Top 9 R Machine Learning Packages In 2020
Tags: machinelearning, rlanguage
Published: 2020-09-09 08:18:47

There is a big confusion between data scientists and machine learning developers to choose the programming language. Python, R, Java, Julia and Scala are some of the popular languages for data science and machine learning. The choice of programming language depends on developer’s preference and project requirements. Among these languages, R is the most popular programming language for statistical analysis and computing because of its amazing features. Researchers in the field of data science and statistical computing have been using this language for a couple of years due to its various features like running code without compiler, open-source, robust visualization library and so on. Let us see the top 9 R machine learning packages in 2020.

#Top 9 R Machine Learning Packages in 2020
**1. Dplyr-**
It is one of the most widely used R package for data science. Dplyr provides some easy to use, fast and consistent functions for data manipulation. It works with data frame like objects, both in memory and out of memory. It is also called as the grammar of data  manipulation which provides methods that are a consistent set of verbs to solve the common data manipulation challenges. This package consists of set of verbs i.e., mutate(), select(), filter(), summarise(), and arrange().

To install this package, one has to write this code-

install.packages(“dplyr”)
And to load this package, you have to write this syntax:

library(dplyr)

**2. Data Explorer-**
It is a popular easy to use R package for data science. Among numerous data science tasks, exploratory data analysis (EDA) is one of them. In exploratory data analysis, the data analyst needs to give more attention in data. But, it is not a simple task to look at or handle data manually or to use poor coding. Automation of data analysis is required. Data explorer provides automation of data exploration and is used to scan and analyze every variable and also visualize them. It is helpful at the case where the dataset is too vast. Thus, the data analysis can extract the hidden information on data efficiently and easily. This package can  be installed from CRAN by using the code:

install.packages(“DataExplorer”)To load this R package, you have to write: library(DataExplorer)

**3. MICE Package-**
MICE refers to Multivariate Imputation by Chained Equations. It is a package that implements various imputation using FCS(Fully Conditional Specification). Here each variable has its own imputation model and built-in imputation models are provided for continuous data, binary data, unordered categorical data. This package includes some functions like inspecting missing data patterns, diagnosis fo quality of imputed values, analyses completed datasets, store and export imputed data in various formats, and so on.

**4. Classification And Regression Training (Caret)-**
This package is a set of functions that tries to streamline the method for creating predictive models. It has tools for splitting data, pre-processing, feature selection, model tuning with resampling, variable importance estimation and so on. The package began as a technique to provide a uniform interface with the functions, including the ways to normalize basic tasks, for example, parameter tuning, variable importance, among others. After completing the installation of this package, developer can run names (getModelInfo()) to see the 217 functions that can be run through only one function. To build predictive model, CARET package use train() function having syntax as- 

train(formula, data, method)

**5. ggplot 2-**
It is a popular package for data visualisation and is a system for declaratively creating graphics, based on the Grammar of Graphics. Using this package, one can create interactive data visualisations and make millions of plots of various models.

**6. Shiny-**
It is a web app framework for data science that helps to build up web apps with R rapidly. Either you can install software on each client system or cab host a webpage. Apart from this, you can build dashboards or can embed them in R markdown documents. Shiny can be extended with multiple scripting languages like CSS themes, html widgets and JavaScript actions. 

Features-
* Attractive default UI theme based on Bootstrap.
* A highly customizable slider widget with built-in support for animation.
* Prebuilt output widgets for displaying plots, tables, and printed output of R objects.
* Fast bidirectional communication between the web browser and R using the httpuv package.
* Uses a reactive programming model that eliminates messy event handling code, so you can focus on the code that really matters.
* Develop and redistribute your own Shiny widgets that other developers can easily drop into their own applications (coming soon!)

Know more at- [https://solaceinfotech.com/blog/top-9-r-machine-learning-packages-in-2020/]