R at Microsoft
Revolutions Milestones in AI, Machine Learning, Data Science, and visualization with R and Python since 2008 I was honoured to be the presenter at last night's meeting of SURF, the 11th largest R user group worldwide. The topic of my presentation was "R at Microsoft", where I described how Microsoft has enthusiastically embraced R for internal use (such as for Azure capacity planning, and Xbox gam
Julia入門
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Revolution Analytics joins Microsoft
Revolutions Milestones in AI, Machine Learning, Data Science, and visualization with R and Python since 2008 by David Smith, Chief Community Officer On behalf of the entire Revolution Analytics team I am excited to announce that Revolution Analytics is joining forces with Microsoft to bring R to even more enterprises. Microsoft announced today that it will acquire Revolution Analytics. Now, Micros
「R for Cloud Computingã€ã®ç´¹ä»‹
最高ã®çµ±è¨ˆã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ã¯ã©ã‚Œã‹ï¼Ÿ "What’s the Best Statistical Software? A Comparison of R, Py...
R言語ã§çµ±è¨ˆè§£æžå…¥é–€ï¼š 目次1　テクニカルデータプレゼンテーション　 梶山　喜一郎
Technical Data presentation in R コピペã§å¦ã¶ Rã§ãƒ†ã‚¯ãƒ‹ã‚«ãƒ«ãƒ‡ãƒ¼ã‚¿ãƒ—レゼンテーション １．基礎統計解æžç·¨ グラフィックス・リテラシï¼æ•™è‚²: ã€Œå›³å¦ I ãƒ»å›³å½¢æƒ…å ± I ・統計å¦ã€ç§‘目　修了後ã®ã‚³ãƒ¼ã‚¹ã‚¦ã‚§ã‚¢ ç¦å²¡å¤§å¦å·¥å¦éƒ¨å›³å¦æ•™å®¤ã€€ã€€ã€€æ¢¶å±±ã€€å–œä¸€éƒŽ ・ã¤ã¾ã¿é£Ÿã„ã§ï¼Œå¦ç¿’ã—ãªã„よã†ã«é¡˜ã„ã¾ã™ï¼Ž ・データã®å¯è¦–化を体系・系統ã ã£ãŸã‚¹ã‚ルã«ã™ã‚‹ãŸã‚ã«é †ã‚’追ã£ã¦å¦ç¿’ã™ã‚‹ï¼Ž ・統計ブームã«ä¹—ã£ã¦ã„ã‚‹å¦ç¿’者も先人ã«æ„Ÿè¬ã®æ°—æŒã¡ã‚’．ã•ã‚‰ã«ï¼Œ ・確ã‹ãªã‚¹ã‚ルã«ã™ã‚‹ãŸã‚ã«ï¼Œæ•™ç§‘書・解説書をç†è§£ã—，Rスクリプトã§ç¢ºèªï¼Ž A.　ã¯ã˜ã‚ã«--ã“ã“ã¯çµ±è¨ˆãƒ»è§£æžã®å¿…è¦ã‚’味ã‚ã£ãŸå¾Œã§èªã‚ã°ã‚ˆã„ ã¾ãšï¼Œçµ±è¨ˆã®æ‰‹ç¶šãを実行ã™ã‚‹ï¼Žæ…£ã‚ŒãŸã‚‰çµ±è¨ˆçš„ã«è€ƒãˆã‚ˆã†ï¼Ž å¦æ ¡ã®çµ±è¨ˆå¦ã‚’復習--è²·ã£ãŸæ•™ç§‘書ã¨ãƒŽãƒ¼ãƒˆã‚’ã¾ãŸèªã‚€ã ã‘ a. 測定ã¨å°ºåº¦ã€€Measurement and scale b. 記述統計å¦ã®
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R AnalyticFlow ウェブサイトã«ç§»å‹•ã—ã¾ã™ã€‚ 自動的ã«ç§»å‹•ã—ãªã„å ´åˆã¯ リンクをクリックã™ã‚‹ã‹ã€ãƒ–ラウザã®ã‚¢ãƒ‰ãƒ¬ã‚¹ãƒãƒ¼ã« 直接 "r.analyticflow.com/ja/" ã¨å…¥åŠ›ã—ã¦ãã ã•ã„。
無料統計ソフトEZR (Easy R)
