#author("2019-03-21T17:33:30+09:00","default:insider","insider")
#author("2019-03-21T17:36:09+09:00","default:insider","insider")
*2005年度統計数理研究所共同研究集会「データ解析環境Rの整備と利用」 [#x7283ecc]
(RjpWikiの「なんでも掲示板」(現在は閉鎖中)の過去ログから、間瀬先生の投稿を一部修正してコピペしたものです。中谷)

統計数理研究所の中野先生の御世話でRに関する講演会が下記の要領(一部変更の可能性あり)で開かれることになりました。特に二日目は RjpWiki でもおなじみの顔ぶれがほとんどの、実質的な R ユーザー会になりますので、御参加のほどよろしくおねがいします。参加は自由でもちろん無料です。

以下中野先生からのメイルを転載:

 みなさま
 
 フリーの統計解析システムRに関する下記の共同利用研究会を開催します。参加は自由で, 一日だけの参加も歓迎します。多くの方のご参加をお待ちしています。中野純司@統計数理 研究所
 
 平成17年度統計数理研究所共同利用研究集会
 「データ解析環境Rの整備と利用」
 プログラム
 開催場所:東京都港区麻布4-6-7 統計数理研究所新館2階研修室
 日程: 2005年12月9(金),10(土)日
 
 12月9日(金)  (使用言語:英語)
 Talks by Simon Urbanek (AT&T Research Labs),
            a member of the R core development team
 
 13:30-14:30 Talk 1:
    iPlots - Interactive Graphics for Data Analysis in R
 
 14:45-15:45 Talk 2:
    JGR - Java GUI for R and Java/R interfaces
 
 16:00-17:00
    Parallel computing in R - Top Trees models space exploration
 
       (それぞれの talk の abstract が下のほうにあります)
 
12月10日(土) (使用言語:日本語)
 
 10:00-10:30  舟尾 暢男 (武田薬品)
    R 初心者からみた R 初心者の現況
 10:40-11:10  中間 栄治 (COM-ONE)
    私的R利用方法
 11:20-11:50  岡田 昌史 (筑波大学)
    RjpWikiのこれまでとこれから
 12:00-13:30  昼食
 13:30-14:00 久保 拓弥 (北海道大学)
    生態学分野での R の使われかた紹介
 14:10-14:40 中澤 港 (群馬大学)
    医学統計教育におけるRの利用
 14:50-15:20  中野 純司 (統計数理研究所)
    統計数理研究所でのR
 15:30-16:00  間瀬 茂 (東京工業大学)
    R の S4 クラスとメソッド入門
 16:10-16:40  谷村 晋 (長崎大学)
    Thematic Cartography with R
 16:50-17:20  総合討論
 18:00-       懇親会(広尾近辺)

----- Abstracts of Simon's talks  ------------------------------

 Abstract of talk 1:
 
  R is a very flexible and powerful software for statistical computing.
 It extensibility is leveraged by hundreds of packages offering a wide
 variety of tools in many areas in applied sciences. However, the is
 one aspect that covered only partially and that is graphics. R
 produces high-quality static graphics for publication purposes, but
 it lacks interactive graphics for explorative data analysis.
 
  iPlots attempt to remedy this shortcoming by providing platform-
 independent interactive graphics for R. The iPlots package offers a
 wide range of fully interactive graphics including scatterplots,
 histograms, barchars, mosaic plots, parallel coordinates plots and
 many others. Moreover iPlots are based on a very flexible and
 extensible engine for creation of arbitrary interactive graphics: iBase.
 
  In this talk we will demonstrate the use of iPlots as a tool for
 exploratory data analysis and discuss the basic concepts of iPlots.
 We will also provide insights into the iBase framework for creating
 new interactive graphics. All this will demonstrated on practical
 real-word examples.
 
 Abstract of talk 2:
 
  R can be made accessible to a broader range of users by using
 graphical user interface (GUI) to provide additional support for the
 user. JGR - Java GUI for R is a cross-platoform GUI for R. It
 flattens the learning curve, is valuable for teaching statistics, but
 also includes features that will be welcome even by power-users.
 
  In this talk we will demonstrate various features of JGR, such as the
 intelligent console, code editor, object browser, help system and
 package installer. In addition we will discuss integration of iPlots
 (interactive graphics for R) and iWidgets (custom user interface
 elements in R) in JGR.
 
  We will also look behind the scenes and consider several R/Java
 interfaces, their differences and applications. Furthermore we will
 show how to integrate own Java code into R and use R function from
 within Java.
 
 Abstract of talk 3:
 
  For historical reasons, R is a single-threaded application. Nevertheless,
 in this talk we will show how can R be successfully
 leveraged for parallel computing. Most common approaches involve
 running several R processes using various kinds of inter-process
 communications.
 
  In this talk we will discuss several different methods of parallel
 computing in R. We will also show step by stop how parallel computing
 can be made easy using the snow package for R. In addition, we will
 present in detail a practical example of model space exploration of
 tree models that we performed using massive parallel computing.

トップ   編集 差分 履歴 添付 複製 名前変更 リロード   新規 一覧 検索 最終更新   ヘルプ   最終更新のRSS