Almost all novice data scientists and machine learning developers are being confused about picking a programming language. They always ask which programming language will be best for their machine learning and data science project. Either one will go for python, R, or MatLab. Well, the choice of a programming language depends on developers’ preference and system requirements. Among other programming languages, R is one of the most potential and splendid programming languages that have several R machine learning packages for ML, AI, and data science projects.

As a consequence, one can develop his project effortlessly and efficiently by using these R machine learning packages. According to a survey of Kaggle, R is one of the most popular open-source machine learning languages.

Table of Contents

## Best R Machine Learning Packages

R is an open-source language so people can contribute from anywhere in the world. You can use a Black Box in your code, which is written by someone else. In R, this Black Box is refereed to as a package. The package is nothing but a pre-written code that can be used repeatedly by anyone. Below, we are showcasing the top 20 best R machine learning packages.

## CARET

The package CARET refers to classification and regression training. The task of this CARET package is to integrate the training and prediction of a model. It is one of the best packages of R for machine learning as well as data science.

The parameters can be searched by the integration of several functions to calculate the overall performance of a given model by using the grid search method of this package. After successful completion of all trials, the grid search finally finds the best combinations.

After installation of this package, the developer can run names (getModelInfo()) to check the 217 possible functions which can be run through only a single function. For building a predictive model, the CARET package uses a train() function.

### Features of CARAT are as follows:

- It integrates the training and prediction of a model.
- The syntax of the train() function used by Carat is: train(formula, data, method)

## RandomForest

RandomForest is one of the most widely used R machine learning packages. This R machine learning package can be employed for solving regression and classification works. Additionally, it can be used for training missing values and outliers.

This machine learning package with R generally is used to generate multiple numbers of decision trees. Basically, it takes random samples. And then, observations are given into the decision tree. Lastly, the common output that comes from the decision tree is the ultimate output.

randomForest(formula=, data=) is the syntax of this function.

### Features of RandomForest are as follows:

- You can use it to solve classification tasks and regression.
- Missing values and outliers can be trained using randomForest.

## e1071

This e1071 is one of the most popular R machine learning packages. Using this package, a developer can implement support vector machines (SVM), shortest path computation, bagged clustering, Naive Bayes classifier, short-time Fourier transform, fuzzy clustering, etc.

### Features of e1071 are as follows:

- Fuzzy Clustering, Support vector machines, Naïve Bayes classifier, etc. are used by this package to perform machine learning algorithms.
- For IRIS data SVM syntax is:

svm(Species ~Sepal.Length + Sepal.Width, data=iris)

## Rpart

Rpart stands for recursive partitioning and regression training. This R package for machine learning can be performed both of the tasks: classification and regression. It acts using a two-stage step. The output model a binary tree. The plot() function is used to plot the output result. Also, there is an alternative function, prp() function, that is more flexible and powerful than a basic plot() function.

The function rpart() used to establish a relationship between independent and dependent variables. The syntax is:

rpart(formula, data=, method=,control=)

where the formula is the combination of independent and dependent variables, data is the name of the dataset, the method is the objective, and control is your system requirement.

### Features of Rpart are as follows:

- It can be used for both classification and regression.
- rpart(formula, data=, method=,control=) is the syntax of this package.

## KernLab

If someone wants to develop your project based on kernel-based machine learning algorithms, then they can use this R package for machine learning. This package is used for SVM, kernel feature analysis, ranking algorithm, dot product primitives, Gaussian process, and so on. KernLab is widely used for SVM implementations.

There are various kernel functions available. Some kernel functions are mentioned here: polydot (polynomial kernel function), tanhdot (hyperbolic tangent kernel Function), laplacedot (laplacian kernel function), etc. These functions are used for performing pattern recognition problems. But users can use their kernel functions instead of predefined kernel functions.

### Features of Kernlab are as follows:

- It is customizable in the sense that users can use their own kernel functions instead of the predefined ones.
- Kernel functions can be used to perform Patten recognition problems.

## Nnet

If you want to develop your machine learning application using the artificial neural network (ANN), then this nnet package might help you in this regard. It is one of the most popular and easy to implement a package of neural networks. But it’s a limitation that is it’s a single layer of nodes.

### Features of Nnet are as follows:

- Nnet provides help one way or the other in the development of machine learning application through the use of artificial neutral network (ANN)
- This package’s syntax is: nnet(formula, data, size)

## Dplyr

One of the most popular R machine learning packages. Also, it provides some easy-to-access, fast, and consistent functions for data manipulation. Hadley Wickham writes this r programming package for data science. This package consists of set of verbs i.e., mutate(), select(), filter(), summarise(), and arrange().

### Features of Dplyr are as follows:

- To load this package, you have to write this syntax: library(dplyr)
- To install this package, one has to write this code: install.packages(“dplyr”)

## ggplot2

Another one of the most elegant and aesthetic graphics framework R packages for data science is ggplot2. It’s a system of creating graphics based on the grammar of graphics. The installation syntax for this data science package is:

install.packages(“ggplot2”)

## Wordcloud

When a single image consists of thousands of words, then its called a Wordcloud. Basically, its a visualization of text data. This machine learning package using R used to create a representation of words, and the developer can customize the Wordcloud according to his preference, like arranging the words randomly or same frequency words together or high-frequency words in the centre etc.

