Programmimg language comparisons

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Features of 3 popular data science languages (R/Python/Julia)

	R	Python	Julia
General	a statistical language and also used for graphical techniques.	a general-purpose language for development and deployment.	a general purpose language but many of its features are well-suited for numerical analysis and computational science.
Ease of use	Easy to start with. It has simpler libraries and plots.	Learning python libraries can be a bit complex.	Easy to start with
Type of programming	Supports only procedural programming for some functions and object-oriented programming for other functions.	"A multi-paradigm language, supporting object-oriented structured, functional and aspect-oriented programming.	A multi-paradigm language, combining features of imperative, functional, and object-oriented programming.
Available Libraries/Packages (June 2020)	more than 15K	more than 200K	around 3K
Use	developed for data analysis, hence has more powerful statistical packages Easy to use for complicated mathematical calculations and statistical tests Better for visualization Can be used for Deep Learning	good for building something new from scratch Used for application development Python’s statistical packages are less powerful Better for Deep Learning	developed for scientific use Can be used for Deep Learning
Speed	Slower than Python	Faster than R but not much	Faster than both R and Python - very fast
Popularity	Less popular although it has many users	More popular	Has less users than R and Python

References

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