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R-Based Strategies for DH in English Linguistics: a Case Study
R-based strategies for DH in English Linguistics: a case study Nicolas Ballier Paula Lissón Université Paris Diderot Université Paris Diderot UFR Études Anglophones UFR Études Anglophones CLILLAC-ARP (EA 3967) CLILLAC-ARP (EA 3967) nicolas.ballier@univ- [email protected] paris-diderot.fr paris-diderot.fr language for research in linguistics, both in a Abstract quantitative and qualitative approach. Developing a culture based on the program- This paper is a position statement advocating ming language R (R Core Team 2016) for NLP the implementation of the programming lan- among MA and PhD students doing English Lin- guage R in a curriculum of English Linguis- guistics is no easy task. Broadly speaking, most tics. This is an illustration of a possible strat- of the students have little background in mathe- egy for the requirements of Natural Language matics, statistics, or programming, and usually Processing (NLP) for Digital Humanities (DH) studies in an established curriculum. R feel reluctant to study any of these disciplines. plays the role of a Trojan Horse for NLP and While most PhD students in English linguistics statistics, while promoting the acquisition of are former students with a Baccalauréat in Sci- a programming language. We report an over- ences, some MA students pride themselves on view of existing practices implemented in an having radically opted out of Maths. However, MA and PhD programme at the University of we believe that students should be made aware of Paris Diderot in the recent years. We empha- the growing use of statistical and NLP methods size completed aspects of the curriculum and in linguistics and to be able to interpret and im- detail existing teaching strategies rather than plement these techniques. -
F¢:1 Nesday with a 20 Percent Chance of Showers, Changing to Snow Late in the Afternoon
----- -------------------------------------------------------------------------------------------------------------~ Business as usual Mostly cloudy and cooler Wed ~~A=C=C=E=N=T==: ==Th==·e====st==u==de==n==t==v==e==rd==ic==t==o==n==S==Y==R==~==· ====~~~ : f¢:1 nesday with a 20 percent chance of showers, changing to snow late in the afternoon. IVIEWPOINT: It's time to go coed . ~ High in the lower 40s. VOL. XXII, NO. 65 WEDNESDAY, DECEMBER 7, 1988 the independent newspaper serving Notre Dame and Saint Mary's Library hours may be extended in '89 By CHRIS MURPHY through Thursday nights, Staff Reporter Feles said. A second proposal would There are no immediate switch the opening hours on plans to extend library hours weekends, Krebs said. Cur though possible changes are rently, the library opens Satur "certainly worth looking at," day at 9 a.m. and Sunday at 12 according to Robert Miller, p.m. director of University Libra "It makes more sense to ries. have the library open early on Student government and Sunday since more students Graduate Student Union repre would like to study on Sunday," sentatives have met with li Krebs said. brary personnel to discuss A possible change in hours proposals for change, accor could occur after a new com ding to Victor Krebs, president puter system is installed in the of the Graduate Student Union library over the Christmas and Arty Feles, judicial council break, Miller added. Support signed The Observer I Sheila Lombard coordinator. "That could have an impact if there's a demand for longer Father Malloy shows his support as he signs the Father Malloy, Kerri Gustafson and Father Andre The first proposal would ex hours," Miller said. -
R for Statistics: First Steps
R for statistics: first steps CAR meeting, Raleigh, Feb 26 2011 Peter Aldhous, San Francisco Bureau Chief [email protected] A note of caution before using any stats package: Beware running with scissors! You can bamboozle yourself, and your readers, by misusing statistics. Make sure you understand the methods you are using, in particular the assumptions that must be met for them to be valid (e.g. normal distribution for many common tests). So before rushing in, consult: • IRE tipsheets (e.g. Donald & LaFleur, #2752; Donald & Hacker, #2731) • Statistical textbooks (e.g. http://www.statsoft.com/textbook/ is free online) • Experts who can provide a reality check on your analysis! Getting started: Download R, instructions at: http://www.r-project.org/ Start the program: Prepare the data: 1) Save as a csv file 2) Point R at the data First steps in the R command line: Load and examine the data: View a basic summary: What is the mean age and salary for CEOs in each sector? An alternative, computing mean and standard deviation in one go: Is there a correlation between age and salary? Draw a graph to explore the correlation analysis: Does mean CEO age differ significantly across sectors? Does mean CEO salary differ significantly across sectors? Draw a graph to explore the distribution of salary by sector: Moving beyond the basic functions: R packages The R community has written 2800+ extensions to R’s basic statistical and graphical functions, available from the CRAN repository: http://cran.r-project.org/web/packages/ Follow the links to find a PDF Reference Manual for each package. -
