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R programming is considered as one of the oldest programming languages. However with a sudden blare in data science, this programming language has gained intensive popularity. R is not an easy language so students require a lot of time to gain command over it. This is the reason why students are continuously looking for R programming online assignment help experts.

R is a software package setting and programming language for applied analysis in mathematics, coverage, and graphics illustration. The core R is an interpreted language that allows branching and iteration just as programming exploitation functions. It permits integration with the methods written within the .Net, C, C++, algebraic or Python language.

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Major Topics Covered in R Programming Language

Few of the key topics in R programming language are demonstrated below:

• Function:It includes a set of organised statements to execute a specific task. R comprises a substantial quantity of in-built functions and the user will also generate their functions.
• Matrices:they are the R objects during the organisation of an element during a 2D rectangular layout. They comprises equivalent data types.
• Vectors or Lists:Vectors in R programming are homogeneous data structures comprising elements of the similar data type whereas lists are heterogeneous data structures comprising elements of mixed data type.
• Data Frames:They could be either a table or 2D array-like structure wherein each column comprises values of 1 variable and row comprises one set of values from every column.
• Factors:They are data structures which signify levels and considered as the most adequate for categorical variables
• R packages:They are libraries comprising library functions. For instance: dplyr comprises library functions, select(), mutate(), filter(), arrange(), and summarise().

Machine Learning Algorithms in R Programming

Common machine learning algorithms in R programming language are mentioned below:

• Linear Regression:lm() is utilised for training a Linear Regression Model in R. It frames a linear relationship model between X, features, and continuous target y.
• Naive Bayes:Naive_bayes() is utilised for training a Naive Bayes Model in R.
• Logistic Regression:glm() is utilised for training a Logistic Regression Model in R.
• SVM:SVM() is for training a Support Vector Machine Model in R.
• Decision Tree:tree() is for training a Decision Tree in R.
• KNN:kNN()is for training a k-nearest neighbor model in R.
• Clustering:kmeans() performs k-Means Clustering on data matrix in R.

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Characteristics of R Programming Language

• R is a very flexible language and thus, it is predominantly utilised in data science and statistics field. Moreover, it is also employed in biology and genetics field to draw analysis and predictions.
• Since R is a vector language, it is convenient to append functions into a single vector without using it in a loop.
• Itis considered as the best option for businesses since it is affordable language. It also provides outstanding graphics and visualizations.
• Itenables the performance of virtual statistical computations in a short span of time and provides error free content.
• It provides the most efficient data handling as well as storage facilities.
• It allows the performance of multiple calculations at one time utilising a single command.
• It is capable to work on multiple operating systems such as Mac, Linux, Windows, etc.
• It supports arrays, data frames, and several other data types at one time and it is also compatible with distinct other programming languages.

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