R for Beginners
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Research Brief March 2017 Publication #2017-16
Research Brief March 2017 Publication #2017-16 Flourishing From the Start: What Is It and How Can It Be Measured? Kristin Anderson Moore, PhD, Child Trends Christina D. Bethell, PhD, The Child and Adolescent Health Measurement Introduction Initiative, Johns Hopkins Bloomberg School of Every parent wants their child to flourish, and every community wants its Public Health children to thrive. It is not sufficient for children to avoid negative outcomes. Rather, from their earliest years, we should foster positive outcomes for David Murphey, PhD, children. Substantial evidence indicates that early investments to foster positive child development can reap large and lasting gains.1 But in order to Child Trends implement and sustain policies and programs that help children flourish, we need to accurately define, measure, and then monitor, “flourishing.”a Miranda Carver Martin, BA, Child Trends By comparing the available child development research literature with the data currently being collected by health researchers and other practitioners, Martha Beltz, BA, we have identified important gaps in our definition of flourishing.2 In formerly of Child Trends particular, the field lacks a set of brief, robust, and culturally sensitive measures of “thriving” constructs critical for young children.3 This is also true for measures of the promotive and protective factors that contribute to thriving. Even when measures do exist, there are serious concerns regarding their validity and utility. We instead recommend these high-priority measures of flourishing -
Introduction to IDL®
Introduction to IDL® Revised for Print March, 2016 ©2016 Exelis Visual Information Solutions, Inc., a subsidiary of Harris Corporation. All rights reserved. ENVI and IDL are registered trademarks of Harris Corporation. All other marks are the property of their respective owners. This document is not subject to the controls of the International Traffic in Arms Regulations (ITAR) or the Export Administration Regulations (EAR). Contents 1 Introduction To IDL 5 1.1 Introduction . .5 1.1.1 What is ENVI? . .5 1.1.2 ENVI + IDL, ENVI, and IDL . .6 1.1.3 ENVI Resources . .6 1.1.4 Contacting Harris Geospatial Solutions . .6 1.1.5 Tutorials . .6 1.1.6 Training . .7 1.1.7 ENVI Support . .7 1.1.8 Contacting Technical Support . .7 1.1.9 Website . .7 1.1.10 IDL Newsgroup . .7 2 About This Course 9 2.1 Manual Organization . .9 2.1.1 Programming Style . .9 2.2 The Course Files . 11 2.2.1 Installing the Course Files . 11 2.3 Starting IDL . 11 2.3.1 Windows . 11 2.3.2 Max OS X . 11 2.3.3 Linux . 12 3 A Tour of IDL 13 3.1 Overview . 13 3.2 Scalars and Arrays . 13 3.3 Reading Data from Files . 15 3.4 Line Plots . 15 3.5 Surface Plots . 17 3.6 Contour Plots . 18 3.7 Displaying Images . 19 3.8 Exercises . 21 3.9 References . 21 4 IDL Basics 23 4.1 IDL Directory Structure . 23 4.2 The IDL Workbench . 24 4.3 Exploring the IDL Workbench . -
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya 1 Introduction
A Comparative Evaluation of Matlab, Octave, R, and Julia on Maya Sai K. Popuri and Matthias K. Gobbert* Department of Mathematics and Statistics, University of Maryland, Baltimore County *Corresponding author: [email protected], www.umbc.edu/~gobbert Technical Report HPCF{2017{3, hpcf.umbc.edu > Publications Abstract Matlab is the most popular commercial package for numerical computations in mathematics, statistics, the sciences, engineering, and other fields. Octave is a freely available software used for numerical computing. R is a popular open source freely available software often used for statistical analysis and computing. Julia is a recent open source freely available high-level programming language with a sophisticated com- piler for high-performance numerical and statistical computing. They are all available to download on the Linux, Windows, and Mac OS X operating systems. We investigate whether the three freely available software are viable alternatives to Matlab for uses in research and teaching. We compare the results on part of the equipment of the cluster maya in the UMBC High Performance Computing Facility. The equipment has 72 nodes, each with two Intel E5-2650v2 Ivy Bridge (2.6 GHz, 20 MB cache) proces- sors with 8 cores per CPU, for a total of 16 cores per node. All nodes have 64 GB of main memory and are connected by a quad-data rate InfiniBand interconnect. The tests focused on usability lead us to conclude that Octave is the most compatible with Matlab, since it uses the same syntax and has the native capability of running m-files. R was hampered by somewhat different syntax or function names and some missing functions. -
