The R Jounral, December 2009
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Progress in the R Ecosystem for Representing and Handling Spatial Data
Journal of Geographical Systems https://doi.org/10.1007/s10109-020-00336-0 ORIGINAL ARTICLE Progress in the R ecosystem for representing and handling spatial data Roger S. Bivand1 Received: 9 October 2019 / Accepted: 8 September 2020 © The Author(s) 2020 Abstract Twenty years have passed since Bivand and Gebhardt (J Geogr Syst 2(3):307–317, 2000. https ://doi.org/10.1007/PL000 11460 ) indicated that there was a good match between the then nascent open-source R programming language and environment and the needs of researchers analysing spatial data. Recalling the development of classes for spatial data presented in book form in Bivand et al. (Applied spatial data analysis with R. Springer, New York, 2008, Applied spatial data analysis with R, 2nd edn. Springer, New York, 2013), it is important to present the progress now occurring in representation of spatial data, and possible consequences for spatial data handling and the statistical analysis of spatial data. Beyond this, it is imperative to discuss the relationships between R-spatial software and the larger open-source geospatial software community on whose work R packages crucially depend. Keywords Spatial data analysis · Open-source software · R programming language JEL Classifcation C00 · C88 · R15 1 Introduction While Bivand and Gebhardt (2000) did provide an introduction to R as a statistical programming language and to why one might choose to use a scripted language like R (or Python), this article is both retrospective and prospective. It is possible that those approaching the choice of tools for spatial analysis and for handling spatial data will fnd the following less than inviting; in that case, perusal of early chapters of Lovelace et al. -
A Word from Our 2006 Section Chairs
VOLUME 17, NO 1, JUNE 2006 A joint newsletter of the Statistical Computing & Statistical Graphics Sections of the American Statistical Association A Word from our 2006 Section Chairs PAUL MURRELL STEPHAN R. SAIN GRAPHICS COMPUTING I would like to begin by When Carey Priebe asked me highlighting a couple of to run for one of the section interesting recent offices a couple of years ago, I developments in the area of wasn’t exactly sure what I was Statistical Graphics. getting into. Now that I have There has been a lot of been chair for a couple of activity on the GGobi project months, I’m still not totally lately, with an updated web sure what I’ve gotten into. site, new versions, and The one thing I do know, improved links to R. I though, is that I’m very happy encourage you to (re)visit www.ggobi.org and see what to be involved in the section. they’ve been up to. There are a lot of very interesting things going on and, as always, a lot of opportunity for people who have an The third volume of the Handbook of Computational interest in statistical computing. Statistics, which is focused on Data Visualization, is scheduled for publication at the end of this year and Continues on Page 2.......... there will be a workshop as a satellite of Compstat 2006. This important volume will contain over 30 contributions and will provide a comprehensive overview of all areas of data visualization. Information about this project is available at gap.stat.sinica.edu.tw/ HBCSC. -
The Rattle Package September 30, 2007
The rattle Package September 30, 2007 Type Package Title A graphical user interface for data mining in R using GTK Version 2.2.64 Date 2007-09-29 Author Graham Williams <[email protected]> Maintainer Graham Williams <[email protected]> Depends R (>= 2.2.0) Suggests RGtk2, ada, amap, arules, bitops, cairoDevice, cba, combinat, doBy, e1071, ellipse, fEcofin, fCalendar, fBasics, foreign, fpc, gdata, gtools, gplots, Hmisc, kernlab, MASS, Matrix, mice, network, pmml, randomForest, rggobi, ROCR, RODBC, rpart, RSvgDevice, XML Description Rattle provides a Gnome (RGtk2) based interface to R functionality for data mining. The aim is to provide a simple and intuitive interface that allows a user to quickly load data from a CSV file (or via ODBC), transform and explore the data, and build and evaluate models, and export models as PMML (predictive modelling markup language). All of this with knowing little about R. All R commands are logged and available for the user, as a tool to then begin interacting directly with R itself, if so desired. Rattle also exports a number of utility functions and the graphical user interface does not need to be run to deploy these. License GPL version 2 or newer URL http://rattle.togaware.com/ R topics documented: audit . 2 calcInitialDigitDistr . 3 calculateAUC . 3 centers.hclust . 4 drawTreeNodes . 5 drawTreesAda . 6 evaluateRisk . 7 1 2 audit genPlotTitleCmd . 8 rattle_gui . 9 listRPartRules . 9 listTreesAda . 10 plotBenfordsLaw . 11 plotNetwork . 11 plotOptimalLine . 12 plotRisk . 14 printRandomForests . 16 randomForest2Rules . 17 rattle . 18 savePlotToFile . 19 treeset.randomForest . 19 Index 21 audit Sample dataset for illustration Rattle functionality. -
