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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). -
Critical Review of Patient Education Materials from the American Academy of Orthopaedic Surgeons
An Original Study D. P. Feghhi et al Critical Review of Patient Education Materials From the American Academy of Orthopaedic Surgeons Daniel P. Feghhi, MD, Nitin Agarwal, MD, David R. Hansberry, MD, PhD, Wayne S. Berberian, MD, and Sanjeev Sabharwal, MD the medical conditions and symptoms they are experiencing.3 Abstract About 80% of Americans report using an Internet resource to We performed an expanded readability analysis to seek answers to health questions.3,4 Studies have identified a determine if the American Academy of Orthopae- correlation between “health literacy” and clinical outcomes: dic Surgeons (AAOS) had sufficiently improved its lower health literacy is associated with adverse patient out- patient education materials since they were originally comes, such as more frequent hospitalizations and emergency studied in 2007. department visits, and higher health literacy is associated with 4-6 In March 2013, we downloaded patient education more favorable patient outcomes. Given these findings, it is materials from the AAOS patient information website, essential to ensure that the health care information being ac- cessed is easily comprehended. Your Orthopaedic Connection. Using 10 different Orthopedic conditions are among the most common condi- readability formulas, we found that the mean grade tions that cause patients to seek medical care.6-9 Many ortho- level of patient education materials on the website is pedic conditions call for surgical intervention. Controversy 8.84. Flesch-Kincaid analysis showed a mean grade arises regarding nonoperative and operative treatments for level of 9.98 (range, 6.6-12.6). Nine other readability certain diseases. Once a patient has been diagnosed with a analyses showed a mean reading level of 7.7 (range, particular injury or condition, it is almost instinctive to seek 6.5-13.7). -
A New Vocabulary-Based Readability Index for Thai
A NEW VOCABULARY-BASED READABILITY INDEX FOR THAI UNIVERSITY STUDENTS Patteera Thienpermpool A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in English Language Studies Suranaree University of Technology Academic Year 2009 ดัชนีวัดความยากงายของบทอานแบบอิงคําศัพทใหมสําหรับนักศึกษา ไทยในมหาวิทยาลัย นางสาวภัทรธีรา เทียนเพิ่มพูล วิทยานิพนธนี้เปนสวนหนึ่งของการศึกษาตามหลักสูตรปริญญาศิลปศาสตรดุษฎีบัณฑิต สาขาภาษาอังกฤษศึกษา มหาวิทยาลัยเทคโนโลยีสุรนารี ปการศึกษา 2552 A NEW VOCABULARY-BASED READABILITY INDEX FOR THAI UNIVERSITY STUDENTS Suranaree University of Technology has approved this thesis submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy. Thesis Examining Committee _____________________________ (Dr. Dhirawit Pinyonatthagarn) Chairperson _____________________________ (Dr. Sarit Srikhao) Member (Thesis Advisor) _____________________________ (Prof. Dr. Chaiyong Brahmawong) Member _____________________________ (Dr. Jitpanat Suwanthep) Member ______________________________ (Dr. Suksan Suppasetseree) Member ______________________________ _____________________________ (Prof. Dr. Sukit Limpijumnong) (Dr. Peerasak Siriyothin) Vice Rector for Academic Affairs Dean of Institute of Social Technology ภัทรธีรา เทียนเพิ่มพูล : ดัชนีวัดความยากงายของบทอานแบบอิงคําศพทั ใหมสําหรบั นักศึกษาไทยในระดับมหาวทยาลิ ัย (A NEW VOCABULARY-BASED READABILITY INDEX FOR THAI UNIVERSITY STUDENTS) อาจารยที่ปรึกษา : รองศาสตราจารย ดร. เจเรมี วิลเลียม วอรด, 351หนา การศึกษานี้มีจุดมุงหมายเพื่อสรางดัชนีวัดความยากงายของบทอานแบบอิงคําศัพทและ -
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. -
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. -
Plain Language Overview and Analysis
Plain Language Overview and Analysis An assessment of 13 College of Liberal Arts department pages Prepared for CLA Web Champions Group Prepared by Jonathan Nixon September 17, 2019 Table of Contents Section I – Introduction to the Analysis Introduction...........................................................................................................................................1 Statement of the Problem....................................................................................................................1 Significance of the Analysis.................................................................................................................1 Scope of the Analysis............................................................................................................................2 Evaluation Methods..............................................................................................................................3 