See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/341419997
An Analytical Comparison Between Python Vs R Programming Languages
Which one is the best for Machine Learning and Deep Learning?
Article · July 2020
CITATION
READS
1
7,257
3 authors:
Joab O. Odhiambo
Winnie Mokeira Onsongo
University of Nairobi
University of Ghana
12 PUBLICATIONS 52 CITATIONS
11 PUBLICATIONS 11 CITATIONS
SEE PROFILE
SEE PROFILE
Shaibu Osman
University of Health and Allied Sciences
36 PUBLICATIONS 283 CITATIONS
SEE PROFILE
Some of the authors of this publication are also working on these related projects:
Re-thinking The Life Tables For Assured Lives In Kenya View project
Mathematical Model for Anthrax and Listeriosis Co-infection Dynamics in Human and Animal Populations with Optimal Control. View project
All content following this page was uploaded by Joab O. Odhiambo on 16 May 2020.
The user has requested enhancement of the downloaded file.
An Analytical Comparison Between Python
Vs R Programming Languages
Which one is the best for Machine Learning
and Deep Learning?
By
Joab O. Odhiambo, Bsc.(Actuarial Science), MSc.(Actuarial Science),PhD
(Actuarial Science).
Data Scientist and Actuarial Science Specialist
Author’s Email: joabodhiambo2022@gmail.com
For many years, data scientists have been battling between Python & R programs especially on the levels of superiority. For any data scientist who would like build a
machine or deep learning project, he or she may be stuck between selecting the right
programming language when build it, you need to know that this article will help you
make an informed decision. Why is this important? This article should elaborate the
difference between these two commonly used languages in algorithms coding namely:
Python and R; but also assist you know among the two languages, which has an upper
edge over the other in a couple of ways.
Both Python and R have indistinguishable features, which makes them highly popular
coding tools amongst many data scientists. Worldwide, about 65 % of all developers use Python when working on their machine learning when compared to 25% of
the developers who R programming language. While both are open-source meaning
that they are free in the market, Python has been structured to make it a widely-used
programming language whilst R has been developed for statistical data analysis.
Artificial Intelligence and data analysis are two distinct territories whereas open source
has now become nearly the absolute permit for creative novel instruments. Both the R
and Python dialects have developed strong environments of libraries and open-source
devices, which help data scientists of any skill level, all with the aim of effectively
and performing scientific works even more in terms of accuracy when delivering the
results.
The difference between data analysis and machine learning is comparatively fluid, nevertheless, the primary thought is that machine and deep learning techniques organizes
prescient precision over model interpretability, whereas data analysis underscores interpretability as well as factual surmising. In today’s world, Python being progressively
worried about prescient accuracy, has built up progressive notoriety in machine and
deep learning. On the other side R programming as a language for statically inference
and factual deduction, has built its brand in statistical data analysis.
That does not mean that you can categorize either of them into a class since Python can
be easily utilized sufficiently as a statistical data analysis instrument, at the same time
R has enough adaptability when accomplishing some great work in both machine and
1
deep learning. This means it has a huge variety of bundles for the main two dialects,
which look to show the usefulness of either of the programming languages. Python has
in-built libraries to aid its capability of measuring induction whereas R has bundles that
improve its ability to have predictive precision during statistical data analysis.
Let us now discuss these two languages in details, which will assist you as a researcher
to significantly make an informed choice of suitable programming language when doing your project.
1. Python Programming Language
The Python programming language has been around since the early 90s and it assumes
a vital job of driving Google internal framework. Python includes passionate designers and now it is been applied in wide range of areas in the uses such as YouTube,
Instagram, Facebook, Dropbox and Quora among other areas. Python has also been
comprehensively used over many IT businesses and grants straightforward exertion of
synchronized effort inside many development groups. Therefore, if you want a versatile at the same time multi-reason programming language that has a supporting gigantic
system of inventers close by the extendable Artificial Intelligence packages then, you
should choose Python.
Benefits of Python
• It is a general-purpose language —Since many people view Python as a superior
decision, you can use it in your project venture when dealing with something
more than just statistics or measurements e.g. when designing a new and functional website.
