# R Programming

> Join Edoxi’s 40-hour online R Programming for Data Analytics course. Learn data wrangling, visualization, & machine learning. Enrol now to unlock data insights.

## Course Details
- Rating: 4.9/5 (100 reviews)
- Category: Software & Technology
- Sub-Category: Programming

## Course Introduction
​Edoxi’s 40-hour online R Programming training equips you with essential skills in data wrangling, visualisation, and statistical analysis.​ Master R fundamentals, Tidyverse packages (dplyr, tidyr, ggplot2), and machine learning model development through hands-on projects and real-world datasets. This R Programming course enables you to transform data into actionable business intelligence. Enrol now to enhance your analytical capabilities and drive data-driven decisions.

## Course Overview
- Delivery Modes: Online
- Course Duration: 40 Hours
- Corporate Days: 5 Days
- Learners Enrolled: 50+
- Modules: 10

## What Do You Learn from Edoxi's R Programming Training

**Data Structures and Manipulation in R**
Learn R fundamentals and learn to organise, manipulate, and transform data efficiently using core R data structures and tidyverse packages.

**Professional Data Visualisation**
Create compelling visualisations using ggplot2 with customised themes, colours, and layouts to effectively communicate insights from complex datasets.

**Statistical Analysis and Hypothesis Testing**
Apply descriptive statistics, probability distributions, and hypothesis testing techniques to make data-driven decisions with confidence.

**Machine Learning Model Development**
Build predictive models using regression, classification techniques, and evaluate model performance using industry-standard metrics.

**Real-world Data Cleaning Techniques**
Handle missing values, outliers, and inconsistencies in datasets using tidyr, dplyr, and other specialised packages for effective preprocessing.

**Data Manipulation with R**
Transform, aggregate, and restructure complex datasets using specialised functions and the tidyverse ecosystem for efficient data processing.

## About This Course
## About Our Online R Programming Course

​Edoxi's 40-hour online R Programming course equips you with essential skills in data wrangling, visualisation, and statistical analysis.​ This R Programming training is designed to help individuals and corporate teams leverage data for business insights, focusing on practical applications across various sectors.

The R Programming for Data Analytics training includes extensive sessions where you work with real-world datasets. From setting up R and RStudio to building predictive models and creating sophisticated visualisations, you gain hands-on experience applicable to workplace challenges. Our curriculum combines statistical foundations with modern R programming techniques using the Tidyverse ecosystem.

Corporate teams particularly benefit from customisable training approaches, with specialised modules designed to address industry-specific data challenges. This ensures you can directly apply their newly acquired skills within their organisational context, transforming data into actionable intelligence and enhancing decision-making capabilities. Upon completing our R Programming course, you will receive Edoxi’s R Programming Course Completion Certificate. This credential validates your expertise in using R for data analysis, visualisation, and statistical modelling. 

Enrol now to advance your analytical capabilities and drive data-driven success in your organisation.

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## Key Features of Edoxi's R Programming Training

**Hands-on RStudio Environment**
Practice in the industry-standard RStudio interface with immediate feedback on coding techniques and analysis approaches.

**Tidyverse Package Ecosystem**
Learn the powerful collection of R packages designed for data science, including dplyr, tidyr, and ggplot2 for efficient data workflows.

**Interactive Coding Sessions**
Engage in live coding with instructors to develop practical skills in data manipulation, visualisation, and modelling techniques.

**Capstone Analytics Project**
Apply all learned concepts in a comprehensive project using real-world datasets from finance, healthcare, or marketing domains.

**Statistical Methods Application**
Implement hypothesis testing, regression analysis, and other statistical techniques to extract meaningful insights from business data.

**Real-World Analytics Activities**
Apply learned concepts to analyse data, create visualisations, and deliver actionable recommendations.

## Who Can Join Edoxi’s Online R Programming Course

**Data and Business Analysts**
Professionals working with data who want to enhance their analytical capabilities using R's powerful statistical and visualisation tools.

**Finance, Marketing and Operations Professionals**
Industry practitioners seeking to apply data-driven approaches to financial modelling, market research, or operational efficiency analysis.