R in a Nutshell
R IN A NUTSHELL Second Edition Joseph Adler Beijing • Cambridge • Farnham • Köln • Sebastopol • Tokyo R in a Nutshell, Second Edition by Joseph Adler Copyright © 2012 Joseph Adler. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://my.safaribooksonline.com). For more infor- mation, contact our corporate/institutional sales department: 800-998-9938 or [email protected]. Editors: Mike Loukides and Meghan Blanchette Indexer: Fred Brown Production Editor: Holly Bauer Cover Designer: Karen Montgomery Proofreader: Julie Van Keuren Interior Designer: David Futato Illustrators: Robert Romano and Re- becca Demarest September 2009: First Edition. October 2012: Second Edition. Revision History for the Second Edition: 2012-09-25 First release See http://oreilly.com/catalog/errata.csp?isbn=9781449312084 for release details. Nutshell Handbook, the Nutshell Handbook logo, and the O’Reilly logo are registered trade- marks of O’Reilly Media, Inc. R in a Nutshell, the image of a harpy eagle, and related trade dress are trademarks of O’Reilly Media, Inc. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in this book, and O’Reilly Media, Inc., was aware of a trademark claim, the designations have been printed in caps or initial caps. While every precaution has been taken in the preparation of this book, the publisher and author assume no responsibility for errors or omissions, or for damages resulting from the use of the information contained herein. -
Eviews 9 Command and Programming Reference Eviews 9 Command and Programming Reference Copyright © 1994–2015 IHS Global Inc
EViews 9 Command and Programming Reference EViews 9 Command and Programming Reference Copyright © 1994–2015 IHS Global Inc. All Rights Reserved ISBN: 978-1-880411-29-2 This software product, including program code and manual, is copyrighted, and all rights are reserved by IHS Global Inc. The distribution and sale of this product are intended for the use of the original purchaser only. Except as permitted under the United States Copyright Act of 1976, no part of this product may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of IHS Global Inc. Disclaimer The authors and IHS Global Inc. assume no responsibility for any errors that may appear in this manual or the EViews program. The user assumes all responsibility for the selection of the pro- gram to achieve intended results, and for the installation, use, and results obtained from the pro- gram. Trademarks EViews® is a registered trademark of IHS Global Inc. Windows, Excel, PowerPoint, and Access are registered trademarks of Microsoft Corporation. PostScript is a trademark of Adobe Corpora- tion. X11.2 and X12-ARIMA Version 0.2.7, and X-13ARIMA-SEATS are seasonal adjustment pro- grams developed by the U. S. Census Bureau. Tramo/Seats is copyright by Agustin Maravall and Victor Gomez. Info-ZIP is provided by the persons listed in the infozip_license.txt file. Please refer to this file in the EViews directory for more information on Info-ZIP. Zlib was written by Jean-loup Gailly and Mark Adler. -
The Use of Statistical Software to Teach Nonparametric Curve Estimation: from Excel to R
ICOTS8 (2010) Invited Paper Refereed Cao & Naya THE USE OF STATISTICAL SOFTWARE TO TEACH NONPARAMETRIC CURVE ESTIMATION: FROM EXCEL TO R Ricardo Cao and Salvador Naya Research Group MODES, Department of Mathematics, University of A Coruña, Spain [email protected] The advantages of using R and Excel for teaching nonparametric curve estimation are presented in this paper. The use of these two tools for teaching nonparametric curve estimation is illustrated by means of several well-known data sets. Computation of histogram and kernel density estimators as well as kernel and local polynomial regression estimators is presented using Excel and R. Interactive changes in the sample and the smoothing parameter are illustrated using both tools. R incorporates sophisticated routines for crucial issues in nonparametric curve estimation, as smoothing parameter selection. The paper concludes summarizing the relative merits of these two tools for teaching nonparametric curve estimation and presenting RExcel, a free add-in for Excel that can be downloaded from the R distribution network. INTRODUCTION There has been an enormous expansion, over the past few years, on the use of computer and communication technologies for teaching statistics at different levels. Microsoft Excel is the most popular spreadsheet program that is used to store information in columns and rows, which can then be organized and/or processed. Many authors consider Microsoft Excel as an excellent tool for statistical education (see, for example, Giles, 2002). On the other hand, the use of free software is also one of the most interesting available tools for teaching statistics. Universal access to the Internet enables easy installation of free software. -
Appendix C Some Details of Matrix.Xla(M)