******** Click here for English version ******** EZRè«–æ–‡ã®å¼•ç”¨å›žæ•°ãŒ2023/11/29ã«1万回を超ãˆã¾ã—ãŸ!! 2024å¹´6月30日　EZR version 1.68公開 ã€é‡è¦ã€‘ EZR Ver 1.60ã‹ã‚‰ãƒãƒ¼ã‚¸ãƒ§ãƒ³ 4.20以é™ã®Rを使用ã—ã¦ã„ã¾ã™ã€‚Rã¯4.20ã‹ã‚‰æ¨™æº–ã®æ–‡å—コードã¨ã—ã¦UTF-8を使用ã™ã‚‹ã‚ˆã†ã«ãªã£ãŸãŸã‚ã€å¾“æ¥ã®Windowså½¢å¼ã®æ—¥æœ¬èªžã‚’å«ã‚€ãƒ•ã‚¡ã‚¤ãƒ«ãŒã†ã¾ãèªã¿è¾¼ã‚ãªã„å ´åˆãŒã‚ã‚Šã¾ã™ã€‚対応方法ã«ã¤ã„ã¦ã¯FAQã‚’å‚考ã«ã—ã¦ãã ã•ã„。ã¾ãŸã€32ビット版ã¯å»ƒæ¢ã•ã‚Œã¾ã—ãŸã€‚ EZRã®ã‚°ãƒ©ãƒ•ã®ã‚µãƒ³ãƒ—ル (棒グラフã€Waterfall plotã€ç´¯ç©ç™ºç”ŸçŽ‡ã®ç©ã¿ä¸Šã’グラフ サブグループ解æžã®forest plotã€Swimmer plotã€current survival CONSORTダイアグラムã€ã‚µãƒ³ã‚ーダイアグアム)
Java R Interface (JRI) を用ㄠ統計解æžç’°å¢ƒ R ã‚’ Java ã‹ã‚‰ä½¿ç”¨ã™ã‚‹ - hamadakoichi blog
統計解æžãƒ»ãƒ‡ãƒ¼ã‚¿ãƒžã‚¤ãƒ‹ãƒ³ã‚°ç’°å¢ƒã® R ã‚’ Java ã‹ã‚‰ä½¿ç”¨ã™ã‚‹æ–¹æ³•ã‚’以下ã«ç´¹ä»‹ã™ã‚‹ã€‚ ç¾åœ¨ã€Rã‚’Javaã§ä½¿ç”¨ã™ã‚‹æ–¹æ³•ã«é–¢ã™ã‚‹æ–‡çŒ®ã¯éžå¸¸ã«å°‘ãªã„ãŒå‚考サイトをã¾ã¨ã‚ãŸã‚‚ã®ã‚‚下部ã«è¨˜è¿°ã™ã‚‹ã€‚ rJava ã¨ã„ã†Packageã«å«ã¾ã‚Œã¦ã„ã‚‹ Java R Interface (JRI) を用ㄠR ã‚’Javaã‹ã‚‰å‘¼ã³å‡ºã™ã“ã¨ãŒã§ãる。 ※rJava: Download: http://www.rforge.net/rJava/files/ SVN: svn://svn.rforge.net/rJava/ ※以下ã€Eclipse 3.4.2ã®ç’°å¢ƒã‚’例ã«èª¬æ˜Žã™ã‚‹ã€‚ ※ã¾ãŸã€Eclipse㧠R言語環境自体を使ã†æ–¹æ³•ã¯R ã‚’ Eclipse ã§ä½¿ã†ã‚’å‚照。 統計解æžç’°å¢ƒ R ã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ãƒ»ç’°å¢ƒè¨å®šï¼š 統計解æžç’°å¢ƒ R 自体ã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ãƒ»ç’°å¢ƒè¨å®šã¯R ã®ã‚¤ãƒ³ã‚¹ãƒˆãƒ¼ãƒ«ãƒ»ç’°å¢ƒè¨å®šã‚’å‚照。 rJava
((Rã§) 書ã ((ã‚‚ã£ã¨Rã£ã½ã„) Lisp) インタプリタ)
((Rã§) 書ã (Lisp) インタプリタ)ã®ç¶šãã§ã™ã€‚ 以å‰ã®è¨˜äº‹ã‚’見ãŸ@kohskeã•ã‚“ã‹ã‚‰ã€ 環境ã¯Rã®ç’°å¢ƒã‚ªãƒ–ジェクトを使ãˆã°è‰¯ã„ã®ã§ã¯ã€ã¨ã‚¢ãƒ‰ãƒã‚¤ã‚¹ã‚’é ‚ãã¾ã—ãŸã€‚ ã‚„ã£ã¦ã¿ãŸã‚‰è‡ªå‰ã§ç’°å¢ƒã‚’実装ã—ãªãã¦è‰¯ã„分簡潔ã«ãªã£ãŸã—ã€ã‚ˆã‚Šé¢ç™½ã„使ã„æ–¹ãŒã§ãるよã†ã«ãªã‚Šã¾ã—ãŸã€‚ コード å‰å›žåŒæ§˜Gistã«ä¸Šã’ã¦ãŠãã¾ã—ãŸã€‚ 変ãªã¨ã“ã‚ãŒã‚ã£ãŸã‚‰æ•™ãˆã¦ãã ã•ã„。 主ãªå¤‰æ›´ç‚¹ã¯ Rã®ç’°å¢ƒã‚ªãƒ–ジェクトをæµç”¨ Lispã®é–¢æ•°ã®å‘¼ã³å‡ºã—方法を変更: proc(exps) ã‹ã‚‰ do.call(proc, exps) ã« éŠã³æ–¹ å‰å›žã¨åŒæ§˜ã§ã™ã€‚ Rインタプリタを起動ã—ã¦ã‚³ãƒ¼ãƒ‰ã‚’èªã¿è¾¼ã¾ã›ã¾ã™ã€‚ repl() を実行ã™ã‚‹ã¨Lispã®å¯¾è©±å¼ã‚¤ãƒ³ã‚¿ãƒ—リタãŒèµ·å‹•ã—ã¾ã™ã€‚ $ ls lispr.R $ R -q > source("lispr.R") > repl() lispr> (+ 1 2) 3 lisp
Post by @things-about-r
Last quarter was a bit too busy to write some new blog post because of a new job. And changing the job often come along with changing the tools you work with. That was my way to Tableau. Tableau is one of the new stars in the BI/Analytics world and definitely worth a look. The people at Tableau describe their tool as an instrument that combines interactive visual analytics, sophisticated visualiza
