Now, it’s the time for a battle of two most demanding programming languages that is R vs Python. We will go deep in understanding the differences between the two languages. And, I assure you that you will not have any confusion left after completing this article i.e. R vs Python – the most trending debate of aspiring data scientists. Learn more at :- https://data-flair.training/

1. R Vs Python – The most
trending debate of aspiring
Data Scientists

2. Now, it’s the time for a battle of two most demanding programming languages
that is R vs Python. We will go deep in understanding the differences between
the two languages. And, I assure you that you will not have any confusion left
after completing this article i.e. R vs Python – the most trending debate of
aspiring data scientists.
R vs Python
To explore everything about R vs Python, first, you must know what exactly R
and Python are. Below is the brief introduction to both the languages. Have a
look –
What is R?

3. R is a programming language that is specifically for statistical computing and
graphical computation. R project is an extension of the S language that was
developed at Bell Laboratories by John Chambers and his colleagues. It is a
language and environment for statistical computing and graphics.
Data Analysts, Data Scientists, Statisticians make use of R programming
language to analyze the data and carry out statistical analysis through graphs
and other types of visualizations. R allows the users to perform analysis on
large datasets.
This language is continuously growing with thousands of packages that can be
used readily for many applications.
There are various statistical and graphical techniques that R provides such as
linear and non-linear modeling, statistical testing, time-series analysis,
classification, regression, clustering and many more.
The main strength of R is its well-publicized quality plots that we can produce
using its many functionalities and packages. The user also retains full control
over the design choices in their graphics.
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R Environment
R is a comprehensive suite of various facilities and functionalities that we can
use for data manipulation, calculations as well as graphical display. These
facilities comprise of –

4. ● It provides efficient data handling as well as a storage facility.
● It provides a suite of operators which we can utilize to perform
calculations on arrays and in particular higher-dimensional
matrices.
● The abundance of graphical facilities and functionalities geared
towards data analysis provide various display exports in different
formats.
● It provides a large, coherent and integrated collection of several
tools that we can utilize for data analysis.
● It is a well developed, simple as well as an efficient programming
language that involves loops, conditions, recursive functions as well
as several input-output functionalities.
What is Python?
Python is an interpreter based, high level and an object-oriented
programming language that consists of dynamic semantics. Python is built-in
high-level data structures that we can combine with dynamic typing as well as
dynamic binding to make it very attractive for Rapid Application
Development.
We can also avail it for scripting or an adhesive language to combine several
components into one framework.
Python is simple, easy to learn and also provides emphasis on readability that
significantly reduces the overall cost of program maintenance. Python has an
easy learning curve and advocates for program modularity as well as code
reusability. The Python interpreter, as well as the extensive standard library,
are present in the binary form that do not charge for major platforms and
provides free distribution.

5. Programmers who are new to programming often fall in love with Python due
to its vast functionalities and ease of learning. Since Python has no
compilation step, the provision of the edit-test-debug cycle is significantly fast.
In Python, the process of debugging programs is incredibly easy. Unlike other
languages like C and C++, an inappropriate input will never lead to a
segmentation fault. When an interpreter finds an error, it raises an exception
to the user right after we enter the input. If however, the program does catch
the exception, the interpreter provides a stack trace. All of these features
added with the massive libraries for a variety of purposes make Python an
efficient programming language. Using a source-level debugger one can easily
inspect local as well as global variables.
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Difference between R and Python
Below we will discuss R vs Python on the basis of definition, responsibilities,
career opportunities, advantages, and disadvantages –
R Vs Python – Definition
R
● It was in particular, geared towards addressing the statistical
techniques.
● R has several graphical libraries like ggplot2 and plotly which make
it highly popular owing to quality reports and images that we can
generate.

6. ● We can use R for several statistical functions like regression,
clustering, classification, statistical testing, etc. Therefore, R has a
variety of supported operations for machine learning and statistical
tasks.
● While R suffers from a steep learning curve, it proves to be an ideal
language for expressing statistical methodologies, allowing the data
scientists to wrangle the data as per their requirements.
● R is an open-source programming language meaning that users can
actively participate in contributing towards the growing language as
well as avail the many benefits without any license.
● Being an interpreter based language, R can run the script
immediately and provide output to the user. This allows ease of code
debugging.
Python
● It is a multi-purpose programming language that we can utilize for a
variety of tasks.
● Python has a library for every task. From web application
development to penetration testing and data science, Python has a
solution for everything.
● It provides an ample number of libraries that we can avail for
various machine learning operations as well as advanced deep
learning methodologies.
● Python provides a smooth learning curve that makes it highly
popular among novices as well as experienced programmers who
want to transition into development using Python as their primary
language. Due to the vast support libraries, it is becoming the go to
choice for many operations as the Python users perceive the
language to be the one solution to many problems.
● Python is also an open-source programming language that has many
open-source projects in which the users can actively contribute.
● We can parse the Python scripts and can also run them immediately.
Furthermore, we can save Python as compiled programs that can be
referenced by other programs.

