Python Vs R for Data Science. What should you learn?
29 Sep 2020 | Nikhil Nair
For years, R was the obvious choice for those who start their career in data science. R being an open-source programming language was designed with statistics in mind, has a long history in the industry, has thousands of public packages, and integrates very well with languages such as C, C ++, and Java. Launched in 1997, R is common in a wide range of sectors and can be found from Wall Street to Silicon Valley as a good alternative to Matlab and SAS.
On the other hand, Python is a syntax that has its origin in 1991. Created by Guido Van Rossem with the aim of making an agile and simple programming language, with a very short learning curve, Python has a great advantage for the growth in the use of syntax internationally. Since its inception, it is intended for professionals from the world of statistics, but its characteristics have greatly expanded the field of use of Python for other professionals as well. Now it is also used to make graphics with large data.
Companies are increasingly incorporating Python programmers into their teams, both back-end and front-end. This means that an increasing number of people are adopting Python. Hence it won’t be wrong to say that Python is challenging the already established position of R as a programming language for data science.
While both languages are in a competitive mode for being the topmost choice of language for data scientists, it is essential to understand their usage in the process of data science, respectively.
Usage in Data Pipeline
A large part of the data science community believes in commitment to a single programming language, however, there are people who still wish to access or are using both languages. For instance, lack of object-oriented capacities in R sometimes increases the urge of the Data Scientists to use python and similarly the limited range of statistical distribution in Python diverts the users towards the usage of R.
According to a survey conducted by Red Monk in 2018, both Python and R are equally popular on Github and Stack Overflow, which clearly means that the divergence in the choice of programming language lies with the people using it and their requirements, and not with the functionalities of the languages themselves. Hence, before selecting one of the two most popular choices of programming languages among data scientists, let’s dig deeper into the features and frameworks of Data Science.
Process of Data Science
Like traditional scientists, data scientists need a fundamental route or ground plan that serves as an advisor to solve issues. This fundamental guide must provide a framework to continue with the methods and processes that will be used to obtain answers and results.
The process of data science advances through the following major steps in the data pipeline and both Python and R have their own methods of operating the same.
Whether its JSON sourced from the web or just comma-separated value documents (CSVs), with Python one can easily play around with all different kinds of data formats. Even, if your programming plans include importing SQL tables into source code directly, it is possible with Python.
The python requests library is an amazing way of creating datasets as it gives you permission to access data from different websites. This also includes access to Wikipedia tables. Also, with a single line of code, one can simplify the HTTP requests and centralize data in an organized way.
Once the data is organized with beautifulsoup, in-depth data analysis also becomes quite easy.
b) R (programming language)
R allows you to import data only from text files, CSV and Excel. There are few exceptions like the files built-in SPSS or Minitab. These files can also be changed into data frames of R.
Although R has the capabilities of handling data from some of the most common sources, it is not as versatile as Python in digging through the web for information.
The language developers of R are continuously working on its advancement and to address the data collection issue with the language many updated packages have also been introduced.
While Rvest allows you to work on the basic web scraping, magrittr helps clean it up, dissect and parse the data further.
When it comes to data exploration through Python, to dig out the insights from the collected information, a data scientist needs to use Python’s data analysis library, Pandas. With this library the problem of lag that mostly occurs with the use of Excels completely disappears. Here one can sort, filter and display very quickly.
Data frames of Pandas can be redefined as many times as required throughout the project. Pandas also make it quite easy to scan and clean up the non-factual data from the table. You can simply replace NaN( not a number) or any such non-valid values with a value that has verifiable value like 0 for numerical analysis.
b) R (programming language)
As the name suggests, data exploration is all about exploring data through numerical and statistical analysis. And this is the reason why R is the best-suited companion for this section, as it’s basic functionality encircles the basic optimizations, analytics, random number generation, statistical processing, machine learning and signal processing. Although if you are into a deeper exploration of data you will have to take help from the third-party libraries.
With R you can also apply a number of statistical tests on your data, build probability distributions, inherit data mining techniques and incorporate standard machine learning functions.
Python gives you a varied list of support options that can make your work as easy as possible. While with Numpy you can do modeling analysis in a numerical way, SciPy helps you complete scientific calculations and computing in a span of few minutes. On the other hand, the scikit-learn code library gives you access to a more complex yet vigorous machine learning algorithms, with an intuitive and user-friendly interface, so that you can utilise all the powers of machine learning without getting into the many complexities.
b) R (programming language)
R was created just for numerical and statistical analysis for both small and large data sets, hence t is no surprise that to use R for a specific data modeling analysis, you would require some outside help. There are many packages available outside R’s core functionality that you can incorporate within R in this process, for instance, the Poisson distribution and mixtures of probability laws.
To visualize data using Python there are plenty of powerful options available within the IPython Notebook that comes with Anaconda. You can even generate basic charts and graphs of the Python embedded data with the help of Matplotlib library. Even if you are looking for a more advanced graph or a better design, Python offers a solution for that in the form of Plot.ly. It is quite a handy solution for data visualization that initiates the process through its intuitive Python API and pours out the user-friendly designer dashboards and graphs to make sure you express your point with potency, energy and grace.
