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Which is ideal programming for data visualization: Python Vs R?

Here, we are going to learn about the features of Python and R programming language and will learn which is ideal programming for data visualization: Python Vs R?
Submitted by Bhavya Sri Khandrika, on December 08, 2020

As we all are acquainted with the scenario that the 21st century is completely digital and even turning into automated. And the world of automation needs an advancing programming language to configure the models and program them such that they can perform real-time applications.

The automation includes the usage of Artificial Intelligence. In addition to that, the Artificial Intelligence principles are mostly implemented by using two prime programming languages, they are R programming language and Python Programming language.

Here, the war between these two popular programming languages begins. The question is how can one conclude whether either one of these is superior or user friendly when it comes to practical implementations.

So here is an attempt by includehelp.com, where we have gathered some information and structured the practical implementations. The later text will state both pros and cons for implementing R and Python as default programming languages.

Let us start with the R programming language and extend our discussion to the Python programming language.

R language is one of the most prominent programming languages where all the statistics and data analysis are widely implemented with ease.  The usage of R in the data analysis has been continued for almost 2 decades and thus even now it's showing the appreciable change. R language has a wide ecosystem when it comes to data analytics. The outstanding performance of the R programmer can be felt from the various 12000 packages of the R.

Why did all the statisticians prefer R programming language?

The R language as stated earlier is considered the best option for performing the data analytics, this is because the R is rich with the inbuilt comprehensive lists that encompass several statistical and other graphical techniques in it. Strictly speaking, R is counted as a scripting language which is quite flexible with its wide range of resource banks and libraries that are included in it.

The later text will deal with the comparison between the R and Python programming language.

  • The ease of learning can be easily compared between the R and Python languages. It is believed that the R language has a very steep learning curve that shows the user's excitement to work on it and learn it eagerly. The advantage of the R language lies in its simple and very comfortable syntax.
  • The R language is the low-level language when compared to the Python Programming language. Based on the above statement, the R language can have longer codes which is a bit difficult for beginners.
  • The prime objective or application of the R language lies in the data analysis. So the R programmer will be more convenient in coding the longer programs. The larger number of packages and libraries will add to the R language.
  • The R language has a great benefit over the Python language when it comes to the graphical representation. The numerous packages provide the path for giving attractive visualization to the data considered.

Well, it is time to consider the autonomous features of Python language over R language.

Python programming language has a lot of common characteristics that of R language. The functions that include data wrangling, feature selection web scraping, and so on are quite identical to the functions and attributes of R.

The beauty of Python can be felt when it is implemented in working with machine learning projects that show a practical solution to the problem statements. Python is widely implemented as its code is easier to maintain and also in fact stronger than the R language's code.

The history of Python doesn't include graphical methods or functions in it unlike that of R language. Whereas the recent improvements in the Python language have bought many remarkable changes in the libraries and the functions in it. There are five most significant Python libraries in the Python language that are executed in the Machine Learning projects. They are:

  • Pandas
  • Numpy
  • Scipy
  • Seaborn
  • Scikit-learn

Python programming is quite manageable in real-time examples. Also, Python gives prominence to code readability and productivity too. The beginners are quite educated or guided to learn the Python language than the R language.

One must note that Python is the high-level language that is majorly accepted and used in programming the crucial code for the problem statements. The positive side of Python language is the code written using Python will get executed fast compared to the R language code. The length of the Python code can be reduced to a greater extent thus improving the execution time.

The earlier context appreciated the R language for its libraries and functions that make Data Analysis easier. Now, when we consider the same aspect with the Python language it is notable that the previous versions didn't consist of the data analysis packages. As time passed by, the later versions of Python included special packages like Numpy and Pandas for data analytics. Now the above two packages are ruling the automation world.

The raw data must be analyzed and then represented in infographics for better understanding. Coming to the Python programming language, it has numerous libraries that can be used for better visualization. The visualizations play a major role in choosing the software for data analysis. Python comprises advanced visualization libraries and also they are quite complex too. Even though the Python programming language possesses advanced libraries and other functions, they are not in line with the R language libraries. This is because the R libraries and functions are more advanced and its graphical capability is much more than that of the Python libraries. So in this case, the R wins more majority when it comes to graphical visualization techniques.

Python language is more flexible in building the lex code than the R language code.

The above features of both R and Python language vary significantly. Yet their need and usage depend on the problem statement and the skills of the programmer.

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