Is r language processing data slower than python?

What is R language?

R language, a free software programming language and operating environment, is mainly used for statistical analysis, graphics, and data mining. R was originally developed by Ross Ihaka and Robert Jetman from the University of Auckland, New Zealand (also called R), and is now developed by the "R Development Core Team". R is a GNU project based on the S language, so it can also be regarded as an implementation of the S language. Usually, codes written in the S language can be run in the R environment without modification. R's syntax is derived from Scheme. (Recommended learning: Python Video Tutorial)

The source code of R can be freely downloaded and used, and there are also compiled executable file versions available for download, which can be used on a variety of platforms Runs on UNIX (also FreeBSD and Linux), Windows and MacOS. R is mainly operated from the command line, and several graphical user interfaces have been developed.

R's functionality can be enhanced through user-written packages. Added capabilities include special statistical techniques, graphing capabilities, as well as programming interfaces and data export/import capabilities. These packages are written in R, LaTeX, Java and most commonly C and Fortran. The downloaded executable version will come with a batch of core functional software packages, and according to CRAN records, there are more than a thousand different software packages. Several of them are commonly used, such as for economic econometrics, financial analysis, humanities research, and artificial intelligence.

Common features of Python and R languages

Both Python and R have relatively professional and comprehensive modules in data analysis and data mining, including many commonly used functions, such as matrix operations, Vector operations and so on have relatively advanced usage

Python and R languages ​​​​are multi-platform adaptable, can be used on linux and window, and the code is highly portable

Python and R are closer to commonly used mathematical tools such as MATLAB and minitab

The difference between Python and R language

In terms of data structure, because it is from the perspective of science From a computing perspective, the data structures in R are very simple, mainly including vectors (one-dimensional), multi-dimensional arrays (matrix when two-dimensional), lists (unstructured data), and data frames (structured data). Python contains richer data structures to achieve more precise access to data and memory control, such as multi-dimensional arrays (readable, writable, ordered), tuples (read-only, ordered), sets (unique, unordered), and dictionaries. (Key-Value) and so on.

Python is faster compared to R. Python can directly process the data of G; R cannot. When R analyzes the data, it needs to convert the big data into small data through the database (through groupby) before it can be handed over to R for analysis. Therefore, it is impossible for R to directly analyze the behavior details. It can only Analyze statistical results.

#Python is a relatively balanced language that can be used in all aspects, whether it is calling other languages, connecting and reading data sources, or operating the system. , or regular expressions and word processing, Python has obvious advantages. And R is more prominent in statistics.

Python's pandas draws on R's dataframes, and R's rvest draws on Python's BeautifulSoup. The two languages ​​are complementary to a certain extent. Generally, we think that Python is better than R. It has more advantages in computer programming and web crawlers, while R is a more efficient independent data analysis tool in statistical analysis. Therefore, learning Python and R at the same time is the king of data science.

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