Top 10 R Packages for Data Science You Must Know in 2021

Introduction

R is one most famous programming languages for statistical analysis and computing. Because it provides a large number of features, researchers and data scientists are using it for data science and machine learning. Some of these features include open source, fast, interactive visualization libraries, running code without a compiler, good community, and many more.

One of the main reasons it is becoming very famous because of the large number of R packages for data science, machine learning, artificial intelligence projects. By using these packages, one can develop predictive models easily and efficiently. This blog list down the top 10 R packages you must know in 2021 for data science and machine learning.

Table of Contents

1. Dplyr
2. ggplot2
3. KernLab
4. DataExplorer
5. Caret
6. randomForest
7. Shiny
8. mboost
9. Plotly
10. SuperML

Dplyr

It is one of the most used R packages for data science and machine learning tasks. This package is written by Hadley Wickham. It is used to solve data manipulation tasks. It has a set of functions for data manipulation. It is also called a grammar of data manipulation. It has s set of verbs that help us to solve the most challenging data manipulation tasks such as mutate(), select(), filter(), summarise(), arrange().

To install this package, use the below code:

install.packages('dplyr')

For more information, check the below link: Introduction to dplyr 

ggplot2

One of the most popular and widely used R package for data visualization and exploratory data analysis. You can create interactive data visualizations using this package. It provides a wide range of pretty plots that take care of minute details as well as drawing legends. This package works under deep grammar called “Grammar of graphics”. It provides a wide range of plots like scatterplot, bubble plots. Jitter plots are charts, histograms, density plots, box plots, violin plots, dendrograms, and many more.

To install this package, use the below code:

install.packages('gglpot2')

Some examples of plots using this package are shown below:

For more information, check the below link:  ggplot2

KernLab

This package also called a Kernel-Based Machine Learning Lab. This package is used for regression, classification, dimensionality reduction, anomaly detection, clustering. If you want to use algorithms that involve a kernel-based approach then you can use it like SVM, ranking algorithm, kernel feature analysis ad many more. It is widely used for SVM implementations. It has a wide range of kernel functions like for polynomial kernel function we can use polydot(), hyperbolic tangent kernel function for tanhdot() etc.

To install this package, use the below code:

install.packages('kernlab')

For more information, check the below link: Package kernlab

DataExplorer

This R package is one of the easiest to use packages for data science and machine learning. This package mainly focuses on three goals:

1. Exploratory Data analysis
2. Feature Engineering
3. Data Reporting

This package automated exploratory data analysis for analytics tasks and predictive modeling by visualizing each feature present in our dataset.

To install this package, use the below code:

install.packages('DataExplorer')

To find a broad overview of our dataset, we can use the below code:

introduce(data)

To visualize the above table, use the below code:

plot_intro(data)

For more information, check the below link: Introduction to DataExplorer

Caret

This is also called Classification and Regression Training. It is one of the best packages for data science and machine learning tasks. It contains a set of functions that are used to create predictive models. It has other functionalities as well like feature selection, data splitting, data pre-processing, model tuning, feature importance, and many more.

To install this package, use the below code:

install.packages('caret')

For more information, check the below link: Package caret

randomForest

Random Forest is one of the most popular packages in R for machine learning. This package is used to create random forests in R. This can be used for both classification and regression tasks. We can also use it for training missing values and outliers. This package uses Breiman’s random forest algorithm for building decision trees.

To find a broad overview of our dataset, we can use the below code:

install.packages('randomForest')

For more information, check the below link: Random Forest 

Shiny

 

It is an R package that is used to build an interactive web application for data science. It helps us to make R web apps without much effort. Shiny creates web apps that are deployed on the web using your server or R shiny’s hosting services. Features of R shiny include create an application with less knowledge of web tools, provides live visualizations, render functions, and many more.

Example of web-app using shiny:

For more information, check the below link: Shiny

mboost

This package is used in data science for model-based boosting package and has a functional gradient descent algorithm for optimizing decision trees. It also provides an interaction model to potentially high-dimensional data.

To install this package, use the below code:

install.packages('mboost')

For more information, check the below link: mboost

Plotly

It is a graphing library that makes interactive graphs. It is a high-level interface to plotly.js, based on d3.js. It provides an easy-to-use user interface to generate slick D3 interactive graphics. These interactive graphs provide many functionalities like the ability to zoom in and zoom out the plots, hover over a point to get additional information, filtering of data, and much more.

It provides an example of charts like scatter plots, line plots, bar plots, pie carts, bubble charts, Box plots, histograms, error bars, violin plots, and much more.

For more information, check the below link: Plotly

SuperML

Superml is one of the famous R bundles for AI that gives a standard interface to the clients who utilize both the programming dialects Python and R for building AI models. This bundle essentially gives the highlights of Scikit Learn and predicts the interface to prepare AI models in R. As well as building AI models, there are convenient functionalities to do feature engineering.

To install this package, use the below code:

install.packages('superml')

For more information, check the below link: SuperML

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