Sage 9.4 Reference Manual: Finite Rings Release 9.4
Sage 9.4 Reference Manual: Finite Rings Release 9.4 The Sage Development Team Aug 24, 2021 CONTENTS 1 Finite Rings 1 1.1 Ring Z=nZ of integers modulo n ....................................1 1.2 Elements of Z=nZ ............................................ 15 2 Finite Fields 39 2.1 Finite Fields............................................... 39 2.2 Base Classes for Finite Fields...................................... 47 2.3 Base class for finite field elements.................................... 61 2.4 Homset for Finite Fields......................................... 69 2.5 Finite field morphisms.......................................... 71 3 Prime Fields 77 3.1 Finite Prime Fields............................................ 77 3.2 Finite field morphisms for prime fields................................. 79 4 Finite Fields Using Pari 81 4.1 Finite fields implemented via PARI’s FFELT type............................ 81 4.2 Finite field elements implemented via PARI’s FFELT type....................... 83 5 Finite Fields Using Givaro 89 5.1 Givaro Finite Field............................................ 89 5.2 Givaro Field Elements.......................................... 94 5.3 Finite field morphisms using Givaro................................... 102 6 Finite Fields of Characteristic 2 Using NTL 105 6.1 Finite Fields of Characteristic 2..................................... 105 6.2 Finite Fields of characteristic 2...................................... 107 7 Miscellaneous 113 7.1 Finite residue fields........................................... -
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. -
UNIX Workshop Series: Quick-Start Objectives
Part I UNIX Workshop Series: Quick-Start Objectives Overview – Connecting with ssh Command Window Anatomy Command Structure Command Examples Getting Help Files and Directories Wildcards, Redirection and Pipe Create and edit files Overview Connecting with ssh Open a Terminal program Mac: Applications > Utilities > Terminal ssh –Y [email protected] Linux: In local shell ssh –Y [email protected] Windows: Start Xming and PuTTY Create a saved session for the remote host name centos.css.udel.edu using username Connecting with ssh First time you connect Unix Basics Multi-user Case-sensitive Bash shell, command-line Commands Command Window Anatomy Title bar Click in the title bar to bring the window to the front and make it active. Command Window Anatomy Login banner Appears as the first line of a login shell. Command Window Anatomy Prompts Appears at the beginning of a line and usually ends in $. Command Window Anatomy Command input Place to type commands, which may have options and/or arguments. Command Window Anatomy Command output Place for command response, which may be many lines long. Command Window Anatomy Input cursor Typed text will appear at the cursor location. Command Window Anatomy Scroll Bar Will appear as needed when there are more lines than fit in the window. Command Window Anatomy Resize Handle Use the mouse to change the window size from the default 80x24. Command Structure command [arguments] Commands are made up of the actual command and its arguments. command -options [arguments] The arguments are further broken down into the command options which are single letters prefixed by a “-” and other arguments that identify data for the command. -
CIS 90 - Lesson 2
