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This article mainly shows you "what are the famous open source analysis tools on the Internet", the content is simple and clear, and I hope it can help you solve your doubts. Let me lead you to study and learn the article "what are the famous open source analysis tools on the Internet".

Famous open source analysis tools include r language, python, Julia and so on. R language is an excellent tool for statistical calculation and statistical mapping; Julia is an advanced and efficient programming language designed for technical computing; python is an interpretive, object-oriented high-level programming language, which can be used in scientific computing, data mining and so on.

The operating environment of this tutorial: windows7 system, Dell G3 computer.

Famous open source analysis tools include r language, python, Julia and so on.

Julia is an advanced and efficient programming language designed for technical computing (technical computing), and its syntax is similar to that of other computing environments. Designed for distributed computing and parallelism, it is best known for its proximity to the high efficiency of the C language.

In the words of the developer, "We want this open source language to be as fast as C … as general as Python, as simple as R to do statistics, as convenient as Perl for text processing, as powerful as Matlab's linear algebra, and as capable of linking programs together as Shell."

Python was designed by Guido Van Rosum of the Dutch Society for Mathematical and computer Science Research in the early 1990s as an alternative to a language called ABC. Python provides efficient advanced data structures and simple and effective object-oriented programming. Python syntax and dynamic typing, as well as the nature of the interpreted language, make it a programming language for scripting and rapid development of applications on most platforms. With the continuous update of versions and the addition of new features of the language, it is gradually used for the development of independent and large-scale projects.

The Python interpreter is easy to extend and can extend new functions and data types in C or C++ (or any other language that can be called through C). Python can also be used for extension program languages in customizable software. Python's rich standard library provides source code or machine code for all major system platforms.

Python syntax is very simple, and has a wealth of modules and packages, can be used for scientific computing, data mining and deep learning and so on.

R language is a language and operating environment for statistical analysis and drawing. R is a free, free and open source software belonging to GNU system. It is an excellent tool for statistical calculation and statistical mapping.

As a statistical analysis software, R integrates statistical analysis and graphic display. It can run on UNIX, Windows and Macintosh operating systems, and embeds a very convenient and practical help system. Compared with other statistical analysis software, R has the following characteristics:

R is free software. This means that it is completely free and open source. You can download any relevant installer, source code, package and its source code and documentation from its website and its mirror image. The standard installation file itself comes with many modules and embedded statistical functions, which can directly realize many commonly used statistical functions after installation.

R is a programmable language. As an open statistical programming environment, the grammar is easy to understand, and it is easy to learn and master the language grammar. And after learning, we can write our own functions to expand the existing language. This is why it updates much faster than general statistical software, such as SPSS, SAS and so on. Most of the latest statistical methods and techniques are available directly in R.

All R functions and data sets are stored in the package. Only when a package is loaded can its contents be accessed. Some common and basic packages have been included in the standard installation files, and with the emergence of new statistical analysis methods, the packages contained in the standard installation files are constantly changing with the update of the version. In the other version of the installation file, the packages that have been included are: base-R basic module, mle- maximum likelihood estimation module, ts- time series analysis module, mva- multivariate statistical analysis module, survival- survival analysis module and so on.

R is highly interactive. Except that the graphic output is in another window, its input and output windows are all in the same window. If there are errors in the input syntax, you will be prompted in the window immediately, and you can remember the previously entered commands. You can reproduce, edit and modify at any time to meet the needs of users. The output graphics can be directly saved to JPG, BMP, PNG and other picture formats, but also can be directly saved as PDF files. In addition, there is a good interface with other programming languages and databases.

If you join R's help mailing list 1, you may receive dozens of email messages about R every day. You can discuss various issues with the world's first-class experts in statistical computing, which can be said to be the gathering place for the thinking of the largest and most cutting-edge statisticians in the world.

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