R Programming

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

Choose the training format that best fits your schedule and learning style:

Online Instructor-Led Training – Learn directly from certified experts in live interactive sessions.
Online Self-Paced Learning – Study anytime, anywhere with flexible access to course materials.
Onsite / Classroom Training – Experience hands-on, instructor-led sessions in a collaborative environment.

What’s Included:

Our comprehensive training program is designed to ensure a complete, career-ready learning experience. You’ll get:

Onsite Instructor-Led Sessions
Live Role
Certified Trainers
Post-Training Support
Resource Materials including eBooks, eGuides, and reference documentation.
Real-World Scenarios & Case Studies
Networking Opportunities
Group Projects and Collaborative Activities
Best Practices and Industry Insights
Access to Recorded Sessions (available only for online programs) for flexible review.
Follow-Up Sessions
Tool Demonstrations and Walkthroughs

Empower your career with expert-led, practical, and flexible training designed to help you master real-world skills and stay ahead in the industry.

Course Content

Getting Started with R and RStudio

Introduction to R Programming Language
Installing and Setting Up R and RStudio
Understanding the RStudio Interface: Console, Script, Environment, and Plots Pane
Basic R Syntax: Operators, Variables, and Data Types

Data Structures in R

Basic Operations in R

Importing Data into R

Exporting Data in R

Exploratory Data Analysis (EDA)

Data Manipulation with dplyr

Introduction to ggplot2

Advanced ggplot2 Techniques

Creating Statistical Plots

Plot Customization and Saving Plots

Control Flow in R

Functions in R

Error Handling and Debugging

Basic Statistical Techniques

Advanced Statistical Methods

Probability Distributions in R

Introduction to Time Series Data

Time Series Analysis in R

Forecasting with Time Series

Data Reshaping with tidyr

Working with Big Data

Advanced Visualization Techniques

R Programming Project Development

Finalizing and Presenting Your Work

R Programming Career Pathways

Course Duration: 32 Hours of Comprehensive Learning

Mode of Learning:

What’s Included:

Course Content

Getting Started with R and RStudio

Introduction to R Programming Language

Installing and Setting Up R and RStudio

Understanding the RStudio Interface: Console, Script, Environment, and Plots Pane

Basic R Syntax: Operators, Variables, and Data Types

Data Structures in R

Vectors: Creating, Accessing, and Manipulating

Lists: Understanding Lists and Operations on Lists

Matrices and Arrays: Structure, Indexing, and Operations

Data Frames: Creating, Accessing, and Modifying Data Frames

Basic Operations in R

Arithmetic Operations and Functions

Logical Operations and Comparison Operators

Handling NA values in R

Importing Data into R

Reading Data from CSV, Excel, and Text Files

Importing Data from Web (APIs, Web Scraping)

Using the readr and data.table Packages for Efficient Data Import

Exporting Data in R

Writing Data to CSV, Excel, and Other Formats

Exporting Results to Text and HTML Reports

Exploratory Data Analysis (EDA)

Summary Statistics: mean(), median(), sd(), etc.

Inspecting Data: head(), tail(), str(), summary()

Data Cleaning: Handling Missing Data, Duplicates, and Outliers

Data Manipulation with dplyr

Introduction to dplyr Package: select(), filter(), mutate(), arrange(), and summarise()

Grouping Data and Applying Functions with group_by() and summarise()

Joins: Inner, Left, Right, and Full Joins in R

Introduction to ggplot2

Basic Syntax and Principles of ggplot2

Creating Basic Plots: Scatterplots, Line Plots, Bar Plots

Customizing Plots: Titles, Axis Labels, and Legends

Advanced ggplot2 Techniques

Adding Multiple Layers to a Plot: Points, Lines, and Smoothing

Faceting for Subplots (facet_wrap() and facet_grid())

Customizing Aesthetics: Color, Size, Shape, and Transparency

Creating Statistical Plots

Histograms, Boxplots, Density Plots, and Violin Plots

Customizing Statistical Plots for Better Understanding

Plot Customization and Saving Plots

Customizing Themes: Changing Axis Scales, Themes, and Colors

Saving and Exporting Plots to Files (PNG, PDF, SVG)

Control Flow in R

Conditional Statements: if, else, ifelse()

Loops: for(), while(), and repeat()

Vectorized Operations vs Loops

Functions in R

Defining Functions: Syntax, Arguments, and Return Values

Scope and Environments in Functions

Best Practices for Writing Functions in R

Error Handling and Debugging

Common R Errors and How to Resolve Them

Using try(), tryCatch(), and stop() for Error Handling

Debugging Code: browser(), traceback(), and debug()

Basic Statistical Techniques

Descriptive Statistics: Measures of Central Tendency, Dispersion

Hypothesis Testing: t-tests, chi-square tests, and ANOVA

Correlation and Regression Analysis

Advanced Statistical Methods

Linear and Logistic Regression in R

ANOVA and MANOVA in R

Non-Parametric Tests: Wilcoxon, Kruskal-Wallis

Probability Distributions in R

Normal Distribution, Binomial Distribution, Poisson Distribution

Generating Random Variables and Visualizing Distributions

Probability Functions: dnorm(), pnorm(), qnorm(), rnorm()

Introduction to Time Series Data

Time Series Data: Concepts and Applications

Importing and Preparing Time Series Data

Visualizing Time Series Data in R

Time Series Analysis in R

Decomposition of Time Series: Trend, Seasonality, and Residuals

Moving Averages and Smoothing Techniques

Autocorrelation and Cross-Correlation Functions