When it comes to data science, two major programming languages are in use. The two programming languages are R and Python. R is a programming language that is popular among statisticians and data miners. Python is one of the most popular programming languages used to provide the back end for many data analysis solutions. So, which one should you use? This blog will give you a very brief difference between R vs Python as well as an introduction.
What is R?
R programming is an open-source language used for statistical computing. The use of this language for a vast range of data analysis and statistical modelling applications.
In other words, it is a programming language with built-in functions for data analysis. This language can handle large data sets and model complex relationships among variables. These features make the R language very popular in sectors like Banking, Insurance, retail, telecom, pharmaceutical, biomedical and government.
Moreover, thousands of people use R for different purposes because it’s easy to learn and has a huge online community.
Advantages of R
- R is a cross-platform and platform-independent programming language, meaning its code runs across all operating systems.
- It lets us perform different machine learning tasks like classification and regression.
- It is most commonly referred to as the language used in statistics. This is the principal reason R is preferred over other programming languages for creating and using statistics tools.
- R permits us to do data manipulation.
- It is the open-source language we can use without licensing or paying a cost.
Disadvantages of R
- Firstly, the R programming language runs slower than the other languages, such MATLAB as well as Python.
- Secondly, it is not able to support 3D or dynamic graphics. R is a complicated language and has an extremely steep learning curve.
- Thirdly, R is not secure enough.
What is Python?
Python is a powerful, flexible and easy-to-use programming language. In addition, it is great for writing web applications. It’s popular for web applications because it’s easy to learn, it’s easy to read, and it’s easy to maintain.
In other words, Python is a relatively small language, which is great for building a small application. However, you can also build massive applications with it. It comes with many modules, has a huge community, and is used by YouTube, Spotify, and many other popular services.
Advantages of Python
- Python is a high-level programming language with a syntax similar to English.
- The library standard of Python is massive. In addition, you will find nearly all the functions required for the task.
- Python does not know what variable it is until we run the program.
- It is an interpreter language. This implies it executes code line by line. Python directly executes code line-by-line.
Disadvantages of Python
- Firstly, Python is not 100 % secure. Therefore, in this situation, it is imperative to take the necessary steps to ensure the security of the code.
- Secondly, it’s slower than other non-compiled languages because it needs a lot of computation power.
- Thirdly, the Python language has use of memory that is high.
Differences between R vs Python
Here are the differences between R and Python programming languages.
Introduction
R is a programming language and software environment. It designs for statistical programming, encompassing statistical computing and graphics.
Python is a general-purpose programming language. For scientific computing and data analysis, use Python.
Workability
The other significant factor is workability in the differences between R vs Python.
R offers a variety of easy-to-use applications for completing tasks.
On the other hand, Python can easily do multi-dimensional computations and optimisation.
Objective
R contains many features that can use to represent and analysis of statistics.
Whereas Python is a programming language that can help build GUI and web-based applications.
Integrated development environment
This is another essential factor in the difference between R vs Python.
Many well-known R IDEs include Rstudio, The RKward, R commander, etc.
On the other hand, many well-known Python IDEs include Spyder, Eclipse+Pydev, Atom, and many more.
Scope
R is used primarily to perform complex analyses of data within data science.
Whereas, Python is a more simplified approach to data science projects.
There are numerous libraries and programs like ggplot2, Caret, etc.
Libraries and packages
Some essential libraries and programs include Pandas, Numpy, Scipy and more.
R is a surprisingly complicated syntax, and the learning curve isn’t simple.
On the other hand, Python is a very simple language that is simple to master.
Popular
You need to know the popular factor in the differences between R vs Python.
R is not as popular with users. Its users are scientists and Research & Development who frequently depend on data analysis.
Whereas Python is more well-known and has a large number of users. The most frequent users of Python include programmers and developers.
Conclusion
We hope you enjoyed this blog post about the differences between R vs Python. These two languages are among the most popular in the data science and machine learning fields.
R is a well-known programming language used for in-depth statistical analysis. Python is a general-purpose programming language. For scientific computing, data mining and machine learning, use Python. R is the way to go if you want to do data science and machine learning tasks. It is very popular in this domain, and the libraries are very powerful. Python is the better option if you want to do coding and build your software.
