Book Image

R Programming By Example

By : Omar Trejo Navarro
Book Image

R Programming By Example

By: Omar Trejo Navarro

Overview of this book

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.

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Table of Contents (12 chapters)
Preface
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
Introduction to R
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
2
Understanding Votes with Descriptive Statistics
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
3
Predicting Votes with Linear Models
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
4
Simulating Sales Data and Working with Databases
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
5
Communicating Sales with Visualizations
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
6
Understanding Reviews with Text Analysis
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
7
Developing Automatic Presentations
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
8
Object-Oriented System to Track Cryptocurrencies
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
9
Implementing an Efficient Simple Moving Average
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
10
Adding Interactivity with Dashboards
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
11
Required Packages
Required Packages
External requirements – software outside of R
Internal requirements – R packages
Loading R packages

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...
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End of Section 5
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