R Programming Course And Training
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
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
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Duration: 30 hours
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Effort: 5 hours per week
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Price With GST: 17700/-
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Subject: Data Science
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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.