The two most popular choices in programming language for data science are R and Python. It is often a difficult choice to choose between both languages.
R is commonly preferred by researchers and statisticians with no background in programming. Python is a general-purpose language and is mostly learnt by developers and students inclined towards data science and machine learning. Let’s discuss the major difference between Python and R.
Data Science and Machine Learning are the most trending topics and the subject of learning. Data science has even been termed as the sexist job of the 21st century. We live in an era where data is the biggest asset of any organisation.
There are servers and data lakes where communication and storage of data take place. Every post, like, article, tweet, feed and upload is data. This data serves the organisation for getting insights and patterns for business analytics and intelligence.
It helps them make strategies and data-driven informed decisions. You will be amazed to know that a 10% increase in data accessibility can add 65$ million to the net income of the fortune 1000 company. It is estimated that the data science and analytics sector in India will grow eightfold to around 16$ billion.
Learn the difference:
Python first emerged as a general-purpose language in 1991. It is an interpreted high-level language. The syntax is quite simple and similar to pseudo-code making it easier to adjust with. Python is a general-purpose language is also used for backend development, automation, web scraping and scripting. Python is the language to choose for end-to-end implementation of machine learning algorithms. Python is well known for its stability, code readability and modular design. Python has compatibility with other data storage and manipulation software such as MS-Excel and MySQL.
|R language first appeared in 1993. It is a multi-paradigm programming language but is popularly used in statistical computing by statisticians and data miners. It is free software available under the GNU General Public License. R language gives you more than 12000 packages available through CRN(open-source repository) to enable you to perform statistical analysis with pre-built modules and packages. R is the first choice among scholars and academicians.|
|Advantages of Python |
General-purpose language gives the flexibility to explore other domains of development with python. Ease of deployment and compatibility with related data storage software. A Large number of packages and APIs to perform the heavy computation for machine learning and deep learning use cases. Great user and developer community for support and issue resolution.
Disadvantages of Python
The number of packages specifically for data science is comparatively lesser than that in R language. It needs intensive testing as errors to come up during runtime.
|Advantages of R |
Best suited for making intuitive and interactive graphs and visualisations. It is most preferred for statistical analysis. Platform independent. So it can easily run on Windows, Linux and Mac systems. Constantly updated and maintained.
Disadvantages of R
Lacks support for dynamic or 3D graphics. Need packages such as Ggplot2 and Plotly for 3d and animated graphics. It requires more memory to data when compared to python. It is not preferable for big data analytics. Lack of basic security features.Cannot be used for making end-to-end applications due to restriction on embedding into a web-application.Steep learning curve. Difficult for beginners and favourable if having prior programming experience. It is slower than other similar languages such as MATLAB and Python.
Trends and Rating
Stackoverflow developer survey 2020 – Most wanted language
In the StackOverflow developer survey, the python was first in the most wanted language of 2020. R language held the 14th position. This survey collected data from more than 65,000 developers.
Stackoverflow developer survey 2020 – Salary by developer type
You can observe that data engineers, data scientists and machine learning specialists are among the top five most paid developer roles. According to Glassdoor, the average salary of a data science engineer is Rs. 9,27,000 in India.
StackOverflow trends analysis
According to StackOverflow trends, we can see python has always been more preferred than R. The possible reasons can be its fast learning curve. It also often said as the beginner’s programming language. Python has seen exponential growth in the number of questions asked every year. This implies the growing number of users and developers.
Packages and Libraries
Packages and Libraries are highly relied upon when working around data. There are multiple processes involved such as structuring, preprocessing, cleaning, transformation, visualisation and modelling data in the study of data science. Are you wondering what is the difference between packages and libraries? A package is a collection of related modules to provide specific functionality. A library is an umbrella term used for “a bundle of code”. It contains multiple modules to perform a wide range of functions. There are no strict definitions but you may interpret them on the scale and usability.
Commonly used packages and libraries in python for data science are:
|Scrapy – Data Mining Scrapy is an application framework. It is used for large scale web scraping. It is a python framework. Web scraping is an efficient method to extract data from websites. |
BeautifulSoup – Data Mining BeautifulSoup is another important python package for data mining. It is used for web scraping.
NumPy – Data Manipulation NumPy stands for numerical python. It is a library that supports multiple functions and a large collection of high-level mathematical functions to deal with numerical data in python.
Pandas – Data Manipulation Pandas is the most used and popular python library used for data manipulation and analysis. It is useful in dealing with tabular data and time series handling.
Seaborn – Data Visualisation Seaborn is a data visualisation library based on matplotlib. It is used for drawing informative statistical graphics.
Matplotlib – Data Visualisation Matplotlib is a plotting library in python for creating static, animated and interactive visualisations.
Scikit learn – Machine learning Scikit learn is a machine learning python library. It has various algorithms pre-built to be directly used such as regression, classification, support vector machine and naive Bayes.
TensorFlow – Deep learning Tensorflow is an open-source software library by Google to enable you to build end-to-end machine learning projects and train deep neural networks. It is based on differentiable programming and dataflow. Keras – Deep learning Keras is an open-source software library to build artificial neural networks. Keras uses multiple backends such as Tensorflow, Microsoft Cognitive Toolkit, R, Theano and PlaidML
|DBI – Establish a connection between database and RTidyverse –|
Data preprocessing and Visualisation A complete package for data science in R. It encompasses many packages such as dplyr, tidyr, readr, purr and tibble.
Dplyr – Data Manipulation It provides you with a set of tools for effective dataset manipulation. It uses data frames for fast and easy data utilisation.
Tidyr – Data Manipulation Tidyr is used for data manipulation. It is often referred to as “tidy the data”. Ggplot2 – Data Visualisation It is a data visualisation library to make declarative graphics and visualisations.
|Package Manager and Repository |
Python has The Python Package Index(PyPi) and Ananconda as the repository of all the required libraries. Users can install the packages with pip and conda package managers.
Python currently has many popular IDEs. The most preferred and used among data scientists is Jupyter Notebook and Spyder. You can also see other alternatives such as Pycharm, Jetbrains, and Vscode for the same purpose. Recently Rodeo has gained popularity as the “data science IDE for python”. Explore and choose that best suits your needs and comfort of use.
|Package Manager and Repository |
RStudio Package Manager is the repository management server to organise and manage package across organisations and teams. You can get access to packages using package repositories such as CRAN, PyPi and Bioconductor. Packrat is also used as a dependency management system in R.
RStudio is the most popular IDE used by most statisticians. RStudio Desktop is used for running locally and RStudio Server for remote access. There are other alternatives also such as IntelliJ IDEA, Eclipse, Jupyter Notebook and Visual Studio.
Both languages have their own merits and demerits. You get to know that python can be a great choice if you are not already into software development or programming. It is easy to learn and fast to master. You can get ample community support and tutorials to learn. R is recommended for someone who has a core focus only on statistical analysis and data-focused manipulation.
You can choose any of the two languages. You should just consider certain questions to decide on your language choice. What does your team or organisation have as skillset and tools preference? What is the product requirement – statistical analysis or deployment? How much time can be invested in learning the skill? What is the scalability of the project? These questions can easily lead you to the choice of your language. Hope this article highlights the major differences between python and R language.