Rmarkdown : : CHEAT SHEET RENDERED OUTPUT File Path to Output Document SOURCE EDITOR What Is Rmarkdown? 1

Total Page:16

File Type:pdf, Size:1020Kb

Rmarkdown : : CHEAT SHEET RENDERED OUTPUT File Path to Output Document SOURCE EDITOR What Is Rmarkdown? 1 rmarkdown : : CHEAT SHEET RENDERED OUTPUT file path to output document SOURCE EDITOR What is rmarkdown? 1. New File Write with 5. Save and Render 6. Share find in document .Rmd files · Develop your code and publish to Markdown ideas side-by-side in a single rpubs.com, document. Run code as individual shinyapps.io, The syntax on the lef renders as the output on the right. chunks or as an entire document. set insert go to run code RStudio Connect Rmd preview code code chunk(s) Plain text. Plain text. Dynamic Documents · Knit together location chunk chunk show End a line with two spaces to End a line with two spaces to plots, tables, and results with outline start a new paragraph. start a new paragraph. narrative text. Render to a variety of 4. Set Output Format(s) Also end with a backslash\ Also end with a backslash formats like HTML, PDF, MS Word, or and Options reload document to make a new line. to make a new line. MS Powerpoint. *italics* and **bold** italics and bold Reproducible Research · Upload, link superscript^2^/subscript~2~ superscript2/subscript2 to, or attach your report to share. ~~strikethrough~~ strikethrough Anyone can read or run your code to 3. Write Text run all escaped: \* \_ \\ escaped: * _ \ reproduce your work. previous modify chunks endash: --, emdash: --- endash: –, emdash: — chunk run options current # Header 1 Header 1 chunk ## Header 2 Workflow ... Header 2 2. Embed Code ... 11. Open a new .Rmd file in the RStudio IDE by ###### Header 6 Header 6 going to File > New File > R Markdown. - unordered list • unordered list 22. Embed code in chunks. Run code by line, by - item 2 • item 2 chunk, or all at once. - item 2a (indent 1 tab) • item 2a (indent 1 tab) - item 2b • item 2b 33. Write text and add tables, figures, images, and 1. ordered list 1. ordered list citations. Format with Markdown syntax or the 2. item 2 2. item 2 RStudio Visual Markdown Editor. - item 2a (indent 1 tab) • item 2a (indent 1 tab) VISUAL EDITOR insert citations style options - item 2b • item 2b 44. Set output format(s) and options in the YAML Insert Citations header. Customize themes or add parameters Create citations from a bibliography file, a Zotero library, <link url> http://www.rstudio.com/ to execute or add interactivity with Shiny. or from DOI references. [This is a link.](link url) This is a link. 55. Save and render the whole document. Knit BUILD YOUR BIBLIOGRAPHY [This is another link][id]. This is another link. periodically to preview your work as you write. At the end of the document: • Add BibTeX or CSL bibliographies to the YAML header. [id]: link url 66. Share your work! add/edit --- attributes title: "My Document" ![Caption](image.png) bibliography: references.bib or ![Caption][id2] link-citations: TRUE At the end of the document: --- [id2]: image.png Caption. Embed Code with knitr • If Zotero is installed locally, your main library will `verbatim code` verbatim code automatically be available. OPTION DEFAULT EFFECTS ``` CODE CHUNKS multiple lines echo TRUE display code in output document • Add citations by DOI by searching "from DOI" in the multiple lines Surround code chunks with ```{r} and ``` or use of verbatim code of verbatim code the Insert Code Chunk button. Add a chunk label TRUE (display error messages in doc) Insert Citation dialog. ``` error FALSE FALSE (stop render when error occurs) and/or chunk options inside the curly braces afer r. > block quotes block quotes eval TRUE run code in chunk INSERT CITATIONS include TRUE include chunk in doc afer running • Access the Insert Citations dialog in the Visual Editor ```{r chunk-label, include=FALSE} equation: $e^{i \pi} + 1 = 0$ equation: eiπ + 1 = 0 message TRUE display code messages