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1、R 的数据分析制霸以及企业级应用盘点随着大数据被更多的企业采用，大数据分析算法编写和生产语言也得到了广泛的关注。而在不知不觉中，开源统计语言 R 已基本成为大数据科学家和开发者的必备技能。在所有编程语言和技巧中，人气急剧上升。通过与大数据工具整合，R 提供了大数据集的深度统计能力，包括统计分析以及数据驱动的可视化等。而在金融、药物、媒体及销售这些可直接从数据中获取决策的行业中，R 更得到了深度应用。根据 Rexer Analytics 2013 年对数据挖掘专业人员的调查显示， R 已经成为当下最流行的统计分析工具，至少有 70%被调查者表示有使用过 R 语言 。而在企业市场，R 的受欢迎程度
2、同样如此，多个公司和项目都使用 R，并将其提供给大数据科学家和业务用户，其中包括了微软的云计算 Azure Machine Learning、IBM 的 Big R、Teradata Aster R、Oracle R Enterprise、PivotalR 的 Big Data R 发行版、SAP 的 R for HANA 等，下面做简要分析：搭载了 R 的 Azure Machine Learning。微软在 Azure ML 中提供了 R 语言的 API 和模板，支持了 300 多个使用 R 语言的包，同时用户不用从头做起，Azure ML 允许开发者使用已有的部分来组装适合自己需求的模型
3、。这样做无疑降低了机器学习的使用门槛，让各种背景的数据科学家都可以使用。IBM InfoSphere BigInsights Big R。Big R 是一组功能库，提供了终端到终端的 R 与InfoSphere BigInsights 集成。Big R 可以被用于 InfoSphere BigInsights 服务器上的数据综合分析，降低亲自编写 MapReduce 作业的复杂性，让用户回归常见的 R 语法和范例。Teradata Aster R。Teradata Aster R，通过放宽内存和处理能力限制条件，扩展开源 R语言分析能力。针对 R 语言分析师， Aster R 开发出他们熟悉的
4、 R 语言和工具，并提供强大的处理能力及丰富的分析方法，其主要分为 3 个组件：“Aster R Library”预置 100 余种R 语言功能；“Aster R Parallel Constructor”拥有超过 5500 个 R 语言分析工具包；“Aster SNAP Framework 集成”将开源 R 语言引擎完全整合至 Teradata Aster 无缝网络分析处理框架。Oracle R Enterprise。Oracle R Enterprise 主要提供了该公司 RDBMS 以及 Exadata 设备的 in-database 分析能力。PivotalR。PivotalR 是一个
5、允许 R 用户与 Pivotal （Greenplum）Database 以及 Pivotal HD（用于大数据分析）交互的包，在类似 R 的界面为数据科学家提供 in-database 和 in-Hadoop 计算。HAWQ 是 Pivotal HD Hadoop 技术的核心，通过支持 R 语言，提供了Dynamic Pipelining、世界级的查询优化器、纵向扩展、SQL 依从、交互式查询、深度分析以及常用的 Hadoop 格式。SAP 将 R 与 HANA 集合。SAP 整合了 R 语言和他们的内存数据库 HANA，形成一个服务于移动、分析、数据服务和云集成服务的新平台，SAP 通过
6、Rserve（与 R Server 的通信器）实现了这个功能。因为使用了列存储，HANA 能够与 R 效率的交换数据，SAP 通过预封装快速部署解决方案来简化用户的操作。英语原文：Big Data needs drive R as a powerful enterprise ready languageAs Big Data continues to reach larger enterprise adoption, the programming languages that support writing schema and producing Big Data analysis alg
7、orithms will rush to keep up. As a result, the open source statistical language R has become a go-to skill for Big Data scientists and developers, with its popularity soaring amid languages and skills.Combined with Big Data tools, the R language provides a deep statistical handle for large data sets
8、, conducting statistical analysis, and rendering data-driven visualization. R is particularly widely used in the industries of finance, pharmaceuticals, media and marketing, where it can be used to help guide data-driven business decisions.The popularity of R has grown significantly in recent years.
9、 A 2013 survey of data mining professionals conducted by Rexer Analytics indicated that the R programming language is by far the most popular statistical analysis tool, with 70% of respondents saying they use it at least occasionally. Developers interested in learning more about R can look into trai
10、ning on the subject to get a better grasp of its use in the Big Data paradigm.In the enterprise market numerous companies and projects have risen to harness R and bring it to Big Data scientists and business users alike. These projects and tools include the use of R in Microsofts cloud computing Azu
11、re Machine Learning platform, IBMs Big R, Teradata Aster R, Oracle R Enterprise, PivotalRs Big Data R distribution, and SAPs R for HANA.Azure Machine Learning is a game changer with RMicrosoft last month announced the launch of its new platform Azure Machine Learning (ML). It is a platform dedicated
12、 to cloud predictive analytics on large volumes of data. Azure MLs cloud service allows scientists and developers to effectively integrate predictive analytics data into their applications.What is interesting is that Microsoft is providing APIs and templates based on the R language. Azure ML support
13、s more than 300 packages using the R programming language; and allows users to assemble a model suited to their needs built out of existing pieces rather than forcing developers to build something from scratch. The ease of implementation makes machine learning accessible to a larger number of invest
14、igators with various backgroundseven non-data scientists.Microsoft says the Azure ML platform can predict future trends in systems such as with search engines, online recommendation, ad targeting, virtual assistants, demand forecasting, fraud detection, spam filters and more.IBM integration with Big
15、 RIBM InfoSphere BigInsights Big R is a library of functions that provides end-to-end integration with the R language and InfoSphere BigInsights. Big R can be used for comprehensive data analysis on the InfoSphere BigInsights server, lowering some of the complexity of manually writing MapReduce jobs
16、.Big R provides an end-to-end integration of R within IBM InfoSphere BigInsights. This makes it easy to write and execute R programs that operate on big data. Using Big R, an R user can explore, transform, and analyze big data hosted in a BigInsights cluster using familiar R syntax and paradigm.Teradata Aster RThe rapid adoption of R and its proven value means that organizations looking to drive new revenue-generating insights should make R a part of their predictive analytics st