R Generation [1] 25
IN DETAIL > y <- 25 > y R generation [1] 25 14 SIGNIFICANCE August 2018 The story of a statistical programming they shared an interest in what Ihaka calls “playing academic fun language that became a subcultural and games” with statistical computing languages. phenomenon. By Nick Thieme Each had questions about programming languages they wanted to answer. In particular, both Ihaka and Gentleman shared a common knowledge of the language called eyond the age of 5, very few people would profess “Scheme”, and both found the language useful in a variety to have a favourite letter. But if you have ever been of ways. Scheme, however, was unwieldy to type and lacked to a statistics or data science conference, you may desired functionality. Again, convenience brought good have seen more than a few grown adults wearing fortune. Each was familiar with another language, called “S”, Bbadges or stickers with the phrase “I love R!”. and S provided the kind of syntax they wanted. With no blend To these proud badge-wearers, R is much more than the of the two languages commercially available, Gentleman eighteenth letter of the modern English alphabet. The R suggested building something themselves. they love is a programming language that provides a robust Around that time, the University of Auckland needed environment for tabulating, analysing and visualising data, one a programming language to use in its undergraduate statistics powered by a community of millions of users collaborating courses as the school’s current tool had reached the end of its in ways large and small to make statistical computing more useful life. -
Data Analysis in R
Data Analysis in R Course at a Glance This course will provide an introduction to reproducible data analysis with R (see Syllabus). Instructor Gabriel Baud-Bovy ([email protected] ) Credits: 5 Synopsis This course aims at giving to the student a methodology to analyze experimental results, from how to organize data to the writing of a report. It includes: an introduction to R an introduction to reproducible research with R examples of statistical analysis with R During this course, the student will have to analyze his own data and is expected to read before each course the material that will be made available on this page. The final grade will consist in the evaluation of a report demonstrating familiarity with the concepts and methods presented in the course. As an editor, the instructor will use Notepad++ (together with NppToR) on a Windows Machines but the student might use other ones (e.g., R studio, EMACS+ESS, Lyx, TexWork). The course will use Mardown as typesetting language. For those desirous to work with Latex and/or generate pdf, you will need to install also MikeTex. Syllabus Total of 15 hours Class 1: Case study, Reproducible research Class 2: R fundamental Class 3: Exploratory data analysis and graphical methods in R Class 4: Basic statistics Class 5: To be determined There will be a final examination decided by the instructor. Prerequisites The course assumes some familiarity with programming concepts and data structures (MATLAB, C/C++, Java or any other programming language). Contact the instructor if you have never programmed anything. -
A History of R (In 15 Minutes… and Mostly in Pictures)
A history of R (in 15 minutes… and mostly in pictures) JULY 23, 2020 Andrew Zief!ler Lunch & Learn Department of Educational Psychology RMCC University of Minnesota LATIS Who am I and Some Caveats Andy Zie!ler • I teach statistics courses in the Department of Educational Psychology • I have been using R since 2005, when I couldn’t put Me (on the EPSY faculty board) SAS on my computer (it didn’t run natively on a Me Mac), and even if I could have gotten it to run, I (everywhere else) couldn’t afford it. Some caveats • Although I was alive during much of the era I will be talking about, I was not working as a statistician at that time (not even as an elementary student for some of it). • My knowledge is second-hand, from other people and sources. Statistical Computing in the 1970s Bell Labs In 1976, scientists from the Statistics Research Group were actively discussing how to design a language for statistical computing that allowed interactive access to routines in their FORTRAN library. John Chambers John Tukey Home to Statistics Research Group Rick Becker Jean Mc Rae Judy Schilling Doug Dunn Introducing…`S` An Interactive Language for Data Analysis and Graphics Chambers sketch of the interface made on May 5, 1976. The GE-635, a 36-bit system that ran at a 0.5MIPS, starting at $2M in 1964 dollars or leasing at $45K/month. ’S’ was introduced to Bell Labs in November, but at the time it did not actually have a name. The Impact of UNIX on ’S' Tom London Ken Thompson and Dennis Ritchie, creators of John Reiser the UNIX operating system at a PDP-11. -
Uros2018.Pdf
