R Programming Course And Training

This course provides a broad introduction to R Programming. This course covers topics from basic to advanced level. The course will also discuss recent applications of R, such as in DATA ANALYTICS, data mining and text and web data processing.

What you'll learn

01: History and Overview of R - What is R? - What is S? - The S Philosophy - Back to R - Basic Features of R - Free Software - Design of the R System - Limitations of R - R Resources

02: Getting Started with R - Installation - Getting started with the R interface

03: R Nuts and Bolts - Entering Input - Evaluation - R Objects - Numbers - Attributes - Creating Vectors - Mixing Objects - Explicit Coercion - Matrices - Lists - Factors - Missing Values - Data Frames - Names - Summary

03: Getting Data In and Out of R - Reading and Writing Data - Reading Data Files with readtable() - Reading in Larger Datasets with readtable - Calculating Memory Requirements for R Objects

04: Using the reader Package

05: Using Textual and Binary Formats for Storing Data - Using dput() and dump() - Binary Formats

06: Interfaces to the Outside World - File Connections - Reading Lines of a Text File - Reading From a URL Connection

07: Subsetting R Objects - Subsetting a Vector - Subsetting a Matrix - Subsetting Lists - Subsetting Nested Elements of a List - Extracting Multiple Elements of a List - Partial Matching - Removing NA Values

08: Vectorized Operations - Vectorized Matrix Operations

09: Dates and Times - Dates in R - Times in R - Operations on Dates and Times - Summary

10: Managing Data Frames with the dplyr package - Data Frames - The dplyr Package - dplyr Grammar - Installing the dplyr package - select() - filter() - arrange() - rename() - mutate()

11: Control Structures - if-else - for Loops - Nested for loops - while Loops - repeat Loops - next, break - Summary

12: Functions - Functions in R - Your First Function - Argument Matching - Lazy Evaluation - The Argument - Arguments Coming After the Argument - Summary

13: Scoping Rules of R - A Diversion on Binding Values to Symbol - Scoping Rules - Lexical Scoping: Why Does It Matter? - Lexical vs Dynamic Scoping - Application: Optimization - Plotting the Likelihood - Summary

14: Coding Standards for R

15: Loop Functions - Looping on the Command Line - lapply() - sapply() - split() - Splitting a Data Frame - tapply - apply() - Col/Row Sums and Means - Other Ways to Apply - mapply() - Vectorizing a Function - Summary

16: Debugging - Something’s Wrong! - Figuring Out What’s Wrong - Debugging Tools in R - Using traceback() - Using debug() - Using recover() - Summary

17: Profiling R Code - Using systemtime() - Timing Longer Expressions - The R Profiler - Using summaryRprof() - Summary

18: Simulation - Generating Random Numbers - Setting the random number seed - Simulating a Linear Model - Random Sampling - Summary

19: Data Analysis Case Study

Course Details
• Duration: 30 hours

• Effort: 5 hours per week

• Price With GST: 17700/-

• Subject: Data Science

• Level: Beginner

Prerequisites

Students are expected to have the following background: Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. Familiarity with the probability theory. Familiarity with linear algebra.