Many students in programming and statistics can find a very remunerative career in data science, as it is an ever-growing field with a lot of potential. But right from the start, you have to ask yourself how you are going to approach this discipline and how you are going to tackle the programming challenges ahead of you. And here comes the question that all data scientists had to answer at the very beginning of their careers: should I learn Python or R programming to start working on data analysis? This is a tough question since Python and R are both versatile programming languages in data statistics. They were born in the same period (the late 80s or the start of the 90s) and both have proven themselves as very useful tools in data mining.
In this article, we will try to figure out which language will suit you the best based on various factors given below:
- Ease of learning
- Usability
- Ecosystem
- Popularity index
- Percentage of people switching
- Job Opportunity
- Advantages and disadvantages over one another
Before moving on to these factors, let us have an overview of both the languages and see how they are different from each other.
Python was developed to offer a way to write scripts to automate some of the routine tasks encountered on a daily basis. However, as time went by, Python has evolved and become quite useful in many other fields, especially data analysis.
On the other hand, R is a programming language as well as an open source software for both graphics and data analytics. It has the advantage of running on any computer system and is used by data miners and statisticians for both presentation and analysis of their data.
It is a common challenge for a data scientist to decide whether to use Python or R for data analysis. While R was purely developed for statisticians, making it portray analysis a specific advantage for visualizing data, Python stands out with its general-purpose characteristics and the fact that it has a very regular syntax. Here are some of the differences between these two languages that can help you decide which one to choose:
| Python Programming Language | R Programming Language |
| – Python programming language was inspired by Modula-3, ABC and C languages | – S programming language inspired R. |
| – Python focuses on code readability and productivity | – Emphasizes on data analysis methods, graphical models and statistics that are user-friendly. |
| – It is easier to develop code and debug because of its easy-to-use and simple syntax | – It is slightly hard to use since statistical models are only written using few lines. |
| – All pieces of functionality are often written in the same style | – There are many ways of representing or writing the same functionality piece. |
| – Python is very flexible and can also be used in web scripting. | – Offers the ease of using complex R formulas. For its many statistical models and tests. |
| – It has a relatively gradual and low learning curve for it focuses on simplicity and readability | – Has a learning curve that is steep at the beginning when learning the basics. But it becomes very easy to learn advanced topics later on |
| – Suitable for those beginning to program | – Not very hard for expert programmers. |
| – Its Package index is called PyPi. Its Python’s software repository with libraries. Although users have the option of contributing to Pypi. It is difficult in practice. | – Comprehensive R Archive Network (CRAN). CRAN is the R repository package that is easily contributed to by the users. |
| – RPy2 is the library which can be used within Python to run R code. Used in providing a low level to R from Python. | – The rpython package is used from R to run Python code.Call Python methods or functions and for getting data. |
| – In 2020, Dice Tech Salary Survey showed the average salary of an experienced expert was $112,076 | – In 2020, Dice Tech Salary Survey showed the average salary of an experienced expert was $112,074 |
| – It is mainly applied when there is a need for integrating the data analyzed with a web application or the statistics is to be used in a database production | – Mainly applied when the analysis requires independent computing or individual servers. |
| – The capability to handle data was a challenge for it in the past although it has improved, this was due to its package infancy in data handling | – Ideal for handling data from its large package number. Usable tests and the use of formulas. |
| – You must use tools like pandas and NumPy to enable it to be used for data analysis | – R does not require additional packages for basic analysis. It only requires packages like dplyr for big datasets. |
| – IDEs available include Spyder, IPython Notebook. | – Uses R studio IDE |
These were the differences between the two languages described briefly but now let us take all the factors mentioned above into consideration and see which language is better suited for an individual.
Ease of learning
If you are a complete beginner in programming, I would suggest Python is the language for you as it is really simple for beginners to understand. Whereas R is not considered as a good first language to learn but if you are already familiar with programming languages,R should not be that hard to understand.
Python focuses on simplicity and readability thus giving it a relatively linear and smooth learning curve whereas R might seem easy to get started with but the learning curve increases exponentially when you dive into more complex concepts.
Usability of Python vs R
Here we will discuss the usability along with the general users for Python and R programming languages.
People having a software engineering background may find Python comes more naturally to them as compared to R.Thus Python is used more by programmers that tend to delve into data analysis or apply statistical techniques, and by developers and programmers that turn to data science. It is a production-ready language, which means that it has the capacity to be a single tool that integrates with every part of the workflow.Python has certain advantages such as coding and debugging is easy because of the simple syntax.Also,the indentation of code affects how the lines in program are interpreted.
