R Programming - a Comprehensive Guide: Software

Chapter 1 – Introduction

Ris a programming language and environment which is widely used in different fields. It was developed by Ross Ihaka and Robert Gentleman from University of Auckland. R is one of the versions of S language which is developed by the Bell laboratories in United States of America.

R core development team monitors the development process. R language contents are updated by its vast community of users through the modules called ‘packages’. R language is frequently updated and the recent version 4.0.0 as of April 2020. R environment has some built in packages and more packages can be downloaded from The Comprehensive R Archive Network (CRAN) website (https://cran.r-project.org/).

R programming is platform independent and can be deployed in any operating system. It is compatible with other languages such as Java and Python programing. 

R lets the users to modify the existing packages and allows them to update with their own codes and functions thus offering greater flexibility in programming.

R Programming language is helpful for data handling, data visualization and data analysis capabilities. It also includes reporting and dash board features.

R is mainly useful for carrying out statistical analysis such as descriptive, inferential, regression modelling, clustering, discriminant analysis, factor analysis and survival analysis.

Getting started

R recent version can be downloaded from cran-r project website (https://cran.r-project.org/).

R Environment

The GUI version of R environment

There are Integrated Development Environment is available for R such as R-Studio and Rattle which provides Graphical User Interface (GUI) to the R environment. Both the IDEs are also freely available for download.

Apart from the above R Commander an interface for R environment provides GUI as well as menu based application for frequently used functions such as statistical functions along with the facility to write r codes.

R-Studio

Writing code in the R Studio

R studio has the following four default windows (panes) with each having sub tabs in it

Top left

Source

Bottom Left

Console

Terminal

Jobs

Top Right

Environment

History

Connection

Bottom right

Files

Plots

Packages

Help

Viewer

Find out version of R

After installing R and R studio we can what R version we have installed by executing the following code. It also provides the base packages which are attached the R environment. To execute a statement or block of statement we can use the run symbol in the source window(pane) or(CTRL+enter) to execute the code.  

Writing Codes for execution in R studio

Users can start writing their codes or statements in the Ssource window which is located at the top left window.

There are built in functions available in the R ‘base’ package which can be viewed by executing the following help code. One need to invoke the library function to start the execution related to a package

Similarly we can use library (help=stats) to see the functions associated with the package stats

Getting help

If want to know about any specific package or function we can use the help menu in the right bottom window (pane). We can search using specific function name such as sum in the search box.

Saving R files

By default R programming files are saved with .R extension which is nothing but the R script file. Files can be saved with following extensions also which will be discussed in detail in the later chapters.

.r – R script file

.rmd – R markdown file

.rnw – R Sweave file

Creating working directory

We can set the working directory by using the statement setwd along with the path reference to the working directory.

Installing packages

Packages or modules can be installed using install.packages statement or through R studio’s Tools - install packages menu

Calling packages to the R environment

library statement is used to call the packages into R environment

REFERENCE

https://cran.r-project.org/

R Core Team (2017). R: A language and environment for

statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL