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If you are studying statistics, you might have heard of R programming. The R programming language is very popular because it has powerful statistical packages, is flexible, and can show data in graphs. People are very interested in the sudden rise in data science. Because of this, more and more students want to learn this programming language.

Since it has some things to do with statistics, some students find it hard to do their homework and look for online R programming assignment help. But it's not easy to find this kind of R tutor because there are so many options. But don't worry, we're here to answer all of your R assignment questions.

Students have to put in a lot of hard work to learn this language. For this to happen, they need to put in their best time and effort. These qualities include being able to think logically, solve problems, think critically, etc. When students have a good mix of these skills, they are more likely to be able to master it.

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About R Programming Language

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.

Machine Learning Algorithms in R Programming Language

Below we have listed some of the common ML algorithms which are implemented using R programming:

• Naive Bayes: naive_bayes() under the naive Bayes package is utilized for training a Naive Bayes Model in the R language. It is a distributed algorithm based on Previous and Posterior Probabilities.
• Logistic Regression: glm() under the stats package is utilized for preparing a Logistic Regression Model in R language. It creates a linear decision boundary for the distribution of data points.
• Linear Regression: lm() under the stats package is utilized for preparing a Linear Regression Model on Training Data in R language. It models a linear correlation between characteristics, X, and constant target y.
• SVM: SVM() under the e1071 package is for preparing a Support Vector Machine Model in R language. Both Regression and classification can be achieved along with density estimation.
• Decision Tree: tree() under the tree package is for preparing a Decision Tree in R language. Like SVM, both Regression and distribution can be done by binary recursive partitioning.
• Clustering: kmeans() under the stats package offers k-Means Clustering on the given data matrix in R language. It is an Unsupervised Machine learning Algorithm utilized for segmentation and data-grouping unlabelled data.
• KNN: kNN() under the DMwR package is for preparing a k-nearest neighbor model in R. It also offers Data Normalization before training the model with Training Data.

R Assignments: Reasons Why Students Seek Best R Assignment Help

Having a limited knowledge of the R language can leave your R programming assignment with many errors, and that is why students look for online R programming assignment help. Students encounter various types of problems while doing their R assignments. Our R programming assignment helper, also known as R programming tutors, will assist you in completing your R programming assignment.

• Error: could not find function
• Error: Object not found.
• Error in if
• Error: cannot open the connection.
• Error: subscript out of bounds
• ‘cannot open the connection’ Error
• ‘non-numeric argument to a binary operator’ Error
• “replacement has” Error.

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In our R assignment help, we cover almost every R programming topic. Our professional R programmers have years of knowledge and covered many topics.

Logistic Regression Multivariate analysis	Non-parametric tests ANOVA
Alternate Hypothesis Chi-square test.	Sampling Box plot
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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