r language is what _r language basic tutorial

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

The R language is an open source data analysis environment that was originally created by digital statisticians to better perform statistical calculations and plotting. This wiki contains some basic information. Since R can be enhanced by installing Packages, its functionality is far from being limited to statistical analysis.

As a statistical analysis software, R is a combination of 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: 1.R is free software. This means it's completely free and open source. You can download any relevant installer, source code, package and its source code, documentation on its website and its image. The standard installation file itself has many modules and embedded statistical functions, which can be directly implemented to implement many common statistical functions.

2.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 grammar of the language. And after learning, we can build our own functions to extend the existing language. This is why it is updated much faster than general statistical software such as SPSS, SAS, etc. Most of the latest statistical methods and techniques are available directly in R.

3. All R functions and data sets are stored in the package. Only when a package is loaded can its contents be accessed. Some common, basic packages have been included in the standard installation files. With the advent of new statistical analysis methods, the packages included in the standard installation files are constantly changing as the version is updated. In the other version of the installation file, the included packages 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 many more.

4.R is very interactive. In addition to the graphic output is in another window, its input and output windows are all in the same window. If there is an error in the input syntax, it will be prompted in the window mouth immediately. It has a memory function for the previously entered commands. Reproduce, edit and modify to meet the needs of users. The output graphics can be saved directly to JPG, BMP, PNG and other image formats, and can also be saved directly as PDF files. In addition, there is a good interface with other programming languages ​​and databases. [2] 5. If you join R's help mailing list 1, you may receive dozens of emails about R every day. It can be discussed with experts in the world's leading statistical computing experts. It can be said that it is the gathering place of the world's largest and most cutting-edge statisticians.

R is a GNU project based on S language, so it can also be used as an implementation of S language. Codes written in S language can be run in R environment without modification. The syntax for R is from Scheme. The use of R has many similarities with S-PLUS, and there is some compatibility between the two languages. The S-PLUS manual can be used as a manual for R with minor modifications. So some people say: R, is a "clone" of S-PLUS. But don't forget: R is free (R is free). The R language source code is hosted on github. The specific address can be found in the reference material.

The R language download can be found by mirroring the CRAN.

The R language has a download address for the domain name .cn, and there are six, two of which are provided by Datagurn, provided by the University of Science and Technology of China. R language Windows version, which is provided by Datagurn and USTC by two download locations.

Before continuing with this tutorial, you should have a basic understanding of computer programming terminology. A basic understanding of any programming language will help you understand the R language programming concept and move quickly on the learning track

This tutorial is designed for software programmers, statisticians, and data miners who are looking to develop statistical software using R programming. If you are trying to understand the R programming language as a beginner, this tutorial will give you enough to understand almost all the concepts of the language, from where you can put your own higher level of expertise.

Maybe you want to say, "I have learned spss/sas/stata...., why should I learn R?" The following aspects may attract you:

R is free and open source software: Many academic journals now have copyright requirements for analytics software, and free analysis tools can make you worry about this. On the other hand, if there is a new method of data analysis in academia, it will take a long time to appear in commercial software. But the benefit of open source software is that someone will soon write this method as an extension, or you can do it yourself.

Command line work: Many people like SPSS menu-like operations, which is easy for beginners to get started with, but for data analysis, command-line operations are more flexible and easier to program and automate. And the command line operation will be easier to play cool, isn't it, the average person sees you get an analysis result after madly knocking the code, the eyes you cast will be different.

Small and exquisite: R language installation package is smaller, about 40M, compared to several other big guys it is very small and fine. At present, the R language is very popular among professionals. According to the survey of the data mining contest winners, the tools they use are basically R language. In addition, it can be learned from the recent R language conferences that the consulting industry, the financial industry, and the pharmaceutical industry are all using R language, and large companies including google/facebook are using it. Therefore, learning R language must be helpful for your career development.

The R language installation package can be downloaded from the official website, and the windows version can be directly clicked on this connection.

It is easier to install R under ubuntu. Run the following command in the terminal.

Sudo apt-get update

Sudo apt-get install r-base

In addition, when learning R language, it is strongly recommended to install Rstudio as the graphical interface of R. There is a brief introduction about the blog post before Rstudio. Click here to go to its official website.

Learning R is not a very easy thing, what beginners need to remember is:

Type the code yourself and understand its meaning

Write down some key points or insights in your notes (personally recommend Evernote)

Persist in practice, application analysis of data on the opponent's side

Understanding the background, the details are important.

1. Official website http://cran.csdb.cn/index.html (official literature focus)

2. Statistics of the Forum

3. NPC Economic Forum - R sub-forum (free information is also a lot)

4.http://library.nu/ This is the most online e-book place, there is a special language bookcase in R language (also known as a shelves)

5. Summary of the textbook on R language

6. The author posted a book on verycd

7. A well-known foreign R language group blog http://

8. Show all kinds of drawing of R language http://addictedtor.free.fr/graphiques/

I also have some information about the R language in my blog: xccds1977.blogspot.com (requires FQ)

If you have some simple getting started questions, you can also follow me twitter on Twitter: @xccds

The goal of this series is to provide beginners with the most concise and clear information to get started quickly. The readers targeted are those who are studying elementary statistics at the university. The series includes: basic commands, data manipulation; description statistics and mapping; important R language function calculations; statistical inference and estimation; nonparametric statistical methods; analysis of variance; linear regression and general linear models.

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