in document by clicking the @ symbol in the toolbar or by clicking summary(mtcars) equation block: ``` warning TRUE display code warnings in document Insert > Citation. equation block: $$E = mc^{2}$$ 2 "asis" (passthrough results) • Add citations with markdown syntax by typing [@cite] E = m c SET GLOBAL OPTIONS results "markup" "hide" (don't display results) or @cite. horizontal rule: horizontal rule: "hold" (put all results below all code) --- Set options for the entire document in the first chunk. fig.align "default" "lef", "right", or "center" fig.alt NULL alt text for a figure | Right | Lef | Default | Center | Right Lef Default Center ```{r include=FALSE} Insert Tables |-------:|:------|-----------|:---------:| fig.cap NULL figure caption as a character string 12 12 12 12 knitr::opts_chunk$set(message = FALSE) Output data frames as tables using | 12 | 12 | 12 | 12 | fig.path "figure/" prefix for generating figure file paths ``` kable(data, caption). | 123 | 123 | 123 | 123 | 123 123 123 123 fig.width & 7 plot dimensions in inches | 1 | 1 | 1 | 1 | 1 1 1 1 INLINE CODE fig.height out.width rescales output width, e.g. "75%", "300px" ```{r} Insert `r <code>` into text sections. Code is evaluated collapse FALSE collapse all sources & output into a single block data <- faithful[1:4, ] HTML Tabsets at render and results appear as text. comment "##" prefix for each line of results knitr::kable(data, # Results {.tabset} Results ## Plots text "Built with `r getRversion()`" --> "Built with 4.1.0" child NULL files(s) to knit and then include caption = "Table with kable") text Plots Tables include or exclude a code chunk when ``` purl TRUE extracting source code with knitr::purl() ## Tables text See more options and defaults by running str(knitr::opts_chunk$get()) Other table packages include flextable, gt, and kableExtra. more text RStudio® is a trademark of RStudio, PBC • CC BY SA RStudio • [email protected] • 844-448-1212 • rstudio.com • Learn more at rmarkdown.rstudio.com • rmarkdown 2.9.4 • Updated: 2021-08 Set Output Formats and their Options in YAML Render IMPORTANT OPTIONS DESCRIPTION Use the document's YAML header to set an output HTML PDF MS Word MS PPT When you render a format and customize it with output options. anchor_sections Show section anchors on mouse hover (TRUE or FALSE) X document, rmarkdown: --- citation_package The LaTeX package to process citations ("default", "natbib", "biblatex") X 1. Runs the code and embeds title: "My Document" code_download Give readers an option to download the .Rmd source code (TRUE or FALSE) X results and text into an .md author: "Author Name" file with knitr. output: code_folding Let readers to toggle the display of R code ("none", "hide", or "show") X html_document: 2. Converts the .md file into the output format with Indent format 2 characters, css CSS or SCSS file to use to style document (e.g. "style.css") X toc: TRUE indent options 4 characters Pandoc. --- dev Graphics device to use for figure output (e.g. "png", "pdf") X X knitr pandoc HTML .Rmd .md PDF df_print Method for printing data frames ("default", "kable", "tibble", "paged") X X X X DOC OUTPUT FORMAT CREATES fig_caption Should figures be rendered with captions (TRUE or FALSE) X X X X html_document .html Save, then Knit to preview the document output. pdf_document* .pdf highlight Syntax highlighting ("tango", "pygments", "kate", "zenburn", "textmate") X X X The resulting HTML/PDF/MS Word/etc. document will word_document Microsof Word (.docx) includes File of content to place in doc ("in_header", "before_body", "afer_body") X X be created and saved in the same directory as the .Rmd file. powerpoint_presentation Microsof Powerpoint (.pptx) keep_md Keep the Markdown .md file generated by knitting (TRUE or FALSE) X X X X odt_document OpenDocument Text Use rmarkdown::render() to render/knit in the R keep_tex Keep the intermediate TEX file used to convert to PDF (TRUE or FALSE) X rtf_document Rich Text