Programming with Big Data in R (Pbdr)
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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). -
Computational Statistics in Practice
Computational Statistics in Practice Some Observations Dirk Eddelbuettel 10 November 2016 Invited Presentation Department of Statistics University of Illinois Urbana-Champaign, IL UIUC Nov 2016 1/147 Motivation UIUC Nov 2016 2/147 Almost all topics in twenty-first-century statistics are now computer-dependent [...] Here and in all our examples we are employing the language R, itself one of the key developments in computer-based statistical methodology. Efron and Hastie, 2016, pages xv and 6 (footnote 3) UIUC Nov 2016 3/147 A View of the World Computational Statistics in Practice • Statistics is now computational (Efron & Hastie, 2016) • Within (computational) statistics, reigning tool is R • Given R, Rcpp key for two angles: • Performance always matters, ease of use a sweetspot • “Extending R” (Chambers, 2016) • Time permitting • Being nice to other (languages) • an underdiscussed angle in industry UIUC Nov 2016 4/147 Overview / Outline / Plan Drawing on three Talks • Rcpp Introduction (from recent workshops / talks / courses) • [if time] If You Can’t Beat ’em (from JSS session at JSM) • [if time] Open Source Finance (from an industry conference) UIUC Nov 2016 5/147 About Me Brief Bio • PhD, MA Econometrics; MSc Ind.Eng. (Comp.Sci./OR) • Finance Quant for 20 years • Open Source for 22 years • Debian developer • R package author / contributor • R Foundation Board member, R Consortium ISC member • JSS Associate Editor UIUC Nov 2016 6/147 Rcpp: Introduction via Tweets UIUC Nov 2016 7/147 UIUC Nov 2016 8/147 UIUC Nov 2016 9/147 UIUC Nov 2016 10/147 UIUC Nov 2016 11/147 UIUC Nov 2016 12/147 UIUC Nov 2016 13/147 UIUC Nov 2016 14/147 UIUC Nov 2016 15/147 UIUC Nov 2016 16/147 Extending R UIUC Nov 2016 17/147 Why R? : Programming with Data from 1977 to 2016 Thanks to John Chambers for high-resolution cover images. -
Frequently Asked Questions About Rcpp
Frequently Asked Questions about Rcpp Dirk Eddelbuettel Romain François Rcpp version 0.12.9 as of January 14, 2017 Abstract This document attempts to answer the most Frequently Asked Questions (FAQ) regarding the Rcpp (Eddelbuettel, François, Allaire, Ushey, Kou, Russel, Chambers, and Bates, 2017; Eddelbuettel and François, 2011; Eddelbuettel, 2013) package. Contents 1 Getting started 2 1.1 How do I get started ?.....................................................2 1.2 What do I need ?........................................................2 1.3 What compiler can I use ?...................................................3 1.4 What other packages are useful ?..............................................3 1.5 What licenses can I choose for my code?..........................................3 2 Compiling and Linking 4 2.1 How do I use Rcpp in my package ?............................................4 2.2 How do I quickly prototype my code?............................................4 2.2.1 Using inline.......................................................4 2.2.2 Using Rcpp Attributes.................................................4 2.3 How do I convert my prototyped code to a package ?..................................5 2.4 How do I quickly prototype my code in a package?...................................5 2.5 But I want to compile my code with R CMD SHLIB !...................................5 2.6 But R CMD SHLIB still does not work !...........................................6 2.7 What about LinkingTo ?...................................................6 -
The Rockerverse: Packages and Applications for Containerisation
