Reproducible Research with R and RStudio 2nd ed

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Title: Reproducible Research with R and RStudio 2nd ed
Authors: Christopher Gandrud
Edition: 2
Finished Date: 2014-03-19
Rating: 5
Language: English
Genres: Programming, R, Software, RStudio
Level: Entry
Publishers: Chapman and Hall/CRC
Publication Date: 2015-06-13
ISBN: 978-1498715379
Format: Pdf
Pages: 324
Download: Pdf

Ch 13

presenting on web with Markdown

  • Markdown

  • mathjax

    • javascript
    • same syntax with LaTex
      • especially used from RStudio
    • Markdwon documents rendered in RSTudio automaticallyt linnk to the mathJax engin online
    • $$latex
      s^{2} = \frac{\sum(x - \bar{x})^2}{n-1}
      $$

      $latex s^{2} = \frac{\sum(x - \bar{x})^2}{n-1} $

  • Pandoc

    • footnote
    • bibliographies
    • 2 ways
      • command
        • system
      • knitr’s pandoc wrapper command
    • mechanism
      • knit convert .Rmd to .md
        • markdownToHTML from markdown package convert md to HTML
      • ToHTML or reset markdownToHTML
  • css
    • look of the document

I getting started

1

  • intro to preproducible research

    • research presents (ads) in
      • slideshows
      • journal articles
      • books
      • websites
    • these documents are not the research
    • the research is

      • full software environment, code , and data that produced the results
      • When separating the research form its ads,
        we are making it difficult for others to verify the findings by reproducing them
    • solution

      • combine research with the presentation of findings
      • workflow
        • data gathering
        • statistical analysis
        • presentation of result
      • tools
        • R
          • gather and analyze data
        • Latex, Markdown
          • create documents
            • slideeshows
            • articles
            • books
            • webpages
          • knitr for R
          • command line shell programs
            • GNU Make
            • Git version control
        • RStudio
    • reproducible research

      • the data and code used to make a finding are available
        and they are sufficient for an independent researcher to recreate the finding
    • advantage

      • better work habits
      • better teamwork
      • changes are easier
      • higher research impact
        • cite more
    • 2 sets of tools

      • reproducible research environment

        • statistical tools
        • the ability to automatically track the provenance of data, analyses,
          and results and to package them for redistribution
      • reproducible research publisher

        • prepare dynamic documents for presenting results
        • easily linked to reproducible research environment
    • tools covered in the book
      • R
      • knitr
        • literate programming
        • work with R, Bash, Python, and Ruby
      • Markup language
      • RStudio
      • cloud storage & versioning
        • dropbox
        • Git/Github
      • Unix-like shell programs
        • GNU Make
          • compiling projects
          • mac
            • Xcode
          • windows
            • included in Rtools
        • Pandoc
          • convert documents from one markup language to another
    • books
      • The R Book
      • R in Action
      • Dynamic Documents with R and knitr
      • The Art of R Programming: A Tour of Statistical Design Software
      • The Linux Command Line: A complete Introduction
        • intro to the command line in Linux and Mac
        • Windows PwoerShell

2

  • getting started with reproducible research: workflow
    • start from beginning of research
    • 3 basic stages of a typical computational empirical research project
      • data gathering
        • Part II
      • data analyis
        • Part III
      • results presentation
        • Part IV

packages used

install.packages(c(“apsrtable”, “brew”, “countrycode”,

"devtools", "digest", "formatR", "gdata",    
"ggplot2", "googleVis", "httr", "knitcitations",    
"knitr", "markdown", "openair", "plyr",   
"quantmod", "repmis", "reshape2",   
"RCurl", "rjson", "RJSONIO", "stargazer",   
"texreg", "tools", "treebase",   
"twitteR", "WDI", "XML",                   
"xtable", "Zelig"))  

install.packages(“ZeligBayesian”, repos = “, type = “source”)

libarary(devtools)

install_github(“slidfy”, “ramnathv”)
install_github(“slidifyLibraries”, “ramnathv”)