R and Python have the same programming features and are both tools commonly used by data scientists. In the field of
machine learning, approximately 69% of developers use Python, and another 24% use R. Both languages are open source, so both are free. However, Python is designed to be a widely applicable programming language, but R is used for statistical analysis.
Artificial intelligence and mathematical analysis are two hot areas of open source tool innovation. Both Python and R have created a good open source ecological environment, which is conducive to data scientists of all levels to complete scientific work more effectively.
The difference between
machine learning and
data analysis is relatively changing over time, but the main difference is that machine learning is biased towards model interpretation, while data analysis focuses on interpretation and factual speculation. In the growing voice of doubt, Python has gained a place in the field of machine learning. R is well-known in the field of data analysis as a language for fact inference and statistical inference.
This does not mean that these two languages must be divided into different fields-python is also sufficient to be used as a tool for data analysis, and R is also fully adaptable to complete important tasks in
machine learning. Each of these two languages has a large number of libraries trying to complete each other's functions. Python has libraries that can improve its remarkable inference ability, and R also has libraries that improve its prediction accuracy.
The next article will further discuss the details of the two languages, which will be very helpful for you to choose the most suitable programming language for your current project.
Python
Python was born in the 1980s and plays an important role in Google's internal framework. Python has a team of passionate designers, and it is now widely used in Youtube, Instagram, Quora, and Dropbox. Python has been widely used in the IT field, and its excellent performance in coordinating internal team work has also been recognized. Therefore, if you need a multi-functional programming language and a strong ecological environment maintained by the designer, Python will be your best choice.
Advantages of Python:
General programming language-Python is a better choice if your business does not need statistical functions. Such as building a website.
Stable learning progress-Python is a programming language that is easier to learn.
A large number of commonly used libraries-Python claims to have countless libraries that can be used to process data. For example, Scikit-learn contains tools for data mining and analysis. In addition, the incomparable structure and information processing functions provided by the Pandas design team can significantly improve development efficiency. If your team specifically requires the use of a unique feature in R, then RPy2 is a suitable choice.
Better integration-Generally, Python is better than R in any design scenario. Regardless of whether the designer may incorrectly use the underlying language such as C, C++, or Java, the Python wrapper can better integrate the various parts. In addition, it is not difficult for data researchers to use python-based construction to complete subsequent work.
Promote productivity-Python syntax is very easy to understand, and like other programming languages, it is not the same as R anyway. This ensures the efficient production of the development team.
Disadvantages of Python:
The lack of general repositories, some R libraries do not have corresponding python packages.
Due to dynamic combination, in some cases, it will search for a certain function and fall into this defect, accompanied by various tasks with data errors.
R
R was created by statisticians for data analysts, and any engineer can understand its syntax by looking at it. If you need to improve the understanding of details and develop creatively, then R is the right choice, because the scientific calculations contained in R are related to machine learning based on statistical analysis.
If your work requires a deeper understanding, then R is a very good choice. It can be used to continuously improve your understanding of the work, requiring only one call to the database. For example, if you want to analyze a corpus by splitting paragraph content into words or phrases to understand these examples, then R is your best choice.
Advantages of R
Suitable for data analysis-if data inspection or data presentation is very important to your business, then R will be your best choice, because it can quickly implement prototype development and design, and can build artificial intelligence/machines with data sets Learning model.
A large number of useful libraries and tools-Like python, R contains a large number of libraries to help companies using machine learning. For example, Caret's unusual features make it very efficient, which also improves R's capabilities in artificial intelligence. The data analysis library that is still being developed has brought huge advantages to R users. These data analysis libraries are not only comprehensive, but also focus on model certification and information presentation.
Suitable for exploratory tasks-if you need to do some exploratory work on model verification in the early stages of the project, then R will make the work easier, because engineers only need to write a few lines of code.
R's disadvantages
It is difficult to learn, and it is easy to write wrong code. Weak typing is dangerous, and functions have the habit of returning objects of unexpected types.
Compared with other programming languages, the uniqueness: the index of the vector starts from 1, not 0.
The syntax for solving some problems is not so obvious. Since R has a large number of libraries, some of the libraries that are not commonly used are not well documented.
in conclusion
For machine learning, Python and R have their own advantages, because they both have a large number of libraries.
If you can master these two languages well, you can become a master, because most of the functions that one language can accomplish can also be accomplished by the other language.
In addition, you can use Python as the data processing in the early stage, and then send the information to R for analysis and processing. R can provide a comprehensive and better data analysis program.
You can think of R as a library of Python, or think of Python as a library of R for preprocessing. Having mastered the advantages and disadvantages of python and R, you can now better choose a programming language that is most suitable for your current project.
Original link:
https://towardsdatascience.com/python-vs-r-which-is-good-for-machine-learning-ecfb87c7f8ca
