# R Programming to support Data Science

R is an open-source programming language extensively used for statistical software and tools for data analysis. R is a key tool in Data Science. It is very well known and is the primary option for many statisticians and data scientists.

But what is it that makes R so well-liked? What is the reason and how to utilize R to do?

## Data Science?

### Information Science within R Programming Language

Data Science has emerged as the most popular field in this century. It's because there's the need to analyze and draw insights from data. Industries convert raw data into prepared data products. To accomplish this it needs a variety of tools that can process raw data. R is one of these programming languages that can provide an extensive environment to study, process, transform and present data. Check out the complete Data Science with R Course to master it.

## Introduction

R is a programming language and an environment for statistical programming, encompassing statistical computing and graphics.

Python is a general-purpose programming language used for data analysis and scientific computing.

### The Goal

It comes with many features that can be useful in the analysis of statistics and representation. It is a great tool to build GUI applications as well as web applications using embedded systems.

### Workability It comes with a variety of easy-to-use applications to perform tasks. It is capable of performing matrix computation and also optimization.

### Integrated development environment Various well-known R IDEs include Rstudio, The RKward, R commander, etc. Many popular Python IDEs include Spyder, Eclipse+Pydev, Atom, and more.

### Libraries and packages

There are a variety of libraries and packages such as ggplot2, caret, etc. The most important libraries and packages include Pandas, Numpy, Scipy, and many more.

### Scope

It is used to perform complex analyses of data for data science. It is a simpler approach to data science-related projects.

## Specifications of R Data Science Data Science

The most important characteristics of R for applications in data science include: R offers a wide range of support for statistical modeling.

R is an ideal tool for many data science applications since it offers visual tools that are attractive. R is widely used in data science applications to ETL (Extract Transform and Load). It is a user interface for numerous databases such as SQL as well as spreadsheets.

R also offers a variety of important applications for data wrangling. With R Data scientists, they can use algorithmic machine-learning to obtain knowledge about the future of developments.

One of the key features of R is its ability to connect directly with NoSQL databases and to analyze data that is not structured.

## Most commonly Data Science in R Libraries

Dplyr: For data wrangling and data analysis, we utilize the dplyr program. This package is used to perform various functions on data frames in R. Dplyr is actually designed around these five functions. It can be used to use local data frames and also using remote database tables. You may have to:

- Select the specific column of information.
- Sort the data you have to filter certain rows.
- Arrange rows of your data in the order you want them to be.
- Change your frame of data to include new columns.
- Summary portions of data a manner.

## Ggplot2:

R is most well-known for its visualization software, ggplot2. It offers a stylish collection of graphics that is also interactive. The library ggplot2 is the concept of a "grammar of graphic images" (Wilkinson 2005). This method provides an organized method to create visualizations that express relationships between properties of data as well as their representation in graphics. Esquisse: This package brings the essential function to Tableau into R. Simply drag and drop to finish your visualization in just a few minutes. It's an improvement to ggplot2. It lets us draw curves, bar graphs, scatter plots, and histograms and export the graph or access the code that created the graph.

Tidyr : Tidyr is a program that can be used to tidy or clean data. We believe that data can be tidy when each of the variables is a column and every row is an observation.

Shiny : This is a well-known program in R. If you're looking to share your work with your friends and family members and allow them to get to know and explore it visually, you could utilize shiny. It's a Data Scientist's ideal friend.

Caret:Carat refers to the concept of classification as well as regression training. By using this feature, you can solve complex classification and regression problems.

E1071 :This program is widely used for creating the clustering process, Fourier Transforms, Naive Bayes, SVM, and other types of functions.

Mlr :This software is absolutely fantastic in the execution of tasks that requires machine learning. It is almost complete with all the necessary and effective algorithms to perform tasks in machine learning. It could also be described as an extensible framework that can be used for regression, classification, multi-classification, clustering, and survival analysis.

## Other Libraries in R:

Lubridate Knitr DT(DataTables) RCrawler Leaflet Janitor Plotly