PREPRINT 1 The Rockerverse: Packages and Applications for Containerisation with R by Daniel Nüst, Dirk Eddelbuettel, Dom Bennett, Robrecht Cannoodt, Dav Clark, Gergely Daróczi, Mark Edmondson, Colin Fay, Ellis Hughes, Lars Kjeldgaard, Sean Lopp, Ben Marwick, Heather Nolis, Jacqueline Nolis, Hong Ooi, Karthik Ram, Noam Ross, Lori Shepherd, Péter Sólymos, Tyson Lee Swetnam, Nitesh Turaga, Charlotte Van Petegem, Jason Williams, Craig Willis, Nan Xiao Abstract The Rocker Project provides widely used Docker images for R across different application scenarios. This article surveys downstream projects that build upon the Rocker Project images and presents the current state of R packages for managing Docker images and controlling containers. These use cases cover diverse topics such as package development, reproducible research, collaborative work, cloud-based data processing, and production deployment of services. The variety of applications demonstrates the power of the Rocker Project specifically and containerisation in general. Across the diverse ways to use containers, we identified common themes: reproducible environments, scalability and efficiency, and portability across clouds. We conclude that the current growth and diversification of use cases is likely to continue its positive impact, but see the need for consolidating the Rockerverse ecosystem of packages, developing common practices for applications, and exploring alternative containerisation software. Introduction The R community continues to grow. This can be seen in the number of new packages on CRAN, which is still on growing exponentially (Hornik et al., 2019), but also in the numbers of conferences, open educational resources, meetups, unconferences, and companies that are adopting R, as exemplified by the useR! conference series1, the global growth of the R and R-Ladies user groups2, or the foundation and impact of the R Consortium3. -
Frequently Asked Questions About Rcpp
Frequently Asked Questions about Rcpp Dirk Eddelbuettel Romain François Rcpp version 0.12.7 as of September 4, 2016 Abstract This document attempts to answer the most Frequently Asked Questions (FAQ) regarding the Rcpp (Eddelbuettel, François, Allaire, Ushey, Kou, Chambers, and Bates, 2016a; Eddelbuettel and François, 2011; Eddelbuettel, 2013) package. Contents 1 Getting started 2 1.1 How do I get started ?.....................................................2 1.2 What do I need ?........................................................2 1.3 What compiler can I use ?...................................................3 1.4 What other packages are useful ?..............................................3 1.5 What licenses can I choose for my code?..........................................3 2 Compiling and Linking 4 2.1 How do I use Rcpp in my package ?............................................4 2.2 How do I quickly prototype my code?............................................4 2.2.1 Using inline.......................................................4 2.2.2 Using Rcpp Attributes.................................................4 2.3 How do I convert my prototyped code to a package ?..................................5 2.4 How do I quickly prototype my code in a package?...................................5 2.5 But I want to compile my code with R CMD SHLIB !...................................5 2.6 But R CMD SHLIB still does not work !...........................................6 2.7 What about LinkingTo ?...................................................6 -
Installing R and Cran Binaries on Ubuntu
INSTALLING R AND CRAN BINARIES ON UBUNTU COMPOUNDING MANY SMALL CHANGES FOR LARGER EFFECTS Dirk Eddelbuettel T4 Video Lightning Talk #006 and R4 Video #5 Jun 21, 2020 R PACKAGE INSTALLATION ON LINUX • In general installation on Linux is from source, which can present an additional hurdle for those less familiar with package building, and/or compilation and error messages, and/or more general (Linux) (sys-)admin skills • That said there have always been some binaries in some places; Debian has a few hundred in the distro itself; and there have been at least three distinct ‘cran2deb’ automation attempts • (Also of note is that Fedora recently added a user-contributed repo pre-builds of all 15k CRAN packages, which is laudable. I have no first- or second-hand experience with it) • I have written about this at length (see previous R4 posts and videos) but it bears repeating T4 + R4 Video 2/14 R PACKAGES INSTALLATION ON LINUX Three different ways • Barebones empty Ubuntu system, discussing the setup steps • Using r-ubuntu container with previous setup pre-installed • The new kid on the block: r-rspm container for RSPM T4 + R4 Video 3/14 CONTAINERS AND UBUNTU One Important Point • We show container use here because Docker allows us to “simulate” an empty machine so easily • But nothing we show here is limited to Docker • I.e. everything works the same on a corresponding Ubuntu system: your laptop, server or cloud instance • It is also transferable between Ubuntu releases (within limits: apparently still no RSPM for the now-current Ubuntu 20.04) -
