R Programming
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Sagemath and Sagemathcloud
Viviane Pons Ma^ıtrede conf´erence,Universit´eParis-Sud Orsay [email protected] { @PyViv SageMath and SageMathCloud Introduction SageMath SageMath is a free open source mathematics software I Created in 2005 by William Stein. I http://www.sagemath.org/ I Mission: Creating a viable free open source alternative to Magma, Maple, Mathematica and Matlab. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 2 / 7 SageMath Source and language I the main language of Sage is python (but there are many other source languages: cython, C, C++, fortran) I the source is distributed under the GPL licence. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 3 / 7 SageMath Sage and libraries One of the original purpose of Sage was to put together the many existent open source mathematics software programs: Atlas, GAP, GMP, Linbox, Maxima, MPFR, PARI/GP, NetworkX, NTL, Numpy/Scipy, Singular, Symmetrica,... Sage is all-inclusive: it installs all those libraries and gives you a common python-based interface to work on them. On top of it is the python / cython Sage library it-self. Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 4 / 7 SageMath Sage and libraries I You can use a library explicitly: sage: n = gap(20062006) sage: type(n) <c l a s s 'sage. interfaces .gap.GapElement'> sage: n.Factors() [ 2, 17, 59, 73, 137 ] I But also, many of Sage computation are done through those libraries without necessarily telling you: sage: G = PermutationGroup([[(1,2,3),(4,5)],[(3,4)]]) sage : G . g a p () Group( [ (3,4), (1,2,3)(4,5) ] ) Viviane Pons (U-PSud) SageMath and SageMathCloud October 19, 2016 5 / 7 SageMath Development model Development model I Sage is developed by researchers for researchers: the original philosophy is to develop what you need for your research and share it with the community. -
A Fast Dynamic Language for Technical Computing
Julia A Fast Dynamic Language for Technical Computing Created by: Jeff Bezanson, Stefan Karpinski, Viral B. Shah & Alan Edelman A Fractured Community Technical work gets done in many different languages ‣ C, C++, R, Matlab, Python, Java, Perl, Fortran, ... Different optimal choices for different tasks ‣ statistics ➞ R ‣ linear algebra ➞ Matlab ‣ string processing ➞ Perl ‣ general programming ➞ Python, Java ‣ performance, control ➞ C, C++, Fortran Larger projects commonly use a mixture of 2, 3, 4, ... One Language We are not trying to replace any of these ‣ C, C++, R, Matlab, Python, Java, Perl, Fortran, ... What we are trying to do: ‣ allow developing complete technical projects in a single language without sacrificing productivity or performance This does not mean not using components in other languages! ‣ Julia uses C, C++ and Fortran libraries extensively “Because We Are Greedy.” “We want a language that’s open source, with a liberal license. We want the speed of C with the dynamism of Ruby. We want a language that’s homoiconic, with true macros like Lisp, but with obvious, familiar mathematical notation like Matlab. We want something as usable for general programming as Python, as easy for statistics as R, as natural for string processing as Perl, as powerful for linear algebra as Matlab, as good at gluing programs together as the shell. Something that is dirt simple to learn, yet keeps the most serious hackers happy.” Collapsing Dichotomies Many of these are just a matter of design and focus ‣ stats vs. linear algebra vs. strings vs. -
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). -
State Notation Language and Sequencer Users' Guide
State Notation Language and Sequencer Users’ Guide Release 2.0.99 William Lupton ([email protected]) Benjamin Franksen ([email protected]) June 16, 2010 CONTENTS 1 Introduction 3 1.1 About.................................................3 1.2 Acknowledgements.........................................3 1.3 Copyright...............................................3 1.4 Note on Versions...........................................4 1.5 Notes on Release 2.1.........................................4 1.6 Notes on Releases 2.0.0 to 2.0.12..................................7 1.7 Notes on Release 2.0.........................................9 1.8 Notes on Release 1.9......................................... 