Format console. See ?render for available options. latex_engine LaTeX engine for producing PDF output ("pdflatex", "xelatex", or "lualatex") X md_document Markdown github_document Markdown for Github reference_docx/_doc docx/pptx file containing styles to copy in the output (e.g. "file.docx", "file.pptx") X X Share ioslides_presentation ioslides HTML slides theme Theme options (see Bootswatch and Custom Themes below) X Publish on RStudio Connect slidy_presentation Slidy HTML slides toc Add a table of contents at start of document (TRUE or FALSE) X X X X to share R Markdown documents beamer_presentation* Beamer slides toc_depth The lowest level of headings to add to table of contents (e.g. 2, 3) X X X X securely, schedule automatic * Requires LaTeX, use tinytex::install_tinytex() toc_float Float the table of contents to the lef of the main document content (TRUE or FALSE) X updates, and interact with parameters in real time. Also see flexdashboard, bookdown, distill, and blogdown. Use ?<output format> to see all of a format's options, e.g. ?html_document rstudio.com/products/connect/ More Header Options PARAMETERS BOOTSWATCH THEMES STYLING WITH CSS AND SCSS INTERACTIVITY Parameterize your documents to reuse with new Customize HTML documents with Bootswatch Add CSS and SCSS to your document by adding a Turn your report into an interactive inputs (e.g., data, values, etc.). themes from the bslib package using the theme path to a file with the css option in the YAML header. Shiny document in 4 steps: --- output option. 1. Add runtime: shiny to the YAML header. 1. Add parameters params: --- in the header as state: "hawaii" Use bslib::bootswatch_themes() to list available title: "My Document" 2. Call Shiny input functions to embed input objects. --- themes. sub-values of author: "Author Name" 3. Call Shiny render functions to embed reactive params. ```{r} --- output: output. html_document: data <- df[, params$state] title: "Document Title" 4. Render with rmarkdown::run() or click Run 2. Call parameters summary(data) author: "Author Name" css: "style.css" in code using ``` output: --- Document in RStudio IDE.
Recommended publications
  • A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research Krzysztof J
    bioRxiv preprint first posted online Feb. 12, 2016; doi: http://dx.doi.org/10.1101/039354. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It is made available under a CC-BY 4.0 International license. A practical guide for improving transparency and reproducibility in neuroimaging research Krzysztof J. Gorgolewski and Russell A. Poldrack Department of Psychology, Stanford University Abstract Recent years have seen an increase in alarming signals regarding the lack of replicability in neuroscience, psychology, and other related fields. To avoid a widespread crisis in neuroimaging research and consequent loss of credibility in the public eye, we need to improve how we do science. This article aims to be a practical guide for researchers at any stage of their careers that will help them make their research more reproducible and transparent while minimizing the additional effort that this might require. The guide covers three major topics in open science (data, code, and publications) and offers practical advice as well as highlighting advantages of adopting more open research practices that go beyond improved transparency and reproducibility. Introduction The question of how the brain creates the mind has captivated humankind for thousands of years. With recent advances in human in vivo brain imaging, we how have effective tools to ​ ​ peek into biological underpinnings of mind and behavior. Even though we are no longer constrained just to philosophical thought experiments and behavioral observations (which undoubtedly are extremely useful), the question at hand has not gotten any easier. These powerful new tools have largely demonstrated just how complex the biological bases of behavior actually are.