Appendix C Some details of Matrix.xla(m) C.1 Matrix nomenclature For the sake of notational compactness, we will denote a square diagonal matrix by D with elements dii, a square tridiagonal matrix by T with elements tij where | j – i | ≤ 1, most other square matrices by S, rectangular matrices by R, and all matrix elements by mij. A vector will be shown as v, with elements vi, and a scalar as s. Particular values are denoted by x when real, and by z when complex. All optional pa- rameters are shown in straight brackets, [ ]. All matrices, vectors, and scalars are assumed to be real, ex- cept when specified otherwise. All matrices are restricted to two dimensions, and vectors to one dimen- sion. Table C.1 briefly explains some matrix terms that will be used in subsequent tables. With some functions, the user is given the integer option Int of applying integer arithmetic. When a matrix only contains integer elements, selecting integer arithmetic may avoid most round-off problems. On the other hand, the range of integer arithmetic is limited, so that overflow errors may result if the ma- trix is large and/or contains large numbers. Another common option it Tiny, which defines the absolute value of quantities that can be regarded as most likely resulting from round-off errors, and are therefore set to zero. When not activated, the routine will use its user-definable default value. Condition of a matrix: ratio of its largest to smallest singular value Diagonal of a square matrix: the set of terms mij where i = j Diagonal matrix D square matrix with mij = 0 for all off-diagonal elements i ≠ j. -
R Course for the Nsos in the Arab Countries Part 3: R Data Management
R Course for the NSOs in the Arab countries Part 3: R Data Management Valentin Todorov1 1United Nations Industrial Development Organization, Vienna 18-20 May 2015 Todorov (UNIDO) R Course for the NSOs in the Arab countriesPart 3: R Data Management18-20 May 2015 1 / 41 Outline 1 Motivation 2 Data exchange with other statistical tools 3 Reading and Writing in Excel Format 4 Reading Data in SDMX Format 5 R data base interfaces and Relational DBMSs 6 Case study: UNIDO database 7 R packages for database access 8 Exercise: The Data Expo 2006 9 Accessing international statistical databases 10 Summary and conclusions Todorov (UNIDO) R Course for the NSOs in the Arab countriesPart 3: R Data Management18-20 May 2015 2 / 41 Motivation Motivation 1. A number of statistical software tools and other programs are in use in a statistical organization 2. The data exchange between such systems (SAS, SPSS, EViews, Stata, Excel, Matlab, Octave, etc) is essential 3. Reading and writing data from/to Excel is very important due to its extreme popularity 4. Often data are stored in relational databases (MS Access, MySql, DB2, MS SQL server, Sybase, etc.) and the size do not allow to extract them into flat files before analysis 5. Using SDMX for data and metadata exchange becomes more and more important Todorov (UNIDO) R Course for the NSOs in the Arab countriesPart 3: R Data Management18-20 May 2015 3 / 41 Data exchange with other statistical tools R as a mediator Todorov (UNIDO) R Course for the NSOs in the Arab countriesPart 3: R Data Management18-20 May 2015 4 / 41 Data exchange with other statistical tools Package foreign • Package foreign reads data stored by Minitab, S, SAS, SPSS, Stata, Systat, Weka, dBase and others and writes in the format of some of these. -
ASA JSM Program Book 06.Indd
GENERAL PROGRAM SCHEDULE ✪ Themed Session ● Applied Session ❖ Presenter CC-Washington State Convention & Trade Center H-Grand Hyatt Seattle S-Sheraton Seattle Hotel & Towers TUESDAY, AUGUST 8 7:00 a.m.–10:00 p.m. CC-Level 4 South Lobby Cyber Center Tuesday 7:00 a.m.–10:00 a.m. S-Metropolitan Ballroom A Tours Council of Chapters Business Meeting and Breakfast (closed) 9:00 a.m.–1:00 p.m. CC-Convention Place Chair(s): Ronald Wasserstein, Washburn University TR06 - Historical Seattle Tour (fee event) 7:00 a.m.–10:00 a.m. S-Ballard 1:00 p.m.–5:00 p.m. CC-Convention Place Section on Physical and Engineering Sciences TR07 - Glassblowing Tour (fee event) Executive Committee Meeting (closed) Chair(s): Robert Wilkinson, Lubrizol Committee/Business Meetings 7:00 a.m.–6:00 p.m. CC-507, CC-508 & Other Activities Speaker Work Rooms 5:15 a.m.–7:00 a.m. Off Property 7:30 a.m.–9:00 a.m. H-Cayuse Gertrude Cox Scholarship Race Project on Filming of Distinguished Statisticians (closed) 7:00 a.m.–8:30 a.m. S-Cedar Room Organizer(s): Nitis Mukhopadhyay, University of Connecticut Section on Statisticians in Defense and National 7:30 a.m.–12:00 p.m. S-Aspen Room Security Business Meeting Chair(s): Ron Fricker, Naval Postgraduate School Biopharmaceutical Section Executive Committee Meeting (closed) 7:00 a.m.–8:30 a.m. H-Excelsior Chair(s): Stacy Lindborg, Eli Lilly and Company Technometrics Editorial Board Meeting (closed) Chair(s): Randy R. Sitter, Simon Fraser University 7:30 a.m.–4:30 p.m. -
Creating and Deploying an Application with (R)Excel and R