12 2 Installation 13 2.1 Prerequisites............................................. 13 2.2 Download............................................... 13 2.3 Unpack................................................ 14 2.4 Configure and Build......................................... 14 2.5 Building the Manual......................................... 14 2.6 Test.................................................. 15 2.7 Use.................................................. 16 2.8 Report Bugs............................................. 16 2.9 Contribute.............................................. 16 3 Tutorial 19 3.1 The State Transition Diagram.................................... 19 3.2 Elements of the State Notation Language.............................. 19 3.3 A Complete State Program...................................... 20 3.4 Adding a -
A Rapid, Sensitive, Scalable Method for Precision Run-On Sequencing
bioRxiv preprint doi: https://doi.org/10.1101/2020.05.18.102277; this version posted May 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 A rapid, sensitive, scalable method for 2 Precision Run-On sequencing (PRO-seq) 3 4 Julius Judd1*, Luke A. Wojenski2*, Lauren M. Wainman2*, Nathaniel D. Tippens1, Edward J. 5 Rice3, Alexis Dziubek1,2, Geno J. Villafano2, Erin M. Wissink1, Philip Versluis1, Lina Bagepalli1, 6 Sagar R. Shah1, Dig B. Mahat1#a, Jacob M. Tome1#b, Charles G. Danko3,4, John T. Lis1†, 7 Leighton J. Core2† 8 9 *Equal contribution 10 1Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA 11 2Department of Molecular and Cell Biology, Institute of Systems Genomics, University of 12 Connecticut, Storrs, CT 06269, USA 13 3Baker Institute for Animal Health, College of Veterinary Medicine, Cornell University, Ithaca, 14 NY 14853, USA 15 4Department of Biomedical Sciences, College of Veterinary Medicine, Cornell University, 16 Ithaca, NY 14853, USA 17 #aPresent address: Koch Institute for Integrative Cancer Research, Massachusetts Institute of 18 Technology, Cambridge, MA 02139, USA 19 #bPresent address: Department of Genome Sciences, University of Washington, Seattle, WA, 20 USA 21 †Correspondence to: [email protected] or [email protected] 22 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.05.18.102277; this version posted May 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. -
Specification of the "Sequ" Command
1 Specification of the "sequ" command 2 Copyright © 2013 Bart Massey 3 Revision 0, 1 October 2013 4 This specification describes the "universal sequence" command sequ. The sequ command is a 5 backward-compatible set of extensions to the UNIX [seq] 6 (http://www.gnu.org/software/coreutils/manual/html_node/seq-invo 7 cation.html) command. There are many implementations of seq out there: this 8 specification is built on the seq supplied with GNU Coreutils version 8.21. 9 The seq command emits a monotonically increasing sequence of numbers. It is most commonly 10 used in shell scripting: 11 TOTAL=0 12 for i in `seq 1 10` 13 do 14 TOTAL=`expr $i + $TOTAL` 15 done 16 echo $TOTAL 17 prints 55 on standard output. The full sequ command does this basic counting operation, plus 18 much more. 19 This specification of sequ is in several stages, known as compliance levels. Each compliance 20 level adds required functionality to the sequ specification. Level 1 compliance is equivalent to 21 the Coreutils seq command. 22 The usual specification language applies to this document: MAY, SHOULD, MUST (and their 23 negations) are used in the standard fashion. 24 Wherever the specification indicates an error, a conforming sequ implementation MUST 25 immediately issue appropriate error message specific to the problem. The implementation then 26 MUST exit, with a status indicating failure to the invoking process or system. On UNIX systems, 27 the error MUST be indicated by exiting with status code 1. 28 When a conforming sequ implementation successfully completes its output, it MUST 29 immediately exit, with a status indicating success to the invoking process or systems. -
GNU Guix Cookbook Tutorials and Examples for Using the GNU Guix Functional Package Manager