Modeling Language Evolution and Feature Dynamics in a Realistic Geographic Environment
Modeling language evolution and feature dynamics in a realistic geographic environment Rhea Kapur Phillip Rogers The Pingry School Department of Linguistics [email protected] The University of California, Santa Barbara [email protected] Abstract Recent, innovative efforts to understand the uneven distribution of languages and linguistic fea- ture values in time and space attest to both the challenge these issues pose and the value in solving them. In this paper, we introduce a model for simulating languages and their features over time in a realistic geographic environment. At its core is a model of language phylogeny and migration whose parameters are chosen to reproduce known language family sizes and geographic disper- sions. This foundation in turn is used to explore the dynamics of linguistic features. Languages are assigned feature values that can change randomly or under the influence of nearby languages according to predetermined probabilities. We assess the effects of these settings on resulting geographic and genealogical patterns using homogeneity measures defined in the literature. The resulting model is both flexible and realistic, and it can be employed to answer a wide range of related questions.1 1 Introduction The distribution of linguistic feature values across time and space is uneven. The overall frequencies of particular feature values can be skewed with some highly frequent and others vanishingly rare, and many feature values have been observed to cluster geographically or within particular language families. Such patterns are most commonly explained in terms of the competing influences of inheritance, diffusion, universal tendencies of language drift, and chance. Understanding these dynamics has long been a goal of linguists, and it has important implications for historical linguistics, areal linguistics, and linguistic ty- pology. -
Seamless R and C Integration with Rcpp
Use R! Series Editors: Robert Gentleman Kurt Hornik Giovanni G. Parmigiani For further volumes: http://www.springer.com/series/6991 Dirk Eddelbuettel Seamless R and C++ Integration with Rcpp 123 Dirk Eddelbuettel River Forest Illinois, USA ISBN 978-1-4614-6867-7 ISBN 978-1-4614-6868-4 (eBook) DOI 10.1007/978-1-4614-6868-4 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2013933242 © The Author 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. -
Hadley Wickham @Hadleywickham Chief Scientist, Rstudio
Packages are easy! Hadley Wickham @hadleywickham Chief Scientist, RStudio May 2014 A package is a set of conventions that (with the right tools) makes your life easier A package is a set of conventions that (with the right tools) makes your life easier # The right tools: # # The latest version of R. # RStudio. # # Code development tools: # # * Windows: Rtools, download installer from # http://cran.r-project.org/bin/windows/Rtools/ # * OS X: xcode, free from the app store # * Linux: apt-get install r-base-dev (or similar) # # Packages that make your life easier: install.packages(c("devtools", "knitr", "Rcpp", "roxygen2", "testthat")) A package is a set of conventions that (with the right tools) makes your life easier R/ R code devtools::create("easy") # Creates DESCRIPTION # Creates rstudio project # Sets up R/ directory ! # NB: All devtools functions take a path to a # package as the first argument. If not supplied, # uses the current directory. Never use package.skeleton()! Why? It only works once, it does something v. easy to do by hand, and automates a job that needs to be manual # Add some R code ! load_all() ! # In rstudio: cmd + shift + l # Also automatically saves all files for you Programming cycle Identify the Modify and task save code NO Reload in R Write an Does it work? YES automated test Document More # Reloads changed code, data, … accurate # cmd + shift + l load_all() ! # Rstudio: Build & Reload # cmd + shift + b # Installs, restarts R, reloads ! # Builds & installs install() # (needed for vignettes) Faster + DESCRIPTION Who can use it, what it needs, and who wrote it Package: easy Title: What the package does (short line) Version: 0.1 Authors@R: "First Last <[email protected]> [aut, cre]" Description: What the package does (paragraph) Depends: R (>= 3.1.0) License: What license is it under? LazyData: true We’re done! ! That’s all you need to know about packages ! But you can also add data, documentation, unit tests, vignettes and C++ code + man/ Compiled documentation Roxygen2 • Essential for function level documentation. -
