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What Is R Programming?

The R programming language is developed as a free language used for graphics support and statistical computing. To solve statistics and graphic computations, one can use various statistical methods and graphical techniques.

It is used to perform various statistics operations such as clustering, statistical hypothesis, data mining, regression modeling, data warehousing, etc. Statisticians around the world widely use the R language.

Apart from that, R programming plays a crucial role in the data science, data analytics, and Big Data industry. There is no limitation with R because it offers various packages and libraries to extend its functionality. That is why it has become one of the most popular programming languages globally.

Example of R Programming

Lets Verify Whether the Number is a Armstrong Number or Not

Input

isArmstrong <- function(p){

add <- 0

q <- p

while(q > 0){

digit <- q %% 10

add <- add + (digit^3)

q <- floor(q/10)

}

if(p == add){

print(paste(p,' ** is the Armstrong Number **'))

}

else

{

print(paste(p,'is not the Armstrong Number'))

}

} # Taking Function

isArmstrong(371)

isArmstrong(1634)

Output

"371 **is the Armstrong Number**"

"1634 is not the Armstrong Number"

Important Features Of R Programming

• R programming is one of the powerful tools that help to solve virtual statistics computation. It can produce error-free solutions and can save time.
• It has the facility of data handling and storage.
• R is an interpreted language; therefore, the user does not require a compiler to develop a program.
• Because of its vector-based language facility, the user can perform several calculations and complicated operations within a command.
• It is highly efficient for running multiple operating systems, such as Macros, Windows, and UNIX, because of its graphical front-ends.
• R programming language supports data frames, arrays, lists, and matrix arithmetic functions.
• It is compatible with other programming languages, including .Net, FORTRAN, C++, and Java.
• R has a significant role in data science because of its statistical flexibility. It is commonly utilized in genetics and biology.

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Logistic Regression Multivariate analysis	Non-parametric tests ANOVA
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Markov chains analysis Statistical process control	Central Limit Theorem Monte Carlo simulation
Simple Linear Regression Multiple regression	Stochastic process Time-series
Correlation analysis Parametric tests	Design of Experiments Null Hypothesis
Bootstrapping Scatter plot	Statistical Inference Biostatistics

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