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author

William Dawson

Full Stack Developer

If you are someone who is a beginner in the field of Data Science and Machine Learning and want to learn it, you must be confused between R and Python as both the languages are widely used for data science.

R and Python are two open-source programming languages with great community support. New libraries or tools are added continuously to their respective spaces. R is mainly used for statistical analysis while Python provides a wider approach to data science.

R vs Python


R

R is a popular statistical modeling language that is used by statistics and data scientists. It provides support for various statistical packages that are most widely used for data analysis and data modeling. Rose Ihaka and Robert Gentleman together developed R in 1995 at the University of Auckland.

There are more than 10,000 packages in the library distribution CRAN repository of R. These packages are tailored for a variety of statistical applications. While R may be a hardcore statistical language, it provides extensible support for various fields, ranging from healthcare to astronomy and genomics.


Popular Packages Of R

  • 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.


Applications of R

Applications of R


Python

Python is a popular programming language used for developing web applications as well as data science operations. Python provides a large number of libraries that appeal to programmers and data scientists alike.

What makes python so popular is its ease of learning. This makes Python a highly popular language among newbies who want to gain in-depth insight into computer programming. Python is highly readable, easy to understand and compresses complex code in single functionalities.


Popular Libraries Of Python

  • 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.


Applications of python

Applications of python


R vs Python for Data Science

R and Python are states of the art in terms of programming language oriented towards data science. Learning both of them is obviously a perfect solution.

With the massive growth in the importance of Big data and Data Science in the software industry, two languages have emerged as the most favorable languages for developers that are R and Python. These two languages have become the first choice of data scientists and data analysts. Both of these are similar yet different in their own ways which makes it difficult for the developer to choose one amongst them.

While R is most widely used for statistical modeling and data analysis, Python is used for data analysis as well as web application development.

R vs Python For Data Science



Although it is suggested to use the language you are most comfortable with and one that suits the needs of your organization, for the purpose of this article, we will evaluate two languages. Here we will compare R and Python in four key categories: Data visualization, Modeling Libraries, Learning Curves, and Community Support.


Data Visualization

Any language or software package for data science should have good data visualization tools. Good data visualization involves clarity. No matter how complicated your model is, there will be a simple and unambiguous way of illustrating your results such that even a layperson would understand.

Data visualization


  • Data visualization in R:- There are many libraries that could be used for data visualization in R but ggplot2 is the clear winner in terms of usage and popularity. The library uses a grammar of graphics philosophy, with layers used to draw objects on plots. Layers are often interconnected with each other and can share many common features. These layers allow one to create sophisticated plots with very few lines of code. The library allows the plotting of summary functions.
    It is, however, worth noting that python includes a ggplot library, based on similar functionality as the original ggplot2 in R. It is for this reason that R and Python both are on par with each other in this department.

  • Data visualization in Python:– Python is renowned for its extensive number of libraries. There are plenty of libraries that can be used for plotting and visualizations. The most popular libraries are matplotlib and seaborn. The library matplotlib is adapted from MATLAB , it has similar features and styles. The library is a very powerful visualization tool with all kinds of functionality built-in. It can work well with other Python data science libraries, pandas and numpy .
    Although matplotlib can make a whole host of graphs and plots, what it lacks is simplicity. seaborn builds on top of matplotlib , including more aesthetic graphs and plots. The library is surely an improvement on matplotlib ‘s archaic style, but it still has the same fundamental problem as creating figures can be very complicated. However, recent developments have tried to make things simpler.


Modeling Libraries

Data science requires the use of many algorithms. These sophisticated mathematical methods require robust computation. It is rarely or maybe never the case that you as a data scientist need to code the whole algorithm on your own. Sometimes it’s very hard to do so, data scientists need languages with built-in modeling support. One of the biggest reasons why R and Python get so much traction in the data science is because of the models you can easily build with them.

  • Modeling Libraries in R:- R was developed by statisticians and scientists to perform statistical analysis. One can build a plethora of models using R. R has plenty of libraries, approximately 10000 of them. The mice package, rpart , party and caret are the most widely used. These packages will have your back, starting from the pre modeling phase to the post model/optimization phase.
  • Since you can use these libraries to solve almost any sort of problem; for this discussion let’s just look at what you can’t model. Python is lacking in statistical non-linear regression and mixed-effects models. Some would argue that these are not major barriers or can simply be circumvented. Kind of true but when the competition is stiff you have to be nitpicky in order to decide which is better.

