R-Programming Lecture Notes Jntuk R16 CSE,IT 2-1
Statistics with R Programming
OBJECTIVE:
After taking the course, students will be able to
 Use R for statistical programming, computation, graphics, and modeling,  Write functions and use R in an efficient way,
 Fit some basic types of statistical models
 Use R in their own research,
 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.
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.
UNIT-III:
Doing Math and Simulation in R, Math Function, Extended Example Calculating ProbabilityCumulative 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,
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.
UNIT-VI:
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,
OUTCOMES:
At the end of this course, students will be able to:
 List motivation for learning a programming language
 Access online resources for R and import new function packages into the R workspace
 Import, review, manipulate and summarize data-sets in R
 Explore data-sets to create testable hypotheses and identify appropriate statistical tests
 Perform appropriate statistical tests using R Create and edit visualizations with
TEXT BOOKS:
1) The Art of R Programming, A K Verma, Cengage Learning.
2) R for Everyone, Lander, Pearson
3) The Art of R Programming, Norman Matloff, No starch Press. REFERENCE
BOOKS:
1) R Cookbook, Paul Teetor, Oreilly.
2) R in Action, Rob Kabacoff, Manning
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