    [Show full text]
  • Reproducible Reports with Knitr and R Markdown
    Reproducible Reports with knitr and R Markdown https://dl.dropboxusercontent.com/u/15335397/slides/2014-UPe... Reproducible Reports with knitr and R Markdown Yihui Xie, RStudio 11/22/2014 @ UPenn, The Warren Center 1 of 46 1/15/15 2:18 PM Reproducible Reports with knitr and R Markdown https://dl.dropboxusercontent.com/u/15335397/slides/2014-UPe... An appetizer Run the app below (your web browser may request access to your microphone). http://bit.ly/upenn-r-voice install.packages("shiny") Or just use this: https://yihui.shinyapps.io/voice/ 2/46 2 of 46 1/15/15 2:18 PM Reproducible Reports with knitr and R Markdown https://dl.dropboxusercontent.com/u/15335397/slides/2014-UPe... Overview and Introduction 3 of 46 1/15/15 2:18 PM Reproducible Reports with knitr and R Markdown https://dl.dropboxusercontent.com/u/15335397/slides/2014-UPe... I know you click, click, Ctrl+C and Ctrl+V 4/46 4 of 46 1/15/15 2:18 PM Reproducible Reports with knitr and R Markdown https://dl.dropboxusercontent.com/u/15335397/slides/2014-UPe... But imagine you hear these words after you finished a project Please do that again! (sorry we made a mistake in the data, want to change a parameter, and yada yada) http://nooooooooooooooo.com 5/46 5 of 46 1/15/15 2:18 PM Reproducible Reports with knitr and R Markdown https://dl.dropboxusercontent.com/u/15335397/slides/2014-UPe... Basic ideas of dynamic documents · code + narratives = report · i.e. computing languages + authoring languages We built a linear regression model.
    [Show full text]
  • R Markdown Cheat Sheet I
    1. Workflow R Markdown is a format for writing reproducible, dynamic reports with R. Use it to embed R code and results into slideshows, pdfs, html documents, Word files and more. To make a report: R Markdown Cheat Sheet i. Open - Open a file that ii. Write - Write content with the iii. Embed - Embed R code that iv. Render - Replace R code with its output and transform learn more at rmarkdown.rstudio.com uses the .Rmd extension. easy to use R Markdown syntax creates output to include in the report the report into a slideshow, pdf, html or ms Word file. rmarkdown 0.2.50 Updated: 8/14 A report. A report. A report. A report. A plot: A plot: A plot: A plot: Microsoft .Rmd Word ```{r} ```{r} ```{r} = = hist(co2) hist(co2) hist(co2) ``` ``` Reveal.js ``` ioslides, Beamer 2. Open File Start by saving a text file with the extension .Rmd, or open 3. Markdown Next, write your report in plain text. Use markdown syntax to an RStudio Rmd template describe how to format text in the final report. syntax becomes • In the menu bar, click Plain text File ▶ New File ▶ R Markdown… End a line with two spaces to start a new paragraph. *italics* and _italics_ • A window will open. Select the class of output **bold** and __bold__ you would like to make with your .Rmd file superscript^2^ ~~strikethrough~~ • Select the specific type of output to make [link](www.rstudio.com) with the radio buttons (you can change this later) # Header 1 • Click OK ## Header 2 ### Header 3 #### Header 4 ##### Header 5 ###### Header 6 4.
