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A QUESTION AS OLD AS TIME
R or Python? Or both? This question may seem quite outdated for experienced developers but for beginners who are simply unsure, this blog is for you.
R and Python have become two of the most popular programming languages for data scientists and data analysts due to the enormous rise in the importance of Big Data in the software industry.
So what works best for Data Science? Keep reading to find out.
Before being tempted to just give a biased opinion straight away, let’s first understand both Python and R and their key differences.
What is Python?
Headphones, Zero Bugs, and Python are a few of the many favorite things of developers.
Python is a widely-used programming language for computers that are used to build websites and applications, automate procedures, and do data analysis.
It’s a multipurpose, object-oriented programming language that places a strong emphasis on the accessibility of its code.
Python enables object-oriented programming, functional programming, and structured programming (OOP). For creating complex applications, it can be transcribed into byte code or deployed as a scripting language. High-level dynamic data types are defined, and dynamic type verification is possible.
Pros
- Easy to Learn
- Flexible
- Supportive Community
- Extensive Library
Cons
- Time Consuming
- Runtime Errors
- Inefficient Memory Consumption
- Weak for Mobile Devices
What is R?
R has recently been setting a trend in Business World
R is a widely used open-source programming language for statistical computing and data analysis. R typically includes a command-line interface. It is accessible on popular operating systems like Windows, Linux, and macOS. It is the newest cutting-edge technology.
The simplicity with which well-designed plots of publishing quality can be created using R, along with mathematical symbols and formulas where necessary, is one of its major strengths. The user still has complete control despite extensive consideration given to the graphics’ minor design choices.
Pros
- Open Source
- Platform Independent
- Array of Packages
- Continuously Growing
Cons
- Weak Origin
- Basic Security
- Complicated Language
- Lesser Speed
Key Difference Between R and Python
| Nos. | R | Python |
|---|---|---|
| 1 | R’s primary objectives are data analysis and statistics. | Python’s primary objectives are deployment and production. |
| 2 | R users are more likely to be academics and R&D experts. | Python users are typically programmers and developers. |
| 3 | R enables flexibility in the application of existing libraries. | Python gives the flexibility to create new models from scratch. |
| 4 | R is difficult to Learn | Python is easy to learn |
| 5 | R is integrated to operate locally. | Python is well-integrated with apps |
| 6 | R visualization tools ggplot2, plotly, ggiraph | Python visualization tools Matplotlib, bokeh, seaborn |
R vs Python for Data Science
Data science is an interdisciplinary area that uses information from data to guide a variety of applications by employing scientific methods, procedures, algorithms, and systems to extrapolate knowledge and insights from noisy, structured, and unstructured data.
Data scientists are concept leaders who use their proficiency in machine learning and statistics to extract valuable knowledge from data. Large datasets, such as social media posts, medical information, transactional data, and more, can be evaluated by them using a variety of techniques.
Python, SQL, C++, Java, R, and Perl are some of the computer languages that data scientists use for data mining, analysis, cleaning, processing, visualizing, indexing, and organizing.
Python is currently the most widely used coding language for data science.
The official Python repository, PyPi, is home to all required Python packages. R uses the CRAN (Comprehensive R Archive Network) repository in contrast. The tens of thousands of libraries and packages that are part of it can be used and improved by users.
Summary
In many ways, these two open-source languages resemble one another significantly. Both languages are available for download without charge and can be used for data science tasks such as processing data, automating data analysis, and doing research. The most crucial distinction would be that R is a statistical analysis tool, whereas Python is a general-purpose programming language.
Multiple factors affect which programming language is best. Python is preferred by software developers, web developers, systems administrators, and software engineers due to its widespread use and wide selection of libraries.
The R programming language is preferred by academics, researchers, scientists, and data engineers because it provides stunning graphics for data visualization and the ability to process massive amounts of data.
R might be a good fit for developers who are enthusiastic about the statistical computation and data visualization parts of data analysis. Python would be a better choice if, on the other hand, they are looking to work as data scientists and use big data, AI, and deep learning methods.
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Communication crafts is a trusted technology partner that offers a wide pool of Remote Web Developers for hire. We leverage cutting-edge technologies like Angular, React, and Vue for high-quality software development solutions that span many industries like banking & finance, food & drink, eCommerce, health & fitness, entertainment, photography, logistics, eLearning, and more.
Our experts are adept at building websites and mobile applications that help your business soar above the ever-growing digital competition. From concept creation to responsive design, avail top-tier developers from our pre-vetted talent pool.
