Top 9 R Machine Learning Packages In 2020

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-

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