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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). -
Other Departments and Institutes Courses 1
Other Departments and Institutes Courses 1 Other Departments and Institutes Courses About Course Numbers: 02-250 Introduction to Computational Biology Each Carnegie Mellon course number begins with a two-digit prefix that Spring: 12 units designates the department offering the course (i.e., 76-xxx courses are This class provides a general introduction to computational tools for biology. offered by the Department of English). Although each department maintains The course is divided into two halves. The first half covers computational its own course numbering practices, typically, the first digit after the prefix molecular biology and genomics. It examines important sources of biological indicates the class level: xx-1xx courses are freshmen-level, xx-2xx courses data, how they are archived and made available to researchers, and what are sophomore level, etc. Depending on the department, xx-6xx courses computational tools are available to use them effectively in research. may be either undergraduate senior-level or graduate-level, and xx-7xx In the process, it covers basic concepts in statistics, mathematics, and courses and higher are graduate-level. Consult the Schedule of Classes computer science needed to effectively use these resources and understand (https://enr-apps.as.cmu.edu/open/SOC/SOCServlet/) each semester for their results. Specific topics covered include sequence data, searching course offerings and for any necessary pre-requisites or co-requisites. and alignment, structural data, genome sequencing, genome analysis, genetic variation, gene and protein expression, and biological networks and pathways. The second half covers computational cell biology, including biological modeling and image analysis. It includes homework requiring Computational Biology Courses modification of scripts to perform computational analyses. -
Introduction to Ggplot2
Introduction to ggplot2 Dawn Koffman Office of Population Research Princeton University January 2014 1 Part 1: Concepts and Terminology 2 R Package: ggplot2 Used to produce statistical graphics, author = Hadley Wickham "attempt to take the good things about base and lattice graphics and improve on them with a strong, underlying model " based on The Grammar of Graphics by Leland Wilkinson, 2005 "... describes the meaning of what we do when we construct statistical graphics ... More than a taxonomy ... Computational system based on the underlying mathematics of representing statistical functions of data." - does not limit developer to a set of pre-specified graphics adds some concepts to grammar which allow it to work well with R 3 qplot() ggplot2 provides two ways to produce plot objects: qplot() # quick plot – not covered in this workshop uses some concepts of The Grammar of Graphics, but doesn’t provide full capability and designed to be very similar to plot() and simple to use may make it easy to produce basic graphs but may delay understanding philosophy of ggplot2 ggplot() # grammar of graphics plot – focus of this workshop provides fuller implementation of The Grammar of Graphics may have steeper learning curve but allows much more flexibility when building graphs 4 Grammar Defines Components of Graphics data: in ggplot2, data must be stored as an R data frame coordinate system: describes 2-D space that data is projected onto - for example, Cartesian coordinates, polar coordinates, map projections, ... geoms: describe type of geometric objects that represent data - for example, points, lines, polygons, ... aesthetics: describe visual characteristics that represent data - for example, position, size, color, shape, transparency, fill scales: for each aesthetic, describe how visual characteristic is converted to display values - for example, log scales, color scales, size scales, shape scales, .. -
Sharing and Organizing Research Products As R Packages Matti Vuorre1 & Matthew J
Sharing and organizing research products as R packages Matti Vuorre1 & Matthew J. C. Crump2 1 Oxford Internet Institute, University of Oxford, United Kingdom 2 Department of Psychology, Brooklyn College of CUNY, New York USA A consensus on the importance of open data and reproducible code is emerging. How should data and code be shared to maximize the key desiderata of reproducibility, permanence, and accessibility? Research assets should be stored persistently in formats that are not software restrictive, and documented so that others can reproduce and extend the required computations. The sharing method should be easy to adopt by already busy researchers. We suggest the R package standard as a solution for creating, curating, and communicating research assets. The R package standard, with extensions discussed herein, provides a format for assets and metadata that satisfies the above desiderata, facilitates reproducibility, open access, and sharing of materials through online platforms like GitHub and Open Science Framework. We discuss a stack of R resources that help users create reproducible collections of research assets, from experiments to manuscripts, in the RStudio interface. We created an R package, vertical, to help researchers incorporate these tools into their workflows, and discuss its functionality at length in an online supplement. Together, these tools may increase the reproducibility and openness of psychological science. Keywords: reproducibility; research methods; R; open data; open science Word count: 5155 Introduction package standard, with additional R authoring tools, provides a robust framework for organizing and sharing reproducible Research projects produce experiments, data, analyses, research products. manuscripts, posters, slides, stimuli and materials, computa- Some advances in data-sharing standards have emerged: It tional models, and more. -
