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R vs Python (Infographic)

R Python
Usage The purpose of R is to develop a
language that focused on
delivering a more user-friendly
way to perform data analysis,
statistics and graphical models.
It is an object-oriented
programming language, which
means it groups data and
codes into objects that can
interact with and modify one
another. It allows developers
to execute tasks with better
stability, modularity, and code
readability.
Statistical analysis R was built to do statistical
and numerical analysis of
large data sets, so that you’ll
have many options while
exploring data with R.
statsmodels in
Python and
other packages
provide decent
coverage for
statistical
methods.
Dashboard Shiny library which allows for
creating rich interactive web
apps.
It is recommended for complex
and customized applications.
Shiny library which allows for
creating rich interactive web
apps.
It is recommended for complex
and customized applications.
Big data R is considered slow. It
requires its objects to be
stored in a physical memory.
R is considered slow. It
requires its objects to be
stored in a physical memory.
Data Analysis R is convenient for analysis
due to the huge number of
inbuilt packages, readily
usable tests and the advantage
of using formulas. R is the
more efficient language for
task.
The Python packages for data
analysis were an issue but this
has improved with the recent
versions. Numpy and Pandas
are used for data analysis in
Python.
Machine learning & Deep
Learning.

For ML problems Caret being a
very popular one and R has
many packages for ML.

The Nnet library is a suitable
platform for modeling neural
networks.

If a machine learning program
requires a wide range of
operations, R may present
some constraints.

Python has popular ML
packages in today’s world.
Scikit-learn presents users with
several fundamental tools for
creating machine learning
models.

For creating DL models
packages such as tensorflow ,
pytorch, theano are more
efficient and robust when
compared with R.

Time series R is better choice for
Advanced Hidden Markov
models, Hierarchical time
series forecasting and advanced econometric
models.
Python is a better choice for
LSTM and other deep learning
models for time series.
NLP R doesn’t offer great built-in
string manipulation. A specific
Rword2vecs package exists to
provide word2vec model, while
the LDA is available within the
MASS package.
Python has a diverse ecosystem
supporting NLP. NLTK that
provides a library for
preprocessing. For modelling it
offers Gensim that provides both
LDA and word2Vec models.
Object-relational mapping
(ORM):
R has none Python has two well used ORM
libraries in Django ORM and SQL
Alchemy.
Authentication & Authorization R lacks proper support for these
kinds of features.
Setting up an authentication &
authorization layer on an API is
done by Flask login, or Auth0 in
Python.
Data Acquisition R has much less coverage, rest
call is being supported by a
mixture of httr & jsonlite, while
soap calls require a mixture of
the Rcurl and XML package. For
GraphQL there are few wrapper
libraries .
Python provides great support
for API calls, Rest API calls are
handled by the requests library,
while SOAP call are usually
handled using Zeep, and offers
a few clients for GraphQL.
Web Scraping R leverages Rvest, a library part
of the Tidyverse.
Python handles web
scraping through the
BeautifulSoup library
In-database computation R is Supported by SQL Server,
Postgres, Teradata and Oracle.
Python has fewer options, it is
supported by Postgres,
Microsoft’s SQL Server.
Workflow Automation R doesn’t have a Workflow
automation tool written in its
language.
Python has two key workflow
automation tools, Airflow and
Luigi.
SDK R on the other hand has no such
support or have to use unofficial
package library such as cloudyr
that contains SDKs features.
On AWS it is supported through
the Boto SDK, on azure python is
supported by a SDK . Google
Cloud also supports through a
SDK client.
Cloud-ML Services R is supported by multiple cloud
service for instance Azure ML
Services and Google CloudML service.
Python is supported by all cloud
platforms, having integration on
AWS, Azure ML Services,
Sagemaker and Google Cloud.
Serverless No official support currently
exists for R on these platforms
Python has serverless function
support for AWS Lambda, Azure
functions or Google Functions.

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