About this course

 

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

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

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

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19: Data Analysis Case Study