Recology

Recology has moved

noreply@blogger.com (Anonymous) — Fri, 06 Jan 2012 18:03:00 +0000

Recology has moved to Github, using Jekyll.

I just finished moving the Recology blog content, etc. to Github. This move is intended to make it easy to do exactly what I want with this blog without the constrictions of Blogger. Thanks for continuing to come back and read. I won't delete the Blogger version of this blog, but only new content will appear on the new site.

I haven't figured out how to get the RSS working yet, but I'm working on it.

Weecology can has new mammal dataset

noreply@blogger.com (Anonymous) — Thu, 29 Dec 2011 14:46:00 +0000

Recology has moved - go to http://recology.info/2011/12/weecology-can-has-new-mammal-dataset

So the Weecology folks have published a large dataset on mammal communities in a data paper in Ecology. I know nothing about mammal communities, but that doesn't mean one can't play with the data...

Their dataset consists of five csv files: communities, references, sites, species, and trapping data.

Where are these sites, and by the way, do they vary much in altitude?

Let's zoom in on just 'the states'?

What phylogenies can we get for the species in this dataset?
We can use the rOpenSci package treebase to search the online phylogeny repository TreeBASE. Limiting to returning a max of 1 tree (to save time), we can see that X species are in at least 1 tree on the TreeBASE database. Nice.

So there are 321 species in the database with at least 1 tree in the TreeBASE database. Of course there could be many more, but we limited results from TreeBASE to just 1 tree per query.

Here's the code:

Recology is 1 yr old...

noreply@blogger.com (Anonymous) — Sat, 24 Dec 2011 00:52:00 +0000

Recology has moved, go to http://recology.info/2011/12/recology-is-1-yr-old

This blog has lasted a whole year already. Thanks for reading and commenting.

There are a couple of announcements:

Less blogging: I hope to put in many more years blogging here, but in full disclosure, I am blogging for Journal of Ecology now, so I am going to be (and already have been) blogging less here.
More blogging: If anyone wants to write guest posts at Recology on the topics of using R for ecology and evolution, or open science, please contact me!
Different blogging: I was going to roll out the new dynamic views for this blog, but Google doesn't allow javascript, which is how I include code using GitHub gists. Oh well...

Anywho, here is the breakdown of visits to this blog, visualized using #ggplot2, of course. There were a total of about 23,000 pageviews in the first year of this blog. Here is the pie chart code I used:

Visits to top ten posts:

Visits by by pages:

Visits by top referring sites:

Visits by country:

Visits by browsers:

Visits by operating system:

Dynamic views don't support javascript-so reverting back to simple views

noreply@blogger.com (Anonymous) — Thu, 22 Dec 2011 17:36:00 +0000

Sorry for the temporary loss of GitHub gists...Hopefully dynamic views will support javascript soon!!

I Work For The Internet !

noreply@blogger.com (Anonymous) — Tue, 13 Dec 2011 15:35:00 +0000

Recology has moved, go to http://recology.info/2011/12/i-work-for-internet

UPDATE: code and figure updated at 647 AM CST on 19 Dec '11. Also, see Jarrett Byrnes (improved) fork of my gist here.

The site I WORK FOR THE INTERNET is collecting pictures and first names (last name initials only) to show collective support against SOPA (the Stop Online Piracy Act). Please stop by their site and add your name/picture.

I used the #rstats package twitteR, created by Jeff Gentry, to search for tweets from people signing this site with their picture, then plotted using ggplot2, and also used Hadley's lubridate to round timestamps on tweets to be able to bin tweets in to time slots for plotting.

Tweets containing the phrase 'I work for the internet' by time:

Here's the code as a GitHub gist. Sometimes the searchTwitter fxn doesn't returns an error, which I don't understand, but you can play with it:

rOpenSci won 3rd place in the PLoS-Mendeley Binary Battle!

noreply@blogger.com (Anonymous) — Wed, 30 Nov 2011 18:28:00 +0000

Recology has moved go to http://recology.info/2011/11/ropensci-won-3rd-place-in-plos-mendeley

I am part of the rOpenSci development team (along with Carl Boettiger, Karthik Ram, and Nick Fabina). Our website: http://ropensci.org/. Code at Github: https://github.com/ropensci

We entered two of our R packages for integrating with PLoS Journals (rplos) and Mendeley (RMendeley) in the Mendeley-PLoS Binary Battle. Get them at GitHub (rplos; RMendeley).

These two packages allow users (from R! of course) to search and retrieve data from PLoS journals (including their altmetrics data), and from Mendeley. You could surely mash up data from both PLoS and Mendeley. That's what's cool about rOpenSci - we provide the tools, and leave it up to users vast creativity to do awesome things.

