Differences Between R Programming vs Python
In this Topic, we are going to learn about R programming vs python which is the best with their amazing differences.
Terms – AI/ Machine learning/ algorithms/ R programming/ Python/ data science.
Please follow this Venn diagramArtificial Intelligence (AI) is a broader term and is a computer science branch that attempts to build machines which are capable of intelligent behavior.
Data Science deals in processes and systems to extract knowledge or useful insights (means meaningful data) from raw data (means unorganized) in various forms.
Machine Learning is nothing but learning from data over a period of time. This makes our computers act without being programmed explicitly. Machine learning is the branch of AI that works best with data science.
Algorithms are a set of rules that are followed when solving problems. Machine learning, algorithms take and use the data to perform calculations and find the desired results. It can be either simple or complex depends upon the complexity of data dealt with. The effectiveness of your algorithm depends upon how well it has been trained (means to what extent of scenarios is being tested).
R is a programming language and free software environment for statistical computing and graphics that is supported by the R Foundation for Statistical Computing. Source – Wikipedia
Python is an interpreted high-level programming language for general-purpose programming. Source – Wikipedia
Best Head to Head Comparison Between R Programming vs Python
Below Is The Top 10 Best Comparison Between R Programming vs Python
Top Key Differences Between R Programming vs Python
Best Comparison Table of R Programming vs Python
As we have already studied about the Amazing differences of R programming vs python. Now we are going to look at the best comparison table Between R Programming vs Python to know which is the best.
Machine learning is divided into 3 kinds of learning algorithms which are –
- Supervised Machine learning algorithms
- Unsupervised Machine learning algorithms
- Reinforcement Machine learning algorithms
The tools may vary as per the convenience based on usability, requirement, and availability but the algorithms will be the same and carried out in a different way.
|The Basis Of Comparison||R PROGRAMMING||PYTHON|
|Objective||Data analysis and statistics||Deployment and production|
|Users||R&D||Programmes and Developers|
|Flexibility||Easy to use a library(readily available)||Easy to construct new models (from scratch).|
|Integration||Runs locally on the systems||Well integrated with the app available|
|Task||Easy to get primary results||Good and easy to deploy algorithms|
|IDE||RStudio is the IDE to be installed||Spyder, Ipython, and Notebook|
|Packages and Library details||Tydiverse, ggplot2, caret, and zoo||Pandas, scipy, scikit-learn, Tensorflow, and caret are some of the most commonly used.|
|Disadvantages||Slow with a high learning curve. A user has to depend upon the libraries||The library quantities are not as much as compared to R|
The choice between R Programming vs Python depends upon the below criteria –
- The kind of problem you want to solve.
- What is the net cost of learning a language – it takes time to learn a new language that fits the problem one wants to solve.
- The kind of community tools used in your field.
- What other tools are available and how well they aligned with the commonly used tools within the organization.
- One should be clear as analysis and deployment are two different things.
- The time factor is also very crucial
This has been a useful guide to Differences Between R Programming vs Python here we have discussed the meaning of both R programming and Python with their Head to Head comparisons, Key differences, and Conclusions. You may also look at the following articles to learn more –