The difference between Python and R

When I decided to start developing my skills and knowledge in Data Science, I was confused if I should start learning Python or R! Since then, I was browsing the web to understand the differences.

Purpose

Python is general-purpose language designed by programmers to do everything a programmer wants to do. It is designed to make it easy and to real projects filled with lots of code (bigger projects). while R does statistics well. In addition, Python emphasizes productivity and code readability while R focuses on better, user friendly data analysis, statistics and graphical models.

Used By

Python is used by programmers that want to delve into data analysis or apply statistical techniques, and by developers that turn to data science. The closer you are to working in an engineering environment, the more you might prefer python. R has been used primarily in academics and search. However, R is rapidly expanding into the enterprise market. The closer you are to statistics, research and data science, the more you might prefer R.

Community

For Python, overall good support for general purpose coding. Python support is found at: Stack overflow, Mailing lists, and user- contributed code and documentation. More adoption from developers and programmers. yet, for R, huge community with coming in the form of: Mailing lists, user contributed documents, and Active stack overflow members. More adaption from researchers, data scientists and statistics.

Usability

Coding and debugging is easier to do in Python, mainly because of the "nice" syntax. The indentation of the code affects its meaning. Any piece of functionality is always written in the same way in Python Statistical Models can be written with only a few lines There are R style sheets but not everyone uses them. The same piece of functionality can be written in several ways in R.

Flexibility

Python is flexible for doing something novel that has never been done before. Developers can also use it for scripting a website or other applications. In R, it is easy to use complex formulas where all kinds of statistical tests and models are readily available and easily used.

Ease of Learning

Python's focus on readability and simplicity makes that its learning curve is relatively low and gradual. Python is considered a good language for starting programmers. R has a steep learning curve at start. Once you know the basics, you can easily learn advanced stuff. R is not hard for experienced programmers.

Usage

Python is generally used when the data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database. R is mainly used when the data analysis tasks require standalone computing or analysis on individual servers.

Improvement

Python is evolving, large improvement while R is staying pure, the step for R is not as large or revolutionary.

In summary, we can make the best of both languages as many data scientists already do by using Python in the first stage of data aggregation and processing. Then the data is fed into R, which applies the well-tested, optimized statistical analysis routines. Python is a full-service, imperative language which makes the processing easier. But this doesn’t mean you can’t use R if you need to clean data.

For me, I decided to start learning Python and follow it with R after reaching a good level in Python. What about you?