    [Show full text]
  • HGC: a Fast Hierarchical Graph-Based Clustering Method
    Package ‘HGC’ September 27, 2021 Type Package Title A fast hierarchical graph-based clustering method Version 1.1.3 Description HGC (short for Hierarchical Graph-based Clustering) is an R package for conducting hierarchical clustering on large-scale single-cell RNA-seq (scRNA-seq) data. The key idea is to construct a dendrogram of cells on their shared nearest neighbor (SNN) graph. HGC provides functions for building graphs and for conducting hierarchical clustering on the graph. The users with old R version could visit https://github.com/XuegongLab/HGC/tree/HGC4oldRVersion to get HGC package built for R 3.6. License GPL-3 Encoding UTF-8 SystemRequirements C++11 Depends R (>= 4.1.0) Imports Rcpp (>= 1.0.0), RcppEigen(>= 0.3.2.0), Matrix, RANN, ape, dendextend, ggplot2, mclust, patchwork, dplyr, grDevices, methods, stats LinkingTo Rcpp, RcppEigen Suggests BiocStyle, rmarkdown, knitr, testthat (>= 3.0.0) VignetteBuilder knitr biocViews SingleCell, Software, Clustering, RNASeq, GraphAndNetwork, DNASeq Config/testthat/edition 3 NeedsCompilation yes git_url https://git.bioconductor.org/packages/HGC git_branch master git_last_commit 61622e7 git_last_commit_date 2021-07-06 Date/Publication 2021-09-27 1 2 CKNN.Construction Author Zou Ziheng [aut], Hua Kui [aut], XGlab [cre, cph] Maintainer XGlab <[email protected]> R topics documented: CKNN.Construction . .2 FindClusteringTree . .3 HGC.dendrogram . .4 HGC.parameter . .5 HGC.PlotARIs . .6 HGC.PlotDendrogram . .7 HGC.PlotParameter . .8 KNN.Construction . .9 MST.Construction . 10 PMST.Construction . 10 Pollen . 11 RNN.Construction . 12 SNN.Construction . 12 Index 14 CKNN.Construction Building Unweighted Continuous K Nearest Neighbor Graph Description This function builds a Continuous K Nearest Neighbor (CKNN) graph in the input feature space using Euclidean distance metric.
    [Show full text]
  • Statistics with Free and Open-Source Software
    Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source
    [Show full text]
  • Conda-Build Documentation Release 3.21.5+15.G174ed200.Dirty
    conda-build Documentation Release 3.21.5+15.g174ed200.dirty Anaconda, Inc. Sep 27, 2021 CONTENTS 1 Installing and updating conda-build3 2 Concepts 5 3 User guide 17 4 Resources 49 5 Release notes 115 Index 127 i ii conda-build Documentation, Release 3.21.5+15.g174ed200.dirty Conda-build contains commands and tools to use conda to build your own packages. It also provides helpful tools to constrain or pin versions in recipes. Building a conda package requires installing conda-build and creating a conda recipe. You then use the conda build command to build the conda package from the conda recipe. You can build conda packages from a variety of source code projects, most notably Python. For help packing a Python project, see the Setuptools documentation. OPTIONAL: If you are planning to upload your packages to Anaconda Cloud, you will need an Anaconda Cloud account and client. CONTENTS 1 conda-build Documentation, Release 3.21.5+15.g174ed200.dirty 2 CONTENTS CHAPTER ONE INSTALLING AND UPDATING CONDA-BUILD To enable building conda packages: • install conda • install conda-build • update conda and conda-build 1.1 Installing conda-build To install conda-build, in your terminal window or an Anaconda Prompt, run: conda install conda-build 1.2 Updating conda and conda-build Keep your versions of conda and conda-build up to date to take advantage of bug fixes and new features. To update conda and conda-build, in your terminal window or an Anaconda Prompt, run: conda update conda conda update conda-build For release notes, see the conda-build GitHub page.
