Introduction to R Programming for Data Science

 

What you'll learn

Manipulate primitive data types in the R programming language using RStudio or Jupyter Notebooks.

Control program flow with conditions and loops, write functions, perform character string operations, write regular expressions, handle errors. 

Construct and manipulate R data structures, including vectors, factors, lists, and data frames.

Read, write, and save data files and scrape web pages using R. 

Join Free:Introduction to R Programming for Data Science

There are 5 modules in this course

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks. 

You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language. 

The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights.  

 

No prior knowledge of R, or programming is required.

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Learn RStudio IDE

Discover how to use the popular RStudio IDE as a professional tool that includes code refactoring support, debugging, and Git version control integration. This book gives you a tour of RStudio and shows you how it helps you do exploratory data analysis; build data visualizations with ggplot; and create custom R packages and web-based interactive visualizations with Shiny. 

In addition, you will cover common data analysis tasks including importing data from diverse sources such as SAS files, CSV files, and JSON. You will map out the features in RStudio so that you will be able to customize RStudio to fit your own style of coding.

Finally, you will see how to save a ton of time by adopting best practices and using packages to extend RStudio. Learn RStudio IDE is a quick, no-nonsense tutorial of RStudio that will give you a head start to develop the insights you need in your data science projects.

What You Will Learn

• Quickly, effectively, and productively use RStudio IDE for building data science applications
• Install RStudio and program your first Hello World application
• Adopt the RStudio workflow 
• Make your code reusable using RStudio
• Use RStudio and Shiny for data visualization projects
• Debug your code with RStudio 
• Import CSV, SPSS, SAS, JSON, and other data

Who This Book Is For

Programmers who want to start doing data science, but don’t know what tools to focus on to get up to speed quickly. 

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Learn RStudio IDE: Quick, Effective, and Productive Data Science Kindle Edition by Matthew Campbell

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Learn RStudio IDE: Quick, Effective, and Productive Data Science Kindle Edition by Matthew Campbell 

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Scatter Plots in R Language

Scatterplots show many points plotted in the Cartesian plane. Each point represents the values of two variables. One variable is chosen in the horizontal axis and another in the vertical axis.

The simple scatterplot is created using the plot()function.

Syntax

The basic syntax for creating scatterplot in R is −

plot(x, y, main, xlab, ylab, xlim, ylim, axes)

Following is the description of the parameters used −

x is the data set whose values are the horizontal coordinates.

y is the data set whose values are the vertical coordinates.

main is the tile of the graph.

xlab is the label in the horizontal axis.

ylab is the label in the vertical axis.

xlim is the limits of the values of x used for plotting.

ylim is the limits of the values of y used for plotting.

axes indicates whether both axes should be drawn on the plot.

Example

We use the data set "mtcars" available in the R environment to create a basic scatterplot. Let's use the columns "wt" and "mpg" in mtcars.

input <- mtcars[,c('wt','mpg')] print(head(input))

When we execute the above code, it produces the following result −

wt mpg Mazda RX4 2.620 21.0 Mazda RX4 Wag 2.875 21.0 Datsun 710 2.320 22.8 Hornet 4 Drive 3.215 21.4 Hornet Sportabout 3.440 18.7 Valiant 3.460 18.1

Creating the Scatterplot

The below script will create a scatterplot graph for the relation between wt(weight) and mpg(miles per gallon).

# Get the input values. input <- mtcars[,c('wt','mpg')] 

# Give the chart file a name. png(file = "scatterplot.png") 

# Plot the chart for cars with weight between 2.5 to 5 and mileage between 15 and 30. plot(x = input$wt,y = input$mpg, xlab = "Weight", ylab = "Milage", xlim = c(2.5,5), ylim = c(15,30), main = "Weight vs Milage" )

 # Save the file. dev.off()

When we execute the above code, it produces the following result −

Scatterplot Matrices

When we have more than two variables and we want to find the correlation between one variable versus the remaining ones we use scatterplot matrix. We use pairs() function to create matrices of scatterplots.

SYNTAX

The basic syntax for creating scatterplot matrices in R is −

pairs(formula, data)

Following is the description of the parameters used −

formula represents the series of variables used in pairs.

data represents the data set from which the variables will be taken.

EXAMPLE

Each variable is paired up with each of the remaining variable. A scatterplot is plotted for each pair.

# Give the chart file a name. png(file = "scatterplot_matrices.png") # Plot the matrices between 4 variables giving 12 plots. 