CIS 90 - Lesson 2 Lesson Module Status • Slides - draft • Properties - done • Flash cards - NA • First minute quiz - done • Web calendar summary - done • Web book pages - gillay done • Commands - done • Lab tested – done • Print latest class roster - na • Opus accounts created for students submitting Lab 1 - • CCC Confer room whiteboard – done • Check that headset is charged - done • Backup headset charged - done • Backup slides, CCC info, handouts on flash drive - done 1 CIS 90 - Lesson 2 [ ] Has the phone bridge been added? [ ] Is recording on? [ ] Does the phone bridge have the mike? [ ] Share slides, putty, VB, eko and Chrome [ ] Disable spelling on PowerPoint 2 CIS 90 - Lesson 2 Instructor: Rich Simms Dial-in: 888-450-4821 Passcode: 761867 Emanuel Tanner Merrick Quinton Christopher Zachary Bobby Craig Jeff Yu-Chen Greg L Tommy Eric Dan M Geoffrey Marisol Jason P David Josh ? ? ? ? Leobardo Gabriel Jesse Tajvia Daniel W Jason W Terry? James? Glenn? Aroshani? ? ? ? ? ? ? = need to add (with add code) to enroll in Ken? Luis? Arturo? Greg M? Ian? this course Email me ([email protected]) a relatively current photo of your face for 3 points extra credit CIS 90 - Lesson 2 First Minute Quiz Please close your books, notes, lesson materials, forum and answer these questions in the order shown: 1. What command shows the other users logged in to the computer? 2. What is the lowest level, inner-most component of a UNIX/Linux Operating System called? 3. What part of UNIX/Linux is both a user interface and a programming language? email answers to: [email protected] 4 CIS 90 - Lesson 2 Commands Objectives Agenda • Understand how the UNIX login • Quiz operation works. -
Percent R, X and Z Based on Transformer KVA
SHORT CIRCUIT FAULT CALCULATIONS Short circuit fault calculations as required to be performed on all electrical service entrances by National Electrical Code 110-9, 110-10. These calculations are made to assure that the service equipment will clear a fault in case of short circuit. To perform the fault calculations the following information must be obtained: 1. Available Power Company Short circuit KVA at transformer primary : Contact Power Company, may also be given in terms of R + jX. 2. Length of service drop from transformer to building, Type and size of conductor, ie., 250 MCM, aluminum. 3. Impedance of transformer, KVA size. A. %R = Percent Resistance B. %X = Percent Reactance C. %Z = Percent Impedance D. KVA = Kilovoltamp size of transformer. ( Obtain for each transformer if in Bank of 2 or 3) 4. If service entrance consists of several different sizes of conductors, each must be adjusted by (Ohms for 1 conductor) (Number of conductors) This must be done for R and X Three Phase Systems Wye Systems: 120/208V 3∅, 4 wire 277/480V 3∅ 4 wire Delta Systems: 120/240V 3∅, 4 wire 240V 3∅, 3 wire 480 V 3∅, 3 wire Single Phase Systems: Voltage 120/240V 1∅, 3 wire. Separate line to line and line to neutral calculations must be done for single phase systems. Voltage in equations (KV) is the secondary transformer voltage, line to line. Base KVA is 10,000 in all examples. Only those components actually in the system have to be included, each component must have an X and an R value. Neutral size is assumed to be the same size as the phase conductors. -
The Constraint on Public Debt When R<G but G<M
The constraint on public debt when r < g but g < m Ricardo Reis LSE March 2021 Abstract With real interest rates below the growth rate of the economy, but the marginal prod- uct of capital above it, the public debt can be lower than the present value of primary surpluses because of a bubble premia on the debt. The government can run a deficit forever. In a model that endogenizes the bubble premium as arising from the safety and liquidity of public debt, more government spending requires a larger bubble pre- mium, but because people want to hold less debt, there is an upper limit on spending. Inflation reduces the fiscal space, financial repression increases it, and redistribution of wealth or income taxation have an unconventional effect on fiscal capacity through the bubble premium. JEL codes: D52, E62, G10, H63. Keywords: Debt limits, debt sustainability, incomplete markets, misallocation. * Contact: [email protected]. I am grateful to Adrien