    [Show full text]
  • Writing Reproducible Reports Knitr with R Markdown
    Writing reproducible reports knitr with R Markdown Karl Broman Biostatistics & Medical Informatics, UW–Madison kbroman.org github.com/kbroman @kwbroman Course web: kbroman.org/AdvData I To estimate a p-value? I To estimate some other quantity? I To estimate power? How many simulation replicates? 2 I To estimate a p-value? I To estimate some other quantity? How many simulation replicates? I To estimate power? 2 I To estimate some other quantity? How many simulation replicates? I To estimate power? I To estimate a p-value? 2 How many simulation replicates? I To estimate power? I To estimate a p-value? I To estimate some other quantity? 2 Data analysis reports I Figures/tables + email I Static Word document I LATEX + R ! PDF I R Markdown = knitr + Markdown ! Web page 3 What if the data change? What if you used the wrong version of the data? 4 rmarkdown.rstudio.com knitr in a knutshell kbroman.org/knitr_knutshell 5 knitr in a knutshell kbroman.org/knitr_knutshell rmarkdown.rstudio.com 5 knitr code chunks Input to knitr: We see that this is an intercross with `r nind(sug)` individuals. There are `r nphe(sug)` phenotypes, and genotype data at `r totmar(sug)` markers across the `r nchr(sug)` autosomes. The genotype data is quite complete. ```{r summary_plot, fig.height=8} plot(sug) ``` Output from knitr: We see that this is an intercross with 163 individuals. There are 6 phenotypes, and genotype data at 93 markers across the 19 autosomes. The genotype data is quite complete. ```r plot(sug) ``` ![plot of chunk summary_plot](RmdFigs/summary_plot.png) 6 html <!DOCTYPE html> <html> <head> <meta charset=utf-8"/> <title>Example html file</title> </head> <body> <h1>Markdown example</h1> <p>Use a bit of <strong>bold</strong> or <em>italics</em>.
    [Show full text]
  • Knitr with R Markdown
    Writing reproducible reports knitr with R Markdown Karl Broman Biostatistics & Medical Informatics, UW–Madison kbroman.org github.com/kbroman @kwbroman Course web: kbroman.org/Tools4RR knitr in a knutshell kbroman.org/knitr_knutshell 2 Data analysis reports I Figures/tables + email I Static LATEX or Word document I knitr/Sweave + LATEX ! PDF I knitr + Markdown ! Web page 3 What if the data change? What if you used the wrong version of the data? 4 knitr code chunks Input to knitr: We see that this is an intercross with `r nind(sug)` individuals. There are `r nphe(sug)` phenotypes, and genotype data at `r totmar(sug)` markers across the `r nchr(sug)` autosomes. The genotype data is quite complete. ```{r summary_plot, fig.height=8} plot(sug) ``` Output from knitr: We see that this is an intercross with 163 individuals. There are 6 phenotypes, and genotype data at 93 markers across the 19 autosomes. The genotype data is quite complete. ```r plot(sug) ``` ![plot of chunk summary_plot](RmdFigs/summary_plot.png) 5 html <!DOCTYPE html> <html> <head> <meta charset=utf-8"/> <title>Example html file</title> </head> <body> <h1>Markdown example</h1> <p>Use a bit of <strong>bold</strong> or <em>italics</em>. Use backticks to indicate <code>code</code> that will be rendered in monospace.</p> <ul> <li>This is part of a list</li> <li>another item</li> </ul> </body> </html> [Example] 6 CSS ul,ol { margin: 0 0 0 35px; } a { color: purple; text-decoration: none; background-color: transparent; } a:hover { color: purple; background: #CAFFFF; } [Example] 7 Markdown # Markdown example Use a bit of **bold** or _italics_.
    [Show full text]
  • 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 ...........................................
    [Show full text]
  • Using GNU Make to Manage the Workflow of Data Analysis Projects
    JSS Journal of Statistical Software June 2020, Volume 94, Code Snippet 1. doi: 10.18637/jss.v094.c01 Using GNU Make to Manage the Workflow of Data Analysis Projects Peter Baker University of Queensland Abstract Data analysis projects invariably involve a series of steps such as reading, cleaning, summarizing and plotting data, statistical analysis and reporting. To facilitate repro- ducible research, rather than employing a relatively ad-hoc point-and-click cut-and-paste approach, we typically break down these tasks into manageable chunks by employing sep- arate files of statistical, programming or text processing syntax for each step including the final report. Real world data analysis often requires an iterative process because many of these steps may need to be repeated any number of times. Manually repeating these steps is problematic in that some necessary steps may be left out or some reported results may not be for the most recent data set or syntax. GNU Make may be used to automate the mundane task of regenerating output given dependencies between syntax and data files. In addition to facilitating the management of and documenting the workflow of a complex data analysis project, such automation can help minimize errors and make the project more reproducible. It is relatively simple to construct Makefiles for small data analysis projects. As projects increase in size, difficulties arise because GNU Make does not have inbuilt rules for statistical and related software. Without such rules, Makefiles can become unwieldy and error-prone. This article addresses these issues by providing GNU Make pattern rules for R, Sweave, rmarkdown, SAS, Stata, Perl and Python to streamline management of data analysis and reporting projects.
