MI4029 R Programming Syllabus:

MI4029 R Programming Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVE:

 To study the fundamentals of R programming to apply in quantitative analysis.

UNIT I GETTING STARTED WITH R

Installing R – The R environment – R packages – Basics of R – Data Structures – Reading data into R – Graphics in R

UNIT II FUNCTIONS AND STATEMENTS

Writing R functions – Control Statements (if and else, switch, if else, compound tests) – Loops in R (for, while, controlling loops) – Applications using the functions and loops.

UNIT III DATA MANIPULATION AND ANALYSIS

Group manipulation – Data Reshaping – Manipulating Strings – Basic Statistics using R (Summaries, Correlation, t-tests, ANOVA)

UNIT IV LINEAR MODELS USING R

Linear Models – Simple and Multiple regression, GLM – Logit Regression, Model diagnostics – Residuals, Cross validation, Boot strapping.

UNIT V NON-LINEAR MODELS, TIME SERIES AND CLUSTERING USING R

Nonlinear Models – Non-Linear least square, Splines, Generalised Additive Models, Decision trees, Random forests. Time Series – Autoregressive moving average, VAR, GARCH. Clustering – K means, PAM and Hierarchical Clustering.

TOTAL: 45 PERIODS

COURSE OUTCOMES:

1. Explore R language fundamentals, including basic syntax, variables, and types.
2. How to create functions and use control flow.
3. Work with data in R.
4. Understand the liner models using R.
5. The student will learn to use R programming to solve decision models.

REFERENCES :

1. Jared P.L., R for Everyone – Advanced Analytics and Graphics, Addison Wesley Data and Analytics series, 2015.
2. SandipRakshit, R Programming for Beginners, McGraw Hill Education, 2017