R modeling libraries


  • Modeling libraries in Python:- As mentioned earlier Python has a very large number of libraries. So naturally, it comes as no surprise that Python has an ample amount of machine learning libraries. There is scikit-learn , XGboost , TensorFlow , Keras and PyTorch just to name a few. Python also has pandas, which allows tabular forms of data. The library pandas makes it very easy to manipulate CSVs or Excel-based data.
  • In addition to this Python have great scientific packages like numpy . Using numpy , you can do complicated mathematical calculations like matrix operations in an instant. All of these packages combined, make Python suited for hardcore modeling.
python modeling libraraies


Learning Curves

Many people are looking to get into the data science bandwagon, many of them have little or no programming experience. Learning a new language can be challenging, especially if it is your first. For this reason, it is appropriate to include ease of learning as a metric when comparing the two languages.

  • Learning Curves in R:- It would be wrong to say that R is a difficult language but yes, R is simpler than many languages like C++ or JavaScript. Like Python, much of R’s syntax is based on C, but unlike Python R was not envisioned as a language that anyone could learn and use, as it was specifically initially designed for statisticians and scientists. IDE’s such as RStudio have made R significantly more accessible, but in comparison with Python, R is relatively more difficult to learn.

  • Learning Curves in Python:- Python was designed in 1989 with a philosophy that emphasizes code readability and a vision to make programming easy or simple, the designers of Python clearly succeeded as the language is fairly easy to learn. Although Python takes inspiration for its syntax from C, unlike C it is uncomplicated. Since anyone can pick it up in relatively less time, you can say it’s a language for beginners.


Community Support

community support



As a data scientist, you are required to solve problems that you haven’t encountered before. Sometimes you may have difficulty finding the relevant library or package that could help you solve your problem. To find a solution, it is not uncommon for people to search in the language’s official documentation or online community forums. Having good community support can help programmers to work more efficiently.


Both of these languages have active Stackoverflow members and also an active mailing list available. R has an online R-documentation where you can find information about certain functions and functions inputs. Most Python libraries like pandas and scikit-learn have their own official online documentation that explains each library.


R vs Python for machine learning

R and Python are the two most commonly used programming languages for Machine Learning and because of the popularity of both the languages fresher are getting confused, whether they should choose R or Python language to commence their career in the Machine learning domain. Here we are discussing R vs Python for machine learning in some factors. It will help you to understand these two languages better.

  • Speed:- When it comes to speed, python is faster than R only till 1000 iteration but after the 1000 iteration, R starts using the lapply function which increases its speed, in that situation R becomes faster than python.
  • Code and Syntax:- R was basically built for statically analysis, so it has many specific libraries for plotting as well. This is the reason R come up with beautiful graphs and charts. On the other hand, Python’s main agenda was not for statistical analysis. So in the early stages of the python packages for data analysis was an issue, but it has improved a lot.
  • Deep Learning:- Deep Learning is the main part of artificial intelligence. When it comes to deep learning Python is more versatile than R as it provides more features to deep learning whereas R is new to Deep Learning.


Business Analytics

Popularity

R vs Python -popularity

that
Here is a five-year graph from 14 Aug 2014 to 14 Jan 2018. It is clearly shown in graph R is more popular than Python according to trends in Google for the last five years.


Jobs

R vs Python Jobs



So this is the five-year graph for job trends in R and Python according to Google. This graph shows that in 2014, the ratio of R jobs was quite high compared to 2018. That means the demand for R developers is decreasing with time. Compare to 2014 jobs in Python, demand for Python developers is increasing.


Salary


R Programmer Salaries in the United States:-

average salary of r programmers


Average Python Developer Salary in the United States is $117,472 per year.

average salary of python programmers



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About The Author

William is a CTO and a full-stack engineer with 10 years of experience. He has spent the past seven years doing web and mobile apps. He’s good at designing architecture and implementing agile development process. The technologies he’s worked with include: Node.js, Elixir, Rails, AngularJS, React, React Native, Objective-C, iOS, Java, Android. He’s also familiar with C++, Haskell, C#/.NET. He is an enthusiastic programmer and a great guy to know

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