R for Programmers Mastering the Tools

Unlike other books about R, written from the perspective of statistics, R for Programmers: Mastering the Tools is written from the perspective of programmers, providing a channel for programmers with expertise in other programming languages to quickly understand R. The contents are divided into four sections: The first section consists of the basics of R, which explains the advantages of using R, the installation of different versions of R, and the 12 frequently used packages of R. This will help you understand the tool packages, time series packages, and performance monitoring packages of R quickly. The second section discusses the server of R, which examines the communication between R and other programming languages and the application of R as servers. This will help you integrate R with other programming languages and implement the server application of R. The third section discusses databases and big data, which covers the communication between R and various databases, as well as R’s integration with Hadoop. This will help you integrate R with the underlying level of other databases and implement the processing of big data by R, based on Hadoop. The fourth section comprises the appendices, which introduce the installation of Java, various databases, and Hadoop. Because this is a reference book, there is no special sequence for reading all the chapters. You can choose the chapters in which you have an interest. If you are new to R, and you wish to master R comprehensively, simply follow the chapters in sequence.

BASICS OF RBasic R PackagesR Is the Most Worthwhile Programming Language to LearnInstallation of Different Versions of Rfortunes: Records the Wisdom of RUsing formatR to Format Codes AutomaticallyMultiuser Online Collaboration of R Development: RStudio ServerFoolproof Programming of R and JSONHigh-Quality Graphic Rendering Library of R CairoA Peculiar Tool Set: caToolsBasic Packages of Time SeriesBasic Time Series Library of R: zooExtensible Time Series: xtsVisualization of Time Series: plot.xtsPerformance Monitoring Packages of RLocal Cache Tool of R: memoisePerformance Monitoring Tool of R: RprofPerformance Visualization Tool of R: LineprofR SERVERCross-Platform Communication of RCross-Platform Communication between Rserve and JavaRsession Makes It Easier for Java to Call RHigh-Speed Channel between R and rJavaCross-Platform Communication between Node.js and RServer Implementation of RA Detailed Elaboration of the Server Program of R: RserveThe Client of Rserve in R: RSclientAn R Program Running on the Web: FastRWebBuilding a WebSocket Server by RDATABASE AND BIG DATADatabase and NoSQLProgramming Guidance for the RMySQL DatabaseConnecting R with NoSQL: MongoDBConnecting R with NoSQL: RedisConnecting R with NoSQL: CassandraConnecting R with NoSQL: HiveExtract Reverse Repurchase Information from Historical Data Using RHiveRHadoopR Has Injected Statistical Elements into HadoopInstallation and Use of RHadoopRHadoop Experiment: Count the Times of the Appearance of Certain E-Mail AddressesImplement the Collaborative Filtering Algorithm by RHadoop Based on MapReduceInstallation and Use of RHBaseSolve the Installation Error of RHadoop PipeMapRed.waitOutputThreads()AppendicesInstallation of a Java EnvironmentInstallation of MySQLInstallation of RedisInstallation of MongoDBInstallation of CassandraInstallation of HadoopInstallation of the Hive EnvironmentInstallation of HBase

Dan Zhang currently works at Qutke in the field of internet finance, leading a startup business of quantitative investment. As a programmer, he has worked in the area of program development for over ten years. Dan has developed mobile games as well as programming tools, and has worked on large Web application systems, internal CRM of companies, system integration of SOA, and big data tools based on Hadoop. Outsourcing, ecommerce, group purchase, payment, SNS and mobile SNS are all within his working range. He is familiar with four programming languages: R, Java, PHP, and JavaScript, and is knowledgeable of mass data storage, data analysis, and machine learning. His blog (http://blog.fens.me/) has a lot of R language original article.