R is a statistical and visualization language which 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 the analysis you want to perform. The rich variety of library 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.
Python is based on C, it is a software development language which 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 code 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.
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 the 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 the 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.
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.
R vs. Python: Popularity in 2018
Till 2015-2016, R has been more popular. But, in the last 2 ΓΆ?? 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 ΓΆ?? 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.
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. The 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: Advantages & Limitations
Advantages of R
1. R is great for statistical analysis.
2. 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.
3. 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. Python's visualizations are a little more convoluted, and there aren't as many visualization libraries to choose.
4. 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.
5. 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:
1. For the users with no programming knowledge, R language will be a little difficult as it has a steep learning curve.
2. 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:
1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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:
1. Python is slower in comparison with other programming languages as it is an interpreted language.
2. Python requires rigorous testing as the errors show up in runtime.
3. 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: Job Opportunities and Salaries
The picture below shows the number of jobs related to data science by programming languages. SQL is far ahead, followed by Python and Java. R ranks 5th. If we focus on the long-term trend between Python (in orange) and R (blue), we can see that Python is more often quoted in job description than R.
In terms of salaries, in 2017, the average annual salaries were $99,000 (R) and $100,000 (Python).