# One variable with 3 others and total 4 variables. pairs(~wt+mpg+disp+cyl,data = mtcars, main = "Scatterplot Matrix") # Save the file. dev.off()

When the above code is executed we get the following output.

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R Projects For Dummies offers a unique learn-by-doing approach. You will increase the depth and breadth of your R skillset by completing a wide variety of projects. By using R’s graphics, interactive, and machine learning tools, you’ll learn to apply R’s extensive capabilities in an array of scenarios. 

The depth of the project experience is unmatched by any other content online or in print. And you just might increase your statistics knowledge along the way, too!

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R Projects For Dummies Paperback – 2018 by Joseph Schmuller (Author) 

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R Projects For Dummies Paperback – 2018 by Joseph Schmuller (Author) 

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Building a Recommendation System with R by Suresh K. Gorakala (Author), Michele Usuelli (Author)

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Building a Recommendation System with R Paperback – Import, 29 Sep 2015 by Suresh K. Gorakala (Author), Michele Usuelli (Author) 

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Building a Recommendation System with R Paperback – Import, 29 Sep 2015 by Suresh K. Gorakala (Author), Michele Usuelli (Author) 

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RStudio for R Statistical Computing Cookbook Paperback – Import, 6 Jan 2016 by Andrea Cirillo (Author)

Over 50 practical and useful recipes to help you perform data analysis with R by unleashing every native RStudio feature About This Book * 54 useful and practical tasks to improve working systems * Includes optimizing performance and reliability or uptime, reporting, system management tools, interfacing to standard data ports, and so on * Offers 10-15 real-life, practical improvements for each user type Who This Book Is For This book is targeted at R statisticians, data scientists, and R programmers. Readers with R experience who are looking to take the plunge into statistical computing will find this Cookbook particularly indispensable. What You Will Learn * Familiarize yourself with the latest advanced R console features * Create advanced and interactive graphics * Manage your R project and project files effectively * Perform reproducible statistical analyses in your R projects *

 Use RStudio to design predictive models for a specific domain-based application * Use RStudio to effectively communicate your analyses results and even publish them to a blog * Put yourself on the frontiers of data science and data monetization in R with all the tools that are needed to effectively communicate your results and even transform your work into a data product In Detail The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. 
This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on.
 In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications. Style and approach RStudio is an open source Integrated Development Environment (IDE) for the R platform. The R programming language is used for statistical computing and graphics, which RStudio facilitates and enhances through its integrated environment.

 This Cookbook will help you learn to write better R code using the advanced features of the R programming language using RStudio. Readers will learn advanced R techniques to compute the language and control object evaluation within R functions. Some of the contents are: * Accessing an API with R * Substituting missing values by interpolation * Performing data filtering activities * R Statistical implementation for Geospatial data * Developing shiny add-ins to expand RStudio functionalities * Using GitHub with RStudio * Modelling a recommendation engine with R * Using R Markdown for static and dynamic reporting * Curating a blog through RStudio * Advanced statistical modelling with R and RStudio
Buy :

RStudio for R Statistical Computing Cookbook Paperback – Import, 6 Jan 2016 by Andrea Cirillo (Author) 


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RStudio for R Statistical Computing Cookbook Paperback – Import, 6 Jan 2016 by Andrea Cirillo (Author) 

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Beginning R: The Statistical Programming Language Paperback – 2013 by Mark Gardener (Author)

This book is about data analysis and the programming language called R. This is rapidly becoming the de-facto standard amongst professionals and is used in every conceivable discipline from science and medicine to business and engineering.

This book delves into the language of R and makes it accessible using simple data examples to explore its power and versatility. In learning how to "speak R" you will unlock its potential and gain better insights into tackling even the most complex of data analysis tasks.

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 Beginning R: The Statistical Programming Language Paperback – 2013 by Mark Gardener (Author)

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Domain-Specific Languages in R

Gain an accelerated introduction to domain-specific languages in R, including coverage of regular expressions. This compact, in-depth book shows you how DSLs are programming languages specialized for a particular purpose, as opposed to general purpose programming languages. 

Along the way, you'll learn to specify tasks you want to do in a precise way and achieve programming goals within a domain-specific context. Domain-Specific Languages in R includes examples of DSLs including large data sets or matrix multiplication; pattern matching DSLs for application in computer vision; and DSLs for continuous time Markov chains and their applications in data science. 