CONTRIBUTED RESEARCH ARTICLES 5 Creating and Deploying an Application with (R)Excel and R Thomas Baier, Erich Neuwirth and Michele De Meo Prediction of a quantitative or categorical variable, is done through a tree structure, which even non- Abstract We present some ways of using R in professionals can read and understand easily. The Excel and build an example application using the application of a computationally complex algorithm package rpart. Starting with simple interactive thus results in an intuitive and easy to use tool. Pre- use of rpart in Excel, we eventually package the diction of a categorical variable is performed by a code into an Excel-based application, hiding all classification tree, while the term regression tree is used details (including R itself) from the end user. In for the estimation of a quantitative variable. the end, our application implements a service- Our application will be built for Microsoft Excel oriented architecture (SOA) with a clean separa- and will make use of R and rpart to implement the tion of presentation and computation layer. functionality. We have chosen Excel as the primary tool for performing the analysis because of various advantages: Motivation • Excel has a familiar and easy-to-use user inter- Building an application for end users is a very chal- face. lenging goal. Building a statistics application nor- • Excel is already installed on most of the work- mally involves three different roles: application de- stations in the industries we mentioned. veloper, statistician, and user. Often, statisticians are programmers too, but are only (or mostly) fa- • In many cases, data collection has been per- miliar with statistical programming (languages) and formed using Excel, so using Excel for the anal- definitely are not experts in creating rich user inter- ysis seems to be the logical choice. -
It Must Be a Dream: Hypnotist on Campus
Des Moines Area Community College Boone Campus Wednesday, September 16, 2009, Vol. 9, No. 1 It must be a dream: Hypnotist on campus By Jamie Stueve hypnotism and students who never experienced hypnotism Staff Writer volunteered to gather in front of The Student Activities eager students and faculty to enjoy Council created an activity the show. An easy count down known as Welcome Fest to from ten, Wand puts the students welcome new and returning into hypnosis with only a couple students. SAC member Steve stragglers who didn’t have much Krafcisin, introduced well success. “He could hypnotize me renowned hypnotist, Jim Wand. in three seconds and it actually Wand has worked with many worked,” said Madison Hudnutt, celebrities such as: Jay Leno, a participant of Wand’s hypnosis. Chris Rock, Michael Jordan and Students and faculty bellowed Larry the Cable Guy. He has a out laughter as they proceeded doctorate in psychology and uses to watch peers play balloon hypnosis to help clients work on instruments, mimic a doll, and concentration, motivation and imitate Taylor Swift along with a inspiration. “Hands down Jim handful of other shenanigans for Wand is the best hypnotist I have a mere 60 minutes. ever had,” said Taylor Perdue, a Coming out of hypnosis participant of hypnosis, “ You supposedly feels like a full night’s know you’re good when you can rest and your recollection of the put somebody in that deep of event takes up to a few hours. Photo by Derrick Roffman a sleep and hardly remember “After I woke up [from hypnosis] I Far right, Taylor Perdue, Quinn Devlin and Kyle Mathes attempt to mimic a doll anything.” felt so energetic that I didn’t even used as a prop in the performance with Jim Wand. -
Non-Programming Introduction to R
sheepsqueezers.com Non-Programming Introduction to R Copyright ©2011 sheepsqueezers.com Legal Stuff sheepsqueezers.com This work may be reproduced and redistributed, in whole or in part, without alteration and without prior written permission, provided all copies contain the following statement: Copyright ©2011 sheepsqueezers.com. This work is reproduced and distributed with the permission of the copyright holder. This presentation as well as other presentations and documents found on the sheepsqueezers.com website may contain quoted material from outside sources such as books, articles and websites. It is our intention to diligently reference all outside sources. Occasionally, though, a reference may be missed. No copyright infringement whatsoever is intended, and all outside source materials are copyright of their respective author(s). Copyright ©2011 sheepsqueezers.com R Lecture Series sheepsqueezers.com Non- Programming Programming Programming I II Introduction Graphics Advanced I Topics Copyright ©2011 sheepsqueezers.com Charting Our Course Goal of this Presentation sheepsqueezers.com What is R? Installing the Software Installing R Commander and Rattle Packages Installing RExcel for R Commander Installing RGGobi Installing Additional Packages Introducing Rterm and RGui Introducing R Commander Introducing Rattle Introducing RExcel Introducing RGGobi Appendix A: References Appendix B: R-Related Websites Copyright ©2011 sheepsqueezers.com Goal of this Presentation The goal of this presentation is to show you how to use R through its many sheepsqueezers.com graphical user interfaces (GUIs). We decided to start with this instead of charging directly into programming to ease you into R rather than throwing you to the wolves. Many of you will be casual R users and will not need or not want to learn the R programming language itself and it is our hope that this presentation will be to your benefit.