Is being a python developer better or is R better? Are python coders more in demand than R developers? Which language is a better choice to learn and advance your career in? Which developer should you choose for your business? In this article we will clear the confusion by making a comparison between Python and R.
What is Python?
Python being a general purpose high-level programming language is designed to make the code more readable and the syntax simple. Learning python is easy and there are tons of free source material to learn it from.
Python has many libraries for tasks like data processing and data analysis etc,. With functionalities like easy debugging, versatility to code anything, efficiency to write and understand code to name a few among many, is the prime reason why many python developers choose python over R.
Related Post: Python vs JavaScript for data science
What is R?
R is graphics, quantitative analysis, and statistical processing focused open-source programming language and environment. Data analysis, data processing, statistical computing, graphical representation of data are its primary uses among many others.
R has many libraries and packages that contribute to its wide range of applications which is why many data scientists and statisticians rely on it.
The language also has data structures list, matrix, vector, factors, and data frames. It is suitable for tasks like intensive computations, graphs representation and manipulation, and a variety of statistical operations.
Data Science: Python vs R
What is data science
If you are a python developer or R programmer, or if you know a little about coding, then chances are you already know about data science. For those of you who do not, here an overview.
Data science is an amalgam of mathematics, statistics, complex programming, and analytics that then relies on artificial intelligence and machine learning to get the required information from a data set. The data collected is often unorganized, which is then cleaned, organized, and labeled. This data is then fed to a piece of code that uses it to learn patterns and differences. This is the basics of data science.
Python vs R
From beginner to skilled and polished developers rely on python for data science, statistics, software development and other tasks. According to this report more than 85% of developers globally use python for data science. After that R was the second most popular programming language being used.
The credit for python being so popular goes to its syntactic sugar and its wide range of uses. For instance, python has thousands of libraries and packages like keras, pandas, tensorflow etc and it can perform complex web scraping which R cannot. However, R has a very specific use case which statistations, mathematicians, and for specific tasks data scientists use.
Which is the better choice?
Hiring python programmers or R developers depends on your needs and circumstances. Similarly, if you are a developer then choosing between R and Python is heavily dependent on what you choose it for.
Python is preferred due to its wide range of applications and easy to understand syntax. Data scientists prefer it due to scalability and the wide range of libraries and tools available. On the other hand R is preferred due to its ability to process huge amounts of data and its ease of use for data visualization and ability to perform complex operations on large data sets quickly.
Wrap up
Choosing Python and R have their own advantages and pitfalls. While choosing one, consider what you would do with it. Mostly beginner developers go for python because of the ease but professional data scientists chose R over python because of its analytical abilities and dealing with raw data.
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