3rd place gives us a $1,000 prize, plus a Parrot AR Drone helicopter.

Public vote open for Mendely-PLoS Binary Battle: vote rOpenSci!

noreply@blogger.com (Anonymous) — Sat, 19 Nov 2011 15:00:00 +0000

Recology has moved, go to http://recology.info/2011/11/public-vote-open-for-mendely-plos
http://www.surveygizmo.com/s3/722753/Mendeley-PLoS-Binary-Battle-Public-Vote

My talk on doing phylogenetics in R

noreply@blogger.com (Anonymous) — Fri, 18 Nov 2011 19:15:00 +0000

Recology has moved, go to http://recology.info/2011/11/my-talk-on-doing-phylogenetics-in-r

I gave a talk today on doing very basic phylogenetics in R, including getting sequence data, aligning sequence data, plotting trees, doing trait evolution stuff, etc.

Please comment if you have code for doing bayesian phylogenetic inference in R. I know phyloch has function mrbayes, but can't get it to work...

Phylogenetics in R

View more presentations from schamber

Check out a video of my research at RocketHub

noreply@blogger.com (Anonymous) — Tue, 01 Nov 2011 14:36:00 +0000

Recology has moved, go to http://recology.info/check-out-video-of-my-research-at

Okay, so this post isn't at all about R - but I can't resist begging my readers for some help.

I’m trying to get some crowdfunding for my research on the evolution of native plants in agricultural landscapes. My campaign is part of a larger project by about 50 other scientists and me to see how well it works to go straight to the public to get funding for science research. All these projects, including mine, are hosted at a site called RocketHub - a site that hosts crowdfunding projects of all sorts – and now they have science.

It is important to get a few bucks at the beginning so that the people that don’t know me with deep pockets will hopefully chip in once they see the money ball rolling.

The funding will go towards paying some students to collect data in the lab for me.

Here’s the link if you want to donate, or just to check out the video I made about my research!
http://www.rockethub.com/projects/3790-evolution-in-agriculture

And watch the video here too:

My little presentation on getting web data through R

noreply@blogger.com (Anonymous) — Fri, 28 Oct 2011 16:16:00 +0000

Recology has moved, go to http://recology.info/my-little-presentation-on-getting-web
With examples from rOpenSci R packages. p.s. I am no expert at this...
Web data from R

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Two new rOpenSci R packages are on CRAN

noreply@blogger.com (Anonymous) — Thu, 27 Oct 2011 12:27:00 +0000

Recology has moved, go to http://recology.info/2011/10/two-new-ropensci-r-packages-are-on-cran

Carl Boettiger, a graduate student at UC Davis, just got two packages on CRAN. One is treebase, which which handshakes with the Treebase API. The other is rfishbase, which connects with the Fishbase, although I believe just scrapes XML content as there is no API. See development on GitHub for treebase here, and for rfishbase here. Carl has some tutorials on treebase and rfishbase at his website here, and we have an official rOpenSci tutorial for treebase here.

Basically, these two R packages let you search and pull down data from Treebase and Fishbase - pretty awesome. This improves workflow, and puts your data search and acquisition component into your code, instead of being a bunch of mouse clicks in a browser.

These two packages are part of the rOpenSci project.

Two-sex demographic models in R

noreply@blogger.com (Anonymous) — Wed, 26 Oct 2011 14:31:00 +0000

Recology has moved, go to http://recology.info/2011/10/two-sex-demographic-models-in-r

Tom Miller (a prof here at Rice) and Brian Inouye have a paper out in Ecology (paper, appendices) that confronts two-sex models of dispersal with empirical data.

They conducted the first confrontation of two-sex demographic models with empirical data on lab populations of bean beetles Callosobruchus.

Their R code for the modeling work is available at Ecological Archives (link here).

Here is a figure made from running the five blocks of code in 'Miller_and_Inouye_figures.txt' that reproduces Fig. 4 (A-E) in their Ecology paper (p = proportion female, Nt = density). Nice!
A: Saturating density dependence
B: Over-compensatory density dependence
C: Sex-specific gamma's (but bM=bF=0.5)
D: Sex-specific b's (but gammaM=gammaF=1)
E: Sex-specific b's (but gammaM=gammaF=2)

New food web dataset

noreply@blogger.com (Anonymous) — Fri, 14 Oct 2011 18:00:00 +0000

Recology has moved, go to http://recology.info/2011/10/new-food-web-dataset

So, there is a new food web dataset out that was put in Ecological Archives here, and I thought I would play with it. The food web is from Otago Harbour, an intertidal mudflat ecosystem in New Zealand. The web contains 180 nodes, with 1,924 links.

