R Programming

Master Data Analysis and Visualization with Our R Programming Course

Unlock the potential of R, the powerful language for statistical computing and data visualization. Whether you're a beginner looking to break into the world of data science or a professional aiming to expand your skillset, our R Programming Course is your gateway to mastering data analysis.

Course Highlights

Mode of Learning: Live Online Interactive Sessions

Instructors: Industry Experts with Proven Expertise in R Programming

Fee: Affordable Pricing With Easy Instalment options.

Certification: Receive a recognized certification upon completion

Free Trial Class: Evaluate the course before enrolling

Placement Assistance: Comprehensive support for job seekers

Internship: Work on real-time projects to gain practical experience

Syllabus

THE ART OF R PROGRAMMING

INTRODUCTION

Why Use R for Your Statistical Work?
Object-Oriented Programming
Functional Programming?
Functional Programming?

INSTALLING R

Downloading R from CRAN
Installing from Source

GETTING STARTED

How to Run R

Interactive Mode
Batch Mode

First R Session

Introduction to Functions

Variable Scope
Default Arguments

Preview of Some Important R Data Structures

Vectors, the R
Character Strings
Matrices
Lists
Arrays
Data Frames

VECTORS

Scalars, Vectors, Arrays, and Matrices

Adding and Deleting Vector Elements
Obtaining the Length of a Vector
Matrices and Arrays as Vectors

Declarations

Common Vector Operations

Vector Arithmetic and Logical Operations
Vector Indexing
Generating Useful Vectors with the : Operator
Generating Vector Sequences with seq()
Repeating Vector Constants with rep

Vectorized Operations

Vector In, Vector Out
Vector In, Matrix Out

NA and NULL Values

Using NA
Using NULL

Filtering

Generating Filtering Indices
Filtering with the subset() Function
The Selection Function which

A Vectorized if-then-else: The ifelse() Function

Extended Example: A Measure of Association
Extended Example: Recoding an Abalone Data Set

Testing Vector Equality

Vector Element Names

MATRICES AND ARRAYS

Creating Matrices

General Matrix Operations
Performing Linear Algebra Operations on Matrices
Matrix Indexing
Filtering on Matrices

Applying Functions to Matrix Rows and Columns

Using the apply() Function
Extended Example: Finding Outliers
Adding and Deleting Matrix Rows and Columns
Changing the Size of a Matrix

Avoiding Unintended Dimension Reduction

Naming Matrix Rows and Columns

Higher-Dimensional Arrays

LISTS

Creating Lists

General List Operations

List Indexing
Adding and Deleting List Elements
Getting the Size of a List

Accessing List Components and Values

Applying Functions to Lists

Using the lapply() and sapply() Functions

ARRAYS

Naming Columns and Rows
Accessing Array Elements
Check if an Item Exists
Amount of Rows and Columns
Array Length
Manipulating Array Elements
Calculations Across Array Elements

DATA FRAMES

Creating Data Frames

Accessing Data Frames

Other Matrix-Like Operations

Extracting Subdata Frames
More on Treatment of NA Values
Using the rbind() and cbind() Functions and Alternatives .
Applying apply()

Merging Data Frames

Extended Example: An Employee Database

Applying Functions to Data Frames

Using lapply() and sapply() on Data Frames

FACTORS AND TABLES

Factors and Levels

Common Functions Used with Factors

The tapply() Function
The split() Function
The by() Function

Working with Tables

Matrix/Array-Like Operations on Tables
Extended Example: Extracting a

Other Factor- and Table-Related Functions

The aggregate() Function
The cut() Function

R PROGRAMMING STRUCTURES

Control Statements

Loops
Looping Over Non vector Sets

Arithmetic and Boolean Operators and Values

Default Values for Arguments

Return Values

Deciding Whether to Explicitly Call return()
Returning Complex Objects

Functions Are Objects

Environment and Scope Issues

The Top-Level Environment

The Scope Hierarchy
More on ls()
Functions Have (Almost) No Side Effects

No Pointers in R

Writing Upstairs

Writing to Nonlocals with the Super assignment Operator
Writing to Nonlocals with assign()

When Should You Use Global Variables?

Replacement Functions

What’s Considered a Replacement Function?

Tools for Composing Function Code

Text Editors and Integrated Development Environments

The edit() Function

Writing Your Own Binary Operations

Anonymous Functions

DOING MATH AND SIMULATIONS IN R

Math Functions

Extended Example
Cumulative Sums and Products
Minima and Maxima

Functions for Statistical Distributions

Sorting

Linear Algebra Operations on Vectors and Matrices

Extended Example: Vector Cross Product
Set Operations

Simulation Programming in R

Built-In Random Variate Generators
Obtaining the Same Random Stream in Repeated Runs

INPUT/OUTPUT

Accessing the Keyboard and Monitor

Using the scan() Function
Using the readline() Function
Printing to the Screen

Reading and Writing Files

Reading a Data Frame or Matrix from a File
Reading Text Files
Introduction to Connections
Extended Example
Accessing Files on Remote Machines via URLs
Writing to a File
Getting File and Directory Information

STRING MANIPULATION

An Overview of String-Manipulation Functions

grep()
nchar()
paste()
sprintf()
substr
strsplit()
regexpr()

Regular Expressions

Extended Example

R DATA INTERFACES

R - CSV Files

Reading a CSV File
Analyzing the CSV File
Writing into a CSV File

R - Excel Files

Install xlsx Package
Reading the Excel File

R - Binary Files

Writing the Binary File
Reading the Binary File

R - XML Files

Reading XML File
XML to Data Frame

R - JSON Files

Install rjson Package
Read the JSON File
Convert JSON to a Data Frame

R - Database

RMySQL Package
Connecting R to MySql
Querying the Tables
Query with Filter Clause
Updating Rows in the Tables
Inserting Data into the Tables
Creating Tables in MySql
Dropping Tables in MySql

GRAPHICS

Creating Graphs

The Workhorse of R Base Graphics: The plot() Function
R - Pie Charts
R - Bar Charts
R - Boxplots
R - Histograms
R - Line Graphs
R - Scatterplots
Starting a New Graph While Keeping the Old Ones
Extended Example
Adding Points: The points() Function
Adding a Legend: The legend() Function
Adding Text: The text() Function
Pinpointing Locations: The locator() Function
Restoring a Plot
Customizing Graphs
Changing Character Sizes: The cex
Changing the Range of Axes: The xlim and ylim Options
Graphing Explicit Functions
Extended Example

Saving Graphs to Files

R Graphics Devices
Saving the Displayed Graph
Closing an R Graphics Device

Creating Three-Dimensional Plots

R Statistics

R Statistics Intro

R Data Set

R Max and Min

R Mean Median Mode

R Percentiles

INSTALLING AND USING PACKAGES

Package Basics

Loading a Package from Your Hard Drive

Downloading a Package from the Web

Installing Packages Automatically

Installing Packages Manually

Listing the Functions in a Package

Duration: 20 Hrs

Attend a Free Trial Class

Syllabus -> R Programming

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R Programming Training