Python vs R: What is the difference between Python and R languages

 

We have discussed some of the main differences in the Python vs R languages in this blog. Python and R are both good and popular choices. It is possible that certain factors may change your decision.

Python vs R

The Python and R programming languages are both open-source and have a large user community. Libraries and tools are continuously being added to their respective catalogs. While R is mainly used for statistical analyses, Python expands the scope of data science. Data science-oriented technologies include the Python and R programming languages. In order to be able to communicate effectively in both languages, it is necessary to learn them both. The learning process for Python and R takes more time, which isn't available to everyone.

What is Python?

There are many tasks that Python and R do that are similar: engineering, data wrangling, app development, etc. In addition to being a tool for high-scale machine learning, Python can also be used to implement data analysis. It is easier and more robust to maintain Python code as compared to R. Data analysis and machine learning libraries weren't abundant in Python before. Recently, Python has come out with cutting-edge APIs for Machine Learning and Artificial Intelligence.

What is the R language?

Over the past two decades, statisticians and academics have developed R. In any case, this language is a statistical one. Data analysis and development of statistical software are two of the key uses of R. There has been an increase in interest in data and data mining since then, and R is becoming increasingly popular. 

Key Difference between Python vs R

Speed and performance:

When it comes to making critical yet fast applications, Python outperforms R for making large data analyses. While R is slower than Python, it's still capable of dealing with large datasets.

Graphics and visualization:

A visual representation of data makes it easier to understand. R provides different packages to interpret data graphically. Although it can be slightly more complex than R, Python has libraries for visualizing data.

Deep Learning:

Machine learning and data science have been trending upwards due to the popularity of Python vs R. As compared to Python's many finely tuned libraries, R has a python-like interface like Keras. Neither language has a dearth of packages that are useful for deep learning.

Statistical correctness:

Furthermore, R is developed for data statistics, which is why R provides better libraries and support in this area. Python is the best choice for deploying and developing applications at that time. R implements a wide variety of graphical and statistical techniques, but for data analysis it relies on its large library of R functions. 

Unstructured Data:

Social media produce a lot of unstructured data. Data is unstructured in Python through PyPI, scikit-image, NLTK. Although R offers libraries for unstructured data analysis, they are less comprehensive than Python's. Although the Python and R languages are both capable of analyzing unstructured data, they do so in different ways.

Conclusion

It is hard to decide which language is better between Python and R, because both have advantages and disadvantages. R language isn't a complete failure just because Python seems to be more successful among data scientists. Statisticians can use R to analyze data and it is very good at this.