R has been primarily used by academic researchers and is a great tool for exploratory data analysis. But in recent years, its enterprise usage has rapidly expanded. It is mostly preferred by statisticians, engineers, and scientists without any prior computer programming skills. It’s popular in academia, finance, pharmaceuticals, media, and marketing. R also has certain advantages over Python ,one being that the statistical models can be written with only a few lines.Also,the same piece of functionality can be written in several ways in R programming language.
Ecosystem in Python vs R
Python has a robust ecosystem and is commonly considered one of the easier programming languages to read and learn. Its programming syntax is simple and its commands mimic the English language. E.g. print(“Hello world!”) will print Hello world! On the screen.Its code is syntactically clear and elegant, easily interpretable, and easy to type.
Python is great for building data science pipelines and machine learning products integrated with web frameworks at scale. But we may need to use a lot of 3rd party dependencies.
The Python Package Index (PyPi) and Anaconda are two major repositories of Python software with all libraries. Users can contribute to these repositories, but it’s a bit complicated in practice to do so.
Now moving on to R which also has a rich ecosystem of cutting-edge interface packages available to communicate between open-source languages. This allows users to string their workflows together which is especially useful for data analysis.
Packages are available at Comprehensive R Archive Network (GRAN),Bioconductor, and GitHub.
Popularity index of Python vs R
In recent years,the popularity of Python programming languages has significantly increased,even surpassing the Java programming language.In the below table you can see the PYPL popularity of various programming languages.Also you can see that R is at number seven.But remember that R is more generally used in statistical analysis and data analytics whereas Python is more of a general purpose programming language.
Now if we compare the PYPL popularity of R and Python over previous years,we can see that Python has a consistent lead on R.
Percentage of people switching
In the above section,we compared the overall popularity of Python and R programming languages,but now we will compare them in the field of Analytics,Data Science and Machine learning.
R programming language was very popular in the early 2000’s for data analytics and even it was more popular than Python few years back,but in recent years people have been switching to Python.Below in the graph of poll conducted by KDnuggets, you can get a much better picture of how Python is becoming number one choice for data analytics and Machine learning.
Here is a plot showing the share of R,Python and other tools in the recent years, and we can see that R and Python are really close to each other,but number of Python users has recently surpassed R users when it comes to data analytics,Machine learning and data science.
Job Opportunity in Python vs R
Now if you want to get hired as a Data Scientist,what are the tools that should be in your arsenal? Here is a list of tools that are mostly mentioned in the job listings of Data Scientists.As we can see Python and R are at the top ,followed by various other tools.
Companies using Python
Companies using R
As we can see, R is mostly used by analytics companies and consulting firms, whereas Python is more commonly used by IT companies. But it is important to note here that R is also used by big tech companies such as Google, Facebook, Microsoft, and many others for data visualization, analytics and advertising effectiveness, and economic forecasting.
Advantages and disadvantages over one another
Python – Pros
- The IPython Notebook facilitates and makes it easy to work with Python and data. This is from the fact that you can share notebooks with other people without necessarily telling them to install anything. Which reduces code organizing overhead, hence allowing one to focus on doing other useful work.
- Given that it is a general-purpose language, it is intuitive and simple. It enables a data scientist with a flat learning curve which in turn allows him to increase his program writing skills. Python also has an inbuilt framework for testing which encourages improved test coverage, which in turn is a guarantee of one’s code being dependable and reusable
- It is a multi-purpose programming language bringing together people with various backgrounds, that is, statisticians and programmers.
Python – Cons
- Visualization is a crucial factor when determining the data analysis software to use. Python offers several libraries for visualization like Boken, Pygal, and Seaborn which may, in turn, be too many to pick. And unlike R, its visualizations are convoluted and not attractive to look.
- Python is just an R Challenger and doesn’t substitute the many R packages that are essential.
R – Pros
- R offers clear visualization of data, making the data efficiently designed and understood. Examples of its visualization packages are ggvis, ggplot2, rChart, and googleVis.
- R has a broad ecosystem of active community and desirable packages. The packages are available at Github, BioConductor, and CRAN.
- It was developed, for statisticians, by statisticians. Hence, they can communicate concepts and ideas through R packages and code.
R – Cons
- If you compare the speed of Python vs R, R is slow because of its code that is poorly written. Packages that can improve its performance include Renjin, PQR, FastR.
- R has a very steep non-trivial learning curve. Especially if you have a graphical user interface (GUI) background that was used for statistical analysis. Finding simple utilities and packages can be very hard.
Conclusion
It is clear that both languages have their own advantages and disadvantages and it depends on your personal preferences to pick one that will solve your problems. But based on the factors we can say that Python is picking up and may have an edge over R in years to come. Yet I would suggest that learning both R and Python is the best choice if you already are an expert in one of them. And if you are just a beginner choose one that suits you best according to your needs and learn the next one in the future.
