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Dr. Harpreet Singh

Head, PG Department of Bioinformatics,

Hans Raj Mahila Maha Vidyalaya,

Jalandhar, Punjab, India

e-module

Introduction to the R Programing Interface

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What is R

Software for Statistical Data Analysis
Based on S
Programming Environment
Interpreted Language
Data Storage, Analysis, Graphing
Free and Open Source Software

Introduction

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Current Version: R-4.3.1
Comprehensive R Archive Network:

http://cran.r-project.org

Binary source codes
Windows executables
Compiled RPMs for Linux
Can be obtained on a CD

Obtaining R

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The R Project

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Downloading R from different mirror sites

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Different R distributions for Windows, Linux, macOS etc.

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Different R distributions

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Downloading & Installing R for Windows

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Downloading & Installing R for Windows

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Downloading & Installing R for Windows

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Downloading & Installing R for Windows

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Downloading & Installing R for Unix/Linux

$ tar –zxvf “filename.tar.gz”

$ cd filename

$ ./configure

$ make

$ make check

$ make install

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Windows, Double-click on Desktop Icon

Linux, type R at command prompt

$ R

Starting R

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Strengths

Free and Open Source
Strong User Community
Highly extensible, flexible
Implementation of high end statistical methods
Flexible graphics and intelligent defaults

Weakness

Steep learning curve
Slow for large datasets

Strengths and Weaknesses

R is a great software. It is freely distributed (free both in price as well as in freedom of usage, no restrictions). It has a very strong user community who are ready to help newbies and share information. It has extensive documentation. Best of all, it is extremely scalable, meaning from very low end to very high end, all types of statistical methods can be easily implemented using R. The graphics of R are very flexible and there are many intelligent defaults. Intelligent defaults mean R can guess what you are trying to do and act accordingly.

On the downside, it can be time-consuming to learn to use it effectively. The learning process is slow, sometimes frustrating, but in the end, it is a rewarding experience. However, for very large datasets, R can sometimes be slow, but there are several ways to speed up R. The newer versions are invariably faster than the older ones, so continuous upgrading of the software is a good way to speed things up.

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Highly Functional

Everything done through functions
Strict named arguments
Abbreviations in arguments OK (e.g. T for TRUE)

Object Oriented

Everything is an object
“<-” is an assignment operator
“X <- 5”: X GETS the value 5

Basics

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From Documentation:

?WhatIWantToKnow
help(“WhatIWantToKnow”)
help.search(“WhatIWantToKnow”)
help.start()
getAnywhere(“WhatIWantToKnow”)
example(“WhatIWantToKnow”)

Documents: “Introduction to R”
Active Mailing List

Archives
Directly Asking Questions on the List

Getting Help in R

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Supports virtually any type of data
Numbers, characters, logicals (TRUE/ FALSE)
Arrays of virtually unlimited sizes
Simplest: Vectors and Matrices
Lists: Can Contain mixed type variables
Data Frame: Rectangular Data Set

Data Structures

R supports all types of data. The simplest type of data is vector, which is one dimensional representation of a string of similar entities. An example of vector is this: a = (“Kolkata”,”Rome”,”New York”, “London”, “Melbourne”). This represents a series of strings (names of cities). Or this, b = (2,3,4,5) – representing a series of numbers. Moving up, a list is also one dimensional, but it consists of a mixture of different types of data. For example, c = (2,”London”,”YES”) is an example of a list, one that contains a number, a string, and a logical YES in it. Extending this concept further, a matrix is two dimensional (or at least n dimensional) that contains only _one_ type of data in it. A data frame is a two dimensional ( or n dimensional) entity that contains mix of different data types in it. In terms of statistical data analysis, a data frame is the most commonly encountered rectangular dataset that most people work with.

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	Linear	Rectangular
All Same Type	VECTORS	MATRIX*
Mixed	LIST	DATA FRAME

Data Structure in R

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Directly in the Windowing System (Console)
Using Editors

Notepad, WinEdt, Tinn-R: Windows
Xemacs, ESS (Emacs speaks Statistics)

On the Editor:

source(“filename.R”)
Outputs can be diverted by using

sink(“filename.Rout”)

Running R

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The R interface for Windows

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R Working Area

This is the area where all commands are issued, and non-graphical outputs observed when run interactively

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https://favtutor.com/blogs/r-programming-projects

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Thanks

https://data-flair.training/blogs/r-applications/