Unified Framework for Development, Deployment and Robust Testing Of
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Boise State University - ScholarWorks Boise State University ScholarWorks Computer Science Faculty Publications and Department of Computer Science Presentations 3-1-2011 Unified rF amework for Development, Deployment and Robust Testing of Neuroimaging Algorithms Alark Joshi Boise State University Dustin Scheinost Yale University Hirohito Okuda GE Healthcare Dominique Belhachemi Yale University Isabella Murphy Yale University See next page for additional authors This is an author-produced, peer-reviewed version of this article. The final publication is available at www.springerlink.com. Copyright restrictions may apply. DOI: 10.1007/s12021-010-9092-8 Authors Alark Joshi, Dustin Scheinost, Hirohito Okuda, Dominique Belhachemi, Isabella Murphy, Lawrence H. Staib, and Xenophon Papademetris This article is available at ScholarWorks: http://scholarworks.boisestate.edu/cs_facpubs/5 Unified framework for development, deployment and robust testing of neuroimaging algorithms Alark Joshi · Dustin Scheinost · Hirohito Okuda · Dominique Belhachemi · Isabella Murphy · Lawrence H. Staib · Xenophon Papademetris Received: date / Accepted: date Abstract Developing both graphical and command- 1 Introduction line user interfaces for neuroimaging algorithms requires considerable effort. Neuroimaging algorithms can meet Image analysis algorithms are typically developed to their potential only if they can be easily and frequently address a particular problem within a specific domain used by their intended users. Deployment of a large (functional MRI, cardiac, image-guided intervention plan- suite of such algorithms on multiple platforms requires ning and monitoring, etc.). Many of these algorithms consistency of user interface controls, consistent results are rapidly prototyped and developed without consid- across various platforms and thorough testing. -
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. -
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. -
R Programming for Data Science
R Programming for Data Science Roger D. Peng This book is for sale at http://leanpub.com/rprogramming This version was published on 2015-07-20 This is a Leanpub book. Leanpub empowers authors and publishers with the Lean Publishing process. Lean Publishing is the act of publishing an in-progress ebook using lightweight tools and many iterations to get reader feedback, pivot until you have the right book and build traction once you do. ©2014 - 2015 Roger D. Peng Also By Roger D. Peng Exploratory Data Analysis with R Contents Preface ............................................... 1 History and Overview of R .................................... 4 What is R? ............................................ 4 What is S? ............................................ 4 The S Philosophy ........................................ 5 Back to R ............................................ 5 Basic Features of R ....................................... 6 Free Software .......................................... 6 Design of the R System ..................................... 7 Limitations of R ......................................... 8 R Resources ........................................... 9 Getting Started with R ...................................... 11 Installation ............................................ 11 Getting started with the R interface .............................. 11 R Nuts and Bolts .......................................... 12 Entering Input .......................................... 12 Evaluation ........................................... -
Simpleitk Tutorial Image Processing for Mere Mortals
SimpleITK Tutorial Image processing for mere mortals Insight Software Consortium Sept 23, 2011 (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 1 / 142 This presentation is copyrighted by The Insight Software Consortium distributed under the Creative Commons by Attribution License 3.0 http://creativecommons.org/licenses/by/3.0 What this Tutorial is about Provide working knowledge of the SimpleITK platform (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 3 / 142 Program Virtual Machines Preparation (10min) Introduction (15min) Basic Tutorials I (45min) Short Break (10min) Basic Tutorials II (45min) Co↵ee Break (30min) Intermediate Tutorials (45min) Short Break (10min) Advanced Topics (40min) Wrap-up (10min) (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 4 / 142 Tutorial Goals Gentle introduction to ITK Introduce SimpleITK Provide hands-on experience Problem solving, not direction following ...but please follow directions! (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 13 / 142 ITK Overview How many are familiar with ITK? (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 14 / 142 Ever seen code like this? 1 // Setup image types. 2 typedef float InputPixelType ; 3 typedef float OutputPixelType ; 4 typedef itk ::Image<InputPixelType ,2> InputImageType ; 5 typedef itk ::Image<OutputPixelType,2> OutputImageType ; 6 // Filter type 7 typedef itk ::DiscreteGaussianImageFilter< 8 InputImageType , OutputImageType > 9 FilterType ; 10 // Create a filter 11 FilterType ::Pointer filter = FilterType ::New (); 12 // Create the pipeline 13 filter >SetInput( reader >GetOutput () ); 14 filter−>SetVariance(1.0);− 15 filter−>SetMaximumKernelWidth(5); 16 filter−>Update (); 17 OutputImageType− ::Pointer blurred = filter >GetOutput (); − (Insight Software Consortium) SimpleITK - MICCAI 2011 Sept 2011 15 / 142 We are here to tell you that you can.. -
