Parallel Programming with R & RStudio: Complete Tutorial

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Arkadi Avanesyan

1:30:07

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1. Introduction

1. Introduction and Course Structure.mp4

01:21

2. Workspace Setup.mp4

05:15

3.1 Download Git URL.html

3.2 Github Repo with Code necessary for this Course.html

3.3 RParallelCompute-main.zip

3. Download R Scripts from Github for Coding Sessions.mp4

04:16

2. Fundamental Concepts of Parallel Computing

1. Parallel Computing - Sequential, Sessions and Cores.mp4

06:35

2. Parallel Computing Fundamentals Mastery Quiz.html

3. Introduction to Inefficient Code

1. Running Slow Codes in R.mp4

06:17

2. Coding Session Scrapping Wikipedia.mp4

05:26

4. Slow Data Mining Script

1. Coding Session Real Example of Slow Web Scrapping.mp4

04:05

2. Coding Session Sequential Script Execution.mp4

04:02

5. Error Handling in R Scripts

1. Coding Session A Deep Dive into Error Handling in R.mp4

09:44

6. Parallelizing For Loops

1. Coding Session doSNOW and foreach.mp4

14:42

7. furrr Package for Enhanced Parallelization

1. Coding Session future parallel processing.mp4

17:15

8. Advanced Parallelization with multidplyr

1. Coding Session Data Manipulation with multidplyr.mp4

06:49

9. Coding Session - Wordcloud of Results

1. Coding Session Text Visualization with wordcloud.mp4

04:20

Description

R Programming, RStudio, Parallel Computing, Multicore, R for Data Science, R Speed Optimization

What You'll Learn?

Understand core parallel computing concepts.
Explore essential R packages for parallel computing.
Implement parallel computing on multicore processors.
Improve R programming script and data analysis performance.
Apply parallel computing in practical RStudio data science projects.
Learn to identify and resolve parallel computing issues.
Follow coding best practices for reliable and efficient R programming.
Analyze real-world examples of parallel computing in R and RStudio.

Who is this for?

Novice to Advanced RStudio Users: Individuals at various levels of R proficiency

Professionals handling large datasets in business consulting.

Beginners focusing on R programming before advanced topics.

Data Scientists and RStudio Developers

Excel Users Transitioning to R Programming

R programmers exploring parallel computing.

What You Need to Know?

Familiarity with the R programming language is beneficial.

A general understanding of data analysis in R is helpful.

A computer or laptop with R and RStudio is optional.

Enthusiasm and a willingness to learn R and RStudio.

More details

Description
Parallel Programming with R & RStudio: Complete Tutorial Guide!
In this course, we'll start by introducing the fundamentals of parallel programming with R, breaking down how it works.
Following that, we'll walk through examples of R code that's slow and needs speeding up.
We'll then download R, install, and explore the R packages designed for this, discussing the advantages and disadvantages of each tool.Â We will learn how the R Compiler can be leveraged to optimize parallel programming processes.
The goal is to make the complex world of parallel programming with R accessible and practical for everyone.

Why R, RStudio, and Posit?
R is one of the simplest languages to learn and is very friendly with data manipulation.
R is open source and is part of a large community of developers that create and maintain packages we will explore during this course.
RStudio is probably the best IDE for programmers (also supports C++, Python, SQL, and other languages).
As of the end of 2023, R is rocking it with these cool numbers:
RStudio has an active user base of 3.5 million.
Posit cloud has a 65,000 userbase.
Over the year, an impressive 2 billion packages were downloaded.

Embark on this learning journey today!
Download R and RStudio to get hands-on with parallel computing, and let's unlock the full potential together!
Which R Packages will be covered?
Learn how to install R packages for parallel programming:
purrr:Â set of tools for working with functions and vectors
doSNOW: parallel backend of "for" loops
furrr: combine purrrâ€™s family of mapping functions with futureâ€™s parallel processing capabilities
multidplyr:Â backend for dplyr that spreads work across multiple processes
Supporting Packages used:
base R: for loops, apply functions
dplyr: data manipulation with a very user-friendly syntax
tidyr: data clean-up, remove duplicates, NA's etc.
rvest: web scraping
tidytext:Â text mining for statistical analysis

About Arkadi
Arkadi Avanesyan is a world-class expert in Finance, Investment Banking, Technology, and Data Science.
Arkadi has a BSc in Engineering and MSc in Quantitative Finance from the Free University of Brussels. During his 8-year investment banking career, he contributed to the development of dozens of investable indices with over â‚¬1.3bn AUM via structured products successfully commercialized by SociÃ©tÃ© GÃ©nÃ©rale, Goldman Sachs, Deutsche Bank, and other large European financial institutions.
Since 2019, he has provided consulting services alongside developing business and software solutions for a range of companies across the United States, Europe, and Dubai. His clients include Fortune 500 companies, investment funds, and niche SMEs.
Through codementor, he has mentored over 1,000 clients in data science, finance, and programming, achieving a 5-star rating and becoming a Featured Mentor for 10 consecutive months in 2020.
He has contributed to several international R workshops hosted by Aigora in the field of automation and sensory science. At Aigora, he developed the cloud infrastructure for over 20 projects, and he continues to work with them as an external technical advisor.
Currently, he conducts corporate training, creates high-quality courses, and trains private clients on a one-to-one basis.
Who this course is for:
Novice to Advanced RStudio Users: Individuals at various levels of R proficiency
Professionals handling large datasets in business consulting.
Beginners focusing on R programming before advanced topics.
Data Scientists and RStudio Developers
Excel Users Transitioning to R Programming
R programmers exploring parallel computing.

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R Programming

Arkadi Avanesyan

Instructor's Courses

Arkadi graduated from the Free University of Brussels with a BSc in Engineering and MSc in Quantitative Finance. During his exchange year in Greece, he studied advanced mathematics applied to finance.He started his career in 2013 as an Investment Banker in Luxembourg and transitioned to Programming, Consultancy, and Mentoring in 2019. He is a Codementor with over 1,000 mentoring sessions.In 2022, Arkadi joined the World Bank as an external consultant and for a period of four months trained teams across three time zones worldwide.Currently, he conducts corporate training, creates high-quality courses, and trains private clients on a one-to-one basis.His interests are IT, finance, technology, blockchain and cryptography, history, chess, and strategy online games.He speaks English, French, Russian, Dutch, German, and Greek.

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