R Vs Python For Data Science and Statistics

1. R vs Python – Objective

In this tutorial, we will learn first what is R and Python. Moreover, we will understand what is the difference between R vs Python for data science and data analysis. Along with this, we will cover the pros and cons of both Python vs R and understand R vs Python for Data Science. Also, we will see is R better than Python for data analysis.

So, let’s start with R vs Python.

R Vs Python For Data Science and Statistics

R Vs Python For Data Science and Statistics

2. Difference Between R and Python

R:

R language is an open source programming language. It is maintained by the R core-development team – team of volunteer developers from across the globe. Also, we use this language to perform statistical operations. And it is available from the R-Project website www.r-project.org. Also, R is a command line driven program.

Python:

It is very easy to learn. Although, its feature itself is modest. They didn’t require too much time in investment. Also, its syntax is easily readable.
Moreover, Python is an ideal teaching language because of this simplicity. Thus it also allows newcomers to pick it up quickly. As we have seen that developers spend their time in thinking about the problem they’re trying to solve, and less time thinking about language complexities.

3. R vs Python for Data Science wars

As we are well known to these that both the languages are gaining height in the data analyst community. Moreover, results show that both the languages are fighting to become data scientist’s language of choice. This is the main Pint in R vs Python.

4. Introducing the opponents

i. Current Versions

R – Its current version is 3.1.3 March 2015
Python – The current version of python is 3.4.3 February 2015 /2.7.9 December 2014

ii. History

R
Creators: It was created by Ross Ihaka and Robert Gentleman.
Release Year: 1995
Must Knowns

  • Basically, R is an implementation of S programming language.
  • Also, it’s design and evolution is being handled by the R-core group. 
  • Moreover, it’s software environment was written in C, Fortran, and R.

Python
Creators: Python was created by Guido Van Rossum.
Release Year: 1991
Must Knowns

  • There is one important thing about python. That was inspired by C, Modula-c and particularly by ABC.
  • This language gets its name from the “Monty Python’s Flying Circus” comedy series.
  • Basically, there is one software present- Python Software Foundation(PSF). That was responsible to take care of python advances. 

iii. Purpose

R: its main focus is on statistics, data analysis, and graphical models.
Python: It highlights only productivity as well as code readability.

iv. Used By

R: Since long used in academics and research. Although, it is expanding itself into the business market.
Python: It has been used by the Programmer. As they want to move into data analysis. 
“Someone working in an engineering environment, they might prefer python”

v. Community

R: It has been getting support from the huge community. And that support is coming in the form of-
a. Different mailing-List
b. Documents that are contributed by different users.
Python:  It is getting very good support for general purpose coding. Python support is being found at:

  • Mainly from StackOverflow
  • More and more adoption from developers and programmers.

vi. Usability

R

  • In this, with the help of only a few lines, you can write statistical models.
  • It has different R style sheets. But it can’t be used by everyone.
  • It has a very good feature that in several ways we can write the same piece of functionality.

Python

  • As it’s syntax is very easy. Therefore, to perform coding and debugging in python is a very easy task. 
  • Particularly in python, in the same way, we can write a piece of functionality.

vii. Flexibility

R: We can easily use complex formulas. Also, we can use a kind of statistical tests and models. That is available in R
Python: it is an important feature of it that it is used for doing something unique. Also, it is used for scripting a website by developers.

viii. Ease of Learning

R: In R, at the start, it is having a steep learning curve. But as soon as you know the basics, you can learn advanced stuff. And a good thing about R is that it’s not hard for experienced programmers.
Python: It is best for readability and it’s simplicity. It is also being considered a good language for beginners.

5. The Case For Python and R

i. Why is Python great for data science?

  • It was released in 1989.
  • IPython / Jupiter’s notebook IDE is excellent.
  • There’s a very large ecosystem for python.

ii. Why is R great for data science?

  • It was created after python in 1992.
  • In this programming language, Rcpp helps to make it very easy to extend with C++.
  • In R, we use RStudio to call a mature and excellent IDE.

6. Introduction to R and Python for data analysis wars

After learning about both the technologies, let us now see the comparison between R vs Python.
I’ll compare PYTHON AND R languages on following attributes:

  • Availability / Cost
  • Ease of learning
  • Data handling capabilities
  • Graphical capabilities
  • Advancements in tool
  • Job scenario
  • Deep Learning Support
  • Customer service support and Community

I give a score to each of these 2 languages (1 – Low; 5 – High).

i. Availability/Cost

Both, are completely free. Here are my scores for this parameter:
R – 5
Python – 5

ii. Ease of Learning

As R has the steepest learning curve. It’s necessary to learn and understand coding. In it, simple procedures can take longer codes as it is a low-level language. This created the main difference between R vs Python.
Python is known just because of its simplicity. Also, it has excellent features for documentation and sharing.
R – 2.5