**Research and Academic Personnel**
Researchers and academics who need to perform statistical analysis and create professional visualisations for their work.

**IT Professionals Transitioning to Data Science**
Technical professionals looking to expand their skill set into data science and analytics using R programming.

**Corporate Teams**
Departments seeking to build internal analytical capabilities across team members with consistent methodologies and tools.

**Beginners in Data Analytics**
Individuals with basic computer literacy who want to start their journey in data analysis with R's accessible programming environment.

## R Programming Course Modules

### Module 1: Introduction to R & Setup

**Chapter 1.1: R Programming Fundamentals**
- Lesson 1.1.1: Why R for Data Analytics
- Lesson 1.1.2: Installing R & RStudio

**Chapter 1.2: RStudio Environment**
- Lesson 1.2.1: RStudio Interface (Console, Script, Plots)
- Lesson 1.2.2: Basic R Commands and Operations

### Module 2: Data Structures in R

**Chapter 2.1: Basic Data Structures**
- Lesson 2.1.1: Vectors and Factors
- Lesson 2.1.2: Lists and Matrices

**Chapter 2.2: Working with Data Frames**
- Lesson 2.2.1: Creating and Manipulating Data Frames
- Lesson 2.2.2: Indexing and Subsetting Techniques
- Lesson 2.2.3: Type Conversions

### Module 3: Importing & Exporting Data

**Chapter 3.1: Data Import Methods**
- Lesson 3.1.1: Working with CSV Files
- Lesson 3.1.2: Importing Excel Spreadsheets
- Lesson 3.1.3: Reading JSON and Other Formats

**Chapter 3.2: Data Export Techniques**
- Lesson 3.2.1: Writing Data to Files
- Lesson 3.2.2: Database Connections

### Module 4: Data Manipulation with dplyr & tidyr

**Chapter 4.1: Tidyverse Introduction**
- Lesson 4.1.1: Philosophy of Tidy Data
- Lesson 4.1.2: Core Tidyverse Packages

**Chapter 4.2: Data Transformation with dplyr**
- Lesson 4.2.1: Filtering and Selecting Data
- Lesson 4.2.2: Arranging and Summarising Data
- Lesson 4.2.3: Grouping Operations

**Chapter 4.3: Reshaping Data with tidyr**
- Lesson 4.3.1: Long and Wide Format Conversions
- Lesson 4.3.2: Handling Missing Data
- Lesson 4.3.3: Combining Datasets

### Module 5: Data Visualisation with ggplot2

**Chapter 5.1: Grammar of Graphics**
- Lesson 5.1.1: ggplot2 Philosophy and Structure
- Lesson 5.1.2: Layers and Aesthetics

**Chapter 5.2: Creating Basic Visualisations**
- Lesson 5.2.1: Bar Charts and Histograms
- Lesson 5.2.2: Scatter Plots and Line Graphs
- Lesson 5.2.3: Boxplots and Violin Plots

**Chapter 5.3: Advanced Visualization Techniques**
- Lesson 5.3.1: Customizing Themes and Colors
- Lesson 5.3.2: Multi-Panel Plots with Facets
- Lesson 5.3.3: Interactive Visualizations

### Module 6: Exploratory Data Analysis (EDA)

**Chapter 6.1: Descriptive Statistics**
- Lesson 6.1.1: Measures of Central Tendency
- Lesson 6.1.2: Measures of Dispersion
- Lesson 6.1.3: Identifying Outliers

**Chapter 6.2: Relationship Analysis**
- Lesson 6.2.1: Correlation Analysis
- Lesson 6.2.2: Cross-Tabulations
- Lesson 6.2.3: Pattern Discovery Techniques

### Module 7: Statistical Analysis in R

**Chapter 7.1: Probability Distributions**
- Lesson 7.1.1: Common Distributions in R
- Lesson 7.1.2: Random Sampling

**Chapter 7.2: Hypothesis Testing**
- Lesson 7.2.1: t-Tests and z-Tests
- Lesson 7.2.2: ANOVA
- Lesson 7.2.3: Chi-Square Tests