Use of R in O cial Statistics 6th International Conference 2018 2018OV010 Eventbanner uRos2018 Rolbanner 100x200_DEF OPTIES .indd 1 23-7-2018 09:58:34 Eventbanner uRos2018 1920x400.jpg Eventbanner uRos2018 1920x400.bb Welcome The global community of R users is growing, and the number of Naonal and Interna- onal Stascal Offices that are adopng R is growing as well. About five years ago, when this conference was organized as an internaonal conference for the first me in Romania, we felt a bit like outlaws using Free and Open Source Soware (FOSS) in an area where commercial packages rule the land. How mes have changed: in the mean me FOSS, and in parcular R is considered a driving force of innovaon in academia, industry and government. The popularity of R is demonstrated by the hundreds of local R user groups, the thousands of R packages, and the RConsorum. The current conference, at Stascs Netherlands, marks the first occasion outside of the place where it was conceived: Romania. We are therefore especially pleased that our keynote speakers have roots in both countries. Alina Matei is a professor of stascs in Switzerland with Romanian roots. She will talk about opmal sample coordinaon using R. An important topic in mes where the reducon of response burden and increasing nonresponse rates force us to use smaller, more complex sampling methods. Not many R users are aware that there is a ‘touch of Dutch’ in R. Since 2017, Jeroen Ooms (UC Berkeley) is the maintainer of both Rtools and R for Windows. He will tell us about what it takes to compile, release, and modernize a system on which more than 12,500 R packages and millions of users rely every day. -
Statcharrms R Version Installation Guide
StatCharrms R Version Installation Guide 2014-07-14 Written and Programmed By: Joe Swintek, BTS Based off StatCharrms SAS version developed by: Dr. John Green, DuPont Applied Statistics Group, Stine-Haskell Research Center Additional Testing By: Kevin Flynn, USEPA Jon Haselman, USEPA Funded By: USEPA Under Contract EP-D-13-052 Installation StatCharrms is a graphical user interface front end for R, designed for ease of operation that performs the recommended statistical procedure used in the Medaka Extended One Generation Test (MEOGRT) and Larval Amphibian Growth and Development Assay (LAGDA). The statistical procedures implemented within StatCharrms are; the Rao-Scott adjusted Cochran-Armitage trend test by slices (RSCABS), a repeated measures ANVOA using time and treatment as fixed effects, Jonckheere-Terpstra trend test, Dunnett test, Kruskal Wallis, Dunns Test, one way ANOVA, weighted one way ANOVA, mixed effect ANOVA for imbalanced replicate structures, and a mixed effect Cox proportional model for imbalanced replicate structures. StatCharrms is implemented as an R workspace preloaded with the required functions. To Start StatCharrms double click on the R icon labeled StatCharrms-V##.RData. Now the installation of the required packages can begin by typing : Install.StatCharrms() into the R console and then hitting enter. R is case sensitive so you will need to type the command exactly as it is above. Figure one shows what is should look like. Executing the installation command will, by default, create a folder on the C drive called “RLib” that will contain the libraries needed for StatCharrms to run. Figure 1: Next a window asking to select CRAN (Comprehensive R Archive Network) mirror will popup. -
Conference Brochure S P E a K E R S
04.07.2017 – 07.07.2017 www.user2017.brussels @useR_Brussels #user2017 CONFERENCE BROCHURE S P E A K E R S 2 As a student my use of R for data analyses was frowned upon - the suggestion was to stick with the existing software. P Access to R related literature was difficult. To admire R the very first R books one had to travel far, to the few enlightened universities. As a researcher I needed the help E of a secret floppy disk to dual boot into a GNU/Linux system with R, in order to escape from unnamed products. As a F starter in industry, people from unnamed companies made clear to my management that setting up R events for customers was A giving a ‘wrong signal to the market’. C Look how ‘wrong’ we have been... Fifteen years later R is shining brightly in the data science field. Students are E learning R before anything else and if statistics research is published without accompanying R package, it is considered a bad sign. Publishers have hundreds of books on display and major software houses are in a constant race against time to further integrate R into their product offerings. And now, more than 1000 people from all over the world have come to Brussels to discover the state of R at this conference. R has come a great way and the community that formed around the R project is and has always been critical to its success. The community has evolved in many ways and is active in many places throughout the year, but of all things the official useR conference still is the major community event of the year. -
August 12-14, Dortmund, Germany
