Showing posts with label R. Show all posts
Showing posts with label R. Show all posts

Tuesday, 10 June 2025

StanfordOnline: R Programming Fundamentals

 

Deep Dive into StanfordOnline's R Programming Fundamentals: A Launchpad for Data Science Mastery

In an era dominated by data, proficiency in statistical programming is becoming not just an asset, but a necessity across disciplines. Whether you’re in public health, finance, marketing, social sciences, or academia, data analysis informs critical decisions. Among the many tools available for this purpose, R stands out for its power, flexibility, and open-source nature. Recognizing the growing demand for R programming expertise, Stanford University, through its StanfordOnline platform, offers an exceptional course titled “R Programming Fundamentals.”

This blog takes a comprehensive look at this course, breaking down its structure, educational philosophy, theoretical underpinnings, and the real-world skills you’ll develop by the end of it.

Course Snapshot

Title: R Programming Fundamentals

Institution: Stanford University (via StanfordOnline or edX)

Instructor: Typically taught by faculty in the Department of Statistics or Stanford Continuing Studies

Delivery Mode: Fully online, asynchronous

Level: Introductory (no prior programming experience required)

Duration: 6–8 weeks (self-paced)

Certification: Available upon completion (fee-based)

Language: English

Course Objective: Why Learn R?

The course is built on the premise that understanding data is a universal skill. R is a statistical programming language specifically built for data manipulation, computation, and graphical display. With over 10,000 packages in CRAN (the Comprehensive R Archive Network), R is used by statisticians, data scientists, and researchers across disciplines.

Stanford’s course seeks to:

Introduce foundational programming concepts through the lens of data

Develop computational thinking required for statistical inference and modeling

Teach students how to write reusable code for data tasks

Equip learners with the skills to clean, analyze, and visualize data

In-Depth Theoretical Breakdown of Course Modules

1.  Introduction to R and Computational Environment

Theory:

R is an interpreted language, which means you write and execute code line-by-line.

The RStudio IDE is introduced to provide an intuitive interface for coding, debugging, and plotting.

Key Concepts:

Working with the R Console and Script Editor

Understanding R packages and the install.packages() function

Basic syntax: variables, arithmetic operations, and assignments

2. Data Types and Data Structures in R

Theory:

At its core, R is built on vectors. Even scalars in R are vectors of length one. Understanding data types is essential because type mismatches can lead to bugs or erroneous results in statistical operations.

Key Concepts:

Atomic types: logical, integer, double (numeric), character, and complex

Data structures:

Vectors: homogeneous types

Lists: heterogeneous data collections

Matrices and Arrays: multi-dimensional data structures

Data Frames: tabular data with mixed types

Type coercion, indexing, and subsetting rules

3.  Control Flow and Functional Programming

Theory:

Programming is about automating repetitive tasks and making decisions. Control structures are the tools that allow conditional execution and iteration, while functions promote code modularity and reuse.

Key Concepts:

Control structures: if, else, for, while, and repeat loops

Writing and invoking custom functions

Scope rules and the importance of environments in R

Higher-order functions: apply(), lapply(), sapply()

4. Data Import, Cleaning, and Transformation

Theory:

Raw data is often messy and requires significant preprocessing before analysis. This module explores how to bring real-world data into R and transform it into a usable format using the tidyverse philosophy.

Key Concepts:

Reading data with read.csv(), read.table(), and readxl::read_excel()

Handling missing values (NA) and type conversion

Tidy data principles (from Hadley Wickham): each variable forms a column, each observation a row

Data manipulation with dplyr: filter(), mutate(), group_by(), summarize()

5. Data Visualization with R

Theory:

Visualization is a form of exploratory data analysis (EDA), helping uncover patterns, outliers, and relationships. R’s base plotting system and the ggplot2 package (based on the Grammar of Graphics) are introduced.

