Parallel Computing With R & Rstudio: Complete Tutorial Guide

R Programming, RStudio, Parallel Computing, Multicore, Data Science, 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.

Requirements

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.

Description

Parallel Computing with R & RStudio: Complete Tutorial Guide!In this course, we'll start by introducing the fundamentals of parallel computing, breaking down how it works. Following that, we'll walk through examples of code that's slow and needs speeding up. We'll then download, install, and explore the R packages designed for this, discussing the advantages and disadvantages of each tool.  We will learn how the R Compiler can be leveraged to optimize parallel computing processes.The goal is to make the complex world of parallel computing 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 Packages will be covered?Learn how to install R packages for parallel computing:purrr: set of tools for working with functions and vectorsdoSNOW: parallel backend of "for" loopsfurrr: combine purrr’s family of mapping functions with future’s parallel processing capabilitiesmultidplyr:  backend for dplyr that spreads work across multiple processesSupporting Packages used:base R: for loops, apply functionsdplyr: data manipulation with a very user-friendly syntax tidyr: data clean-up, remove duplicates, NA's etc.rvest: web scrapingtidytext: text mining for statistical analysisAbout ArkadiArkadi 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 €1.3bn AUM via structured products successfully commercialized by Société Générale, 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.

Overview

Section 1: Introduction

Lecture 1 Introduction and Course Structure

Lecture 2 Workspace Setup

Lecture 3 Download R Scripts from Github for Coding Sessions

Section 2: Fundamental Concepts of Parallel Computing

Lecture 4 Parallel Computing - Sequential, Sessions and Cores

Section 3: Introduction to Inefficient Code

Lecture 5 Running Slow Codes in R

Lecture 6 Coding Session: Scrapping Wikipedia

Section 4: Slow Data Mining Script

Lecture 7 Coding Session: Real Example of Slow Web Scrapping

Lecture 8 Coding Session: Sequential Script Execution

Section 5: Error Handling in R Scripts

Lecture 9 Coding Session: A Deep Dive into Error Handling in R

Section 6: Parallelizing For Loops

Lecture 10 Coding Session: doSNOW and foreach

Section 7: furrr Package for Enhanced Parallelization

Lecture 11 Coding Session: future parallel processing

Section 8: Advanced Parallelization with multidplyr

Lecture 12 Coding Session: Data Manipulation with multidplyr

Section 9: Coding Session - Wordcloud of Results

Lecture 13 Coding Session: Text Visualization with wordcloud

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.