In the R machine learning language, two libraries are available to develop word cloud: Wordcloud and Worldcloud2. Here we will show the syntax for WordCloud2. To install WordCloud2, you have to write: library(wordcloud2)

### Features of Wordcloud are as follows:

- It is customizable, as you can have it to your taste.
- You need two libraries, WordCloud and WordCould 2, before you can create a WordCloud.

## Tidyr

Another widely used r package for data science is tidyr. The goal of this r programming for data science is tidying the data. In tidy, variable is placed into the column, observation placed into the row, and the value is in the cell. This package describes a standard way of sorting data.

### Features of Tidyr are as follows:

- For installation, you can use this code fragment: install.packages(“tidyr”)
- For loading, the code is: library(tidyr)

## Shiny

The R machine learning package, Shiny, is one of the web application frameworks for data science. It helps to create web applications from R effortlessly. Either the creator can install the software on each client system or cab host a webpage.

Additionally, Shiny apps can be extended with various scripting languages like HTML widgets, CSS themes, and JavaScript actions.

### Features of Shiny are as follows:

- The developer can build dashboards or can embed them in R Markdown documents.
- This package is a combination of the computational power of R with the interactivity of the modern web.

## MICE Package

The machine learning package with R, MICE refers to Multivariate Imputation via Chained Sequences. Almost all the time, the project developer faces a common problem with the machine learning dataset that is the missing value. This package can be used to impute the missing values via multiple techniques.

This package contains several functions like inspecting missing data patterns, diagnosis of the quality of imputed values, analyses completed dataset, store and export imputed data in various formats, and so on.

## tm

Needless to say, text mining is an emerging application of machine learning nowadays. This R machine learning package provides a framework for solving text mining tasks. In a text mining application i.e., sentiment analysis or news classification, a developer has to various types of tedious work like removing unwanted and irrelevant words, removing punctuation marks, removing stop words and many more.

### Features of tm are as follows:

- It consists of removeNumbers() function: for removing Numbers from the given text document.
- It also consists of weightTfIdf() function: for term Frequency and inverse document frequency.
- tm has tm_reduce() function: for joining transformations.
- It comprises removePunctuation() function: for removing punctuation marks from the given text document, etc.

## igraph

The network analysis package, igraph is one of the powerful R machine learning packages. It is a collection of powerful, efficient, easy to access, and portable network analysis tools. Also, this package is open source and free. Additionally, this package can be programmed on Python, C/C++, and Mathematica.

This package has several functions to generate random and regular graphs, visualization of a graph, etc. Also, you can work with your big graph using igraph. There are some requirements to use this package: for Linux, a C and a C++ compiler are required.

### Features of igraph are as follows:

- The installation of this R programming is: install.packages(“igraph”)
- For loading this package, you have to write: library(igraph)

## ROCR

The R machine learning package, ROCR, is used to visualize the performance of scoring classifiers. This package is flexible and easy to use. Only three commands and default values for optional parameters are needed. This package is used to creating cutoff-parameterized 2D performance curves. In this package, there are several functions like prediction(), which is used to create prediction objects, performance() that is used to create performance objects, etc.

## Data Explorer

DataExplorer is one of the most extensively easy to use R machine learning package. Among various data science tasks, exploratory data analysis (EDA) is one of them. In exploratory data analysis, the data analyst has to pay more attention to data. But, it not an easy job to check out or handle data manually or to use poor coding. Automation of data analysis is required.

This R package for data science provides automation of data exploration. This package used to scan and analyze every variable and visualize them. It is useful when the dataset is massive. So, the data analysis can extract the hidden knowledge of data efficiently and effortlessly.

## mlr

One of the most popular packages of R machine learning is the mlr package. This package is an encryption of several machine learning tasks. That means you can perform several tasks by only using a single package, and you no need to use three packages for three different tasks.

The package mlr is an interface for numerous classification and regression techniques. The techniques consist machine-readable parameter descriptions, clustering, generic re-sampling, filtering, feature extraction, and many more. Also, parallel operations can be done.

### Features of mlr are as follows:

- You need to install.packages(“mlr”) and library(mlr) to install and load this package respectively.
- It serves as a connection between several classifications and regression techniques.

## Arules

The package, arules (Mining association rules, and Frequent Itemsets) is an extensively used R machine learning package. By using this package, several operations can be done. The operations are the representation and transaction analysis of data and patterns and data manipulation. The C implementations of Apriori and Eclat association mining algorithms are also available.

## mboost

One of the best R machine learning packages for data science is mboost. This model-based boosting package has a functional gradient descent algorithm for optimizing general risk functions by utilizing regression trees or component-wise least squares estimates.

The interaction model is made available by mboot to potentially high-dimensional data.

## Party

Another package in machine learning with R is a party. This computational toolbox is used for recursive partitioning. The main function or core of this R machine learning package is ctree(). It is an extensively used function which reduces the time of training and bias.

The syntax of ctree() is: ctree(formula,data)

### Conclusion

R is such a prominent programming language that uses statistical methods and graphs to explore data. Needless to say, this language has several numbers of R machine learning packages, an incredible RStudio tool, and easy to understand the syntax to develop advanced machine learning projects. In an R ml package, there are several default values. Before applying it to your program, you must have to know about the various options in detail. By using these machine learning packages, anyone can build an efficient machine learning or data science, model. Lastly, R is an open-source language, and its packages are continually growing.