high-quality code
Summary
Predicting Votes with Linear Models Chevron down icon Chevron up icon
Predicting Votes with Linear Models
Required packages
Setting up the data
Predicting votes with linear models
Checking model assumptions
Measuring accuracy with score functions
Programatically finding the best model
Predicting votes from wards with unknown data
Summary
Simulating Sales Data and Working with Databases Chevron down icon Chevron up icon
Simulating Sales Data and Working with Databases
Required packages
Designing our data tables
Simulating the sales data
Simulating the client data
Simulating the client messages data
Working with relational databases
Summary
Communicating Sales with Visualizations Chevron down icon Chevron up icon
Communicating Sales with Visualizations
Required packages
Extending our data with profit metrics
Building blocks for reusable high-quality graphs
Starting with simple applications for bar graphs
Graphing disaggregated data with boxplots
Scatter plots with joint and marginal distributions
Developing our own graph type – radar graphs
Exploring with interactive 3D scatter plots
Looking at dynamic data with time-series
Looking at geographical data with static maps
Navigating geographical data with interactive maps
Summary
Understanding Reviews with Text Analysis Chevron down icon Chevron up icon
Understanding Reviews with Text Analysis
This chapter's required packages
What is text analysis and how does it work?
Preparing, training, and testing data
Building the corpus with tokenization and data cleaning
Training models with cross validation
Improving our results with TF-IDF
Adding flexibility with N-grams
Reducing dimensionality with SVD
Extending our analysis with cosine similarity
Digging deeper with sentiment analysis
Testing our predictive model with unseen data
Retrieving text data from Twitter
Summary
Developing Automatic Presentations Chevron down icon Chevron up icon
Developing Automatic Presentations
Required packages
Why invest in automation?
Literate programming as a content creation methodology
The basic tools for an automation pipeline
A gentle introduction to Markdown
Header Level  1
Extending Markdown with R Markdown
Developing graphs and analysis as we normally would
Building our presentation with R Markdown
Summary
Object-Oriented System to Track Cryptocurrencies Chevron down icon Chevron up icon
Object-Oriented System to Track Cryptocurrencies
This chapter's required packages
The cryptocurrencies example
A brief introduction to object-oriented programming
Introducing three object models in R – S3, S4, and R6
The architecture behind our cryptocurrencies system
Starting simple with timestamps using S3 classes
Implementing cryptocurrency assets using S4 classes
Implementing our storage layer with R6 classes
Retrieving live data for markets and wallets with R6 classes
Finally introducing users with S3 classes
Helping ourselves with a centralized settings file
Saving our initial user data into the system
Activating our system with two simple functions
Some advice when working with object-oriented systems
Summary
Implementing an Efficient Simple Moving Average Chevron down icon Chevron up icon
Implementing an Efficient Simple Moving Average
Required packages
Starting by using good algorithms
How fast is fast enough?
Calculating simple moving averages inefficiently
Understanding why R can be slow
Measuring by profiling and benchmarking
Easily achieving high benefit - cost improvements
Using parallelization to divide and conquer
Using C++ and Fortran to accelerate calculations
Looking back at what we have achieved
Other topics of interest to enhance performance
Summary
Adding Interactivity with Dashboards Chevron down icon Chevron up icon
Adding Interactivity with Dashboards
Required packages
What is functional reactive programming and why is it useful?
Designing our high-level application structure
Inserting a dynamic data table
Introducing interactivity with user input
Adding a summary table with shared data
Adding a simple moving average graph
Adding interactivity with a secondary zoom-in graph
Styling our application with themes
Other topics of interest
Summary
Required Packages Chevron down icon Chevron up icon
Required Packages
External requirements – software outside of R
Internal requirements – R packages
Loading R packages

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John Beck Aug 29, 2018
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Great content and very well explained. At first I was hesitating about buying it because of the other comment here, but I had heard good things about the book so I bought it, and I’m glad I did. Never had a problem with the data files or source code files. Easy to download and follow, and great material. Thanks! Definitely recommended.
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John Stewart Sep 06, 2019
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I know programming at an intermediate level, and I've recently started to focus in Data Science. This book was the right one for me! It does a great job at introducing R from a programmer's perspective, not only showing you how to do interesting Data Science projects, but actually explaining the underlying programming concepts which I had not seen well explained in other R books. Thanks!
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S N Jul 10, 2018
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Pros:-The book seems to have practical, detailed examples and quality instruction.Major Con:-The data files required to use the book are NOT available. The author only provides links to Git Hub "pointer files", which have to be downloaded via a separate command prompt utility (Git LFS). The author provides vague, incorrect instructions for how to download the files. You will need to spend several hours researching and troubleshooting just to figure out how to correctly run the utility and which commands to run. What's worse: once you get the utility commands to successfully execute, you'll get an error message indicating that the .csv files are not available on the server.-The publisher and author REFUSE to provide accessible links to the .csv files, or correct/detailed instructions for how to retrieve them. If you contact the publisher or author, the author will ignore your feedback about error messages and will simply copy/paste vague instructions from Errata which he posted previously on the publisher's website (i.e. indicating that previous people have complained).I cannot recommend this book at all until the author/publisher corrects this. Very unprofessional behavior.
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R Programming By Example is not worth. Instead to buy this book we have somany alternate books which they explain very clear.
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About the authors

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Profile icon Omar Trejo Navarro
Omar Trejo Navarro
Omar Trejo Navarro is a data consultant. He co-founded Datata, is actively working on CVEST, and maintains a personal website (otrenav.com). He is an applied mathematics and economics double major from ITAM in Mexico City, where he continues to work as a research assistant. He does software development with a focus on data platforms, data science, and web applications. He has worked with clients from all over the world, and is a keen supporter of open source, open data, and open science in general.
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Profile icon Trejo Navarro
Trejo Navarro
Github icon
Omar Trejo Navarro is a data consultant. He co-founded Datata, is actively working on CVEST, and maintains a personal website (otrenav.com). He is an applied mathematics and economics double major from ITAM in Mexico City, where he continues to work as a research assistant. He does software development with a focus on data platforms, data science, and web applications. He has worked with clients from all over the world, and is a keen supporter of open source, open data, and open science in general.
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Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela