For years, R was the obvious choice for those with careers in data science. R, being an open-source programming language 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 its use to 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. Which means that an increasing number of people are adopting Python. Hence it wouldn't be incorrect to speculate that Python is challenging the already established position of R as the programming language for Data Science.
While it is common to pit both languages against each other in a competitive mode, it is far more essential to understand their usage in the process of data science.
Usage in the data pipeline
A large part of the data science community believes in commitment to a single programming language, however, there are people who use both languages. For instance, lack of object-oriented capacities in R sometimes necessitates the use of 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 operation.
Whether JSONs sourced from the web or comma-separated value documents (CSVs), with Python one can easily play around with all 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 to create 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 HTTP requests and centralize data in an organized way.
Once the data is organized with beautifulsoup, in-depth data analysis also becomes quite easy.
R allows you to import data only from text files, CSV and Excel. There are exceptions like the files built in SPSS or Minitab. These files can be changed into the data frames of R. Therefore, although R can handle data from some common sources, it is not as versatile as Python in digging through the web for information.
R developers are continuously working on its advancement and to address the data collection issue, and thus many updated packages have 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 insights out of collected information, a data scientist needs to use Python’s data analysis library, Pandas. With this library, the lag that often appears with the use of Excel 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 tables. For instance, 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.
As the name suggests, data exploration is all about exploring data through numerical and statistical analysis. And this is the reason why R is best-suited, as it’s basic functionality encompasses basic optimizations, analytics, random number generation, statistical processing, machine learning and signal processing. Although if you need deeper exploration of data, you would need 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 has a list of support options that can make work really easy. Numpy can do modeling analysis in a numerical way, whereas SciPy can handle complete scientific calculations and computing in a span of few minutes. On the other hand, the scikit-learn code library gives you access to yet more complex and 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 its many complexities.
R was created just for numerical and statistical analysis for both small and large data sets, hence it is no surprise that to use R for a specific data modeling analysis, you would require some help. There are many packages available outside R’s core functionality that can be incorporated 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 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 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 this, Python has an nbconvert function that helps embed snippets from a cleanly-formatted coding to an engaging website or interactive online portfolio.
As mentioned above, R was initially crafted to do large statistical analysis, hence it provides a powerful environment to demonstrate end results. With specialized packages for graphical result display, R makes it easier to have 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 the importance of ease of learning when comparing Python and R comes in.
Python is a language built to make programming scenarios simple, easy and flexible. Therefore it emphasizes code reliability and thus makes the language fairly easy to understand and learn. Although it has its roots in C, Python, as compared to C, is uncomplicated. In short, Python is highly recommended for beginners.
With all the updates by R’s language developers, IDE’s such as RStudio have made R simpler and accessible but it is still more difficult as compared to Python.
There are times when data scientists come across problems that they haven’t encountered before or they can struggle to find 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 quality community support can help data scientists to work more efficiently.
Members from both language communities are quite active on StackOverflow. These communities also have an active mailing list of subject experts available for everyone.
If you are not 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 utilise the advantages of R and Python together?
Even though born out of the same programming family, both Python and R have different specialities. But, funnily enough, few hardcore adherents consider using the advanced programming capabilities of Python along with statistical prowess of R in tandem.
As a matter of fact, there are actually two ways to use both R and Python hand in hand for a single project.
R within Python
- PypeR: As the name suggests, PypeR allows you 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. This is especially the case if your project doesn’t include frequent data transfers between both the languages. By allowing a 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.
- pyRserve: This tool 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: rpy2 embeds R in the Python programming process. It develops a framework to easily translate Python objects into R objects, transfer them into R functions and finally transform the output generated by R into Python objects to complete the process.
Python within R
- Jython: With the help of Jython this package helps instrument an interface to Python. It is envisioned to help other packages embed Python side by side with R.
- rPython: 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 within R.
- SnakeCharmR: SnakeCharmR is a renovated version of rPython. As it is an updated version, it has more variations when compared to rPython. It uses jsonlite.
- PythonInR: By using PythonInR you can directly use functions from within R to interact with Python. It makes accessing Python from R quite simple.
- reticulate: With the 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 packages we've covered so far. With reticulate, you can easily embed any Python session into an 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.
The final word
R and Python are two powerful, flexible and accessible languages. So, the question remains, which one do you use?
Even though we have advocated the use of both, the real answer is that it depends on the project. Ask the following questions to get a start:
- 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 something else entirely. 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 I work with? You must know the environment in which you work and what programs you will have to manage for big data and business intelligence, and thus choose the programming language that best integrates with your tools.