pbdR - Programming with Big Data in R
pbdR - Programming with Big Data in R
Introduction of stan
This document provides an introduction and overview of Stan, a programming language for Bayesian statistical modeling and inference. It discusses Stan's motivation as a faster alternative to BUGS that compiles models to C++. Key points covered include: - How Stan models are specified using blocks like data, transformed data, parameters, model, and generated quantities. - Stan's support for scalar,
VOYAGE GROUP　エンジニアブãƒã‚° : Rを使ã£ã¦ãŠçµµæã（アンパンマン方程å¼ï¼‰
2011å¹´09月16æ—¥13:03 カテゴリRãƒã‚¿ Rを使ã£ã¦ãŠçµµæã（アンパンマン方程å¼ï¼‰ ã“ã‚“ã«ã¡ã¯ã€ECナビã®æ°´è¶Š(@Akiyah)ã§ã™ã€‚ Rã§ãŠçµµæãã®å®Œçµç·¨ã§ã™ã€‚アンパンマン方程å¼ã¨ã„ã†ã‚‚ã®ã‚’作ã£ã¦ã¿ã¾ã—ãŸã€‚ ã“ã®æ–¹ç¨‹å¼ã‚’解ãã¨ã€ã“ã†ãªã‚Šã¾ã™ã€‚ アンã€ã‚¢ãƒ³ã€ã‚¢ãƒ³ãƒ‘ãƒ³ãƒžãƒ¼ãƒ³ï¼ Rã®ã‚³ãƒ¼ãƒ‰ã¯ã“ã‚“ãªæ„Ÿã˜ã§ã™ã€‚library(rgl) # パッケージã®å‘¼ã³å‡ºã— open3d() # デãƒã‚¤ã‚¹ã®èµ·å‹• rgl.bg(color="#808080") rgl.light(theta = 5, phi = 5) x <- seq(-110, 110, length = 880) y <- x anpanman <- function(x,y) { s(((x )/85)^2+((y )/85)^2-1) * # face s(((x-55)/24)^2+((y +5)/24)^2-1) * # c
Renjin | The JVM-based interpreter for the R language for statistical computing
Renjin is a JVM-based interpreter for the R language Renjin was developed between 2010 and 2021 but is no longer actively maintained. Read more about the project here or browse the source code on GitHub. Blog archive Integrating R and Java (2019-08-16) How to integrate R and Java in three steps using Renjin. RIOT 2019: The role of compilers in the Renjin Project (2019-07-18) At RIOT 2019, Alex rec
7 Command-Line Tools for Data Science | Jeroen Janssens
Blog article by Jeroen Janssens. Sep 19, 2013. 21 min read. Data science is OSEMN (pronounced as awesome). That is, it involves Obtaining, Scrubbing, Exploring, Modelling, and iNterpreting data. As a data scientist, I spend quite a bit of time on the command-line, especially when there’s data to be obtained, scrubbed, or explored. And I’m not alone in this. Recently, Greg Reda discussed how the cl
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