**Chapter 7.3: Regression Analysis**
- Lesson 7.3.1: Simple Linear Regression
- Lesson 7.3.2: Multiple Regression Basics

### Module 8: Introduction to Machine Learning with R

**Chapter 8.1: Machine Learning Fundamentals**
- Lesson 8.1.1: Supervised vs. Unsupervised Learning
- Lesson 8.1.2: Train-Test Splits

**Chapter 8.2: Classification Models**
- Lesson 8.2.1: Logistic Regression
- Lesson 8.2.2: Decision Trees
- Lesson 8.2.3: Random Forests

**Chapter 8.3: Model Evaluation**
- Lesson 8.3.1: Confusion Matrices
- Lesson 8.3.2: Accuracy, Precision, Recall
- Lesson 8.3.3: RMSE and Other Metrics

### Module 9: Working with Real-World Data

**Chapter 9.1: Large Dataset Handling**
- Lesson 9.1.1: Memory Management in R
- Lesson 9.1.2: Efficient Data Processing

**Chapter 9.2: Data Cleaning Challenges**
- Lesson 9.2.1: Complex Missing Data Patterns
- Lesson 9.2.2: Inconsistent Formatting
- Lesson 9.2.3: End-to-End Workflow Practice

### Module 10: Capstone Project & Presentation

**Chapter 10.1: Project Development**
- Lesson 10.1.1: Problem Definition and Data Selection
- Lesson 10.1.2: Analysis Implementation

**Chapter 10.2: Results Presentation**
- Lesson 10.2.1: Creating Compelling Data Stories
- Lesson 10.2.2: Communicating Insights Effectively

## Hands-On Lab Activities

**Comprehensive Data Analytics Capstone**
You can choose a dataset (finance, healthcare, marketing, etc.), perform data cleaning, exploratory data analysis, visualisation, and predictive modelling, then present insights and recommendations based on their findings.

**R and RStudio Setup**
Set up and configure the R environment and RStudio interface, write simple scripts, and run basic commands to establish a functional analytics workspace.

**Data Structures Implementation**
Organise and manipulate datasets using R's data structures, including vectors, factors, lists, matrices, and data frames, with exercises.

**Dataset Loading and Saving**
Practice importing data from various sources (CSV, Excel, JSON) and exporting processed datasets back to different file formats.

**Data Transformation Workshop**
Clean, transform, and prepare datasets for analysis using tidyverse packages to handle real-world data challenges like missing values and inconsistent formats.

**Visualisation Development**
Create professional-level plots and dashboards using ggplot2 with customisation options to effectively communicate analytical findings.

## R Programming Course Outcome and Career Opportunities
​Our R Programming for Data Analytics course delivers significant corporate benefits, empowering organisations to leverage data more effectively.​ Upon completion, your team will contribute to:

- Gain proficiency in the R ecosystem by mastering R fundamentals and core data structures. Learn data manipulation with the Tidyverse packages (dplyr and tidyr) and create professional data visualisations using ggplot2.
- Develop robust statistical analysis skills to apply descriptive statistics, probability distributions, and hypothesis testing for confident, data-driven decision-making.
- Learn to build and evaluate machine learning models using regression and classification techniques with industry-standard performance metrics.
- Master effective data cleaning and preparation methods to handle missing values, outliers, and inconsistencies for reliable analytical outcomes.
- Apply all learned concepts in a comprehensive capstone project using real-world datasets to solve practical business problems.
- Strengthen analytical problem-solving abilities by performing exploratory data analysis and interpreting insights effectively. Communicate your results through clear, impactful visualisations and well-structured reports.

## R Programming Training Options

**Online Training**
- 40-hour online R Programming Training
- Virtual RStudio Environments
- Flexible Scheduling for Working Professionals
- Session Recordings for Review
- Online Support for Coding Challenges

**Corporate Training**
- 5-day R Programming Corporate Training
- Customised Program Based on Company Data
- Industry-Specific Case Studies
- Training delivered at a selected hotel, client premises, or Edoxi
- Fly-Me-A-Trainer Option

## How to Get a R Programming Certification?
Here’s a four-step guide to becoming a certified R Programming professional.