R vs Python

R vs Python

Python – 3.5

iii. Data Handling Capabilities

R computations were limited by the amount of RAM on 32-bit machines.
R – 4
Python – 4

iv. Graphical Capabilities

In R, it is having advanced graphical capabilities.
R – 4.5
Python – 4.5

v. Advancement in Tool

As both languages are open in nature. Thus, the first get the latest features. Also, R and Python have an open contribution. Thus, in the latest developments, there are more chances of errors.
R – 4.5
Python – 4.5

vi. Job Scenario

Both are better for start-ups. Also, both are a better option for companies that are looking for cost efficiency.
R – 4.5
Python – 4.5

vii. Customer service support and community

Both languages don’t have this facility. So if you have any type of trouble, then you are on your own.
R – 3.5
Python – 3.5

7. R (Lingua Franca of Statistics) and Python( A Multi-Purpose Language)

i. R

It was created by statisticians. We can use R packages to communicate ideas and methods for statistical analysis. Hence, engineers, statistician, and scientist those are not having knowledge of computer programming skills find it easy to use.
We can also use R in different fields. Such as in finance, pharmaceuticals, media, and marketing. R’s on the rise as a business analytics tool.
“The number one value to business in using R is access to talent”
R is experiencing a rapid growth. It holds the third place as software, right after SAS and SAP.

ii. Python

Python is a common and easy language, said by many programmers. It always brings peoples with different backgrounds together.
Hence, python is a production ready language. it has the capacity to be a single tool that integrates with every part of your workflow!
For Example:
Some organizations that didn’t want to hire a new data scientist. They are(trained) their existing employees to use python instead.

8. R vs Python in terms of speed

R

  • It is slow, on purpose. That was designed to make data analysis and statistics easier to do. But not to make life easier for your computer.
  • It requires defining how it’s implementation works.
  • As R is poorly written, a lot of R code is slow.

Python

“Visualizations are important criteria in choosing data analysis software”
Python has some nice visual visualization libraries which are the key difference between R vs Python:

  • Seaborn Library based on matplotlib
  • Bokeh Interactive visualization library
  • Pygal To create dynamic SVG charts

9. Positive points of R and Python

Let us see features that are common to both python and R that does not create the difference in R vs Python.
a. Open Source
Both languages are free to download for everyone. That is in comparison to SAS and SPSS. This both SAS and SPSS are commercial tools.
b. Advanced Tool
R and Python are having advanced tool.  As new developments and changes appear first in R. And python, before making their way to commercial platforms.
c. Online Communities
Both dispose of online communities. Thus they offer support to their respective users.

R Quiz

10. Advantages and Disadvantages of R and Python

Let us finally see the advantages and disadvantages of Python and R to get a clear understanding of R vs Python.

i. Advantages of R

  • Basically, it is most comprehensive statistical analysis package. As new technology and ideas often appear first in R.
  • As R is open-source software. Hence anyone can use and change it.
  • It is an open source. We can run R anywhere and at any time, and also even sell it under conditions of the license.
  • As we know that R is good for GNU/Linux and Microsoft Windows. Also, it is having a cross-platform which runs on many operating systems.
  • Anyone can perform bug fixing, code enhancements, and new packages.

ii. Disadvantages of R

  • In R, quality of some packages is less than perfect.
  • In R, no one to complain, if something doesn’t work.
  • It is a software Application. Hence, many people devote their own time to developing.
  • Moreover, R can consume all available memory because of its memory management.

iii. Advantages of python

  • It has free availability and stability.
  • Also, it has easy integration with and extensibility using C and Java.
  • It supports multiple systems and platforms.
  • Generally, it is easy to learn for even a novice developer.
  • As there are too many resources are available for Python.

iv. Disadvantages of Python

  • It has a smaller pool of Python developers compared to other languages. 
  • Software performance
  • It is not good for mobile development.
  • Although, it has limitations on database access.
  • Speed: slower than C or C++.

So, this was all about the R vs Python Tutorial. Hope you like our explanation.

11. Conclusion: Python vs R

We have studied R vs Python with their features and differences. Along with this, we have also learned why R and Python are good for data science and data analysis. After learning all this we have also focused on advantages and disadvantages of R and Python. We have also seen finally which out of the two you should learn to give a boost to your career. Still, if any doubt regarding R vs Python, ask in the comment tab.

Reference for R

Reference for Python

2 Responses

  1. Abdul Rawoof says:

    Excellent article for the people who wants to know about Data Science

  2. Kondisetti Anjanadevi says:

    really awesome your website all in one together really i just admire your work my request you to prepare tutorial for the shiny application too in r programming and NLTK,nlp implementation in R-programming like if i want to do text classification and video detection using R-programming ,if we have tutorials on these it will be very helpful to the people who use R -programming.

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