Fun stuff...

igraph, default layout plot

igraph, circle layout plot, nice

My funky little gggraph function plot

get the gggraph function, and make it better, here at Github

Phylogenetic community structure: PGLMMs

noreply@blogger.com (Anonymous) — Thu, 13 Oct 2011 15:18:00 +0000

Recology has moved, go to http://recology.info/2011/10/phylogenetic-community-structure-pglmms

So, I've blogged about this topic before, way back on 5 Jan this year.

Matt Helmus, a postdoc in the Wootton lab at the University of Chicago, published a paper with Anthony Ives in Ecological Monographs this year (abstract here). The paper addressed a new statistical approach to phylogenetic community structure.

As I said in the original post, part of the power of the PGLMM (phylogenetic generalized linear mixed models) approach is that you don't have to conduct quite so many separate statistical tests as with the previous null model/randomization approach.

Their original code was written in Matlab. Here I provide the R code that Matt has so graciously shared with me. There are four functions and a fifth file has an example use case. The example and output are shown below.

Look for the inclusion of Matt's PGLMM to the picante R package in the future.

Here are links to the files as GitHub gists:
PGLMM.data.R: https://gist.github.com/1278205
PGLMM.fit.R: https://gist.github.com/1284284
PGLMM.reml.R: https://gist.github.com/1284287
PGLMM.sim.R: https://gist.github.com/1284288
PGLMM_example.R: https://gist.github.com/1284442

Enjoy!

The example

..and the figures...

R talk on regular expressions (regex)

noreply@blogger.com (Anonymous) — Thu, 06 Oct 2011 15:50:00 +0000

Recology has moved, go to http://recology.info/2011/10/r-talk-on-regular-expressions-regex

Regular expressions are a powerful in any language to manipulate, search, etc. data.

For example:

> fruit <- c("apple", "banana", "pear", "pineapple")
> fruit
[1] "apple"     "banana"    "pear"      "pineapple"

> grep("a", fruit) # there is an "a" in each of the words
[1] 1 2 3 4
> 
> strsplit("a string", "s") # strsplit splits the string on the "s"
[[1]]
[1] "a "    "tring"

R base has many functions for regular expressions, see slide 9 of Ed's talk below. The package stringr, created by Hadley Wickham, is a nice alternative that wraps the base regex functions for easier use. I highly recommend stringr.

Ed Goodwin, the coordinator of the Houston R Users group, gave a presentation to the group last night on regular expressions in R. It was a great talk, and he is allowing me to post his talk here.

Enjoy! And thanks for sharing Ed!

regex-presentation_ed_goodwin

View more presentations from schamber.

R tutorial on visualizations/graphics

noreply@blogger.com (Anonymous) — Fri, 30 Sep 2011 15:48:00 +0000

Recology has moved, go to http://recology.info/2011/09/r-tutorial-on-visualizationsgraphics

Rolf Lohaus, a Huxley postdoctoral fellow here in the EEB dept at Rice University, gave our R course a talk on basic visualizations in R this morning.

Enjoy!

Short on funding? Can't get a grant? Crowdfunding! #SciFund

noreply@blogger.com (Anonymous) — Tue, 27 Sep 2011 16:16:00 +0000

Recology has moved, go to http://recology.info/2011/09/short-on-funding-cant-get-grant

Crowdsourced funding is becoming a sustainable way for various artists, entrepreneurs, etc. to get their idea funded from individuals. For example, think of Kickstarter and RocketHub.

Jai Ranganathan and Jarrett Byrnes have started an experiment to determine how well crowdfunding can work for scientists: The SciFund Challenge. Go here to signup and here for their website.

The deadline to sign up is Oct. 1

"Ten Simple Rules for OA Publishers" talk by Philip Bourne

noreply@blogger.com (Anonymous) — Fri, 23 Sep 2011 19:44:00 +0000

Recology has moved, go to http://recology.info/2011/09/ten-simple-rules-for-oa-publishers-talk

Ten Simple Rules for Open Access Publishers

View more presentations from Philip Bourne

@drewconway interview on @DataNoBorders at the Strata conference

noreply@blogger.com (Anonymous) — Fri, 23 Sep 2011 04:10:00 +0000

Recology has moved, go to http://recology.info/2011/09/drewconway-interview-on-datanoborders

The O'Reilly Media Strata Summit has many interviews on YouTube (just search YouTube for it)

Drew Conway is the author of a R packages, including infochimps, an R wrapper to the Infochimps API service.