1. Join Edoxi’s Online R Programming Certification Course.
2. Attend our expert-led R Programming training.
3. Complete the R Programming Classes.
4. Earn your R Programming certification from Edoxi.

## Why Choose Edoxi for Online R Programming Training?
With many options available, Edoxi proves to be the best choice. The following are the reasons why Edoxi’s R Programming training is the ideal option for you:


**Industry-Relevant Analytics Curriculum**
Our course covers the complete R ecosystem, including tidyverse, ggplot2, and statistical packages specifically configured for corporate data analytics workflows.

**Business-Focused Implementation Approach**
We emphasise practical business applications over theory, with instructors who bring extensive experience in applying R analytics to corporate decision-making processes.

**Customised Corporate Training Solutions**
Our flexible delivery formats ensure training aligned with your organisation's specific data challenges and industry context for maximum relevance.

**Enterprise-Grade Analytics Practices**
Our curriculum focuses on scalable, reproducible analytics methods used by leading companies for data-driven decision making and reporting.

**Comprehensive Data Skills Development**
Build your analytics capabilities with our related courses in Python, Power BI, and Advanced Statistical Analysis for continued professional growth.

## Frequently Asked Questions

**Q: What prerequisites are needed for this R Programming course?**
A: Basic computer literacy is the only essential requirement. While familiarity with statistics is helpful, the course includes all necessary statistical concepts for complete beginners.

**Q: Can I use my company's data for projects during the R Programming course?**
A: Yes, we encourage corporate teams to work with their actual business data (with appropriate confidentiality measures). This makes the R Programming training immediately applicable to your specific challenges.

**Q: How can I measure the ROI of this R Programming training for my team?**
A: We help you evaluate ROI through clearly defined pre-training benchmarks and post-training performance metrics. These focus on measuring time efficiency, accuracy improvements, and enhanced analytical capabilities that lead to tangible business impact.

**Q: What is the average salary of an R Programming certified professional?**
A: R is a core skill across data roles, with salaries typically ranging from $86,000 to $174,000 per year based on role and experience. Below are the average salaries for common R-related positions:

Job Role
Average Salary (USD)

Data Analyst
$91,000

Statistician
$108,000

Data Scientist
$112,000–$151,000

Data Engineer
$131,000

Data Architect
$174,000

**Q: Can the R Programming corporate training be split across multiple weeks?**
A: Yes, we offer flexible scheduling for corporate clients. The 5-day program can be delivered as weekly sessions or split according to your team's availability and workload considerations.

## Career Opportunities After Completing the R Programming Training
Data Analyst, Data Scientist, Statistical Analyst, Quantitative Analyst, Data Engineer, R Developer/Programmer, Machine Learning Engineer



## Trainer
- Name: Athar Ahmed
Athar Ahmed is a skilled technical trainer with more than 15 years of experience in both educational institutions and the software development business. Athar specialises in technology stacks including Advanced Excel, Python, Power BI, SQL, .NET, Java, PHP, Full Stack Web Development, Agile, Data Science, Artificial Intelligence, Data Analytics, and DevOps.

He holds several certifications and licenses that underscore his expertise in the field. These include MCTS (Microsoft Certified Technology Specialist), MCP (Microsoft Certified Professional), and a Certificate in Artificial Intelligence and Machine Learning for Business. He also completed a Certificate Course in Unix, C++, and C# from CMC Academy, among other qualifications.

Athar also holds a Bachelor of Computer Applications (BCA) and a Master of Computer Applications (MCA). Additionally, he earned a Master of Technology (M. Tech) in Machine Learning and Artificial Intelligence, as well as a Doctorate of Philosophy (PhD) in Computer Applications.

## Enrol in This Course
- Course URL: https://www.edoxi.com/r-programming-course
- Phone: +971 43801666
- Email: info@edoxi.com