Limitations of the Analysis..................................................................................................................6 Section II – Findings, Conclusions, and Recommendations Introduction...........................................................................................................................................7 Findings..................................................................................................................................................7 Conclusions..........................................................................................................................................10 -
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 ........................................... -
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. -
Readability of Ebola Information on Websites of Public Health Agencies, United States, United Kingdom, Canada, Australia, and Europe
Article DOI: http://dx.doi.org/10.3201/eid2107.141829 Readability of Ebola Information on Websites of Public Health Agencies, United States, United Kingdom, Canada, Australia, and Europe Technical Appendix Websites Reviewed European Centre for Disease Control. Ebola factsheet for the general public [cited 2014 Sep 1]. http://www.ecdc.europa.eu/en/healthtopics/ebola_marburg_fevers/factsheet_general_ public/Pages/factsheet-general-public.aspx Centers for Disease Prevention and Control. Questions and answers on Ebola [cited 2014 Sep 1]. http://www.cdc.gov/vhf/ebola/outbreaks/2014-west-africa/qa.html Public Health England. Ebola: public health questions and answers [cited 2014 Sep 1]. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/370777 /141104__Ebola_QA_For_Public_LF.pdf Government of Canada. Ebola virus disease [cited 2014 Sep 1]. http://healthycanadians.gc.ca/diseases-conditions-maladies-affections/disease- maladie/ebola/index-eng.php Government of Australia. Ebolavirus disease outbreaks in West Africa – important information for travellers, patients and consumers [cited 2014 Nov 11]. http://www.health.gov.au/internet/main/publishing.nsf/Content/2974D69E347C60F6 CA257D1E00106707/$File/ebola-travellers-patients-consumers.pdf World Health Organization. Advice for individuals and families. Ebola guidance package [cited 2014 Nov 11]. http://apps.who.int/iris/bitstream/10665/136474/1/WHO_EVD_Guidance_AdviceFa m_14.1_eng.pdf?ua = 1 Page 1 of 2 Readability Indicator Definitions Gunning FOG Index, which correlates aspects of a text with its grade level. Flesch Reading Ease score, which measures readability on a scale from 0 to 100 (100 being easiest to read, where a score of 60–70 is considered well written and easy to follow by the average reader). -
Assessing the Readability of Policy Documents on the Digital Single Market of the European Union
Assessing the Readability of Policy Documents on the Digital Single Market of the European Union Jukka Ruohonen University of Turku, Finland Email: juanruo@utu.fi Abstract—Today, literature skills are necessary. Engineering fruit. For instance, anecdotal evidence hints that engineering and other technical professions are not an exception from this and computer science students are receptive to the grammar requirement. Traditionally, technical reading and writing have and vocabulary of law [6]. Yet questions remain whether been framed with a limited scope, containing documentation, specifications, standards, and related text types. Nowadays, and how well they are able to understand and translate law however, the scope covers also other text types, including legal, into technical specifications and implementations. In practice, policy, and related documents. Given this motivation, this paper collaboration between lawyers and engineers is often required evaluates the readability of 201 legislations and related policy for such translations [7]. To ease the translations, different documents in the European Union (EU). The digital single market formalization methods and tools have long been proposed [8]. (DSM) provides the context. Five classical readability indices provide the methods; these are quantitative measures of a text’s Eventually, such technical solutions may eliminate the need readability. The empirical results indicate that (i) generally a to have lawyers in situations requiring the engineering of Ph.D. level education is required to comprehend the DSM laws requirements from law. These requirements have increased and policy documents. Although (ii) the results vary across the particularly in the EU as regulation of the information technol- five indices used, (iii) readability has slightly improved over time.