Python and R are the two most commonly used languages in data science
and nowadays, most of the fresher's get confused, whether they should use R or
Python to kick-start their career in the field of data science domain.
I am gonna tell you the long and the short of both of these topics. So,
without wasting more time, let's get started. I am gonna start off with their
basic definitions:
Starting with R- R is a programming language made by statisticians and
data miners for statistical analysis and graphics supported by the R foundation
for statistical computing. R also provides high-quality graphics and it has
some popular libraries, which help in analytical parts such as R Markdown and
Shiny.
Python, on the other hand, is a fully-fledged, Object-oriented &
high-level programming language made by programmers and developers' for
general-purpose programming. Python is widely used in GUI based applications
such as games, graphic designs, Web applications and many more.
So, we can say that R's functionality is developed by statisticians
mind, thereby giving it a field-specific advantages while Python is often
praised for being a general-purpose language with an easy-to-understand syntax.
Let us start from the first factor, that is speed. When it comes to
speed, python is faster than R only till 1000 iterations but, after the 1000
iterations, R starts using the lapply function which increases its speed, in
that case, R becomes faster than python. So, both have their own advantages. Right?
Moving forward to the next point: that is, Code and Syntax. In this
topic, I am gonna give you a brief about the variable declaration, Data
handling capacity with the scatterplot visualization and Plot graphics. Starting
off with Variable Declaration. Let's take the case of String here. As R uses
the similar implementation to that of the S programming language, which uses
arrow signs in order to initialize the variable,
which was also present in case of S programming language. These arrows can be
used from right to left or left to right indicating whom to assign the
variables whereas python uses an assignment operator to initialize the
variables. Basically, R developers
thought that it would be better to tell the direction of assignment rather than
just using an assignment operator, which could actually confuse any new
programmer about which variable is being assigned.
Next is the Data Handling capability, here, I am gonna show you the case
of ScatterPlots, by which you will see the visualizations in R and python. These
are the piece of codes in R and Python and after running these codes, you will
get the very similar plot results in both the cases, if you check the code
here, then this shows that how R data science ecosystem has many smaller
packages like GGally, which basically is a package that helps ggplot2 and also,
it is the most-used R plotting package, whereas in Python, matplotlib is the
primary plotting package, and seaborn is a widely used layerover the
matplotlib.
So, guys, these are the plot results that I was talking about, you can
see that the graph results for both R and Python are similar, but the only
difference is their visualization. So guys, based on these points and plot
results, we can conclude that R has Many packages supporting different methods
of doing things. Whereas there is usually one way to do something in python.
Moving on to the next point that is Graphics. Here we will take the case
of ClusPlots. So Guys, as we already discussed that R was basically built for
statistical analysis, so it has many specific libraries for plotting. This is
the reason R comes up with beautiful charts and graphs whereas Python's main agenda
was not a statistical analysis, so in the early stages of Python, packages for data
analysis was an issue, but it has improved a lot.
Here is the plot result: As you know that a picture says more than a
thousand words. Here You can see by yourself that R comes up with beautiful
graphical representations. So here we can say that R is handy when it comes to
Data Handling. Our next point of attention is Deep Learning, which is today's
trend. As you all know, almost the majority of the companies are working on
Artificial Intelligence, And Deep Learning is the main part of Artificial
intelligence. So, When it comes to Deep Learning, Python is more versatile than
R as it provides more features to deep learning whereas R is new to Deep
Learning. R has newly added APIs like Keras and KerasR, which are written in
Python. Right?
So now somewhere in your mind, this question might be floating why
Keras? Actually, Keras in Python has the capabilities to run over python's
strong APIs like tensorflow or Theano or Microsoft's CNTK. So we can say that
Python has a greater advantage here. Till now, we have seen that both are
useful in their own terms.
Now if we look at the Ease of Learning Point: Python is easy to start
with as its languages are based on standardized format, i.e. people find it
easy to read. It looks like you are reading English. R, on the other hand, is an
unstandardized language. It is quite hard to learn as compared to Python.
Beginners may find this hurdle in the starting. In the past years of research,
the percentage of people switching from R to Python are more as compared to
Python to R.
Let's say, if 10% people are switching from Python to R then, 20% are
switching from R to Python, which is twice as compared to the before scenario. Next,
we are gonna look at the trends, community support, and Jobs: Before 2016, R
was more in use. But here we can see that from 2016, Python is in trend. So,
it's more popular than R. And because of its popularity, it has overall good
support for general purpose programming. Well if we talk about the community
support, Then Python and R support aspects are almost similar as Python's
support is found at: Mailing list, user-contributed code & documentation
& StackOverflow. Basically, it has more adoption from developers &
programmers end.
Whereas R language support is also found at: Mailing list,
user-contributed documentation & active StackOverflow members. Basically, R
has more adoption from researchers, data scientist and statisticians end. Now
if we talk about Job trends, let's check the Google Job Trends graph right
here, this is the Job postings for R and Python in past 12 months
"WORLDWIDE" where python is asked more as compared to R. How is it
possible? Because of its popularity and its need in the current industry. Since
Python is more versatile and an all-rounder programming language which can be
used for majority of the purposes such as web and application development, game
development, artificial intelligence, data science, statistical analysis etc,
whereas R language is used among statisticians and data miners for developing
statistical software and data analysis. Which clearly depicts that, there are
more jobs for python than R.
Now let's move forward! So, Which one to choose for Data Science R or
Python? Guys, this the frequently asked question by the majority of the
learners in this domain. I would suggest using both if you have the choice. They
complete each other gracefully and will make your life better if you leverage
their strengths and avoid their weaknesses. Everything has their own pros as
well as cons, so as in the case of R and Python.
If we talk about pros in R, well, then R is great for prototyping and
for statistical analysis. It has a huge set of libraries which are available
for different statistical type analysis. Even RStudio IDE is definitely a big
plus as it eases most of the tedious tasks and fastens your workflow. Talking
about its cons, well The syntax could be obscure sometimes. And it is harder
for it to integrate to production workflow. In my opinion, it is better suited
for "consultancy-type" tasks. The libraries documentation isn't
always user-friendly.
Talking about the pros in Python, Python is great for scripting and
automating your different data mining pipelines. It is the de facto scripting
language nowadays. And it also integrates easily in a production workflow.
Besides, it can be used across different parts of your software engineering team
(like for back-end, cloud architecture etc. The scikit-learn library in python
is awesome for machine-learning tasks. Python (and its notebook) is also a
powerful tool for exploratory analysis and presentations.
Talking of its cons Then python isn't as thorough for statistical analysis
as R, but it has come a long way these recent years. In my opinion, the
learning curve is steeper than R, since you can do much more with Python.
To conclude it, I'd like to that you can use R and Python both. Learn how
they inter-operate together. Start with one and then add the other to your
workflow. It only adds another skill-set into your resume, which comes as an
added bonus to your career, Isn't it? So, guys, now it's a wrap time. Thank you
so much for reading this article session. I'd love to hear from you guys that
which one according to you is better and why?
Please reply to us in the comment section below.
