R vs Python: Meta-review on Usability, Popularity, Pros & Cons, Jobs, and Salaries

If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Pythonusability, popularity index, advantages & limitations, job opportunities, and salaries. In this article, you will get to know r vs python for data science, r vs python for machine learning, r vs python for data analysis etc.

R vs Python

Meta-review on Usability, Popularity, Pros & Cons, Jobs, and Salaries

Introduction to R

R is a statistical and visualization language that is deep and huge and mathematical. R was developed in 1992 and was the preferred programming language of most data scientists for years. R makes it possible to find a library for whatever analysis you want to perform. The rich variety of libraries makes R the first choice for statistical analysis, especially for specialized analytical work. Additionally, one of the standout features of using R is you can create beautiful data visualization reports and communicate the findings.

R: Popular Packages for Coders

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

Check out the Data Science Certification Course using R by Edureka

Introduction to Python

Python is based on C, it is a software development language that is deep and huge, and intuitive.  It is easier to learn than many other languages, and you don’t need to be totally fluent in order to make use of it for genomics or other biological data analysis.  It can do some statistics and is a great scripting language to help you link your workflow or pipeline components together.

Python was released in 1989 with a philosophy that emphasizes code readability and efficiency. It is an object-oriented programming language, which means it groups data and codes into objects that can interact with and modify one another. Java, C++, and Scala are other examples.

Python is a tool to deploy and implement machine learning at a large scale. It can pretty much do the same tasks as R: data wrangling, engineering, feature selection web scrapping, app, and so on. But, Python codes are easier to maintain and more robust than R. It provides cutting-edge API for machine learning or Artificial Intelligence.

Most of the data science jobs can be done with five Python libraries: Numpy, Pandas, Scipy, Scikit-learn, and Seaborn. Additionally, Python makes reproducibility and accessibility easier than R. If you need to use the results of your analysis in an application or website, Python is the best choice.

Python: Popular Libraries for Coders

  • pandas for data manipulation
  • SciPy/NumPy for scientific computing
  • scikit-learn for machine learning
  • matplotlib for graphics
  • statsmodels to explore data, estimate statistical models, and perform statistical tests and unit tests

Check out the Best Python Online Courses and Python Certification Training for Data Science from Edureka

R vs Python
Source: DataCamp

R vs Python: Usability

According to Chris Groskopf, Quartz’s former Data Editor, Python is better for data manipulation and repeated tasks, while R is good for ad-hoc analysis and exploring datasets.

He further added that from pulling the data, to running automated analyses over and over, to producing visualizations like maps and charts from the results, Python was the better choice when he was working on elections coverage.

“If I had done the analysis in R, then I would have had to switch to a different tool to create the website and automate the process, but Python also works well for those things,” he says.

In contrast, R is good for statistics-heavy projects and one-time dives into a dataset. Take text analysis, where you want to deconstruct paragraphs into words or phrases and then identify patterns.

“I often don’t know where I’ll end up when I start a process like that, and R makes it easy to try a lot of different ideas quickly,” Groskopf says. “In Python, I would inevitably end up writing a bunch of generic code to solve this pretty narrow problem.”

R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.

Python is a great go-to tool for programmers and developers.

Another advantage of Python is that it is a more general programming language: For those interested in doing more than statistics, this comes in handy for building a website or making sense of command-line tools. Python is a pure player in Machine Learning. But, Python is not entirely mature (yet) for econometrics and communication.

Python is the best tool for Machine Learning integration and deployment, but not for business analytics.

R is meant for academicians, scholars, and scientists. R is designed to answer statistical problems, machine learning, and data science. R is the right tool for data science because of its powerful communication libraries. Besides, R is equipped with many packages to perform time series analysis, panel data and data mining.

Python vs R
Source: DataCamp

R vs Python: Usage in Statistics, Data Science, Machine Learning, and Software Engineering

When it comes to usage in data science, some data scientists prefer R to Python because of its visualization libraries and interactive style.

R comes with great abilities in data visualization, both static and interactive. Interactive visualization built with R packages like Plotly, Highcharter, Dygraphs, and Ggiraph take the interaction between the users and the data to a new level.

