Python and R are the two most commonly usedlanguages 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 sciencedomain.
Hey Guys! 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 off with R-R is a programming language made by statisticians and data miners for statistical analysis andgraphics supported by the R foundation for statistical computing.R also provides high-quality graphics and It also has some popular libraries whichhelp 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 generalpurpose programming.
Python is widely used in GUI based applicationssuch as games, graphic designs, Web applications and many more So, we can say that R's functionality isdeveloped by statisticians mind, thereby giving it a field-specific advantages while Python is often praised for being ageneral-purpose language with an easy-to-understand syntax.
Let us start from the first factor, that isspeed. When it comes to speed, python is fasterthan R only till 1000 iterations but, after the 1000 iterations, R starts using the lapplyfunction 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 handlingcapacity with the scatterplot visualization and.. the ClusPlot graphics.
Starting off with Variable Declaration.Let's take the case of String here. As R uses the similar implementation to that ofthe S programming language, which uses arrow signs in order to initialize the variablewhich was also present in case of S programming language.
These arrows can be used from rightto left or left to right indicating whom to assign the variables whereas python uses anassignment operator to initialize the variables.
Basically, R developers thought that it wouldbe 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 inR and python. These are the piece of codes in R and Pythonand after running these codes, you will get the very similar plot results in both thecases, if you check the code here, then this shows that how R data science ecosystem hasmany 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 plottingpackage, and seaborn is a widely used layer over the matplotlib.
So, guys, these are the plot results that I was talking about, you can see that thegraph 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 Whereasthere 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 beautifulcharts and graphs whereas Python's main agenda was not a statistical analysis, soin the early stages of Python, packages for data analysis was an issue, but it has improveda 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 itcomes 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 workingon Artificial Intelligence, And Deep Learning is the main part of Artificial intelligenceSo, When it comes to Deep Learning, Python is more versatile than R as it provides morefeatures to deep learning whereas R is new to Deep Learning.
R has newly added APIs like Keras and KerasRwhich are written in Python. Right?So now somewhere in your mind, this question might be floating why Keras?
Actually, Kerasin Python has the capabilities to run over python's strong APIs like tensorflow orTheano or Microsoft's CNTK So we can say that Python has a greater advantagehere. Till now, we have seen that both are usefulin their own terms.
Now if we look at the Ease of LearningPoint: Python is easy to start with as its languagesare based on standardized format, i.e. people find it easy to read. It looks like you arereading English. R, on the other hand, is an unstandardized language.
It is quite hardto 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 moreas compared to Python to R.
Let's say, if 10% people are switching fromPython to R then, 20% are switching from R to Python, which is twice as compared to thebefore scenario Next, we are gonna look at the trends,community support, and Jobs:
Before 2016, R was more in use. But here wecan 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 almostsimilar 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, datascientist and statisticians end.
Now if we talk about Job trends, let'scheck the Google Job Trends graph right here, this is the Job postings for R and Pythonin past 12 months "WORLDWIDE" where python is asked more as compared to R. How is itpossible? Because of its popularity and its need in the current industry.
Since Pythonis more versatile and an all-rounder programming language which can be used for majority ofthe purposes such as web and application development, game development, artificial intelligence,data science, statistical analysis etc, whereas R language is used among statisticians anddata 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 thechoice. They complete each other gracefully and willmake 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 statisticalanalysis. It has a huge set of libraries which are availablefor different statistical type analysis. Even RStudio IDE is definitely a big plusas it eases most of the tedious tasks and fastens your workflow.
Talking about its cons, wellThe syntax could be obscure sometimes. And it is harder for it to integrate to productionworkflow. 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 automatingyour 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 forback-end, cloud architecture etc. The scikit-learn library in python is awesomefor machine-learning tasks. Ipython (and its notebook) is also a powerfultool for exploratory analysis and presentations.
Talking of its consThen python isn't as thorough for statistical analysis as R, but it has come a long waythese recent years In my opinion, the learning curve is steeperthan R, since you can do much more with Python.
To conclude it, I'd like to that you can use R and Pythonboth. Learn how they inter-operate together. Start with one and then add the other to yourworkflow.
It only adds another skill-set into your resume, which comes as an added bonusto your career, Isn't it? So, guys, now it's a wrap time.