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Domain-Specific Languages in R: Advanced Statistical Programming Paperback – Import, 22 Feb 2019 by Thomas Mailund 
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Natural Language Processing Recipes starts by offering solutions for cleaning and preprocessing text data and ways to analyze it with advanced algorithms. You’ll see practical applications of the semantic as well as syntactic analysis of text, as well as complex natural language processing approaches that involve text normalization, advanced preprocessing, POS tagging, and sentiment analysis. You will also learn various applications of machine learning and deep learning in natural language processing.

By using the recipes in this book, you will have a toolbox of solutions to apply to your own projects in the real world, making your development time quicker and more efficient. 

What You Will Learn

• Apply NLP techniques using Python libraries such as NLTK, TextBlob, spaCy, Stanford CoreNLP, and many more
• Implement the concepts of information retrieval, text summarization, sentiment analysis, and other advanced natural language processing techniques.
• Identify machine learning and deep learning techniques for natural language processing and natural language generation problems

Who This Book Is For

Data scientists who want to refresh and learn various concepts of natural language processing through coding exercises. 

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Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python Paperback – Import, 30 Jan 2019 by Akshay Kulkarni (Author), Adarsha Shivananda (Author)

 

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Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python Paperback – Import, 30 Jan 2019 by Akshay Kulkarni (Author), Adarsha Shivananda (Author) 

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R packages

R packages are a collection of R functions, complied code and sample data. They are stored under a directory called "library" in the R environment. By default, R installs a set of packages during installation. More packages are added later, when they are needed for some specific purpose. When we start the R console, only the default packages are available by default. Other packages which are already installed have to be loaded explicitly to be used by the R program that is going to use them.

All the packages available in R language are listed at R Packages.

Below is a list of commands to be used to check, verify and use the R packages.

Check Available R Packages

Get library locations containing R packages

.libPaths()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

[2] "C:/Program Files/R/R-3.2.2/library"

Get the list of all the packages installed

library()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

Packages in library ‘C:/Program Files/R/R-3.2.2/library’:

base                    The R Base Package
boot                    Bootstrap Functions (Originally by Angelo Canty
                        for S)
class                   Functions for Classification
cluster                 "Finding Groups in Data": Cluster Analysis
                        Extended Rousseeuw et al.
codetools               Code Analysis Tools for R
compiler                The R Compiler Package

Get all packages currently loaded in the R environment

search()

When we execute the above code, it produces the following result. It may vary depending on the local settings of your pc.

[1] ".GlobalEnv"        "package:stats"     "package:graphics" 
[4] "package:grDevices" "package:utils"     "package:datasets" 
[7] "package:methods"   "Autoloads"         "package:base" 

Install a New Package

There are two ways to add new R packages. One is installing directly from the CRAN directory and another is downloading the package to your local system and installing it manually.

INSTALL DIRECTLY FROM CRAN

The following command gets the packages directly from CRAN webpage and installs the package in the R environment. You may be prompted to choose a nearest mirror. Choose the one appropriate to your location.

 install.packages("Package Name")
 
# Install the package named "XML".
 install.packages("XML")

INSTALL PACKAGE MANUALLY

Go to the link R Packages to download the package needed. Save the package as a .zip file in a suitable location in the local system.

Now you can run the following command to install this package in the R environment.

install.packages(file_name_with_path, repos = NULL, type = "source")

# Install the package named "XML"
install.packages("E:/XML_3.98-1.3.zip", repos = NULL, type = "source")

Load Package to Library

Before a package can be used in the code, it must be loaded to the current R environment. You also need to load a package that is already installed previously but not available in the current environment.

A package is loaded using the following command −

library("package Name", lib.loc = "path to library")

# Load the package named "XML"
install.packages("E:/XML_3.98-1.3.zip", repos = NULL, type = "source")

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Learn R in a Day

'Learn R in a Day' provides the reader with key programming skills through an examples-oriented approach and is ideally suited for academics, scientists, mathematicians and engineers. The book assumes no prior knowledge of computer programming and progressively covers all the essential steps needed to become confident and proficient in using R within a day. 

Learn R in a Day

 Learn R in a Day
Topics include how to input, manipulate, format, iterate (loop), query, perform basic statistics on, and plot data, via a step-by-step technique and demonstrations using in-built datasets which the reader is encouraged to replicate on their computer. Each chapter also includes exercises (with solutions) to practice key skills and empower the reader to build on the essentials gained during this introductory course.
Learn R in a Day  

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