R Language for Data Analytics - Detailed Syllabus

01

Introduction to R Basics

Start your R journey — arithmetic, variables, data types, and your first lines of real code in RStudio.

Arithmetic in R

Variables in R

Basic Data Types

Vectors, Lists, Arrays

Vector Operations

Comparison Operators

Logical Operators

Indexing and Slicing

Type Conversion

Handling NA, NaN, NULL

02

R Data Structures

Understand the full spectrum of R's data containers and when to use each one effectively.

Vectors (In Depth)

Lists

Arrays

Factors & Categorical Data

Vector vs List vs Matrix

03

R Matrices

Master matrix operations — the backbone of numerical computing and linear algebra.

Creating Matrices

Matrix Arithmetic

Selection & Indexing

rowSums, colMeans

apply() on Matrices

04

R Data Frames

Work with tabular data like a pro — the most common real-world data structure in R.

Data Frame Basics

str() & summary()

Indexing & Selection

Adding/Removing Columns

Handling Missing Data

Sorting and Ordering

05

Control Structures

Write intelligent, branching, looping R programs — the foundation of all automation.

if / else / else if

for Loops

while Loops

break and next

Functions in R

Vectorized Operations

06

Advanced R Programming

Level up with custom functions, error handling, and the apply family.

Custom Functions

Recursion & Scoping

Error Handling: tryCatch

apply, lapply, sapply

Regular Expressions

Dates & Timestamps

07

Data Manipulation (Tidyverse)

Master the Tidyverse — dplyr and tidyr for elegant, readable data transformation.

filter(), select(), mutate()

summarise() & arrange()

Pipe Operator %>%

pivot_longer/wider

Data Cleaning Techniques

String Manipulation

08

Data Visualization (ggplot2)

Create stunning, publication-quality plots with the grammar of graphics.

Aesthetics (aes)

Histograms & Scatterplots

Barplots & Boxplots

Faceting & Coordinates

Themes & Customization

09

Basic Statistics in R

Apply statistical thinking to your data — from descriptive stats to hypothesis testing.

Mean, Median, Mode

Variance & Std Deviation

Correlation

Distribution Functions

Hypothesis Testing Intro

10

Data Input & Output

Import and export data from various sources seamlessly in R.

CSV Files (read.csv)

Excel Files (readxl)

Writing Output (write.csv)

11

Data Analysis Workflow

Experience the complete data pipeline — from raw data to meaningful insight.

Data Cleaning Pipeline

Insight Discovery

Interpretation of Results

12

Reporting & Export

Communicate your findings professionally with R Markdown and dynamic reports.

Generating Reports

R Markdown Basics

Exporting PDF/HTML/Word