JNTUK R19 R-Programming III Year

DEPARTMENT VISION & MISSION

PROGRAM OUTCOMES

PROGRAM EDUCATION OBJECTIVES

PROGRAM SPECIFIC OUTCOMES

R-Programming

Course Outcomes:

At the end of this course, students will be able to:

1. Demonstration and implement of basic R programming framework and data structures
2. Explain critical R programming language concepts such as control structures and recursion.
3. Applying mathematical and statistical operations data structures in R.
4. Examine data-sets to create testable hypotheses and identify appropriate statistical tests
5. Make use of appropriate statistical tests using R and Create and edit visualizations with regression models
6. Define model choices and results.

Course Objectives:

After taking the course, students will be able to

1. Use R for statistical programming, computation, graphics, and modeling

2. Write functions and use R in an efficient way

3. Fit some basic types of statistical models

4. Use R in their own research

5. Be able to expand their knowledge of R on their own.

UNIT I

Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes.

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UNIT II

R Programming Structures, Control Statements, Loops, - Looping Over Nonvector Sets,- If-Else, Arithmetic and Boolean Operators and values, Default Values for Argument, Return Values, Deciding Whether to explicitly call return- Returning Complex Objects, Functions are Objective, No Pointers in R, Recursion, A Quicksort Implementation-Extended Extended Example: A Binary Search Tree.

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UNIT-III

Doing Math and Simulation in R, Math Function, Extended Example Calculating Probability, Cumulative Sums and Products-Minima and Maxima- Calculus, Functions Fir Statistical Distribution, Sorting, Linear Algebra Operation on Vectors and Matrices, Extended Example: Vector cross Product, Extended Example: Finding Stationary Distribution of Markov Chains, Set Operation, Input /out put, Accessing the Keyboard and Monitor, Reading and writer Files.

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UNIT-IV

Graphics, Creating Graphs, The Workhorse of R Base Graphics, the plot() Function –Customizing Graphs, Saving Graphs to Files.

UNIT-V

Probability Distributions, Normal Distribution- Binomial Distribution- Poisson Distributions Other Distribution, Basic Statistics, Correlation and Covariance, T-Tests,-ANOVA. Linear Models, Simple Linear Regression, -Multiple Regression Generalized Linear Models, Logistic Regression, - Poisson, Regression- other Generalized Linear Models-Survival Analysis, Nonlinear Models, Splines- Decision ,Random Forests