The R User Conference, User! 2017 July 4-7 2017 Brussels, Belgium
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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. -
Navigating the R Package Universe by Julia Silge, John C
CONTRIBUTED RESEARCH ARTICLES 558 Navigating the R Package Universe by Julia Silge, John C. Nash, and Spencer Graves Abstract Today, the enormous number of contributed packages available to R users outstrips any given user’s ability to understand how these packages work, their relative merits, or how they are related to each other. We organized a plenary session at useR!2017 in Brussels for the R community to think through these issues and ways forward. This session considered three key points of discussion. Users can navigate the universe of R packages with (1) capabilities for directly searching for R packages, (2) guidance for which packages to use, e.g., from CRAN Task Views and other sources, and (3) access to common interfaces for alternative approaches to essentially the same problem. Introduction As of our writing, there are over 13,000 packages on CRAN. R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use. At useR!2017 in Brussels, we organized a plenary session on this issue, with three themes: search, guidance, and unification. Here, we summarize these important themes, the discussion in our community both at useR!2017 and in the intervening months, and where we can go from here. Users need options to search R packages, perhaps the content of DESCRIPTION files, documenta- tion files, or other components of R packages. One author (SG) has worked on the issue of searching for R functions from within R itself in the sos package (Graves et al., 2017). -
Computational Statistics in Practice
Computational Statistics in Practice Some Observations Dirk Eddelbuettel 10 November 2016 Invited Presentation Department of Statistics University of Illinois Urbana-Champaign, IL UIUC Nov 2016 1/147 Motivation UIUC Nov 2016 2/147 Almost all topics in twenty-first-century statistics are now computer-dependent [...] Here and in all our examples we are employing the language R, itself one of the key developments in computer-based statistical methodology. Efron and Hastie, 2016, pages xv and 6 (footnote 3) UIUC Nov 2016 3/147 A View of the World Computational Statistics in Practice • Statistics is now computational (Efron & Hastie, 2016) • Within (computational) statistics, reigning tool is R • Given R, Rcpp key for two angles: • Performance always matters, ease of use a sweetspot • “Extending R” (Chambers, 2016) • Time permitting • Being nice to other (languages) • an underdiscussed angle in industry UIUC Nov 2016 4/147 Overview / Outline / Plan Drawing on three Talks • Rcpp Introduction (from recent workshops / talks / courses) • [if time] If You Can’t Beat ’em (from JSS session at JSM) • [if time] Open Source Finance (from an industry conference) UIUC Nov 2016 5/147 About Me Brief Bio • PhD, MA Econometrics; MSc Ind.Eng. (Comp.Sci./OR) • Finance Quant for 20 years • Open Source for 22 years • Debian developer • R package author / contributor • R Foundation Board member, R Consortium ISC member • JSS Associate Editor UIUC Nov 2016 6/147 Rcpp: Introduction via Tweets UIUC Nov 2016 7/147 UIUC Nov 2016 8/147 UIUC Nov 2016 9/147 UIUC Nov 2016 10/147 UIUC Nov 2016 11/147 UIUC Nov 2016 12/147 UIUC Nov 2016 13/147 UIUC Nov 2016 14/147 UIUC Nov 2016 15/147 UIUC Nov 2016 16/147 Extending R UIUC Nov 2016 17/147 Why R? : Programming with Data from 1977 to 2016 Thanks to John Chambers for high-resolution cover images. -
Frequently Asked Questions About Rcpp
Frequently Asked Questions about Rcpp Dirk Eddelbuettel Romain François Rcpp version 0.12.9 as of January 14, 2017 Abstract This document attempts to answer the most Frequently Asked Questions (FAQ) regarding the Rcpp (Eddelbuettel, François, Allaire, Ushey, Kou, Russel, Chambers, and Bates, 2017; Eddelbuettel and François, 2011; Eddelbuettel, 2013) package. Contents 1 Getting started 2 1.1 How do I get started ?.....................................................2 1.2 What do I need ?........................................................2 1.3 What compiler can I use ?...................................................3 1.4 What other packages are useful ?..............................................3 1.5 What licenses can I choose for my code?..........................................3 2 Compiling and Linking 4 2.1 How do I use Rcpp in my package ?............................................4 2.2 How do I quickly prototype my code?............................................4 2.2.1 Using inline.......................................................4 2.2.2 Using Rcpp Attributes.................................................4 2.3 How do I convert my prototyped code to a package ?..................................5 2.4 How do I quickly prototype my code in a package?...................................5 2.5 But I want to compile my code with R CMD SHLIB !...................................5 2.6 But R CMD SHLIB still does not work !...........................................6 2.7 What about LinkingTo ?...................................................6 -
Supplementary Materials
Tomic et al, SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses 1 Supplementary Materials: 2 3 Figure S1. Staining profiles and gating scheme of immune cell subsets analyzed using mass 4 cytometry. Representative gating strategy for phenotype analysis of different blood- 5 derived immune cell subsets analyzed using mass cytometry in the sample from one donor 6 acquired before vaccination. In total PBMC from healthy 187 donors were analyzed using 7 same gating scheme. Text above plots indicates parent population, while arrows show 8 gating strategy defining major immune cell subsets (CD4+ T cells, CD8+ T cells, B cells, 9 NK cells, Tregs, NKT cells, etc.). 10 2 11 12 Figure S2. Distribution of high and low responders included in the initial dataset. Distribution 13 of individuals in groups of high (red, n=64) and low (grey, n=123) responders regarding the 14 (A) CMV status, gender and study year. (B) Age distribution between high and low 15 responders. Age is indicated in years. 16 3 17 18 Figure S3. Assays performed across different clinical studies and study years. Data from 5 19 different clinical studies (Study 15, 17, 18, 21 and 29) were included in the analysis. Flow 20 cytometry was performed only in year 2009, in other years phenotype of immune cells was 21 determined by mass cytometry. Luminex (either 51/63-plex) was performed from 2008 to 22 2014. Finally, signaling capacity of immune cells was analyzed by phosphorylation 23 cytometry (PhosphoFlow) on mass cytometer in 2013 and flow cytometer in all other years. -