Sometimes data scientists also need to turn the Python notebooks into HTML documents for easier functionality. To accomplish the same, Python offers you nbconvert function that helps embed snippets from a cleanly-formatted coding to an engaging website or interactive online portfolio.
b) R (programming language)
As mentioned above R was initially crafted to do large statistical analysis, hence it provides a powerful environment to demonstrate the end results. With specialized packages for graphical result display, R makes it easier to undergo a complete scientific visualization of data.
Whether you are creating plots from data matrices or just a few basic charts, R’s base graphics module gives you the liberty to do all that under one roof. Later, you can simply save the files as images with .jpg extensions or as documents separated as PDFs. For more advanced and complex plots R offers you the newly designed tool, ggplot2.
Ease of Learning
There is no denying the fact that currently, the field of data science is generating some of the most in-demand jobs. Due to which, many newcomers are looking to get into the sphere of data science with little or no experience in programming.
Learning a new language can be quite a challenging task, especially if you are just a beginner. This is where comes the importance of ease of learning when comparing Python and R.
Python is a language built to make programming scenarios simple, easy and flexible. Therefore it emphasizes more on code reliability and said so it makes the language fairly easy to understand and learn. Although it has its ancestral roots attached with C, Python, as compared to C, is uncomplicated. In short, Python is a highly recommendable language for beginners.
b) R (programming language)
With all the updates done by R’s language developers, IDE’s such as RStudio have made R simpler and accessible but it is still more difficult to learn if compared to Python.
There are times when data scientists come across problems that they haven’t encountered before or they face difficulty in getting connected to the right library. This is the time when you look for support either from the language’s official documentation or community forums available online. In such a scenario, having a quality and competitive community support can help data scientists to work more efficiently.
Members from the communities of both the languages are quite active on Stackoverflow. These communities also have an active mailing list of subject experts available for everyone.
However, If you are not quite a community fan and need your answers from official documents, you can simply visit online R-documentation for R and libraries like Scikit-learn and Pandas for Python. You can also visit docs.python.org for Python’s official documentation.
How to utilize the advantages of R and Python together?
Even though born out of the same programming family C, both Python and R have different specialities. Have you ever thought of using the advanced programming capabilities of Python along with statistical prowess of R?
Well, there are actually two methods by which you can use both R and Python hand in hand for a single project.
R within Python
As the name suggests PypeR takes you through a simple route to access R from Python through pipes. Also included in Python’s Package Index, PypeR is the most convenient method for any kind of installation.
Especially if your project doesn’t include frequent data transfers between both the languages (Python and R), PypeR is the right choice for you. By giving gateway to R through pipes, PypeR gives python memory control, flexible sub-process controls and portability across operating systems like GNU, Windows, Mac OS, Linux and more.
For this tool, Rserve is used as an RPC connection gateway, which helps in positioning variables in R from Python. It also helps R-functions to be called remotely. Python implemented classes use exposed R objects as instances, with R-functions that are bound methods to the objects in many cases.
rpy2 embeds R in Python programming process. It develops a framework to easily translate python objects in R objects, transfer them into R functions and finally transform the output by R into Python objects to complete the process.
Python within R
With the help to rJython this package helps instrument an interface to Python. It is envisioned to help other packages embed Python side by side with R.
A simple package that is intended to allow R to Call Python. rPython allows you to make function calls, assign and retrieve complex variables and even run python code from R.
SnakeCharmR is a renovated version of the original rPython. As it is an updated version, it has a lot more variations than rPython. It uses jsonlite.
By using PythonInR you can directly use functions from within R to interact with Python. It makes accessing Python from R quite simple.
With reticulate package and its all-inclusive set of tools, you can increase the ability to exchange and use information between R and Python. Reticulate is being aggressively developed and updated by Rstudio, which is why this is the most popular package among all the above-mentioned packages. With reticulate you can easily embed any Python session into the R session, enabling high-performance and seamless interoperability.
reticulate makes it possible to weave in the two most used programming languages (Python and R) together and develop a new breed of a traditional project.
R and Python are two powerful, flexible, and accessible languages.
So … which one do you keep?
This question may be the most complicated to answer since as we have seen throughout the article, both R and Python have a large number of features that make them optimal for data analysis and help in taking decisions in Business Intelligence.
The use of both must, therefore, be motivated by the answers as users to the following questions:
- What kind of problems do I want to solve?
Choose one or the other depending on the type of data analysis you want to carry out, be it Machine Learning, Data Mining, web analytics or others.
- R is a very good option when data analysis requires independent computing or individual analysis on servers, while Python should be used when data analysis needs to be integrated with web applications or if you need to incorporate the statistical analysis code in a production database.
What tools will be integrated into the environment do I work with?
You must know the environment in which you work and what programs for big data and business intelligence you will have to manage, to choose the programming language that best integrates with your tools.
Answering these questions can guide you in the use of one or another language, since as we have seen, both are very powerful for data analytics, and are constantly developing and updating, as well as integrating seamlessly with different business intelligence tools.