Presence-absence to Occurrence Dataset?

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Elyse--

For the first step of converting your community matrix to triplets of
{site, species, presence}, you can use melt() from the flexible & powerful
reshape package as David suggested, or dematrify() from Dave Roberts'
labdsv package, which was built specifically for this task and has simpler
syntax than melt.  The second step is to then merge your location dataframe
with the long-format occurrence data by sitenames.

Do you need coordinates for only presences, or for presences and absences?
If you need only presences, dematrify() with thresh=0.5 would automatically
drop absences, while melt() would require a second step LongOccurrences <-
LongOccurrences[Freq>0,]

So, (untested code) for dataframes named CommMatrix and SiteCoords and
sitenames as Site:
LongOccurrences <- dematrify(CommMatrix,thresh=0.5)
Presences <- merge(LongOccurrences,SiteCoords,by="Site",all.x=TRUE)

If you have more complex needs for handling community and environmental
data, you might consider the mefa package.

Tom
-------------------------------------------
Tom Philippi
Quantitative Ecologist
Inventory and Monitoring Program
National Park Service
c/o Cabrillo National Monument
1800 Cabrillo Memorial Dr
San Diego, CA 92106
(619) 523-4576
Tom_philippi at NPS.gov
http://science.nature.nps.gov/im/monitor
-------------------------------------------

             David Valentim                                                
             Dias                                                          
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             g                                                     Subject 
                                       Re: [R-sig-eco] Presence-absence to 
                                       Occurrence Dataset?                 
             04/20/2012 01:32                                              
             PM AST                                                        

You can check melt() (from reshape2) and reshape() functions.

2012/4/20 Coffey, Elyse D. (UMSL-Student) <edc6cb at mail.umsl.edu>
Hello everyone,

I am working with a large community dataset where column names are
species
and rows are filled with either 1s or 0s (presence absence of the
species)
and each row corresponds to a site name.  I have long and lati
coordinates
for each site in a separate datasheet.

I am trying to use a program called Maxent in order model species
distributions. The program requires a specific occurrence dataset with
columns in this format: species, Long, Lati.

It is very difficult and time consuming to create such a dataset by hand!
I was wondering if anyone had any ideas as to how I can use R to
manipulate
my original community dataset in order to create the occurrence dataset I
described above?

Any suggestions would be greatly appreciated!

Thanks,

Elyse Coffey
Biology (B.S.) student
University of Missouri-St. Louis

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Hi Elyse,

Tom referred to the mefa package, I'd like to draw the attention to
the mefa4 package which is more efficient and can handle large data
since it uses sparse matrices through the Matrix package. The Melt()
function is the inverse operation to xtabs() or its modified version
Xtabs() in mefa4. It can take a matrix, sparse matrix, S3 mefa or S4
Mefa object as its argument:

library(mefa4)

y <- matrix(sample(0:4, 12, replace=TRUE, prob=c(0.5, rep(0.5/4, 4))), 4, 3)
x <- data.frame(x=runif(4), y=runif(4))
colnames(y) <- LETTERS[1:3]
rownames(y) <- rownames(x) <- paste("row", 1:4, sep="_")

z <- Melt(y)
data.frame(z, x[z$rows,])

There is also the (relatively) new multitable package by Steve Walker
with similar functionality. Lots of ways to do it -- the problem must
bother many of us :)

Cheers,

Peter

P?ter S?lymos
Alberta Biodiversity Monitoring Institute
and Boreal Avian Modelling project
Department of Biological Sciences
CW 405, Biological Sciences Bldg
University of Alberta
Edmonton, Alberta, T6G 2E9, Canada
Phone: 780.492.8534
Fax: 780.492.7635
email <- paste("solymos", "ualberta.ca", sep = "@")
http://www.abmi.ca
http://www.borealbirds.ca
http://sites.google.com/site/psolymos
Elyse--

For the first step of converting your community matrix to triplets of
{site, species, presence}, you can use melt() from the flexible & powerful
reshape package as David suggested, or dematrify() from Dave Roberts'
labdsv package, which was built specifically for this task and has simpler
syntax than melt. ?The second step is to then merge your location dataframe
with the long-format occurrence data by sitenames.