GNU Guix Cookbook Tutorials and examples for using the GNU Guix Functional Package Manager The GNU Guix Developers Copyright c 2019 Ricardo Wurmus Copyright c 2019 Efraim Flashner Copyright c 2019 Pierre Neidhardt Copyright c 2020 Oleg Pykhalov Copyright c 2020 Matthew Brooks Copyright c 2020 Marcin Karpezo Copyright c 2020 Brice Waegeneire Copyright c 2020 Andr´eBatista Copyright c 2020 Christine Lemmer-Webber Copyright c 2021 Joshua Branson Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled \GNU Free Documentation License". i Table of Contents GNU Guix Cookbook ::::::::::::::::::::::::::::::: 1 1 Scheme tutorials ::::::::::::::::::::::::::::::::: 2 1.1 A Scheme Crash Course :::::::::::::::::::::::::::::::::::::::: 2 2 Packaging :::::::::::::::::::::::::::::::::::::::: 5 2.1 Packaging Tutorial:::::::::::::::::::::::::::::::::::::::::::::: 5 2.1.1 A \Hello World" package :::::::::::::::::::::::::::::::::: 5 2.1.2 Setup:::::::::::::::::::::::::::::::::::::::::::::::::::::: 8 2.1.2.1 Local file ::::::::::::::::::::::::::::::::::::::::::::: 8 2.1.2.2 `GUIX_PACKAGE_PATH' ::::::::::::::::::::::::::::::::: 9 2.1.2.3 Guix channels ::::::::::::::::::::::::::::::::::::::: 10 2.1.2.4 Direct checkout hacking:::::::::::::::::::::::::::::: 10 2.1.3 Extended example :::::::::::::::::::::::::::::::::::::::: -
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, .. -
Introduction to GNU Octave
Introduction to GNU Octave Hubert Selhofer, revised by Marcel Oliver updated to current Octave version by Thomas L. Scofield 2008/08/16 line 1 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 8 6 4 2 -8 -6 0 -4 -2 -2 0 -4 2 4 -6 6 8 -8 Contents 1 Basics 2 1.1 What is Octave? ........................... 2 1.2 Help! . 2 1.3 Input conventions . 3 1.4 Variables and standard operations . 3 2 Vector and matrix operations 4 2.1 Vectors . 4 2.2 Matrices . 4 1 2.3 Basic matrix arithmetic . 5 2.4 Element-wise operations . 5 2.5 Indexing and slicing . 6 2.6 Solving linear systems of equations . 7 2.7 Inverses, decompositions, eigenvalues . 7 2.8 Testing for zero elements . 8 3 Control structures 8 3.1 Functions . 8 3.2 Global variables . 9 3.3 Loops . 9 3.4 Branching . 9 3.5 Functions of functions . 10 3.6 Efficiency considerations . 10 3.7 Input and output . 11 4 Graphics 11 4.1 2D graphics . 11 4.2 3D graphics: . 12 4.3 Commands for 2D and 3D graphics . 13 5 Exercises 13 5.1 Linear algebra . 13 5.2 Timing . 14 5.3 Stability functions of BDF-integrators . 14 5.4 3D plot . 15 5.5 Hilbert matrix . 15 5.6 Least square fit of a straight line . 16 5.7 Trapezoidal rule . 16 1 Basics 1.1 What is Octave? Octave is an interactive programming language specifically suited for vectoriz- able numerical calculations. -
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. -
Bash Guide for Beginners
Bash Guide for Beginners Machtelt Garrels Garrels BVBA <tille wants no spam _at_ garrels dot be> Version 1.11 Last updated 20081227 Edition Bash Guide for Beginners Table of Contents Introduction.........................................................................................................................................................1 1. Why this guide?...................................................................................................................................1 2. Who should read this book?.................................................................................................................1 3. New versions, translations and availability.........................................................................................2 4. Revision History..................................................................................................................................2 5. Contributions.......................................................................................................................................3 6. Feedback..............................................................................................................................................3 7. Copyright information.........................................................................................................................3 8. What do you need?...............................................................................................................................4 9. Conventions used in this