Selecting the Right Tool for the Job—R Commander Or Something Else? Jason Wilson∗
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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). -
Data Science with Postgresql
Data Science with PostgreSQL Data Science with PostgreSQL Balázs Bárány Data Scientist pgconf.eu 2015 Data Science with PostgreSQL Contents Introduction What is Data Science? Process model Tools and methods of Data Scientists Data Science with PostgreSQL Business & data understanding Preprocessing Modeling Evaluation Deployment Summary I Who is a Data Scientist? Data Science with PostgreSQL Introduction What is Data Science? Sexiest job of the 21st century I According to Google, LinkedIn, ... Data Science with PostgreSQL Introduction What is Data Science? Sexiest job of the 21st century I According to Google, LinkedIn, ... I Who is a Data Scientist? Data Science with PostgreSQL Introduction What is Data Science? Data Science Venn Diagram (c) Drew Conway, 2010. CC-BY-NC I Mash up & format for analysis I Analyze & visualize I Predict & prescribe I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data scientists I Get data from various sources I Big data? I Analyze & visualize I Predict & prescribe I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data scientists I Get data from various sources I Big data? I Mash up & format for analysis I Predict & prescribe I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data scientists I Get data from various sources I Big data? I Mash up & format for analysis I Analyze & visualize I Operationalize Data Science with PostgreSQL Introduction What is Data Science? Tasks of data -
Software Corner: Rkward: Installation and Use Aaron Olaf Batty [email protected] SRB General Editor
Software Corner Software Corner: RKWard: Installation and use Aaron Olaf Batty [email protected] SRB General Editor Many readers of SRB are likely familiar with the free, open-source statistical software package R. R offers a staggeringly wide range of analyses and outputs, besting every other general statistics package on the market, for free. This value, however, comes at a cost that may seem too steep for many users: it is command-line only. One must learn to write R code to take advantage of this software, and although the code itself is relatively memorable and intuitive, it cannot compete with the ease of use offered by the graphical user interfaces (GUIs) of competing packages, such as SPSS/PASW—a software package whose price can run over a thousand US dollars. What is RKWard? RKWard seeks to lower the usability barrier to entry into statistical analysis with R by providing a GUI frontend that hides much of the code from the use (although it is easy to inspect in the con- sole for those who wish to do so). Furthermore, it streamlines the sometimes-maddening process of importing data into R, and can read SPSS and Stata files natively in addition to standard text formats. Finally, its general look-and-feel will be immediately familiar to those who have used SPSS, further easing entry for novices. It has been in development since 2005, and achieved its first stable release in 2011. Although it uses the KDE desktop environment, which is usually found on Linux systems, it is not limited to computers running Linux/Unix. -
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. -
Molecular Data in R Phylogeny, Evolution & R
Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Molecular data in R Phylogeny, evolution & R Vojtěch Zeisek Department of Botany, Faculty of Science, Charles University, Prague Institute of Botany, Czech Academy of Sciences, Průhonice https://trapa.cz/, [email protected] January 27 to 30, 2020 . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 1 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline I 1 Introduction 2 R Installation Let’s start with R Basic operations in R Packages for our work 3 Data Overview of data and data types Microsatellites AFLP Notes about data DNA sequences, SNP VCF Export . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 2 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline II Tasks 4 Alignment Overview and MAFFT MAFFT, Clustal, MUSCLE and T-Coffee Multiple genes Display and cleaning 5 Basic analysis First look at the data Statistics Genetic distances Hierarchical clustering AMOVA . MSN . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 3 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline III NJ (and UPGMA) tree PCoA Tasks 6 SNP PCA and NJ 7 DAPC Bayesian clustering Discriminant analysis and visualization 8 Structure Running Structure from R ParallelStructure on Windows Post processing 9 Spatial analysis . Vojtěch Zeisek (https://trapa.cz/) Molecular data in R January 27 to 30, 2020 4 / 393 Introduction R Data Alignment Basic analysis SNP DAPC Structure Spatial analysis Trees Evolution The end Outline IV Moran’s I sPCA Monmonier Mantel test Geneland Plotting maps 10 Trees Manipulations MP Seeing trees in the forest Comparisons Notes about plotting the trees . -
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. -
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 ........................................... -
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. -
Deducer: a Data Analysis GUI for R
JSS Journal of Statistical Software June 2012, Volume 49, Issue 8. http://www.jstatsoft.org/ Deducer: A Data Analysis GUI for R Ian Fellows University of California, Los Angeles Abstract While R has proven itself to be a powerful and flexible tool for data exploration and analysis, it lacks the ease of use present in other software such as SPSS and Minitab. An easy to use graphical user interface (GUI) can help new users accomplish tasks that would otherwise be out of their reach, and improves the efficiency of expert users by replacing fifty key strokes with five mouse clicks. With this in mind, Deducer presents dialogs that are understandable for the beginner, and yet contain all (or most) of the options that an experienced statistician, performing the same task, would want. An Excel-like spreadsheet is included for easy data viewing and editing. Deducer is based on Java's Swing GUI library and can be used on any common operating system. The GUI is independent of the specific R console and can easily be used by calling a text-based menu system. Graphical menus are provided for the JGR console and the Windows R GUI. Keywords: GUI, R. 1. Introduction R (R Development Core Team 2012) is a powerful statistical programming language that places the latest statistical techniques at one's fingertips through thousands of add-on packages available on the Comprehensive R Archive Network (CRAN) download servers. The price for all of this power is complexity. Because R analyses must be called as text commands, the user is required to find out the name of the function that will accomplish their task, and then remember that name along with the names of the variables to feed it, and its argument options.