Knowledge Cow Micro Classroom: R programming language in the era of big data

The TIOBE December 2014 programming language rankings show that the R programming language is affected by big data, the industry is sought after, the market share once climbed to the top 12, and last year 38, R language is expected to be the candidate for this year's TIOBE year language.

R language is God horse East? Learn about the cow Micro class with you.

The first glimpse of R language

The R language is used for statistical analysis, drawing of the language and operating environment. R is a free, free, source-code software that belongs to the GNU system and is an excellent tool for statistical computing and statistical mapping.

R is a branch of the S language widely used in the field of statistics, which was born around 1980. You can think of R as an implementation of the S language. The S language is an interpretive language developed by the T-Bell Laboratory for data exploration, statistical analysis and mapping. The original implementation version of the S language was mainly s-plus. S-plus is a commercial software that is based on the S language and is further perfected by the Department of Statistical Science of Mathsoft Corporation. Later Robert Gentleman and Ross Ihaka and other volunteers at Auckland University developed an R system. Developed by the R Development core team. There are many similarities between the use of R and S-plus, the two languages have some compatibility. S-plus Manual, as long as a slight modification can be used as a manual of R. So some people say: R, is a "clone" of S-plus.

R is a GNU project based on the S language, and code written in the S language can be run without modification in the R environment. The syntax for r is from scheme. R language is open source, for all people is completely free, free to use, R language source code hosted on GitHub, can be run in multiple operating systems, such as Windows, Linux and UNIX.

R Language Resources:

• Home: http://www.r-project.org

• cran:http://cran.r-project.org

• Domestic Mirror:

• http://mirror.bjtu.edu.cn/cran/

• http://mirrors.ustc.edu.cn/CRAN/

• http://mirror.lzu.edu.cn/CRAN/

• http://mirrors.xmu.edu.cn/CRAN/

R Advantages and Features

R is a complete set of data processing, calculation and mapping software systems, providing a wide range of statistical analysis and mapping technology environment: including linear and non-linear models, statistical testing, time series, classification, clustering and other methods. , including a number of statistical procedures and a strong variety of mathematical calculations, statistical calculation function library, users can simply specify a database and a number of parameters for a statistical analysis, but also flexible data analysis, to create a new statistical calculation method to meet the needs.

From the development history of R language, R is mainly the language that statisticians develop to solve the problem of data analysis, so R has some unique advantages:

• Efficient data storage and processing system;

• Operators with a complete array of arrays and matrices (especially powerful vectors, matrix operations), statisticians and cutting-edge algorithms that cover almost the entire statistical field (3700+ expansion pack);

• A complete and coherent statistical analysis tool;

• High-quality, extensive statistical analysis, data mining platform

• Repetitive analysis Work (Sweave = r + Latex), with the powerful analytical ability of R language + latex perfect typesetting ability, can automatically generate analysis reports;

• Excellent statistical drawing, drawing function, drawing has the quality of printing, but also can add mathematical symbols;

• A fairly perfect, concise and efficient programming language: can manipulate the input and output of data, can realize branching, looping, user-definable functions;

• R language is a completely object-oriented statistical programming language;

• There is a good interface between r language and other programming languages and databases;

• Open source code (free, in both senses), can be deployed in any operating system, such as Windows, Linux, Mac OS X, BSD, Unix strong community support

• Ease of extensibility

• The database can be connected via the appropriate interface, such as Oracle, DB2, MySQL

• Cross-tune with Python, Java, C, C + + and other languages

• API interface can be called, such as Google, Twitter, Weibo

• Most other statistical software can call R, such as SAS, SPSS, Statistica, etc.

• Even some of the more straightforward business applications, such as Oracle R Enterprise, IBM Netezza, R add-on for Teradata, SAP HANA, Sybase RAP

The functions of R can be enhanced by the user-written suite. Added features include special statistical techniques, drawing functions, as well as programming interfaces and data output/input functions. These packages are written by the R language, LaTeX, Java, and the most commonly used C language and Fortran. Several of them are more commonly used, such as economic metrology, financial analysis, humanities research, and artificial intelligence.

R is more open than Matlab

• R is free software, MATLAB is commercial software;

• R can be easily extended through the "package", the core of R is only 25 packages, but there are thousands of external packages can be called, of course, you can also develop their own;

• R language is more powerful than MATLAB;

• There is a good interface between r and other programming languages/databases, and other languages can easily invoke R's API and result objects.

• R is commonly used in the fields of finance and statistics. Most people use r because of its statistical function, and R's interior implements a lot of classical or fashionable statistical techniques.

Effect Demo

With a simple example, make R look more intuitive.

Enter the following command in the console of R:

> install.packages (' quantmod ') # Install QUANTMOD Package

> Require (QUANTMOD) #引用quantmod包

> Getsymbols ("GOOG", src= "Yahoo", from= "2013-01-01", to= ' 2013-04-24 ') #从雅虎财经获取google的股票数据

> chartseries (goog,up.col= ' red ', dn.col= ' green ') #显示K线图 > ADDMACD () #增加MACD图

will be able to see the effect of:

Knowledge Cow Micro Classroom: R programming language in the era of big data