Online training of R Programming

R Programming



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Online training of R Programming
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Course Details

Duration: 0 hours Effort: hours per week

Price With GST: ₹17700/-

Subject: Level: Beginner
Prerequisites
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About this course

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 readr 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

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