R: a Swiss Army Knife for Market Research
R: A Swiss Army Knife for Market Research Enhancing work practices with R Martin Chan – Consultant, Rainmakers CSI 12th September, 2018 1 2 This presentation is about using R in business environments or conditions where its usage is less intuitively suitable… … and a story of how R has been transformative for our work practices 2 3 Who are we? What do we do? 3 4 What How Where Answer strategic Estimate size of market, and size of Across multiple questions to help our opportunity, anticipating trends industries and clients grow profitably markets Utilise resources available within an Inform strategic decisions organisation (e.g. stakeholder knowledge, • FMCG through creative and existing research) • Finance & analytical thinking Insurance Develop consumer targeting frameworks – • Travel identify core consumers and areas of • Media opportunities 4 5 A team with a mix of backgrounds from strategy, brand planning, marketing, research… (where analytics is a key, but only one of the components of our work) 5 6 What’s different about the use of R in our work? 6 Challenges of Using R Nature of data: Nature of U&A Client 1 2 3 disparate, patchy data requirement and expectations (of outputs) U&A – research with the aim to understand a market and identify growth opportunities by answering questions on whom to target, with what, and how. (Source: https://www.ipsos.com/en/ipsos-encyclopedia- 7 usage-attitude-surveys-ua) Nature of data: 1 disparate, patchy 8 9 Historical survey data – often designed for different purposes Stakeholder Interviews Our data come (Qualitative) together in and collected from different samples different forms, like pieces of Population / Demographic data (from census, World Bank Customer Interviews (Qualitative) JIGSAW research etc.) Pricing data Historical segmentation work (e.g. -
Recent Developments in Free Medical Imaging Software
Recent Developments in Free Medical Imaging Software OrthancCon I, 2019 Andrew Crabb The Johns Hopkins University I Do Imaging Why Free Medical Imaging Software? Why Use It? Why Write It? Medical imaging is well-served by free software Recognition and publicity Benefits from collaborative imaging community Free testing by demanding users Source code often available Contributions and improvements Can address specialist/niche/research needs Sometimes required by sponsor Imaging software is competing for the user’s most valuable asset: time Today’s users are accustomed to high-quality free software Many imaging areas are served by multiple free applications Only the best software becomes self-sustaining Distributions Source Virtual Machines GitHub/BitBucket repo Docker/DockerHub • hg clone bitbucket.org/sjodogne/orthanc • docker run jodogne/orthanc Vagrant/VirtualBox • git clone xnat.git; Platform Specific ./run xnat setup HomeBrew (Mac) • brew install dcmtk apt/yum (Linux) Language Specific • apt-get install Pip (Python) python-dicom • pip search nifti # (12 results) zypper (openSUSE) npm/yarn (Node JS) • zypper install • npm search dicom # (24 results) orthanc Chocolatey (Windows) DICOM Libraries DCMTK (OFFIS) • C++ ‘reference’ DICOM library • Steady enhancements since 2003 • Command line utilities dcm4che (dcm4che.org) • Java DICOM toolkit since ca. 2000 • Many command line applications • Adding DICOMWeb capabilities GDCM (Mathieu Malaterre) • Grassroots DICOM • C++, binds to Python, C#, Java, PHP • SCU network operations DICOM Libraries -
Captura, Visualización Y Extracción Aproximada De Contornos De Imágenes 3D De Arterias Simples Miguel Angel Castañeda Zambra
CAPTURA, VISUALIZACIÓN Y EXTRACCIÓN APROXIMADA DE CONTORNOS DE IMÁGENES 3D DE ARTERIAS SIMPLES MIGUEL ANGEL CASTAÑEDA ZAMBRANO UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERIA DEPARTAMENTO DE SISTEMAS Y COMPUTACIÓN Bogotá, 2004 Miguel Angel Castañeda Zambrano CAPTURA, VISUALIZACIÓN Y EXTRACCIÓN APROXIMADA DE CONTORNOS DE IMÁGENES 3D DE ARTERIAS SIMPLES Tesis de Grado Trabajo de grado presentado como requisito parcial para optar al titulo de Ingeniero de Sistemas y Computación Asesor: Tiberio Hernández UNIVERSIDAD DE LOS ANDES FACULTAD DE INGENIERIA DEPARTAMENTO DE SISTEMAS Y COMPUTACIÓN Bogotá, febrero de 2004 Gracias a mi familia Y a todos los que lo hicieron posible ISC-2003-1-8 CONTENIDO 1. Introducción .......................................................................................... 2 1.1 Descripción del problema .............................................................. 3 1.2 Organización del documento ......................................................... 3 2. Contexto ............................................................................................... 5 2.1 Contexto médico ............................................................................ 5 2.1.1 Arterias principales ............................................................... 5 2.1.2 Enfermedades arteriales: Estenosis .................................... 9 2.2 Contexto mecánico ......................................................................... 9 2.2.1 Modelos computacionales .................................................... 10 2.3