# R vs Python

**R programming Language** and **Python** are both used extensively for Data Sciences. Both are very useful and open source languages as well.

### In this article we will cover the following topics:

**R Programming Language****Python Programming Language****Difference between R Programming and Python Programming****Ecosystem in R Programming and Python Programming****Advantages in R Programming and Python Programming****R and Python usages in Data Science****Example in R and Python**

**R Programming Language**

**R Language** is used for machine learning algorithms, linear regression, time series, statistical inference, etc. It was designed by Ross Ihaka and Robert Gentleman in 1993. R is an open-source programming language that is widely used as a statistical software and data analysis tool. R generally comes with the Command-line interface. R is available across widely used platforms like Windows, Linux, and macOS. Also, the R programming language is the latest cutting-edge tool.

## Python Programming Language

**Python** is a widely-used general-purpose, high-level programming language. It was created by Guido van Rossum in 1991 and further developed by the Python Software Foundation. It was designed with an emphasis on code readability, and its syntax allows programmers to express their concepts in fewer lines of code.

**Difference between R Programming and Python Programming**

Below are some major differences between R and Python:

Feature | R | Python |
---|---|---|

Introduction | R is a language and environment for statistical programming which includes statistical computing and graphics. | Python is a general purpose programming language for data analysis and scientific computing |

Objective | It has many features which are useful for statistical analysis and representation. | It can be used to develop GUI applications and web applications as well as with embedded systems |

Workability | It has many easy to use packages for performing tasks | It can easily perform matrix computation as well as optimization |

Integrated development environment | Various popular R IDEs are Rstudio, RKward, R commander, etc. | Various popular Python IDEs are Spyder, Eclipse+Pydev, Atom, etc. |

Libraries and packages | There are many packages and libraries like ggplot2, caret, etc. | Some essential packages and libraries are Pandas, Numpy, Scipy, etc. |

Scope | It is mainly used for complex data analysis in data science. | It takes a more streamlined approach for data science projects. |

**Ecosystem in R Programming and Python Programming**

**Python** supports a very large community to general-purpose in data science. One of the most basic use for data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas and NumPy is one of those packages and makes importing and analyzing, and visualization data much easier.

**R Programming** has a rich ecosystem to use in standard machine learning and data mining techniques. It works in statistical analysis of large datasets, and it offers a number of different options for exploring data and It makes it easier to use probability distributions, apply different statistical tests.

Features | R | Python |
---|---|---|

Data collection | It is used for data analysts to import data from Excel, CSV, and text files. | It is used in all kinds of data formats including SQL tables |

Data exploration | It optimized for statistical analysis of large datasets | You can explore data with Pandas |

Data modeling | It supports Tidyverse and it became easy to import, manipulate, visualize and report on data | Use can you NumPy, SciPy, scikit-learn |

Data visualization | You can use ggplot2 and ggplot tools to plots complex scatter plots with regression lines. | You can use Matplotlib, Pandas, Seaborn |

**Advantages in R Programming and Python Programming**

Advantage | |
---|---|

R Programming | Python Programming |

It supports a large dataset for statistical analysis | General-purpose programming to use data analyse |

Primary users are Scholar and R&D | Primary users are Programmers and developers |

Support package like tidyverse, ggplot2, caret, zoo | Support packages like pandas, scipy, scikit-learn, TensorFlow, caret |

Support RStudio and It has a wide range of statistics and general data analysis and visualization capabilities. | Support Conda environment with Spyder, Ipython Notebook |

**R and Python usages in Data Science**

Python and R programming language is most useful in data science and it deals with identifying, representing and extracting meaningful information from data sources to be used to perform some business logic with these languages. It has a popular package for Data collection, Data exploration, Data modeling, Data visualization, and statical analysis.

**Example in R and Python**

Program for the addition of two numbers

## R

`# R program to add two numbers` `numb1 <- 8` `numb2 <- 4` `# Adding two numbers` `sum <- numb1 + numb2` `print` `(` `paste` `(` `"The sum is"` `, sum))` |

## Python

`# Python program to add two numbers` `numb1 ` `=` `8` `numb2 ` `=` `4` `# Adding two numbers` `sum` `=` `numb1 ` `+` `numb2` `# Printing the result` `print` `(` `"The sum is"` `, ` `sum` `)` |

**Output:**

The sum is 12

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