Scott Chamberlain

@recology_ & @ropensci

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Science is often not reproducible or repeatable, even within the same lab group over time.

Increasing attention in the press...

...more in the press...

...and a recent initiative to address the problem...


What do we need?



Open Science


Open data + code

Source: Wolkovich et al. GCB 2012.



But, scientists don't want to share :(





Source: http://bit.ly/Tprrx8



But, this =>


Source: PLOS, 2007



R + Open Science



Open Science needs open source tools



Source: Revolution Analytics, 2010, Nature editorial, 2012

The old way...

The R way, e.g.,...

Make an API call

library(RCurl); library(RJSONIO)
dat <- fromJSON(getURL("https://api.github.com/users/hadley/repos"))

Manipulate the data

library(plyr); library(reshape2)
dat_df <- ldply(dat, function(x) as.data.frame(x[names(x) %in% c("name", "watchers_count", 
    "forks", "open_issues")]))
dat_melt <- melt(dat_df)

Run some statistical model

lm(value ~ variable, data = dat_melt)

Visualize results

library(ggplot2)
ggplot(dat_melt, aes(name, value, colour = variable)) + geom_point() + coord_flip()


This is reproducible, repeatable and can serve as a analytic workflow.

Why R...




Wrapping all science APIs




Development team


Carl Boettiger
Karthik Ram
Scott Chamberlain
Edmund Hart



Advisory team


Duncan
Temple Lang
Hadley Wickham
JJ Allaire
Bertram
Ludascher
Matt Jones


rOpenSci packages

Data packages


Literature packages


Hybrid packages



rOpenSci packages

http://ropensci.org/packages/index.html



rOpenSci packages

https://github.com/ropensci



Some stats on rOpenSci packages

Data via GitHub API, run in R function via OpenCPU.org



Data sources (click to go)



Literature sources



Coming when APIs ready/we get around to it...

eLife


Umbrella packages

Abstract away the detail of each package to search across data sources on:



Getting data from the web into R



Authentication: R and APIs - API keys

API keys can be stored in a users.Rprofile file

Click here for help on .Rprofile files 

 
options(MendeleyKey = "uf5daib7wyil7ag5buc")
options(MendeleyPrivateKey = "faj2os5dyd7jop2fok6")
options(PlosApiKey = "ef3vip9yak7od3hud4g")
options(SpringerMetdataKey = "ri9hi7woc6jax4vaf8w")





Note: These keys aren't real.

Wrapping APIs in R


in the browser...

Wrapping APIs in R


same URL in RCurl::getURL...

# Call the github json data within R using the RCurl pkg
library(RCurl)

getURL("https://api.github.com/repos/hadley/ggplot2")  # or getForm()/postForm()

[1] "{\"has_downloads\":true,\"full_name\":\"hadley/ggplot2\",\"owner\":{\"gravatar_id\":\"7ba164f40a50bc23dbb2aa825fb7bc16\",\"login\":\"hadley\",\"avatar_url\":\"https://secure.gravatar.com/avatar/7ba164f40a50bc23dbb2aa825fb7bc16?d=https://a248.e.akamai.net/assets.github.com%2Fimages%2Fgravatars%2Fgravatar-140.png\",\"url\":\"https://api.github.com/users/hadley\",\"id\":4196},\"forks_count\":53,\"homepage\":\"http://had.co.nz/ggplot2\",\"svn_url\":\"https://github.com/hadley/ggplot2\",\"mirror_url\":null,\"git_url\":\"git://github.com/hadley/ggplot2.git\",\"pushed_at\":\"2012-08-17T20:49:44Z\",\"network_count\":53,\"forks\":53,\"has_wiki\":true,\"language\":\"R\",\"created_at\":\"2008-05-25T01:21:32Z\",\"watchers\":392,\"watchers_count\":392,\"description\":\"An implementation of the Grammar of Graphics in R\",\"html_url\":\"https://github.com/hadley/ggplot2\",\"clone_url\":\"https://github.com/hadley/ggplot2.git\",\"open_issues\":106,\"open_issues_count\":106,\"has_issues\":true,\"size\":1722,\"fork\":false,\"updated_at\":\"2012-08-21T16:29:13Z\",\"ssh_url\":\"git@github.com:hadley/ggplot2.git\",\"name\":\"ggplot2\",\"url\":\"https://api.github.com/repos/hadley/ggplot2\",\"private\":false,\"id\":19438,\"master_branch\":\"master\"}"

Wrapping APIs in R


same URL in RCurl::getURL...parsed

# And parse the results to more R friendly list
library(RJSONIO)

fromJSON(getURL("https://api.github.com/repos/hadley/ggplot2"))

$has_downloads
[1] TRUE

$full_name
[1] "hadley/ggplot2"
....

Wrapping APIs in R


same URL in httr::GET...

# Or use httr package by Hadley Wickham
library(httr)
tt <- GET("https://api.github.com/repos/hadley/ggplot2")
content(tt)  # content auto-detects data type, and parses

$has_downloads
[1] TRUE

$full_name
[1] "hadley/ggplot2"
....

