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 structures2. 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 tests5. Make use of appropriate statistical tests using R and Create and edit visualizations with regression models6. 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.
DOWNLOAD
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.
DOWNLOAD
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.
DOWNLOAD
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
No comments