Couturier and Rui Sousa for research assistance, to John Cochrane, Daniel Cohen, Fiorella de Fiore, Xavier Gabaix, N. Gregory Mankiw, Jean-Charles Rochet, John Taylor, Andres Velasco, Ivan Werning, and seminar participants at the ASSA, Banque de France - PSE, BIS, NBER Economic Fluctuations group meetings, Princeton University, RIDGE, and University of Zurich for comments. This paper was written during a Lamfalussy fellowship at the BIS, whom I thank for its hospitality. This project has received funding from the European Union’s Horizon 2020 research and innovation programme, INFL, under grant number No. GA: 682288. First draft: November 2020. 1 Introduction Almost every year in the past century (and maybe longer), the long-term interest rate on US government debt (r) was below the growth rate of output (g). -
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, .. -
Reading Source Data in R
Reading Source Data in R R is useful for analyzing data, once there is data to be analyzed. Real datasets come from numerous places in various formats, many of which actually can be leveraged by the savvy R programmer with the right set of packages and practices. *** Note: It is possible to stream data in R, but it’s probably outside of the scope of most educational uses. We will only be worried about static data. *** Understanding the flow of data How many times have you accessed some information in a browser or software package and wished you had that data as a spreadsheet or csv instead? R, like most programming languages, can serve as the bridge between the information you have and the information you want. A significant amount of programming is an exercise in changing file formats. Source Result Data R Data To use R for data manipulation and analysis, source data must be read in, analyzed or manipulated as necessary, and the results are output in some meaningful format. This document focuses on the red arrow: How is source data read into R? The ability to access data is fundamental to building useful, productive systems, and is sometimes the biggest challenge Immediately preceding the scope of this document is the source data itself. It is left to the reader to understand the source data: How and where it is stored, in what file formats, file ownership and permissions, etc. Immediately beyond the scope of this document is actual programming. It is left to the reader to determine which tools and methods are best applied to the source data to yield the desired results. -
Mandoc: Becoming the Main BSD Manual Toolbox
mandoc: becoming the main BSD manual toolbox BSDCan 2015, June 13, Ottawa Ingo Schwarze <[email protected]> Cynthia Livingston’sOTTB “Bedifferent” (c) 2013 C. Livingston (with permission) > Ingo Schwarze: mandoc page 2: INTROI BSDCan 2015, June 13, Ottawa Brief history of UNIX documentation • The key point: All documentation in one place and one format. Easy to find, uniform and easy to read and write. Be correct, complete, concise. • 1964: RUNOFF/roffmarkup syntax by Jerome H. Saltzer,MIT. Unobtrusive,diff(1)-friendly,easy to hand-edit, simple tools, high quality output. • 1971: Basic manual structure by Ken Thompson and Dennis Ritchie for the AT&T Version 1 UNIX manuals, Bell Labs. • 1979: man(7) physical markup language for AT&T Version 7 UNIX. • 1989: mdoc(7) semantic markup by Cynthia Livingston for 4.3BSD-Reno. Powerful, self-contained, portable. • 1989: GNU troffbyJames Clarke. • 2001: mdoc(7) rewrite by Werner Lemberg and Ruslan Ermilovfor groff-1.17. • 2008: mandoc(1) started by Kristaps Dzonsons. • 2010: mandoc(1) is the only documentation formatter in the OpenBSD base system. • 2014: mandoc(1) used by default in OpenBSD, FreeBSD, NetBSD, illumos. 16:19:30 What is the mandoc toolbox? → < > Ingo Schwarze: mandoc page 3: INTROIIBSDCan 2015, June 13, Ottawa What is the mandoc toolbox? User perspective:man(1), the manual viewer One comprehensive tool! Normal operation always proceeds in three steps: 1. Find one or more manuals in the file system or using a database by manual name — man(1) — or by search query — apropos(1) =man -k The result of this step can be printed out with man -w.