7. R Vs Python – Responsibilities
Responsibilities of an R Developer are as follows –
● An R professional is responsible for providing simulations, carry out
data analysis, visualization using a variety of R packages.
● An R developer provides assistance in the design and development
of the core architecture that will be used in the statistical model.
● The developer also coordinates with the end-user for the design and
development of the solution to the analytics problems that address
the project proposals.
● Another key responsibility is to provide data access, manipulate
information and report the functions.
● Creation of key procedures for integrating R programming with data
sources and delivery systems that export the data file in several
formats.
● An R developer is responsible for providing assistance to the
developers for executing their plans while keeping in mind the client
requirements.
● R programmers have to build a code that consists of a qualitative
variable as well as manipulation tasks with several statistical
procedures.
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Responsibilities of a Python Programmer are as follows –
● A Python Developer must be able to implement server-side logic to
carry out server-based computations.
● Development of asset management software is another key
responsibility of a Python programmer.
● In order to integrate different software systems, a Python developer
must be able to develop a scalable adhesive code.

8. ● Writing reusable and testable code for better operational quality is
one of the main functions of a Python developer.
● Data Security and protection of software is also one of the
requirements of a software solution. Therefore, a Python developer
must be able to address that.
● A Python programmer must be able to implement event-driven
programming.
● Another important job role is to carry out efficient unit testing and
debugging.
R Vs Python – Career Opportunities
Some of the industries that hire R developers are –
● Academia
● Finance
● Banking
● Healthcare
● Manufacturing
● E-commerce
Some of the companies hiring R developers are IBM, Airbnb, Uber, Twitter as
well as several public statistical institutes.
The average salary of an R developer is $67,244.
Discover various career opportunities and scope of R Programming
Some industries that are hiring Python developers are –
● Software Development
● Manufacturing
● Robotics
● Embedded Systems
● Automation and Testing

9. ● Ethical Hacking
Some of the companies hiring Python developers are Google, Amazon,
Dropbox, Quora, Reddit, Pinterest and many more.
The average salary earned by a Python developer is $117,155 per year.
You must check the Future of Python Now!!
R Vs Python – Advantages and
Disadvantages
Advantages of R
● The main advantage of R is its open-source nature. You can,
therefore, work with R without any licence or payment of fees. Being
open-source, you can also contribute towards the customisation of R
packages, newer development as well as resolution of issues.
● R provides exemplary support for data wrangling. The packages like
dplyr, readr are capable of transforming messy data into a
structured form.
● With over 10,000 packages in its CRAN repository, R facilitates
libraries that are diverse and every field that uses data can make use
of them.
● R has some essential features for graph plotting and aesthetic
enhancement of graphs. There are popular libraries such as ggplot2
and plotly that offer a wide range of graph customization options to
the users.
● It is a platform independent language that can execute programs on
Windows, Linux and Mac.
● R is a specific language that is used for statistical modeling. It is the
primary tool for creating statistical tools for data science. This gives
R an essential advantage over other programming languages like
Python.

10. ● R is constantly evolving. It provides various state of the art features
that keep on updating it whenever any new algorithm is released.
● It has an active and engaging community. There are various online
forums in R that provide help and support to the R programmers.
Furthermore, there are various bootcamps and online seminars that
provide active education to aspiring R programmers.
Disadvantages of R
● The R programming language shares its roots with a much older
programming language called S. Because of this, R lacks most of the
features of a modern programming language like support for
dynamic or 3D graphics.
● R requires its objects to be stored in physical memory. As compared
with other statistical tools, R requires more memory for its
programs. Since R requires the entire data to be loaded into its
memory, it is not a good option when dealing with Big Data.
● Since R stems from much older technology, the basic capabilities
like security were not native to R. This restricts R as web
applications cannot embed it or its usage as a backend computation
language like Java, Python or Node.js.
● R poses a steep learning curve. People who had a background in
statistics would find it ideal to use R. Therefore, for people who are
starting afresh in data science may find R a difficult language to
adapt to.
● Packages in R tend to be slower than other competing languages like
Python and MATLAB.
● Most of the R algorithms are implemented on different packages.
This decentralization of packages makes it difficult to apply
algorithms on problems without prior knowledge of the required
package.
Advantages of Python

11. ● Just like R, Python is open-source. You can use Python for free.
Furthermore, you can change, customise and contribute towards
Python libraries.
● Python is a general-purpose programming language that facilitates
its usage over diverse tasks. Areas software development, robotics,
embedded systems, automation, etc. make heavy use of python.
● Python comprises of state of the art APIs like TensorFlow, pytorch,
keras, numpy that are extremely useful in building artificial neural
networks.
● It is a user-friendly programming language. This is one of the main
reasons as to why Python is the standard programming language in
universities.
● Python is secure. These server-side computations involve Python as
it provides various frameworks for the development of web
applications.
● Python is apt at handling large datasets. It can load data files much
faster and can also work with Big Data ecosystems.
Disadvantages of Python
● Being an interpreter based language, Python is slower than other
languages like C, C++, and Java.
● Python lags behind R when it comes to statistical analysis. Python
though may have improved a lot but it still lacks certain statistical
packages as compared to R.
● The dynamically typed nature of Python makes it vulnerable to
runtime errors.
● Python, when compared with JDBC has an underdeveloped
database access layer.
● Tasks that require heavy memory suffer from Python. The flexible
data-types in Python contribute towards its high memory
consumption.

12. Summary
Here comes the conclusion of R vs Python. We discussed all the major points
for understanding the difference between R and Python. Hope now you are
able to pick the best one to start for becoming a data scientist.
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