Rkward: a Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R
JSS Journal of Statistical Software June 2012, Volume 49, Issue 9. http://www.jstatsoft.org/ RKWard: A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with R Stefan R¨odiger Thomas Friedrichsmeier Charit´e-Universit¨atsmedizin Berlin Ruhr-University Bochum Prasenjit Kapat Meik Michalke The Ohio State University Heinrich Heine University Dusseldorf¨ Abstract R is a free open-source implementation of the S statistical computing language and programming environment. The current status of R is a command line driven interface with no advanced cross-platform graphical user interface (GUI), but it includes tools for building such. Over the past years, proprietary and non-proprietary GUI solutions have emerged, based on internal or external tool kits, with different scopes and technological concepts. For example, Rgui.exe and Rgui.app have become the de facto GUI on the Microsoft Windows and Mac OS X platforms, respectively, for most users. In this paper we discuss RKWard which aims to be both a comprehensive GUI and an integrated devel- opment environment for R. RKWard is based on the KDE software libraries. Statistical procedures and plots are implemented using an extendable plugin architecture based on ECMAScript (JavaScript), R, and XML. RKWard provides an excellent tool to manage different types of data objects; even allowing for seamless editing of certain types. The objective of RKWard is to provide a portable and extensible R interface for both basic and advanced statistical and graphical analysis, while not compromising on flexibility and modularity of the R programming environment itself. Keywords: GUI, integrated development environment, plugin, R. -
The Rockerverse: Packages and Applications for Containerisation
PREPRINT 1 The Rockerverse: Packages and Applications for Containerisation with R by Daniel Nüst, Dirk Eddelbuettel, Dom Bennett, Robrecht Cannoodt, Dav Clark, Gergely Daróczi, Mark Edmondson, Colin Fay, Ellis Hughes, Lars Kjeldgaard, Sean Lopp, Ben Marwick, Heather Nolis, Jacqueline Nolis, Hong Ooi, Karthik Ram, Noam Ross, Lori Shepherd, Péter Sólymos, Tyson Lee Swetnam, Nitesh Turaga, Charlotte Van Petegem, Jason Williams, Craig Willis, Nan Xiao Abstract The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software. Introduction The R community continues to grow. This can be seen in the number of new packages on CRAN, which is still on growing exponentially (Hornik et al., 2019), but also in the numbers of conferences, open educational resources, meetups, unconferences, and companies that are adopting R, as exemplified by the useR! conference series1, the global growth of the R and R-Ladies user groups2, or the foundation and impact of the R Consortium3. -
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. -
BUSMGT 7331: Descriptive Analytics and Visualization
BUSMGT 7331: Descriptive Analytics and Visualization Spring 2019, Term 1 January 7, 2019 Instructor: Hyunwoo Park (https://fisher.osu.edu/people/park.2706) Office: Fisher Hall 632 Email: [email protected] Phone: 614-292-3166 Class Schedule: 8:30am-12:pm Saturday 1/19, 2/2, 2/16 Recitation/Office Hours: Thursdays 6:30pm-8pm at Gerlach 203 (also via WebEx) 1 Course Description Businesses and organizations today collect and store unprecedented quantities of data. In order to make informed decisions with such a massive amount of the accumulated data, organizations seek to adopt and utilize data mining and machine learning techniques. Applying advanced techniques must be preceded by a careful examination of the raw data. This step becomes increasingly important and also easily overlooked as the amount of data increases because human examination is prone to fail without adequate tools to describe a large dataset. Another growing challenge is to communicate a large dataset and complicated models with human decision makers. Descriptive analytics, and visualizations in particular, helps find patterns in the data and communicate the insights in an effective manner. This course aims to equip students with methods and techniques to summarize and communicate the underlying patterns of different types of data. This course serves as a stepping stone for further predictive and prescriptive analytics. 2 Course Learning Outcomes By the end of this course, students should successfully be able to: • Identify various distributions that frequently occur in observational data. • Compute key descriptive statistics from data. • Measure and interpret relationships among variables. • Transform unstructured data such as texts into structured format. -
Dyalog User Conference Programme
Conference Programme Sunday 20th October – Thursday 24th October 2013 User Conference 2013 Welcome to the Dyalog User Conference 2013 at Deerfield Beach, Florida All of the presentation and workshop materials are available on the Dyalog 2013 Conference Attendee website at http:/conference.dyalog.com/. Details of the username and password for this site can be found in your conference registration pack. This website is available throughout the conference and will remain available for a short time afterwards – it will be updated as and when necessary. We will be recording the presentations with the intention of uploading the videos to the Internet. For this reason, please do not ask questions during the sessions until you have been passed the microphone. It would help the presenters if you can wait until the Q&A session that concludes each presentation to ask your questions. If your question can't wait, or if the presenter specifically states that questions are welcome throughout, then please raise your hand to indicate that you need the microphone. All of Dyalog's presenters are happy to answer specific questions relating to their topics at any time after their presentation. Scavenger Hunt Have fun and get to know the other delegates by participating in the scavenger hunt that is running throughout Dyalog '13. The hunt runs until 18:30 on Wednesday and prizes will be awarded at the Banquet. Team assignments, rules and the scavenger hunt list will be handed out at registration. Guests are welcome to join in as well as conference delegates. Good luck and happy hunting! For practical information, see the back cover If you have any questions not related to APL, please ask Karen. -
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.