# R Programming

## R Programming Training in Vizag

The R Programming Tutorial is intended for both novices and experts. All of the fundamental and sophisticated ideas of data analysis and visualization are covered in this tutorial.

A software environment called R is used to examine graphical data and statistical data. R makes it possible to use functions for modular programming.

## What is R Programming

“R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand.” R is now developed by the R Development Core Team. In addition, it includes a software environment for data modeling, reporting, graphical display, and statistical analysis. Lexical scoping semantics is paired with the S programming language, which is implemented in R.

In addition to branching and looping, R also enables function-based modular programming. To increase efficiency, R enables integration with procedures written in C, C++,.Net, Python, and FORTRAN.

R is currently one of the most essential tools used by statisticians, academics, data analysts, and marketers for data retrieval, cleaning, analysis, visualization, and presentation.

## History of R Programming

R has a history that dates back roughly 20–30 years. The R Development Core Team is presently responsible for maintaining and developing R, which was created by Ross lhaka and Robert Gentleman at the University of Auckland in New Zealand. The names of the two developers are incorporated into the name of this programming language. In 1992, the initial project was given some thought. 1995 saw the release of the first version, and 2000 saw the release of a stable beta version.

### R Programming Training in Vizag

## Features of R programming

R is a domain-specific programming language which aims to do data analysis. It has some unique features which make it very powerful. The most important arguably being the notation of vectors. These vectors allow us to perform a complex operation on a set of values in a single command. There are the following features of R programming:

- It is a simple and effective programming language which has been well developed.
- It is data analysis software.
- It is a well-designed, easy, and effective language which has the concepts of user-defined, looping, conditional, and various I/O facilities.
- It has a consistent and incorporated set of tools which are used for data analysis.
- For different types of calculation on arrays, lists and vectors, R contains a suite of operators.
- It provides effective data handling and storage facility.
- It is an open-source, powerful, and highly extensible software.
- It provides highly extensible graphical techniques.
- It allows us to perform multiple calculations using vectors.
- R is an interpreted language.

## Why use R Programming?

There are numerous tools on the market for doing data analysis. Acquiring new language skills requires time. Two great technologies that the data scientist can use are R and Python. When we first start learning data science, we might not have the time to master them both. Learning algorithms and statistical modeling is more crucial than learning a programming language. Our finding is computed and communicated using a programming language.

The important task in data science is the way we deal with the data: clean, feature engineering, feature selection, and import. It should be our primary focus. Data scientist job is to understand the data, manipulate it, and expose the best approach. For machine learning, the best algorithms can be implemented with R. **Keras** and **TensorFlow** allow us to create high-end machine learning techniques. R has a package to perform **Xgboost**. Xgboost is one of the best algorithms for **Kaggle competition**.

R communicate with the other languages and possibly calls Python, Java, C++. The big data world is also accessible to R. We can connect R with different databases like **Spark** or **Hadoop**.

In brief, R is a great tool to investigate and explore the data. The elaborate analysis such as clustering, correlation, and data reduction are done with R.

#### R Programming Training in Vizag

## Comparison between R and Python.

Data science deals with identifying, extracting, and representing meaningful information from the data source. R, Python, SAS, SQL, Tableau, MATLAB, etc. are the most useful tools for data science. R and Python are the most used ones. But still, it becomes confusing to choose the better or the most suitable one among the two, R and Python.

Comparison Index | R | Python |
---|---|---|

Overview | “R is an interpreted computer programming language which was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand .” The R Development Core Team currently develops R. R is also a software environment which is used to analyze statistical information, graphical representation, reporting, and data modeling. | Python is an Interpreted high-level programming language used for general-purpose programming. Guido Van Rossum created it, and it was first released in 1991. Python has a very simple and clean code syntax. It emphasizes the code readability and debugging is also simple and easier in Python. |

Specialties for data science | R packages have advanced techniques which are very useful for statistical work. The CRAN text view is provided by many useful R packages. These packages cover everything from Psychometrics to Genetics to Finance. | For finding outliers in a data set both R and Python are equally good. But for developing a web service to allow peoples to upload datasets and find outliers, Python is better. |

Functionalities | For data analysis, R has inbuilt functionalities | Most of the data analysis functionalities are not inbuilt. They are available through packages like Numpy and Pandas |

Key domains of application | Data visualization is a key aspect of analysis. R packages such as ggplot2, ggvis, lattice, etc. make data visualization easier. | Python is better for deep learning because Python packages such as Caffe, Keras, OpenNN, etc. allows the development of the deep neural network in a very simple way. |

Availability of packages | There are hundreds of packages and ways to accomplish needful data science tasks. | Python has few main packages such as viz, Sccikit learn, and Pandas for data analysis of machine learning, respectively. |

## Applications of R

There are several-applications available in real-time. Some of the popular applications are as follows:

- HRDAG
- Sunlight Foundation
- RealClimate
- NDAA
- XBOX ONE
- ANZ

## Prerequisite

R programming is used to represent data and provide statistical insights. Therefore, it is necessary that we understand mathematical statistical theory. Recognizing various graph types for data representation and—above all—having prior programming experience are crucial.