R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, data mining surveys, and studies of scholarly literature databases show substantial increases in popularity; as of January 2021, R ranks 9th in the TIOBE index, a measure of popularity of programming languages
Syntax:
Once you have R environment setup, then it’s easy to start your R command prompt by just typing the following command at your command prompt −
$ RThis will launch R interpreter and you will get a prompt > where you can start typing your program as follows −
> myString <- "Hello, World!"
> print ( myString)
[1] "Hello, World!"Here first statement defines a string variable myString, where we assign a string "Hello, World!" and then next statement print() is being used to print the value stored in variable myString.
R Script File
Usually, you will do your programming by writing your programs in script files and then you execute those scripts at your command prompt with the help of R interpreter called Rscript. So let's start with writing following code in a text file called test.R as under −
# My first program in R Programming
myString <- "Hello, World!"
print ( myString)Save the above code in a file test.R and execute it at Linux command prompt as given below. Even if you are using Windows or other system, syntax will remain same.
$ Rscript test.R When we run the above program, it produces the following result.
[1] "Hello, World!"Comments
Comments are like helping text in your R program and they are ignored by the interpreter while executing your actual program. Single comment is written using # in the beginning of the statement as follows −
# My first program in R ProgrammingR does not support multi-line comments but you can perform a trick which is something as follows −
if(FALSE) {
"This is a demo for multi-line comments and it should be put
inside either a single OR double quote"}
myString <- "Hello, World!"
print ( myString)[1] "Hello, World!"Though above comments will be executed by R interpreter, they will not interfere with your actual program. You should put such comments inside, either single or double quote.
History:
R is an implementation of the S programming language combined with lexical scoping semantics, inspired by Scheme. S was created by John Chambers in 1976 while at Bell Labs. A commercial version of S was offered as S-PLUS starting in 1988. Much of the code written for S-PLUS runs unaltered in R.
In 1991 Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, began an alternative implementation of the basic S language, completely independent of S-PLUS. They publicized this project starting in 1993. In 1995 Martin Maechler convinced Ihaka and Gentleman to make R free and open-source software under the GNU General Public License. The R Development Core Team was created to manage the further development of R. John Chambers became a member at least as of August 2018. R is named partly after the first names of the first two R authors and partly as a play on the name of S.
The first official release came in 1995. The Comprehensive R Archive Network (CRAN) was officially announced 23 April 1997 with 3 mirrors and 12 contributed packages. The first official "stable beta" version (v1.0) was released 29 February 2000.
Installation:
Windows Installation
You can download the Windows installer version of R from R-3.2.2 for Windows (32/64 bit) and save it in a local directory.
As it is a Windows installer (.exe) with a name "R-version-win.exe". You can just double click and run the installer accepting the default settings. If your Windows is 32-bit version, it installs the 32-bit version. But if your windows is 64-bit, then it installs both the 32-bit and 64-bit versions.
After installation you can locate the icon to run the Program in a directory structure "R\R3.2.2\bin\i386\Rgui.exe" under the Windows Program Files. Clicking this icon brings up the R-GUI which is the R console to do R Programming.
Linux Installation
R is available as a binary for many versions of Linux at the location R Binaries.
The instruction to install Linux varies from flavor to flavor. These steps are mentioned under each type of Linux version in the mentioned link. However, if you are in a hurry, then you can use yum command to install R as follows −
$ yum install RAbove command will install core functionality of R programming along with standard packages, still you need additional package, then you can launch R prompt as follows −
$ R
R version 3.2.0 (2015-04-16) -- "Full of Ingredients"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-redhat-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
Now you can use install command at R prompt to install the required package. For example, the following command will install plotrix package which is required for 3D charts.
> install.packages("plotrix")Features of R
As stated earlier, R is a programming language and software environment for statistical analysis, graphics representation and reporting. The following are the important features of R −
R is a well-developed, simple and effective programming language which includes conditionals, loops, user defined recursive functions and input and output facilities.
R has an effective data handling and storage facility,
R provides a suite of operators for calculations on arrays, lists, vectors and matrices.
R provides a large, coherent and integrated collection of tools for data analysis.
R provides graphical facilities for data analysis and display either directly at the computer or printing at the papers.
Advantages:
1) Open Source
An open-source language is a language on which we can work without any need for a license or a fee. R is an open-source language. We can contribute to the development of R by optimizing our packages, developing new ones, and resolving issues.
2) Platform Independent
R is a platform-independent language or cross-platform programming language which means its code can run on all operating systems. R enables programmers to develop software for several competing platforms by writing a program only once. R can run quite easily on Windows, Linux, and Mac.
3) Machine Learning Operations
R allows us to do various machine learning operations such as classification and regression. For this purpose, R provides various packages and features for developing the artificial neural network. R is used by the best data scientists in the world.
4) Exemplary support for data wrangling
R allows us to perform data wrangling. R provides packages such as dplyr, readr which are capable of transforming messy data into a structured form.
5) Quality plotting and graphing
R simplifies quality plotting and graphing. R libraries such as ggplot2 and plotly advocates for visually appealing and aesthetic graphs which set R apart from other programming languages.
6) The array of packages
R has a rich set of packages. R has over 10,000 packages in the CRAN repository which are constantly growing. R provides packages for data science and machine learning operations.
7) Statistics
R is mainly known as the language of statistics. It is the main reason why R is predominant than other programming languages for the development of statistical tools.
8) Continuously Growing
R is a constantly evolving programming language. Constantly evolving means when something evolves, it changes or develops over time, like our taste in music and clothes, which evolve as we get older. R is a state of the art which provides updates whenever any new feature is added.
Disadvantages:
1) Data Handling
In R, objects are stored in physical memory. It is in contrast with other programming languages like Python. R utilizes more memory as compared to Python. It requires the entire data in one single place which is in the memory. It is not an ideal option when we deal with Big Data.
2) Basic Security
R lacks basic security. It is an essential part of most programming languages such as Python. Because of this, there are many restrictions with R as it cannot be embedded in a web-application.
3) Complicated Language
R is a very complicated language, and it has a steep learning curve. The people who don't have prior knowledge or programming experience may find it difficult to learn R.
4) Weak Origin
The main disadvantage of R is, it does not have support for dynamic or 3D graphics. The reason behind this is its origin. It shares its origin with a much older programming language "S."
5) Lesser Speed
R programming language is much slower than other programming languages such as MATLAB and Python. In comparison to other programming language, R packages are much slower.
Source: TutorialPoint, Wikipedia
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