Since R was built as a statistical language, it suits much better to do statistical learning. It represents the way statisticians think pretty well, so anyone with a formal statistics background can use R easily.

But, if you are looking for higher performance or structured code Python is the go-to language. It is because Python has some of the best libraries such as SciKit-Learn, IPython, numpy, scipy, matplotlib, etc.

NumPy is the foundational library for scientific computing in Python, and it introduces objects for multi-dimensional arrays and matrices, as well as routines that allow developers to perform advanced mathematical and statistical functions on those arrays with fewer codes. Matplotlib is the standard Python library for creating 2D plots and graphs.

Python is also a better choice for machine learning with its flexibility for production use, especially when the data analysis tasks need to be integrated with web applications. For rapid prototyping and working with datasets to build machine learning models, R inches ahead. Python has caught up some with advances in Matplotlib but R still seems to be much better at data visualization (ggplot2, htmlwidgets, Leaflet).

Additionally, Python is also great if you want to do a lot of software engineering. It integrates much better than R in the larger scheme of things in an engineering environment. However, to write really efficient code, you might have to employ a lower-level language such as C++ or Java, but providing a Python wrapper to that code is a good option to allow for better integration with other components.

Related: So You Think You Can Become A Data Scientist?

R vs Python: Popularity in 2021

Till 2015-2016, R has been more popular. But, in the last 2 – 3 years, Python gained tremendous popularity. Burtch Works did a comprehensive survey of data scientists and analytics professionals to determine which tool they prefer to use – SAS, R, or Python. KDnuggets also did another survey to figure out the top platforms among data scientists and analytics professionals. Have a look at the results below.

Python vs R - Stackoverflow
Image Source: DZone
Python vs R - platform usage
Image Source: DZone
R vs Python vs SAS
Image Source: Burtch Works
R vs Python
Image Source: KDnuggets

The seasoned pros use R (and SAS) more. In contrast, entry-level data scientists prefer using Python which is no surprise as Python is easier to pick up. Predictive Analytics Professionals prefer using SAS. While for the Data Scientists, Python is a clear winner. Additionally, the usage and popularity also vary from industry to industry and by education level. Have a look at the graphs below.

R vs Python vs SAS
Image Source: Burtch Works
SAS vs R vs Python in Data Science and Predictive Analytics
Image Source: Burtch Works
Python vs R vs SAS - Preference by Industries
Image Source: Burtch Works
SAS vs R vs Python by Education Level
Image Source: Burtch Works

R vs Python: Advantages & Limitations

Advantages of R

  • R is great for statistical analysis.
  • R is also built around a command line, but many people work inside of environments like RStudio or R commander that include a data editor, debugging support, and a window to hold graphics as well. Python has tried to catch up with this with IDEs like Eclipse or Visual Studio.
  • R language is considered as the best tool for data visualization. Visualized data can be better understood than raw numbers. R and visualization go hand-in-hand. It includes quite a few packages that correspond with this. Pythons visualizations are a little more convoluted, and there aren’t as many visualization libraries to choose.
  • R programming produces best results of visualization which can be used in research papers (white papers). The results can be traced when needed and can be reproduced to create a different result structure.
  • R language provides a large community support with 1000 developers and draws talents of data scientists spread across the world. The community includes packages in various domains like finance, machine learning, web technologies, and pharmacy.

Limitations of R:

  • For the users with no programming knowledge, R language will be a little difficult as it has a steep learning curve.
  • Deriving proper solutions with R programming language can be considered as slow if the code is written poorly. To overcome this drawback, it is mandatory to include libraries to achieve proper output.

Advantages of Python:

  • Since Python is a general programming language, learning it gives you the skills to go beyond just data analysis. Python programming is used broadly for web development, automation testing, and ETL.
  • Programmers think Python coincides with the way programmers think more than R does, and therefore it translates over to other languages more easily. As mentioned above, the roots of R lie in statistics, so it has a unique design. If you want to go down the road of learning other general-purpose languages, Python is the language to pursue.
  • A large part of data analysis is cleaning up the data beforehand. It’s nice to clean data with a full-service language like Python because you can add new functions and layers to take apart your data. If these functions require local storage or web access, it’s fairly easy to include these with Python.
  • Python is evolving with time. A new code is being introduced and breaking old code, which makes Python a living language. This leads to more open source code and solutions. R’s steps are not as forward-thinking. Instead, it has stayed pure.
  • Python moves more quickly than R. This is because R was developed to center around the convenience of statisticians, not the convenience of the computer.
  • Python has gained wide popularity as the syntax is crystal clear to understand. Data scientists gain expert knowledge and master programming with Python to get the output as desired with a defined number of steps.

