Python vs. R: What’s the Difference?

O’Reilly emphasized in their report that Python and R are two very popular programming tools for the working of the data science field. This report is based on a data science survey conducted by O’Reilly. Hence it becomes difficult for anyone to choose which one is the best, between Python vs R. In fact, both Python and R are flexible data analytics languages. Both programming languages were developed at the start of the 1990s, although both are open-source and completely free.

 Python is essential for anyone interested in learning about different machines, creating complex data visualizations, and working with large datasets. R is essential as a general-purpose programming language for one interested in statistical analysis, even for python.

If you are a student of programming and confused about the difference between Python vs R., Then you came to the right place. This blog is helpful for you to understand the comparison between Python and R. Before discussing their differences, first of all, let’s check all the essential information about both.

What is Python?

Python is one of the utterly object-oriented programming languages. That means python makes groups of codes and data into objects that can easily modify and interact with one another. Python was released in 1989 to emphasize code efficiency and readability. C++, Java, and some other scales are examples of it. It is helpful for data scientists to execute different programming works with great code readability, stability, and modularity.

Data science is a small portion of the python ecosystem. Python is a suite of specific machine-learning libraries that contains many popular tools. Most data science programming language-related jobs can be done with these top five Python libraries, such as scipy, seaborn, NumPy, sci-kit-learn, and pandas. Most data science jobs can be done with five Python libraries: Numpy, Pandas, Scipy, Scikit-learn, and Seaborn.

Advantages and disadvantages of Python

If you want to understand the difference between Python vs R., then you should know about the advantages and disadvantages of both. So we are going to discuss some important advantages or disadvantages of python-:

Advantages of python

  •  The main reasons for Python’s popularity are its high speed, easy code readability, and many other functionalities.
  • Python provides high ease for deployment or reproducibility.
  • It is one of the general-purpose programming languages useful for data analysis.
  • Python is best for mathematical computations. It also helps you to learn how algorithms work.

Disadvantages of python

  • If you are using python, then it’s required rigorous testing as errors show up in runtime.
  • The visualization system of python is more typical and complicated than R.
  •  Python doesn’t have as many libraries as R.
  •  Python does not allow us to get primary results that are allowed by R.

What is R?

R programming language was released in 1992. From day one of its release, it became a programming language preferred by mostly data scientists for their work for many years.  The R programming language is also called procedural language. Because it works differently, it breaks down a programming task into subroutines, steps, and procedures. That becomes a plus point of R when creating any data model. Because if you are using the R programming language, it makes it easy to understand how we can carry out different complex operations.

The analysis-oriented community of R developed open-source packages for some specific typical models. There are many other features of R that some data scientists prefer to R for different scientific tasks.  The primary reason that most scientists prefer to R is the output. R has excellent tools to communicate with positive results. Such as Xie Yihui, Rstudio, etc.

Advantages and disadvantages of R

As we already discussed, to understand the difference between Python and R. One should know the advantages or disadvantages of both first. So here we mention some important advantages and disadvantages of R-:

Advantages of the R

  • R has many functionalities for all types of data analysis.
  •  Most users are working with R because it is built around a command line.
  • R is widely considered as the best tool that is useful for making beautiful graphs and visualization.
  •  R is the best programming language for statistical analysis.

Disadvantages of R

  • The main problem of using R is that it takes too much time to find out the right packages to use in R.
  • R is completely dependent on some of its libraries.
  • If the code has been written poorly, then R is considered slow.
  •  R is not as popular as python is popular.

Some important differences between Python vs R

The basis of the differencePythonR
Main usersIt is mainly used by developers and programmers.It is mainly used by scholars and R&D.
Learning curvePython is a linear and smooth programming language; hence it’s easy to learn.R seems difficult to learn initially, but it can be learned through practice.
TasksIt is good to deploy the algorithm.
It makes it easy to get primary results.
DisadvantagesIt does not have many libraries like RThe slow high learning curve even that is dependent on the library
ObjectiveIts main objective is to deploy and productionStatistics and data analysis the main objective of R.
FlexibilitiesIf you are using python then you may know that it becomes easy to construct new models from scratch. Such as optimization, matrix, and computations.If you are using R then it becomes easy to use the available library of R, and that is very helpful in different tasks.
The popularity of Programming LanguagePython is the most popular at present.R is also popular but not as popular as python.
IntegrationIt is well-integrated with appR is run locally.
Database sizeIt can easily handle huge sizeIt also can able to handle huge sizes
Important Packages, and libraryPython includes TensorFlow, caret pandas, scipy,  scikit-learnR includes the zoo, diverse, ggplot2, caret, 
AdvantagesPython makes all typical tasks very easily such as speed, deployments, functions, and mathematical computations, etc.R makes graphs beautiful that are made to talk such as RMarkdown, a large catalog for data analysis, and the Github interface.

Other key differences

  • Data collection Python supports all data formats from CSV files to JSON sourced directly from the internet. Importing SQL tables into Python code is also possible. The Python requests library allows you to pull data from the internet for web development easily. On the other hand, R is intended for data analysts who can import data from Excel, CSV, and text files. Minitab and SPSS files can be converted into R data frames. Python is more flexible for pulling data from web pages, but modern R packages such as Rvest are optimized for web scraping.
  • Data exploration In Python, you can explore data using Pandas, the Python data analysis library. In a matter of seconds, you can filter, sort, and display data. R, on the other side, is optimized for statistical analysis and offers many options for exploring data. You can use R to create probability distributions, perform statistical tests and use standard machine learning and Data Mining techniques.
  • Data modeling – Python provides standard libraries for data modeling, such as SciPy for scientific computing, calculations, and sci-kit-learn to assist with machine learning algorithms. Pack packages not part of R’s core functionality will sometimes be required for specific modeling analysis. The Tidyverse package set makes it simple to import, manipulate and visualize data and report on them.
  • Data visualization – While Python isn’t known for its visual abilities, the Matplotlib library can be used to generate basic charts and graphs. The Seaborn library makes it possible to create more informative and attractive statistical graphics in Python. R was designed to show the results of statistical analysis. The base graphics module allows you to create simple charts and plots. For more complex plots, such as scatter plots with regression lines, you can use ggplot2.

Conclusion

We have mentioned all the important differences between Python vs R. We also provided you with detailed information about both programming languages that can help you to understand the difference between them. 

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FAQ

What is better R or Python?

  • R is an excellent choice if you are sincere about data visualization and statistical calculations. Python is better if you are interested in working with data scientists, deep learning algorithms, and artificial intelligence.

Is R easier than Python?

  • R is challenging to learn for beginners due to its non-standard code. Python is easier to learn and has a more linear curve. Python is also more accessible to code because it’s simpler to maintain and has a syntax similar to English.

Can Python do everything R can?

  • Python can do almost any data science task can imagine, but there are areas where one language excels over the other. Python dominates deep learning research, so tools like Keras or PyTorch are Python-first.

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