The R vs Python debate is one of the most common but important questions asked by lots of data scientists. We know that R and Python both are open source programming languages. Both of these languages have a large community. Apart from that, these languages are developing continuously. Hence these languages add new libraries and tools in their catalog continuously. The major purpose of using R is for statistical analysis, while Python provides a more general approach to data science. Both languages can be used for state of the art data science. Python on one hand is one of the simplest programming languages based on its easy to read syntax.That’s why any beginner in a programming language can learn Python without putting extra efforts. On the other hand, R is built by statisticians, so it is a little bit harder to master but very powerful when mastered. Infact, the world leading scientists use R for data analysis.
Which one is easier to learn?
When it comes to the learning curve of these languages, then R is quite hard to learn for the beginners. It requires lots of effort to start with R. But once you start with it, then you can polish your R programming skills with the help of its developer community. Apart from that, if you have the basic knowledge of programming, then you may not find it that difficult.
On the other hand, Python is one of the simplest programming languages with clean syntax. You can start with Python quickly if you have the basic knowledge of programming, then you will find it the most straightforward programming language. But if you are a beginner in programming, then it takes less time to learn Python than R although. It also has a large community that will help you to clear all your doubts. One active community is the R-Ladies Global that has a branch in Nairobi and other major global cities.
Now that we have look at the basic differences between R and python, let’s take a look at the difference between R and python in accounting data.
If you have a finance team or you are working in an accounting firm, a bank, or consulting team with lots of data; then one can easily compare these coding languages. R is better for writing customised functions, statistical applications, and it has standard libraries that can be utilised for statistical work. On the other side, python has its own standard libraries that are built for computations, with some extension of matrix algebra and natural language processing.
The general advice is to use R in more scientific and statistical geared projects and Python for more general processes. Or choose one and stick to it!
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