R is one of the most popular programming languages in the field of data science and machine learning. As it has numerous packages related to Data ingestion, data processing, data visualization, report creation, machine learning etc. through which data scientists and researchers can take advantage of their projects.

Up to now, there are 10k+ packages available in cran. In this article, we will be learning about the most useful r package for data science.

Data.table#

Every Data Science and ML project starts with the ingestion of data. This package is used for reading data and data manipulation. data.table is known for its fast speed of reading data. It is used for loading large volumes of data and for data manipulation. It provides a high-performance version of base R’s data.frame with syntax and feature enhancements due to which it can perform fast data loading.

Learn more about data.table.

Dplyr#

This package is part of tidyverse package family, which is the creation of Hadley Wickham. It is known for its user-friendly functions for data manipulation. It follows the grammar of data manipulation and provides consistent functions for different data manipulation processes. Some of the main functions of dplyr are: mutate(), select() filter(), summarise(), arrange(). Learn more about the dplyr.

Ggplot2#

This package is part of the tidyverse family and is used for data visualization. It is well-known for its powerful visualization capabilities and ease of use. This package is built on the grammar of graphics, which is the deep philosophy of visualization. You can easily create basic visualizations such as barplots, line plots, histograms, and so on. In addition, you can create advanced visualizations such as these: Find out more about ggplot2.

Highcharter#

Although Static visualization are great but interactive visualization is more awesome as it allows users to interact and tell more stories.Highcharter is used for the creation of interactive visualization and It is an R wrapper for Highcharts JavaScript library and its modules. Likewise, it is a very flexible and customizable charting library. The highcharter visualization is stunning, and it can be used in interactive dashboards or reports.

Learn more about the highcharter.

DataExplorer#

EDA is a very important phase for data analysis & Machine learning. Dataexplorers is a package that simplifies the eda process so that users can focus on insight extraction and model creation. It has several functions that allow you to determine the missing value distribution, variable density distribution, and variable correlation.

Learn more about the Dataexplorer

Tidymodels#

It is a set of several packages used in machine learning and predictive modelling. Before Tidymodels, it was difficult to do Machine Learning in R because there were multiple packages for different algorithms, each with its own datatype and processing. Tidymodel has made machine learning in R much easier because it includes packages like parsnip that provide a tidy, unified interface to model building. It also includes data preprocessing, data sampling, feature engineering, and model performance metrics packages. Parsnip, rsample, recipes, tune, and broom are the core tidy models packages.

Learn more about the Tidymodels.

flexdashboard#

It’s used to create an interactive dashboard using R markdown. This package allows you to easily combine the related charts and create an interactive dashboard or storyboard to tell your data story. If you are already familiar with Rmarkdown, the learning curve for this package is not steep. The application is simple to deploy on GitHub Pages or Netlify.

Learn more about the Flexdashboard.

Shiny#

Shiny is a R library that allows you to create interactive web apps directly from R. It can be used to build simple web apps as well as large data applications. Without any prior knowledge of web development, you can create a gleaming app. Shiny is divided into two major parts: UI, which controls the user interface, and Server, which controls all data processing or backend processing. Shiny apps must be installed on a private server or the shinyapps.io server.