Limitations of Python:

  • Python is slower in comparison with other programming languages as it is an interpreted language.
  • Python requires rigorous testing as the errors show up in runtime.
  • Python programming is still considered weak on mobile computing platforms as there are few apps created with Python as a core language.
R vs Python - Pros & Cons
Infographic Credit: DataCamp

R vs Python: Job Opportunities and Salaries

The figure below shows the number of data science jobs by programming language. SQL is the most in-demand language, followed by Python and Java. R is the fifth most popular language. However, if we focus on the long-term trend between Python (in orange) and R (in blue), we can see that Python is becoming increasingly more popular than R.

In terms of salaries, the average annual salaries were $99,000 (R) and $100,000 (Python).

What Is the Average Python Developer Salary? [2022 Guide]

Salaries in the US

Python - Salaries - USA
Image Source: DAXX

R vs Python: Jobs and Salaries in India

Below are the findings from the Analytics India Annual Salary Study that aims to understand a wide range of current and emerging compensation trends in Analytics & Data science organizations across India.

R vs Python - Salaries in India
Image Source: Analytics India Magazine
R vs Python - Salaries in India
Image Source: Analytics India Magazine
Python vs R
Image Source: Analytics India Magazine
R vs Python - Metareview
Image Source: Analytics India Magazine
R vs Python - Jobs and Salaries in India
Image Source: Analytics India Magazine
Data Scientists & Analysts Salaries in India
Image Source: Analytics India Magazine
Python vs R - Jobs and Salaries
Image Source: Analytics India Magazine

Knowledge of multiple tools will obviously allow you to earn more. Have a look at the chart below (data from 2016 – 2017).

Salaries in India - R vs Python
Source: NDTV

Most Popular Online Courses to Learn R & Python

Popular Online Courses on R:

R Programming for Absolute Beginners

R Programming

Statistics with R

Data Science and Machine Learning with R

R Programming A-Z for Data Science with Real Exercises

R Programming for Statistics and Data Science

Text Mining, Scrapping, and Sentiment Analysis with R

Mastering Data Visualization with R (using R Base Graphics, Lattice Package, and ggplot/GGPlot2)

Popular Online Courses on Python:

Data Science with Python for Students and Beginners

Mastering Machine Learning with Python from Scratch

Python for Everybody

Complete Python Bootcamp

Introduction to Data Science in Python

Python for Data Science and Machine Learning Bootcamp

Applied Machine Learning in Python

Machine Learning with Python by IBM

Machine Learning A-Z™: Hands-On Python & R In Data Science

Data Analysis with Pandas and Python

Data Science with Python and Pandas, Numpy, Matplotlib

Data Visualization with Python and Matplotlib

Capstone: Retrieving, Processing, and Visualizing Data with Python

What to do if you are a Newbee in Data Science?

If you are new to data science and have a background in statistics, I recommend learning Python first. Python is a general-purpose programming language that is easy to learn and has a wide range of libraries for data science. You can use Python to build models from scratch, and then use the machine learning libraries to deploy and reproduce your models.

If you already know the algorithms or want to focus on statistical methods, you can start with either Python or R. However, if you want to do more than statistics, such as writing reports and creating dashboards, Python is a better choice. R is a statistical programming language that is better suited for data analysis and visualization.

Ultimately, the best language for you will depend on your specific needs and goals. If you are not sure which language to choose, I recommend starting with Python. It is a versatile language that can be used for a wide range of data science tasks.

R vs Python - which one should you use?
Image Source: Revolutionary Analytics

Python or R? Conclusion

The choice between R and Python really depends on your level of knowledge and objective. But, going ahead you need to learn both.

Day-to-day users and data scientists are getting best of both worlds, as R users can run a rPython package within R to run Python code from R, and Python users who are using RPy2 library can run R code from within the Python environment.

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References: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12.

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