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How the Integration of R and Python Enhances the Data Science Workflow?

 

Everyone, from data scientists to big organizations, knows and agrees that the data-driven transformations, happening at present, are expanding day by day.

Businesses are experiencing a global shift where approx 90% of the enterprises will have their own Chief Data by the end of 2019 because with a high-performance team they can generate huge ROI. However, most of the data scientist’s teams are divided between two leading programming languages: Data Science with Python and data science with R.

In a recent case study, it was found that most of the organizations use a structured workflow with a combination of industry experts, data science education, and with data scientists to achieve the best possible results in this competitive business.

If you would ask any experienced data scientist, they will always advise you to leverage the strength of both programming languages, R and Python and build a high performance driven data science team which can capitalize the positive aspects of both languages.

Using multiple programming languages would seem unfavorable for some, but in long-term it provides high profits in:

  • Improved Efficiency – How fast your team iterates through its workflow?
  • Increased Productivity – How your team would add value and increase profit?
  • Improved Capabilities – How much output can your data science team provide?

Both R and Python have amazing features which when combined, will produce excellent results. Here is a graphic representation which summarizes the strengths of both programming languages.

Source – https://www.business-science.io/business/2018/10/08/python-and-r.html

Both of the data science languages are used for business analytics and have similar workflow when viewed from a machine learning perspective.

The libraries of both of them are dedicated to processing, wrangling and machine learning.

To validate experiments and research processes most organizations look for reproducible research and both Python and R are excellent choices for it.

# R Strengths

Before the development of R, the programming language S was developed by a statistician John Chambers in the year 1976. The primary goal of the S language was to implement statistics.

The statistical programming language R was developed in the year 1993 by the professors of University of Auckland. One thing to notice is that both the developers of S and R were not computer professionals or software engineers.

They were scholars and professors who wanted to develop applications which can help them in performing experiments efficiently.

The roots of the R language is in data exploration, analysis, visualization, and statistics. R has excellent tools for communication and reporting including the tools Shiny and RMarkdown.

R programming language is expanding worldwide quickly with the emergence of Tidyverse which is a set of tools with a common-programming interface that uses functional verbs such as summarize (), mutate(), etc. to perform functions related by the pipe.

Tidyverse helps to explore data efficiently through exploratory analysis. Here’s a sample flowchart which describes how it works:

Source – http://www.storybench.org/getting-started-with-tidyverse-in-r/

R language is mostly used in organizations which need to test theories, study and examine quickly and make faster decisions. Its communication utilities include slide decks, work reporting, which can be used to create a responsive workflow.

# Python Strengths

The Python language was developed in the year 1991 by the computer scientist Guido van Rossum. It was made so that it can become easy to read and write multiple programming paradigms.

The versatility of Python is its major strength which includes networking, web frameworks, web scraping, image processing and many more. Most of these features are essential in machine learning including natural language processing, text processing, and others.

Python roots are concerned with mathematics and computer science. It is developed for people that require versatility into different fields and profession.

It has the most extensive database of any language with over more than 1 lakh open source libraries.

Python data science library Scikit Learn is one of the most widely used machine learning library. It’s another library TensorFlow is used for natural language processing tasks and image recognition.

The flowchart of Scikit Learn is given below which describe its workflow and reach:

Source – http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

The strength of Python lies in its massive learning libraries. The Scikit library has all the algorithm in one place and supports pipelines to simplify workflow.

Data Science Workflow Design

If you know about different programming languages, then you would have the option to use the given applications and tools according to the requirement. If done correctly the results will increase the data science team’s efficiency and productivity.

The motive is to be as flexible as possible so that one can use the strengths of both the programming languages within the same data science workflow which consists of:

  • Exploring data effectively
  • Cross-Validating, Evaluating model and it’s quality
  • Using data science for better communication via reports including web applications, word, excel files, and others.

We need to make a few modifications to the strengths of Python and R visualizations so that we can leverage its power more efficiently.

Source – https://www.business-science.io/business/2018/10/08/python-and-r.html

Conclusion

R is chosen for exploration due to the efficiency and readability of Tidyverse while Python is chosen for machine learning due to the machine learning capability of Scikit Learn.

Both Python and R are powerful and versatile programming languages. But instead of considering them as competitors you should think of them as allies and integrate the strengths of each other which would enhance the data science workflow.

When these two languages are combined a transition takes place which improves the overall productivity and efficiency and helps in building a high-performance data science team.

 

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Written by sanjayit38

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