R vs Python- Which One is Dominant for Machine Learning and Data Science? | by eSparkBiz | Oct, 2022

Most data science or machine learning beginners are often confused about choosing the right programming language. Though R and Python are popular open-source and free programming languages, both have their own weaknesses and strengths. And as per an individual developer’s perspective, either of the languages may be more fitting than the other.

This post will dive into what exactly these two programming languages are and which one is better suited for machine learning and data science and individual use cases.

Created in 1991, Python is a general-purpose, high-level programming language. It’s popular for its easy-to-understand syntax. Python also makes code structuring easy because of the white spaces known as indentation. That, resultantly, helps write simple code results that are more readable than other programming languages.

Python is a multi-paradigm, object-oriented, and interpreted programming language. Dictionary, tuple, list, and set are some of the crucial data structures you will find in Python. Though Python was originally created to cater to the programming language needs in software development, data science features were included later.

Moreover, Python incorporates popular machine learning libraries such as Tensorflow, PyTorch, Keras, Matplotlib, Scikit-learn, Pandas, and Numpy. It is important to be familiar with these open-source Python projects and libraries if you want to be an efficient Python developer.

Python isn’t just suitable for data science or machine learning; it has many other applications, such as mobile app development, AI, web scraping, and more. Hence, it is because of these reasons that Python has a favourable stronghold and why many companies hire Python developers.

Created in 1993, R is a software environment and programming language for graphics, statistical computing, analysis, and data visualization. It offers an array of graphical and statistical techniques like non-linear and linear modelling, clustering, classification, time-series evaluation, statistical tests, etc.

You will get a few basic packages upon R installation. The remaining packages can be availed via the Comprehensive R Archive Network (CRAN) repository. R is an open-source, functional, and object-oriented programming language capable of managing complexity in large issues.

List, factors, data frames, array, matrix, and vectors are some crucial data structures you will find in R. The best aspect of R is that it can offer publication-quality graphs and charts, including various mathematical symbols.

A few R programming projects like Churn Prediction, Credit Card Fraud Prediction, and Loan Applications Classification can give you hands-on experience with R and help you learn and implement several machine learning algorithms to solve data science issues.

R and Python both have amazing data analysis abilities. However, in R, several of these functionalities are in-built. Meanwhile, in Python, you can use them via importing packages such as Numpy, random, math, etc.

R and Python both offer support for several file formats, such as text files, HTML, XML, JSON, CSV, and others. Additionally, SQL queries can be used in R and Python via supporting packages.

Matplotlib is the focus plotting library in Python. Seaborn is a library you can use as a wrapper over Matplotlib. These features are enough to develop incredible plots using Python.

On the other hand, numerous R packages are available for plotting, which you can install easily. You can make the same plot differently with the help of various plotting libraries in R, giving you ample choices.

Both R and Python are capable of developing incredible plots; however, R enjoys a favourable position over Python as it houses various plotting packages.

Python offers many machine-learning tools wrapped in a package known as Scikit-learn. Meanwhile, R has several small individual libraries specific to every machine-learning tool. Although R offers us several options, it’s not considered developer-friendly compared to Python.

As Python is object-oriented, it allows you to write robust and large-scale code more easily and seamlessly than R.

The points below might help us arrive at a fair scenario for using R or Python:

  • The in-built statistical features in R make it a better choice for data analysis.
  • The freedom to choose among the different plotting packages and develop publication-quality charts and graphs make R plotting friendly.
  • The ability to integrate code seamlessly with the remaining architecture does make Python production-ready.
  • The simple syntax and ease of using and importing machine learning packages make Python developer-friendly.

The option to choose between Python and R depends on the type of data scientist you aspire to become. R is ideal if you want to concentrate on probabilities and statistics. It has an enormous community of experienced statisticians that can help solve your queries.

However, if you wish to create apps that process a lot of data, then Python makes a great choice. It comes with a bigger developer ecosystem, and it is easier to locate people who want to collaborate on some project with you.

Though machine learning is an exciting field in the computer science domain, it needs extensive programming skills and knowledge. It’s not so simple to locate people who know programming and statistics well to develop app models. R offers an amazing environment to do such type of work. It is widely used, free, and offers a vibrant community.

On the other hand, Python has become a popular programming language for machine learning due to its enormous library ecosystem, diverse developer community, and simple syntax. That is one of the reasons why companies hire Python programmers to develop quick solutions without heavy infrastructure costs.

The easy availability of machine learning libraries in Python, like Matplotlib, Scikit-learn, and Pandas, helps create models from scratch much easier. In fact, Python was ranked as the most wanted programming language in Stack Overflow Survey 2018 for the second consecutive year, with developers globally implementing it extensively for machine learning projects.

There is no need to fret if you are still unable to decide between R and Python. Agreeing on just one programming language to meet your requirements can be hard sometimes. However, using a hybrid approach, you can avoid compromising on either of the languages.

Seeking the help of a professional who knows both languages will help you get the best of both worlds. You can utilize R to create appealing data visualizations in the initial data analysis phase. Later, using Python, you can create a production-ready model. Hence, it is a win-win situation as, that way, you don’t have to give up on one language over the other.

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