Key Concepts:

Base R plots: plot(), hist(), boxplot(), barplot()

Introduction to ggplot2: aesthetic mappings (aes), geoms, themes

Constructing multi-layered visualizations

Customizing axes, labels, legends, and colors

6. Statistical Concepts and Inference in R

Theory:

This module introduces foundational concepts in statistics, showing how R can be used not just for computation, but also for performing inference — drawing conclusions about populations from samples.

Key Concepts:

Summary statistics: mean, median, standard deviation, quantiles

Probability distributions: Normal, Binomial, Poisson

Simulations using rnorm(), runif(), etc.

Hypothesis testing: t-tests, proportion tests, chi-squared tests

p-values, confidence intervals, type I and II errors

Hands-On Learning and Pedagogy

The course is highly interactive, designed with both conceptual clarity and real-world application in mind. Each module includes:

Video lectures explaining theory with visual aids

Coding exercises using built-in R notebooks or assignments

Quizzes and assessments for concept reinforcement

Final capstone project analyzing a real dataset (varies by offering)

By the end, learners will have a working R environment set up and a portfolio of scripts and visualizations that demonstrate practical ability.

Why Choose StanfordOnline?

Stanford is a global leader in technology and education. The course benefits from:

Expert instruction from professors and statisticians at Stanford

Access to rigorous academic standards without enrollment in a degree program

A curriculum grounded in both theory and practice

Opportunities to network via forums and alumni platforms

Join Now : StanfordOnline: R Programming Fundamentals

Final Takeaways

StanfordOnline’s R Programming Fundamentals is more than just a beginner's course — it's an invitation into a mindset of analytical thinking, reproducible science, and ethical data use. With its blend of clear theory, practical tasks, and academic excellence, it stands out in the crowded landscape of online courses.StanfordOnline's R Programming Fundamentals course is a robust, accessible introduction to one of the most powerful languages for data analysis. It bridges the gap between theory and practice, empowering learners to use R confidently in academic, research, or professional settings. Whether you're charting your path into data science or just curious about R, this course is a smart, well-structured first step into the world of statistical programming.


Tuesday, 2 January 2024

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.

Saturday, 7 December 2019

Python Vs R for Data Science - One Clear Winner

This video titled "Python Vs R for Data Science One Clear Winner" explains and compare both R and Python language on seven parameters when it comes to machine learning. Although both of these languages have their own strengths and weakness yet we will choose a clear winner based on these parameters.




Tuesday, 30 April 2019

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. 

Buy :

PDF Download :


Friday, 12 April 2019

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.

Tuesday, 26 March 2019

R Projects For Dummies by Joseph Schmuller (Author)

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!

Buy :

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

PDF Download :

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





Monday, 25 March 2019

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

If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you.
Buy :

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

PDF Download :

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




Friday, 22 March 2019

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) 


PDF Download:

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

Tuesday, 19 March 2019

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.

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

PDf Download :


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


Monday, 18 March 2019

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. 

After reading and using this book, you'll understand how to write DSLs in R and have skills you can extrapolate to other programming languages. What You'll Learn Program with domain-specific languages using R Discover the components of DSLs Carry out large matrix expressions and multiplications Implement metaprogramming with DSLs Parse and manipulate expressions Who This Book Is For Those with prior programming experience. R knowledge is helpful but not required.
Buy :

Domain-Specific Languages in R: Advanced Statistical Programming Paperback – Import, 22 Feb 2019 by Thomas Mailund 
PDF Download :

Domain-Specific Languages in R: Advanced Statistical Programming Paperback – Import, 22 Feb 2019 by Thomas Mailund 




Natural Language Processing Recipes

Implement natural language processing applications with Python using a problem-solution approach. This book has numerous coding exercises that will help you to quickly deploy natural language processing techniques, such as text classification, parts of speech identification, topic modeling, text summarization, text generation, entity extraction, and sentiment analysis. 

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. 

Buy :

 
PDF Download :


Friday, 1 March 2019

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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