###### R vs Python

R and Python are two programming languages that have a data science orientation. Someone who is just learning data science, of course, confused between the two which language should be used and what is the difference between the two?

**R**

R is a programming language that can be used for statistical computing and graphs. The goal of developing the R language, which is to create a language that can focus on statistics, data analysis, and graphical modeling.

**Python**

Python is an object-oriented programming language that can be used for scientific and numerical computing.

**R vs Python**

**Data collection**

R can import data via Excel, CSV, and text files. Files created using Minitab or SPSS can also be converted into dataframes for use in R. The Rvest and magrittr packages can help us clean data from the web.

Python supports a variety of data formats, such as CSV, JSON files, and SQL files. Python can also be used to extract data directly from the internet.

**Data Exploration**

R provides great results for data exploration, as it was created especially for statisticians and data miners. With R, you can perform data optimization, random number generation, signal processing. R also offers support for third-party libraries. You can apply various tests and techniques, such as probability distribution and data mining using R.

Python has many libraries that can help you in data exploration, some of which are pandas, NumPy, and PyPI. Pandas can be used to organize data into data frames and also makes cleaning easier. Pandas can also store large amounts of data.

**Data Visualization**

To create a standard graph and data visualization, R provides several libraries including ggplot2 , Plotly , and Lattice.

Python can create customizable visualizations in the form of graphs or charts. Python provides IPython and matplotlib libraries to help developers and researchers create visualizations. However, the most commonly used library is matplotlib.

**Data Modeling**

With R, you can do statistical modeling efficiently. R can assist you in statistical modeling and special analysis, such as Poisson Distribution, Linear Distribution and Logistic Regression.

Python provides several libraries that can perform modeling. Python provides the NumPy library for numerical modeling, and for scientific computing, Python provides the SciPy library.

**Performance**

In terms of performance, Python is relatively faster than R. This is one of the main reasons why most programmers prefer Python over R.

**Library**

Python package hosting and management is handled by PyPI, while R is handled by CRAN. PyPI has over 275 thousand packets (a total not only used for data science), while CRAN has more than 16 thousand.

**Popularity**

Of the two languages, Python is more popular among developers and data scientists than R. However, some statisticians and data miners still prefer R due to its numerical processing capabilities and powerful visualizations. In addition, R also provides better control over data analysis due to its tendency towards statistical and numerical computations and provides more sophisticated and in-depth results.