    [Show full text]
  • Healing the Fragmentation to Realise the Full Potential of The
    HealingHealing thethe fragmentationfragmentation toto realiserealise thethe fullfull potentialpotential of thethe IoTIoT Dr.Dr. DaveDave Raggett,Raggett, W3C/ERCIMW3C/ERCIM email:email: [email protected]@w3.org 2525 JuneJune 20202020 ThisThis talktalk isis supportedsupported byby thethe CreateCreate--IoTIoT CoordinationCoordination andand SupportSupport ActionAction withwith fundingfunding fromfrom thethe EuropeanEuropean CommissionCommission Investments in IoT are at risk! Eric Siow, Director, Open Web Platform Standards and Ecosystem Strategies at Intel: • IoT is a little over 10 years old • Hype has been much greater than present reality • IoT is “biting off more that it can chew” • Trying to address too many markets • Involves too many and mostly uncoordinated SDOs and SIGs 2/14 Key IoT Challenges Facing Smart Cities • Lack of coalescence around a set of complementary standards • Hinders scalability, interoperability and evolution • Need to simplify: prioritise and define requirements • Regional regulatory differences adding to the confusion • Diverse requirements impede scalability of the market • Need regulatory agencies to participate and help with standardisation requirements • Lack of interoperability wastes up to 40% of IoT value1 • Cities and technology partners may waste up to $321 billion by 20252 1. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/the-internet-of-things-the-value-of-digitizing-the-physical-world 2. https://machinaresearch.com/news/smart-cities-could-waste-usd341-billion-by-2025-on-non-standardized-iot-deployments/
    [Show full text]
  • B.Tech. in Computer Science and Engineering with Specialization in Big Data Analytics
    B.Tech. in Computer Science and Engineering with Specialization in Big Data Analytics Mission of the Department Mission Stmt - 1 To impart knowledge in cutting edge Computer Science and Engineering technologies in par with industrial standards. To collaborate with renowned academic institutions to uplift innovative research and development in Computer Science and Engineering and Mission Stmt - 2 its allied fields to serve the needs of society To demonstrate strong communication skills and possess the ability to design computing systems individually as well as part of a Mission Stmt - 3 multidisciplinary teams. Mission Stmt - 4 To instill societal, safety, cultural, environmental, and ethical responsibilities in all professional activities To produce successful Computer Science and Engineering graduates with personal and professional responsibilities and commitment to Mission Stmt - 5 lifelong learning Program Educational Objectives (PEO) Graduates will be able to perform in technical/managerial roles ranging from design, development, problem solving to production support in software PEO - 1 industries and R&D sectors. PEO - 2 Graduates will be able to successfully pursue higher education in reputed institutions. Graduates will have the ability to adapt, contribute and innovate new technologies and systems in the key domains of Computer Science and PEO - 3 Engineering. PEO - 4 Graduates will be ethically and socially responsible solution providers and entrepreneurs in Computer Science and other engineering disciplines. PEO - 5 Graduates will possess the additional skills in handling Big Data using the state-of-art tools and techniques PEO - 6 Graduates will be able to apply the principles of data science for providing real world business solutions Mission of the Department to Program Educational Objectives (PEO) Mapping Mission Stmt.
    [Show full text]