The YouTube video:

Open science talk by Carl Boettiger

noreply@blogger.com (Anonymous) — Fri, 23 Sep 2011 03:55:00 +0000

Recology has moved, go to http://recology.info/2011/09/open-science-talk-by-carl-boettiger

Carl Boettiger gave a talk on the topic of open science to incoming UC Davis graduate students.

Here is the audio: http://www.archive.org/details/ThingsIWishIKnewThreeYearsAgo-ByTheDavisOpenScienceGroup&reCache=1

Here are the slides: http://hazelnusse.github.com/DOS_WOW2011/#title-slide

My take on an R introduction talk

noreply@blogger.com (Anonymous) — Fri, 09 Sep 2011 18:03:00 +0000

Recology has moved, go to http://recology.info/2011/09/my-take-on-r-introduction-talk

UPDATE: I put in an R tutorial as a Github gist below.

Here is a short intro R talk I gave today...for what it's worth...

R Introduction

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A Data Visualization Book

noreply@blogger.com (Pascal Mickelson) — Fri, 09 Sep 2011 06:28:00 +0000

Recology has moved, go to http://recology.info/2011/09/data-visualization-book

Note: thanks to Scott for inviting me to contribute to the Recology blog despite being an ecology outsider; my work is primarily in atomic physics. -Pascal
A part of me has always liked thinking about how to effectively present information, but until the past year, I had not read much to support my (idle) interest in information visualization. That changed in the spring when I read Edward Tufte's The Visual Display of Quantitative Information, a book that stimulated me to think more deeply about presenting information. I originally started with a specific task in mind--a wonderful tool for focusing one's interests--but quickly found that Tufte's book was less a practical guide and more a list of general design principles. Then, a few months ago, I stumbled upon Nathan Yau's blog, FlowingData, and found out he was writing a practical guide to design and visualization. Conveniently enough for me, Yau's book, Visualize This, would be released within a month of my discovery of his blog; what follows are my impressions of Visualize This.
I have liked Visualize This a lot. Yau writes with much the same informal tone as on his blog, and the layout is visually pleasing (good thing, too, for a book about visualizing information!). The first few chapters are pretty basic if you have done much data manipulation before, but it is really nice to have something laid out so concisely. The examples are good, too, in that he is very explicit about every step: there is no intuiting what that missing step should be. The author even acknowledges in the introduction that the first part of the book is at an introductory level.
Early in the book, Yau discusses where to obtain data. This compilation of sources is potentially a useful reference for someone, like me, who almost always generates his own data in the lab. Unfortunately, Yau does not talk much about preparation of (or best practices for) your own data. Additionally, from the perspective of a practicing scientist, it would have been nice to hear about how to archive data to make sure it is readable far into the future, but that is probably outside the scope of the book.
Yau seems really big into using open source software for getting and analyzing data (e.g. Python, R, etc…), but he is surprisingly attached to the proprietary Adobe Illustrator for turning figures into presentation quality graphics. He says that he feels like the default options in most analysis programs do not make for very good quality graphics (and he is right), but he does not really acknowledge that you can generate nice output if you go beyond the default settings. For me, the primary advantage of generating output programmatically is that it is easy to regenerate when you need to change the data or the formatting on the plot. Using a graphical user interface, like in Adobe Illustrator, is nice if you are only doing something once (how often does that happen?), but when you have to regenerate the darn figure fifty times to satisfy your advisor, it gets tedious to move things around pixel by pixel.
By the time I reached the middle chapters, I started finding many of the details to be repetitive. Part of this repetition stems from the fact that Yau divides these chapters by the type of visualization. For example, "Visualizing Proportions" and "Visualizing Relationships" are two of the chapter titles. While I think these distinctions are important ones for telling the right story about one's data, creating figures for the different data types often boils down to choosing different functions in R or Python. People with less analysis and presentation experience should find the repetition helpful, but I increasingly skimmed these sections as I went along.
Working through Yau's examples for steps you do not already know would probably be the most useful way of getting something out of the book. So, for example, I started trying to use Python to scrape data from a webpage, something I had not previously done. I followed the book's example of this data-scraping just fine, but as with most things in programming, you find all sorts of minor hurdles to clear when you try your own thing. In my case, I am re-learning the Python I briefly learned about 10 years ago--partly in anticipation of not having access to Matlab licenses once I vacate the academy--since I have forgotten a lot of the syntax. A lot of this stuff would be faster if I were working in Matlab which I grew more familiar with in graduate school.
Overall, Visualize This is a really nice looking book and will continue to be useful to me as a reference. Yau concludes his book with a refreshing reminder to provide context for the data we present. This advice is particularly relevant when presenting to a wider or lay audience, but it is still important for us, as scientists, to clearly communicate our findings in the literature. Patterns in the data are not often self-evident, and therefore we should think carefully about which visualization tools will best convey the meaning of our results.
Edited to add a link to Visualize This here and in the introductory paragraph.