• It offers a smooth learning curve. Python just like any other language may be
difficult when learning, however, it effectively available thus will empowers you
when locating the gifted designers especially on a quicker premise.
• It has a wide variety of essential libraries. Python boosts of many libraries, which
are important for data assembly and control. For example, you can take an event
of Scikit-realize that includes devices for mining of data as well as examination
thus helping in unimaginable Artificial Intelligence comfort when using Python.
In addition, a package of Pandas helps engineers in unrivaled structures as well
as information assessment gadgets thus decreasing the improvement time. For
your advancement group requests, among the significant R functionalities, you
can go for RPy2 when working on a project.
• It has better integration. On a general view, Python incorporates superior features when compared to R. In such a way, whether or not its inventers endeavor
to ever misuse a lower-level language such as C, C++, or Java, it offers the best
predicting language when compared to others that exists in the market. In addition, a python-based stack is never hard when consolidating the rest of the job
that needs to be done through data researchers when bringing it effectively into
new creation.
2
• It boosts productivity. The Python punctuation is predominantly understandable
as well as like other programming dialects when remarkably compared to R.
Going by these features, it ensures a high productivity especially in development
groups.
Shortcomings of Python
The absence of a common repository as well as choices for several R libraries. Due
to its dynamic composing, it is entangled to scan for certain capacities in some cases
and to follow deficiencies associated with the erroneous task of several data types to
similar factors.
2. R Programming Language
R programming was created by statisticians and essentially for the analysts where any
engineer can foresee the comparable by taking a look at its syntax. Just as the language
containing scientific calculations, which are associated with machine and deep learning
derived from statistics, selecting R may become the right decision to a person who
needs to increase a deeper comprehension of the fundamental fabricate and subtleties
inventively.
If you have a project that founded on insights intensely, then you should consider
R since it will enable you do your analysis much easier before making a scientific
decision based on the findings of the dataset. It is just a programming tool used by
data scientist when analyzing statistical data to make inferences.
Advantages of R
• It is Suitable for Statistical Analysis. If the data representation or examination is
at the core of your business venture then you can consider R as your best decision
since it permits fast prototyping at the same time works with the datasets to
configuration AI/deep learning/machine learning models in your projects.
• Has many useful libraries and tools for statistical data analysis. Just like Python,
R language contains many bundles, which will help you in improving your presentation of the machine/deep learning ventures. For instance, Caret supports
the Artificial Intelligence capacities of the R with its unusual arrangement of
abilities that assists with making prescient models in terms of productively. R
users can take an advantage from the propelled data analysis bundles that spread
the pre-and post-demonstrating stages when aiming at explicit assignments such
as model approval or even information representation during research.
• R is suitable for exploratory work. If your project requires any kind of exploratory work in measuring models toward the initial phases of your undertaking then you can use R since it makes it easier and simpler to keep in touch
with them an expert simply you need to include a some simple lines of code to
make it work.
3
Disadvantages of R Program:
• Difficult to learn at the same time easy to code badly. Weak typing may be
dangerous, functions may have a fierce habit of returning an unanticipated type
of object.
• Specificity in comparison with many other languages likes vector indexation
begins with one as opposed to zero.
• The syntax for solving certain problems may not be that obvious. This is because
of the huge large number of libraries, thus making the documentation of less
popular ones can never be considered as complete.
My Concluding Remarks as An Expert
Concerning Machine and deep Learning, both R and Python have their points of interest with wide range of applications depending on the nature of research. When you
ace both these dialects, you may be in a position to make better of these two universes
since most of the basic errands are related to at least one of these dialects.
Alternatively, you can utilize Python for the beginning times of data aggression and
afterward you can feed the final information into R that applies the all-around tried,
which is upgraded for measurable examination schedules after incorporated with the
language. Alongside these lines, you can also utilize R as your library for Python or
Python as a pre-handling library for R program.
Ultimately, you should ensure that you choose a program that will help you get your
results much faster whenever you need to do your project with high levels of precision.
For more information, you can contact the author through the email for more information on how to make the most appropriate language for your project.
Contact author: joabodhiambo2022@gmail.com
4
View publication stats