R and Python: 2 Pillar of Data Science Dynamic

In Data Science R and Python? only R? only Python? These are the prime questions within many aspirants now a days, specially in this summer. However, this choice should depend on the type of data analytical challenge that you’re facing. Both Python and R are popular programming languages for statistics. While R’s functionality is developed with statisticians in mind, Python is often praised for its easy-to-understand syntax!

In this article, we will try to dive deeper to understand which one to use and when to use!

When to use R?

In 1995, two statisticians, Ross Ihaka and Robert Clifford Gentleman created an open-source language as an implementation of the S programming language. Their purpose was to develop a language that would focus on delivering a better and more user-friendly way to perform data analysis, statistics and graphical models. R was primarily used in academics and research, but later it gain popularity within the corporate world what makes R one of the fastest growing statistical languages. However, the popularity of R goes in a roller coaster ride. As per Alex Woodie in datanami.com, R was the 8th popular language in 2018 and dropped to 20 in terms of popularity in 2019. That drop coincided with a surge behind Python, which the folks at TIOBE attributed to a decline in R. Many of us thought that R was a dying language. But, it regained it’s popularity quickly and hold it’s position back in July 2020.

When you need to perform standalone computing or analysis on individual server, there is no alternative for R. It’s great for exploratory work, and it’s handy for almost any type of data analysis because of the huge number of packages and readily usable tests that often provide you with the necessary tools to get up and running quickly. R can even be a part of a big data solution.

To get started with R, a good first step is to install the amazing RStudio IDE. Once this is done, you can have a look at the following popular packages:

  • dplyr, plyr and data.table to easily manipulate packages
  • stringr to manipulate strings,
  • zoo to work with regular and irregular time series,
  • ggvis, lattice, and ggplot2 to visualize data, and
  • caret for machine learning

Fortunately, there are numerous incredible learning resources you can refer these days.

Tutorial Credit: Massachusetts Institute of Technology Open Courseware

When to use Python?

Python is normally chosen when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. Being a fully fledged programming language, it’s a great tool to implement algorithms for production use.

While the infancy of Python packages for data analysis was an issue in the past, this has improved significantly over the years. Make sure to install NumPy /SciPy (scientific computing) and pandas (data manipulation) to make Python usable for data analysis. Also have a look at matplotlib to make graphics, and scikit-learn for machine learning.

Unlike R, Python has no clear “winning” IDE. We recommend you to have a look at Spyder, IPython Notebook and Rodeo to see which one best fits your needs.

Tutorial Credit: Massachusetts Institute of Technology Open Courseware

Which one has popularity in terms of Data Science?

Who does not love comparison? We do comparison for each and every aspect. Neither R nor Python were exempted from that rule! Let’s review..

AreaPythonR
Availability & CostFREEFREE
Ease of LearningPython is known for its simplicity in programming world. This remains true for data analysis as well. It provides an exceptional documentation.In Data Science, R has the steepest learning curve. It requires you to learn and understand coding. It is a low level programming language and hence simple procedures can take longer codes.
Data Handling CapabilitiesPython has good data handling capabilities and options for parallel computations.R computes every thing in memory (RAM) and hence the computations were limited by the amount of RAM on 32 bit machines. This is no longer the case. They both have good data handling capabilities and options for parallel computations.
Graphical CapabilitiesWith the introduction of Plotly in both the languages now and with Python having Seaborn, making custom plots has never been easier.R has highly advanced graphical capabilities. There are numerous packages which provide you advanced graphical capabilities.
Advancements in ToolDue to open in nature, Python get latest features quickly. This is true for R as well. Since R has been used widely in academics in past, development of new techniques is fast.
Job ScenarioPython and R both has demand in recent days. Python is still more dominant than R.The job demand of R is increasing now a days.
Customer Service Support & CommunityPython has the biggest online communities but no customer service support.R has the biggest online communities but no customer service support.
Deep Learning SupportPython has had great advancements in the field and has numerous packages like Tensorflow and Keras.R has recently added support for those packages, along with some basic ones too. The kerasR and keras packages in R act as an interface to the original Python package, Keras.

Pros & Cons for R

Pros:

  1. It is Open Source
  2. R provides exemplary support for data wrangling. The packages like dplyr, readr are capable of transforming messy data into a structured form.
  3. R has a vast array of packages. With over 10,000 packages in the CRAN repository.
  4. R facilitates quality plotting and graphing. The popular libraries like ggplot2 and plotly advocate for aesthetic and visually appealing graphs that set R apart from other programming languages.
  5. R is highly compatible and can be paired with many other programming languages like C, C++, Java, and Python. It can also be integrated with technologies like Hadoop and various other database management systems as well.
  6. R is a platform-independent language.
  7. With packages like Shiny and Markdown, reporting the results of an analysis is extremely easy with R.
  8. R provides various facilities for carrying out machine learning operations like classification, regression and also provides features for developing artificial neural networks.
  9. R is prominently known as the lingua franca of statistics. This is the main reason as to why R is dominant among other programming languages for developing statistical tools.

Cons:

  1. R shares its origin with a much older programming language “S”. This means that it’s base package does not have support for dynamic or 3D graphics.
  2. In R, the physical memory stores the objects. This is in contrast to other languages like Python. Furthermore, R utilizes more memory as compared with Python. Also, R requires the entire data in one single place, that is, in the memory. Therefore, it is not an ideal option when dealing with Big Data. However, with data management packages and integration with Hadoop possible, this is easily covered.
  3. R lacks basic security. This feature is an essential part of most programming languages like Python.
  4. R has a steep learning curve. Due to this, people who do not have prior programming experience may find it difficult to learn R.
  5. R packages and the R programming language is much slower than other languages like MATLAB and Python.

Pros & Cons of Python

Pros:

  1. It’s easy to write
  2. It is an Interpreted Language
  3. It is platform independent
  4. Compact with modules –  A lot of libraries and frameworks
  5.  Large community
  6. Many online and offline, FREE & PAID courses are available for boosting your learning curve.

Cons:

  1. It is relatively slow than C or C++. But of course, Python is a high-level language, unlike C or C++ it’s not closer to hardware.
  2. Python is not a very good language for mobile development.
  3. Python has limitations with database access . As compared to the popular technologies like JDBC and ODBC, the Python’s database access layer is found to be bit underdeveloped and primitive . However, it cannot be applied in the enterprises that need smooth interaction of complex legacy data .
  4. Python is not a good choice for memory intensive tasks. Due to the flexibility of the data-types, Python’s memory consumption is also high.
  5. Python programmers cited several issues with the design of the language. Because the language is dynamically typed , it requires more testing and has errors that only show up at runtime .

Conclusion

Which one resonates you? In Data Science R or Python, which one is going to be the future? Let me know in comment section. I could refer some books and tutorials that I’ve used for preparing them. I can pass it to you if you’re interested.

We see the market slightly bending towards Python in today’s scenario. It will be pre-mature to place bets on what will prevail, given the dynamic nature of industry. But, whatever your choice may be, we can say both the languages have their own facilities and limitation. Both can be useful if you can master them. In the volatile market of Data Science, R and Python both left a strong foot print for us. We can consider them as the dynamic duo of Data Science.

Rating: 4 out of 5.