R Programming Vs Python Programming
An Introduction
R and Python have a huge following and enjoy cult status as the state of the art programming languages. Additionally, these are open-source languages. They continuously add new libraries or tools with a large community. R is used in statistical analysis and Python provides a pathway to data science. Both also need time-investment. R is built by statisticians and carries their specific language, whereas Python is a general-purpose language. The factors worthy of consideration are the objective in designing the language, its user base, the flexibility of usage, role which the language plays to offer good job opportunities, major technical dimensions, to select a tool.
Development process
R was developed over many years by the Academics and statisticians and presently has a rich ecosystem for the data analysis. 12000 packages can be availed in the CRAN repository. Additionally, there are libraries for all types and categories of analysis. This makes R the ideal option for statistical analysis, taking care of specialized analytical work.
The major difference between R and the other products is the output, based on the finest tools, for communicating the results. Rstudio has a library knitr making reporting simple and elegant. Python serves as an effective tool used for deploying and implementing machine learning. It can do the tasks of data wrangling, engineering, as well as feature selection web scrapping, and app similar to R. Python codes are easy to maintain and are comparatively much robust than R. Presently, Python facilitates machine learning the professionals can do data science jobs through Python libraries: Scipy, Numpy, Pandas, Scikit-learn and Seaborn.
In Python, replicability and accessibility are comparatively easier than R. When the results of analysis are needed in the application or website, opt for Python.
A measure of popularity
In the Popularity index also as per the IEEE Spectrum rank, quantifying the popularity of programming languages in 2017, Python held the top place and R was at the sixth place. Besides, when we consider Job Opportunity as a long-term trend Python is quoted more in job description in comparison to R. on the other hand when we consider data analysis jobs, there is a priority for R. A survey has revealed that Python users tend to be more loyal than those who use R.
Relative features
Python is at a bit of disadvantage, as it is not mature enough presently, to deal with econometrics and communication. Python is ideal for Machine Learning integration as well as deployment.
R is a dynamic answer to statistical problems, data science and machine learning, owing to a powerful communication library that can manipulate matrix or code the algorithms. Also, there are packages to do data mining, time series analysis, and panel data.
The learning part and its impact
For the beginners in data science, who are keen on learning the working of algorithm and deploying the model, it is good to learn Python first. The professional already knowing the algorithm or going into the data analysis R and Python both are good to start with. For dealing with statistics, (deployment and reproducibility), Python must get the priority. R serves well for in writing a report and for creating a dashboard.
A feature common to both Python and R is the vast software ecosystems, plus the communities, making them suitable for performing all data science tasks. The transition from Java or C++ to Python is comparatively easier than in the case of R.
In R, people without any prior programming or data science experience, can become more productive, quickly, in comparison to Python. For the professionals with a programming experience, Python is advantageous. On the contrary when employees do not have a background in data science or programming R has an advantage.
Areas of excelling are different
Python Excels in deep learning research, but in R, much of the statistical modeling research can be conducted. Python scores well over R, in deploying models with other pieces of software. In R, dashboard creation is easy, by using Shiny, allowing persons with lesser technical experience in creating and publishing dashboards and also sharing them with their colleagues.
Also, there is an exchange of good and workable ideas between Python programmers and R programmers. The better idea thus can gain currency and become popular in this scenario. Furthermore, we must note that there is fine language interoperability between these two. Many features present in a language are accessible from the other language.
Conclusion
If more persons in the office use one of these speaking the same language helps and we can close, by saying that the choice between R and Python, mainly depends on mission objectives, the time available for investing, and the tool mostly used by the company/industry. The factors of employee background, the issues you work with besides the culture of an industry impact on the decision to choose any of these. When the colleagues use the same programming language, there are benefits of sharing the code, with them and maintaining a simple software process.
