R Programming

Learning Outcomes: 
After successful completion of the course students should be able to
• Understand the basics in R programming in terms of constructs, control statements, string functions
• Understand the use of R for Big Data analytics
• Learn to apply R programming for Text processing
• Able to appreciate and apply the R programming from a statistical perspective
Syllabus: 
Unit NoTopics
1

Introduction:

Introducing to R – R Data Structures – Help functions in R – Vectors – Scalars – Declarations –

recycling – Common Vector operations – Using all and any – Vectorized operations – NA andNULL values – Filtering – Vectorised if-then else – Vector Equality – Vector Element names 

2

Matrices, Arrays And Lists:

Creating matrices – Matrix operations – Applying Functions to Matrix Rows and Columns – Adding

and deleting rows and columns – Vector/Matrix Distinction – Avoiding Dimension Reduction –

Higher Dimensional arrays – lists – Creating lists – General list operations – Accessing listcomponents and values – applying functions to lists – recursive lists

3

Data Frames:

Creating Data Frames – Matrix-like operations in frames – Merging Data Frames – Applyingfunctions to Data frames – Factors and Tables – factors and levels – Common functions used with

factors – Working with tables - Other factors and table related functions - Control statements –

Arithmetic and Boolean operators and values – Default values for arguments - Returning Boolean

values – functions are objects – Environment and Scope issues – Writing Upstairs - Recursion –

Replacement functions – Tools for composing function code – Math and Simulations in R

4

OOP:

S3 Classes – S4 Classes – Managing your objects – Input/Output – accessing keyboard andmonitor – reading and writing files – accessing the internet – String Manipulation – Graphics –Creating Graphs – Customizing Graphs – Saving graphs to files – Creating three-dimensional plots

5

STATISTICS:

Descriptive Statistics (summary Measures) using R –  Hypothesis Testing – I (Parametric) – Hypothesis Testing – II (Non-Parametric) –  Analysis of Variance (One way ANOVA, Two way ANOVA) –  Simple and Multiple Linear Regression Analysis –  Logistic Regression –  Time Series Analysis

6

REPRODUCIBLE RESEARCH USING R:

Reproducible Research using R and Rstudio (knitr, rmarkdown, bookdown, interactive document, shiny presentation, shiny web application)

7

Advance R Programming

Interfacing R to Other Languages, Text mining, Neural Networks, Monte Carlo methods, Markov chains, classification, Market Basket Analysis

Text Books: 
Name : 
The Art of R Programming: A Tour of Statistical Software Design
Author: 
Norman Matloff
Publication: 
NoStarch Press
Edition: 
2011
Name : 
R for Everyone: Advanced Analytics and Graphics
Author: 
Jared P. Lander
Publication: 
Addison-Wesley Data,2013
Reference Books: 
Name: 
Beginning R – The Statistical Programming Language
Author: 
Mark Gardener
Publication: 
Wiley,2013
Name: 
Introductory R: A Beginner's Guide to Data Visualisation, Statistical Analysis and Programming in R
Author: 
Robert Knell
Publication: 
Amazon Digital South Asia Services Inc, 2013.
Syllabus PDF: 
AttachmentSize
PDF icon RP.pdf232.64 KB
branch: 
BDA
Course: 
2018
Stream: 
B.Tech