Determinants of Risk Perception Related to Exposure to Endocrine Disruptors During Pregnancy: a Qualitative and Quantitative Study on French Women
Article Determinants of Risk Perception Related to Exposure to Endocrine Disruptors during Pregnancy: A Qualitative and Quantitative Study on French Women Steeve Rouillon 1,2,3, Houria El Ouazzani 1,2,3, Sylvie Rabouan 1,2, Virginie Migeot 1,2,3 and Marion Albouy-Llaty 1,2,3,* 1 INSERM, University Hospital of Poitiers, University of Poitiers, Clinical Investigation Center 1402, 2 rue de la Milétrie, 86021 Poitiers CEDEX, France; [email protected] (S.Ro.); [email protected] (H.E.O.); [email protected] (S.Ra.); [email protected] (V.M.) 2 Faculty of Medicine and Pharmacy, University of Poitiers, 6 rue de la Milétrie, 86000 Poitiers, France 3 Department of Public Health, BioSPharm Pole, University Hospital of Poitiers, 2 rue de la Milétrie, 86021 Poitiers CEDEX, France * Correspondence: [email protected]; Tel.: +33-549-443-323 Received: 27 August 2018; Accepted: 5 October 2018; Published: 11 October 2018 Abstract: Endocrine disruptors (EDCs) are known as environmental exposure factors. However, they are rarely reported by health professionals in clinical practice, particularly during pregnancy, even though they are associated with many deleterious consequences. The objectives of this study were to estimate the risk perception of pregnant women related to EDC exposure and to evaluate its determinants. A qualitative study based on the Health Belief Model was carried out through interviews of pregnant women and focus group with perinatal, environmental health and prevention professionals in 2015 in the city of Poitiers, France. Then, determinants of risk perception were included in a questionnaire administered to 300 women in the perinatal period through a quantitative study. -
Rhea: a Transparent and Modular R Pipeline for Microbial Profiling Based on 16S Rrna Gene Amplicons
Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons Ilias Lagkouvardos, Sandra Fischer, Neeraj Kumar and Thomas Clavel ZIEL—Core Facility Microbiome/NGS, Technical University of Munich, Freising, Germany ABSTRACT The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well- documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea. -
Using R Via Rocker
USING R VIA ROCKER A BRIEF INTRODUCTION WITH EXAMPLES Dirk Eddelbuettel Köln R User Group 19 Nov 2020 https://dirk.eddelbuettel.com/papers/cologneRUG2020.pdf TODAY’S TALK Brief Outline • Docker Intro: Appeal, Motivation, “Lego” blocks, … • Brief Overview of Docker and its commands • Going Further: More about Rocker Köln RUG, Nov 2020 2/45 DOCKER A Personal Timeline • Docker itself started in 2013 • I started experimenting with it around spring of 2014 • Was convinced enough to say ‘will change how we build and test’ in keynote at useR! 2014 conference in mid-2014 • Started the Rocker Project with Carl Boettiger fall 2014 • Gave three-hour tutorial at useR! in 2015 • Active development of multiple (widely-used) containers • Introductory paper in R Journal, 2017 • Rockerverse paper (lead by Daniel Nuest) in R Journal, 2020 Köln RUG, Nov 2020 3/45 MOTIVATION Source: https://lemire.me/blog/2020/05/22/programming-inside-a-container/ Köln RUG, Nov 2020 4/45 MOTIVATION Excellent discussion by Daniel Lemire The idea of a container approach is to always start from a pristine state. So you define the configuration that your database server needs to have, and you launch it, in this precise state each time. This makes your infrastructure predictable. We can of course substitute “predictable” with “reproducible” … Köln RUG, Nov 2020 5/45 Source: https://commons.wikimedia.org/wiki/File:3_D-Box.jpg ON IN SIMPLER TERMS: Köln RUG, Nov 2020 6/45 ON IN SIMPLER TERMS: Source: https://commons.wikimedia.org/wiki/File:3_D-Box.jpg Köln RUG, Nov 2020 6/45 ON IN