Wrapping APIs in R using httr


httr from Hadley Wickham


occurrencecount <- function(scientificname = NULL, coordinatestatus = NULL, 
	url = "http://data.gbif.org/ws/rest/occurrence/count", 
    	curl = getCurlHandle()) 
{


# The compact fxn is a great way to gather parameters - removes all NULL


    querystr <- compact(list(
    	scientificname = scientificname, coordinatestatus = coordinatestatus
    )) 


    temp <- GET(url, query = querystr)
    out <- content(temp)$doc$children$gbifResponse
    as.numeric(xmlGetAttr(getNodeSet(out, "//gbif:summary")[[1]], "totalMatched"))
}

Wrapping APIs in R using httr


The output from the API that needs to be parsed

	$doc
	$file
	[1] ""

	$version
	[1] "1.0"
		
	</gbif:statements>
	  <gbif:stylesheet>http://data.gbif.org/ws/rest/occurrence/stylesheet</gbif:stylesheet>
	  <gbif:parameter name="request" value="count"/>
	  <gbif:parameter name="service" value="occurrence"/>
	  <gbif:parameter name="scientificname" value="Abies concolor"/>
	  <gbif:parameter name="coordinatestatus" value="true"/>
	  <gbif:summary totalMatched="597"/>
	 </gbif:header>
	</gbif:gbifResponse>

Wrapping APIs in R using httr


Run the occurrencecount function, search for white fir (Abies concolor)


library(XML)	
library(httr)
library(plyr)

occurrencecount(scientificname = "Abies concolor", coordinatestatus = TRUE)
			
			
[1] 597

Examples from some of our packages

Public Library of Science full text - rplos


Public Library of Science 


library(rplos)
plot_throughtime(list("reproducible science"), 500)

Public Library of Science uses rplos!


Public Library of Science uses rplos!




Managing bibliography - RMendeley


Mendeley


Manage libraries and measure impact of research

groupDocInfo(mc, 530031, 4344945792)

$abstract
[1] "SUMMARY: Modern biological experiments create vast amounts of data which are geographically distributed. These datasets consist of petabytes of raw data and billions of documents. Yet to the best of our knowledge, a search engine technology that searches and cross-links all different data types in life sciences does not exist.....

$authors
$authors[[1]]
      forename        surname
   "Dominic S" 	"L\xfctjohann" 
# ....


Accessing data behind papers - rdryad


Dryad 


# Get URL for a specific dataset
dryaddat <- download_url("10255/dryad.1759")

# Download the file from the Dryad servers
file <- dryad_getfile(dryaddat)

# Just first four columns
head(file[, 1:4])

  year nest.identity season clutch.size
1 2001             1      0           6
2 2001             1      0           6
3 2001             1      0           6
4 2001             1      0           6
5 2001             1      0           6
6 2001             1      0           6


Tracking altmetrics - raltmet

Tracks altmetrics across various sources such as GitHub, Total impact, CitedIn, CiteULike, Stackoverflow.

GitHub(userorg = "ropensci", repo = "rmendeley")

totimp(id = "10.5061/dryad.8671")

stackexchange(ids = 16632)

Definitely check out Total-Impact.org and here to learn more about #altmetrics

Mapping biodiversity data - rgbif


Global Biodiversity Information Facility 


distribution <- occurrencelist(sciname = "Danaus plexippus", coordinatestatus = TRUE, maxresults = 1000, latlongdf = TRUE)


Sharing unpublished data - (rfigshare)

Using Figshare's new API, it will soon be possible to share figures, data, and any other object generated in R directly to one's figshare account.


> figshare(data)
# code isn't ready yet but once it is, it will return a persistent identifier






A multi-institution consortium to build infrastructure for open science



DataONE

DataONE creates all the necessary components to support persistent and secure access to earth observation data.




DataONE's upcoming R package will allow users to submit and access data to/from member nodes directly from the console.



Git + Science

Rapid peer-peer sharing of code is great for science



R packages early in development can easily be tested, rapidly deployed from GitHub using devtools and revised before submitting to a persistent repository such as CRAN.


library(devtools)
install_github("RMendeley", "ropensci")


R + collaborative writing


knitr + Markdown



Xie Y (2012). knitr: A general-purpose package for dynamic report generation in R.

knitr + Markdown + GitHub

GitHub automatically renders Markdown and even provides syntax highlighting




knitr + Markdown + GitHub = executible paper




knitr + Markdown + GitHub = pre publication review



Incorporate citations with R + Markdown


knitcitations

citet(c(Halpern2006 = "10.1111/j.1461-0248.2005.00827.x"))
# then cite in your markdown file
citet("Halpern2006")


# or read citations from a bibtex file which can be automatically generated and updated from services like Mendeley

bib <- read.bibtex("example.bib")
# then cite inline
citet(bib[["knitr"]])

- knitcitations by Carl Boettiger @ GitHub
- tutorial

ropensci.org

Please contact us if you have feedback or ideas for collaborations.



All ropensci code is on GitHub.

We're also on Twitter (@ropensci) and G+.

rOpenSci tutorials

rOpenSci tutorials

http://ropensci.org/tutorials/

If you like, play with examples at: http://bit.ly/OoCUg3

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