FigShare Talk

noreply@blogger.com (Anonymous) — Fri, 09 Sep 2011 04:36:00 +0000

Recology has moved, go to http://recology.info/2011/09/figshare-talk

FigShare - I very much like this idea of a place to put your data online that is NOT published. Dryad is a nice place for datastes linked with published papers, but there isn't really a place for datasets that perhaps did not make the cut for a published paper, and if known to the scientific community, could potentially help resolve the "file-drawer" effect in meta-analyses. (wow, run on sentence)

"Figshare - Why don't you publish all your research?" Mark Hahnel Imperial College London from London Biogeeks on Vimeo.

Jonathan Eisen on the Fourth Domain and Open Science

noreply@blogger.com (Anonymous) — Tue, 06 Sep 2011 23:53:00 +0000

Recology has moved, go to http://recology.info/2011/09/jonathan-eisen-on-fourth-domain-and

Stalking the Fourth Domain with Jonathan Eisen, Ph D from mendelspod on Vimeo.

rnpn: An R interface for the National Phenology Network

noreply@blogger.com (Anonymous) — Wed, 31 Aug 2011 15:26:00 +0000

Recology has moved, go to http://recology.info/2011/08/rnpn-r-interface-for-national-phenology

The team at rOpenSci and I have been working on a wrapper for the USA National Phenology Network API. The following is a demo of some of the current possibilities. We will have more functions down the road. Get the publicly available code, and contribute, at Github here. If you try this out look at the Description file for the required R packages to run rnpn. Let us know at Github (here) or at our website http://ropensci.org/, or in the comments below, or on twitter (@rOpenSci), what use cases you would like to see with the rnpn package.

Method and demo of each:
Get observations for species by day
From the documentation: "This function will return a list of species, containing all the dates which observations were made about the species, and a count of the number of such observations made on that date."

#### Note, the data below is truncated for blogging brevity...

> getobsspbyday(c(1, 2), '2008-01-01', '2011-12-31') # Searched for species 1 and 2 from Jan 1, 2008 to Dec 31, 2011
          date count   species
1   2009-03-08     2 species 1
2   2009-03-15     1 species 1
3   2009-03-22     1 species 1
4   2009-03-24     1 species 1
5   2009-03-26     1 species 1
6   2009-04-17     1 species 1
7   2009-04-24     1 species 1
8   2009-05-12     1 species 1
9   2009-05-20     1 species 1
10  2009-11-24     1 species 1
11  2009-12-07     1 species 1
12  2010-01-18     1 species 1
13  2010-01-23     1 species 1
62  2011-05-29     1 species 1
63  2011-06-27     1 species 1
64  2011-06-30     2 species 1
65  2009-03-17     1 species 2
66  2009-04-03     3 species 2
67  2009-04-05     3 species 2
68  2009-04-10     3 species 2
69  2009-04-17     3 species 2

Get individuals at specific stations
From the documentation: "This function returns all of the individuals at a series of stations."

> getindsatstations(c(507, 523)) # Searched for any individuals at stations 507 and 523
   individual_id individual_name species_id kingdom
1           1200         dogwood         12 Plantae
2           1197    purple lilac         36 Plantae
3           1193         white t         38 Plantae
4           3569     forsythia-1         73 Plantae
5           1206            jack        150 Plantae
6           1199      trout lily        161 Plantae
7           1198           dandy        189 Plantae
8           1192           red t        192 Plantae
9           1710    common lilac         36 Plantae
10          1711  common lilac 2         36 Plantae
11          1712       dandelion        189 Plantae

Get individuals of species at stations
From the documentation: "This function will return a list of all the individuals, which are members of a species, among any number of stations."

> getindspatstations(35, c(60, 259), 2009)  # Search for individuals of species 35 at stations 60 and 259 in year 2009
  individual_id individual_name number_observations
1          1715            west                   5
2          1716            east                   5

Get observation associated with particular observation
From the documentation: "This function will return the comment associated with a particular observation."

> getobscomm(1938) # The observation for observation number 1938

$observation_comment
[1] "some lower branches are bare"