AASC Austrian Association for Statistical Computing 2008 Program August 12-14, Dortmund, Germany useR! 2008, Dortmund, Germany 1 Contents Greetings and Miscellaneous 2 Maps 5 Social Program 8 Program 9 Tutorials 9 Schedule 10 List of Talks 14 2 useR! 2008, Dortmund, Germany Dear useRs, the following pages provide you with some useful information about useR! 2008, the R user con- ference, taking place at the Fakultät Statistik, Technische Universität Dortmund, Germany from 2008-08-12 to 2008-08-14. Pre-conference tutorials will take place on August 11. The confer- ence is organized by the Fakultät Statistik, Technische Universität Dortmund and the Austrian Association for Statistical Computing (AASC). Apart from challenging and likewise exciting scientific contributions we hope to offer you an attractive conference site and a pleasant social program. With best regards from the organizing team: Uwe Ligges (conference), Achim Zeileis (program), Claus Weihs, Gerd Kopp (local organiza- tion), Friedrich Leisch, and Torsten Hothorn Address / Contact Address: Uwe Ligges Fakultät Statistik Technische Universität Dortmund 44221 Dortmund Germany Phone: +49 231 755 4353 Fax: +49 231 755 4387 e-mail: [email protected] URL: http://www.R-Project.org/useR-2008/ Program Committee Micah Altman, Roger Bivand, Peter Dalgaard, Jan de Leeuw, Ramón Díaz-Uriarte, Spencer Graves, Leonhard Held, Torsten Hothorn, François Husson, Christian Kleiber, Friedrich Leisch, Andy Liaw, Martin Mächler, Kate Mullen, Ei-ji Nakama, Thomas Petzoldt, Martin Theus, and Heather Turner Conference Location Technische Universität Dortmund Campus Nord Mathematikgebäude / Audimax Vogelpothsweg 87 44227 Dortmund Conference Office Opening hours: Monday, August 11: 08:30–19:30 Tuesday, August 12: 08:30–18:30 Wednesday, August 13: 08:30–18:30 Thursday, August 14: 08:30–15:30 useR! 2008, Dortmund, Germany 3 Public Transport The conference site at the university campus and the city hall are best to be reached by public transport. -
Sexy-Rgtk: a Package for Programming Rgtk2 GUI in a User-Friendly Manner
sexy-rgtk: a package for programming RGtk2 GUI in a user-friendly manner Damien Lerouxa and Nathalie Villa-Vialaneixa;b a INRA, UR875, MIAT F-31326 Castanet Tolosan - France [email protected] b SAMM, Université Paris 1 F-75634 Paris - France [email protected] Keywords: Gtk2, RGtk2, GUI There are many dierent ways to program Graphical User Interfaces (GUI) in R.[1] provides an overview of the available methods, describing ways to program R GUI with RGtk2, qtbase and tcltk. More recently, the package shiny, for building interactive web applications, was also released (the rst version has been published on December, 2012). The package RGtk2 [2] is probably one of the most complete packages to program complex and highly customizable GUI. It is based on GTK2 (the GIMP Toolkit, http://www.gtk. org/), which is a multi-platform toolkit for creating Graphical User Interfaces. GTK2 oers a complete set of widgets and can be used to develop complete application suites working on Linux, Windows and Mac OS X. Although very exible, each RGtk2 interface results in a long script that has a counterintuitive syntax for most R users. For instance, the simple window of Figure1 1 is obtained with the command lines provided in Figure2 (left). Figure 1: A simple GUI interface made with RGtk2. One attempt to overcome the diculty of the RGtk2 syntax is the package gWidgets but, quoting its reference manual The excellent RGtk2 package opens up the full power of the GTK2 toolkit, only a fraction of which is available though gWidgetsRGtk2. -
Pipenightdreams Osgcal-Doc Mumudvb Mpg123-Alsa Tbb
pipenightdreams osgcal-doc mumudvb mpg123-alsa tbb-examples libgammu4-dbg gcc-4.1-doc snort-rules-default davical cutmp3 libevolution5.0-cil aspell-am python-gobject-doc openoffice.org-l10n-mn libc6-xen xserver-xorg trophy-data t38modem pioneers-console libnb-platform10-java libgtkglext1-ruby libboost-wave1.39-dev drgenius bfbtester libchromexvmcpro1 isdnutils-xtools ubuntuone-client openoffice.org2-math openoffice.org-l10n-lt lsb-cxx-ia32 kdeartwork-emoticons-kde4 wmpuzzle trafshow python-plplot lx-gdb link-monitor-applet libscm-dev liblog-agent-logger-perl libccrtp-doc libclass-throwable-perl kde-i18n-csb jack-jconv hamradio-menus coinor-libvol-doc msx-emulator bitbake nabi language-pack-gnome-zh libpaperg popularity-contest xracer-tools xfont-nexus opendrim-lmp-baseserver libvorbisfile-ruby liblinebreak-doc libgfcui-2.0-0c2a-dbg libblacs-mpi-dev dict-freedict-spa-eng blender-ogrexml aspell-da x11-apps openoffice.org-l10n-lv openoffice.org-l10n-nl pnmtopng libodbcinstq1 libhsqldb-java-doc libmono-addins-gui0.2-cil sg3-utils linux-backports-modules-alsa-2.6.31-19-generic yorick-yeti-gsl python-pymssql plasma-widget-cpuload mcpp gpsim-lcd cl-csv libhtml-clean-perl asterisk-dbg apt-dater-dbg libgnome-mag1-dev language-pack-gnome-yo python-crypto svn-autoreleasedeb sugar-terminal-activity mii-diag maria-doc libplexus-component-api-java-doc libhugs-hgl-bundled libchipcard-libgwenhywfar47-plugins libghc6-random-dev freefem3d ezmlm cakephp-scripts aspell-ar ara-byte not+sparc openoffice.org-l10n-nn linux-backports-modules-karmic-generic-pae