Jim Holtman's solution generates a data frame that can be easily indexed by
year:
setwd("/r-help/2010-03-10")
x <- read.csv("restored.csv", as.is=TRUE)
require(reshape)
x.m <- melt(x, id=c('Year', 'LocationID', 'SpeciesCode'),
measure='PlotFreq')
x.binary <- cast(x.m, Year + LocationID ~ SpeciesCode, length)
On Thu, Mar 11, 2010 at 10:55 AM, Lanna Jin <lanna...@gmail.com>wrote:
>>>>
>>>>> Hi Jim,
>>>>>
>>>>> Here's an example of what I mean by a binary matrix ( 1 = species
>>>>> presence, 0 = species absence, cols = species, rows = location). This is
>>>>> just a sample of data from the Year 1998
>>>>>
>>>>> ACMI2 ALLIUM ALST AMAR2 AMCA6 AMPS ANCA8 ANCA9 ANCY ANEMONE
>>>>> ANGE ANNE APCA ARLU ASCA11 ASER3 ASLA5 ASNO ASOV ASSY ASVE BOCU BREU CAREX
>>>>> CASE13
>>>>> PIPE_15 0 0 0 0 0 0 0 0 0
>>>>> 0 0 0 0 0 0 0 0 0 0 0 0 0 0
>>>>> 0 0
>>>>> PIPE_16 0 0 0 0 0 0 0 0 0
>>>>> 0 0 0 0 0 0 0 0 0 0 0 0 0 0
>>>>> 0 0
>>>>> PIPE_17 0 0 0 0 0 0 0 0 0
>>>>> 0 0 0 0 0 0 0 0 0 0 0 0 0 0
>>>>> 0 0
>>>>> PIPE_7 0 0 0 0 0 0 0 0 0
>>>>> 0 0 0 0 0 0 0 0 0 0 0 0 0 0
>>>>> 0 0
>>>>> PIPE_8 0 0 0 0 0 0 0 0 0
>>>>> 0 0 0 0 0 0 0 0 0 0 0 0 0 0
>>>>> 0 0
>>>>> PIPE_9 0 0 0 0 0 0 0 0 0
>>>>> 0 0 0 0 0 0 0 0 0 0 0 0 0 0
>>>>> 0 0
>>>>> PIPE_11 1 0 0 0 1 0 1 0 1
>>>>> 0 0 0 1 1 0 1 1 0 1 1 0 1 0
>>>>> 1 0
>>>>> PIPE_12 1 1 0 0 0 0 1 0 0
>>>>> 0 1 0 1 1 0 0 0 0 0 1 0 0 0
>>>>> 1 0
>>>>> PIPE_13 0 0 0 0 1 0 1 0 0
>>>>> 0 1 0 0 0 0 1 0 0 0 1 0 0 0
>>>>> 1 0
>>>>> PIPE_14 1 0 0 0 1 1 1 0 1
>>>>> 0 0 0 0 1 0 0 1 0 0 1 1 1 0
>>>>> 1 0
>>>>> To give you a better idea of what the data looks like:
>>>>>
>>>>> > head(data)
>>>>> Type Year Location Species
>>>>> 66 Restored 1998 PIPE_7 ACMI2
>>>>> 67 Restored 1998 PIPE_7 AMAR2
>>>>> 68 Restored 1998 PIPE_7 AMCA6
>>>>> 69 Restored 1998 PIPE_7 ANCY
>>>>> 70 Restored 1998 PIPE_7 ASVE
>>>>> 71 Restored 1998 PIPE_7 BOCU
>>>>> > unique(data$Year)
>>>>> [1] 1998 1999 2001 2005 2006 2009
>>>>> > unique(data$Type)
>>>>> [1] "Restored" "Native"
>>>>> > length(unique(data$Species))
>>>>> [1] 107
>>>>>
>>>>> I've managed to individually pull out each matrix for each type (also,
>>>>> for some reason when I do as.matrix, it converts the 1's to -1's. Not sure
>>>>> why, but I've corrected it...).
>>>>>
>>>>> dataT<-table(data$Location,data$Species,data$Year,data$Type)
>>>>>
>>>>> # For Natives:
>>>>> N98<--as.matrix(dataT[,,1,"Native"]);N98[which(N98<0)]<-1
>>>>> N99<--as.matrix(dataT[,,2,"Native"]);N99[which(N99<0)]<-1
>>>>> etc... etc...
>>>>> N09<--as.matrix(dataT[,,6,"Native"]);N09[which(N09<0)]<-1
>>>>>
>>>>> # For Restored:
>>>>> R98<--as.matrix(dataT[,,1,"Restored"]);R98[which(R98<0)]<-1
>>>>> R99<--as.matrix(dataT[,,2,"Restored"]);R99[which(R99<0)]<-1
>>>>> etc... etc...
>>>>> R09<--as.matrix(dataT[,,6,"Restored"]);R09[which(R09<0)]<-1
>>>>>
>>>>> Do you know of a way to efficiently do this in a function? For some
>>>>> reason, I can't figure out how to generate unique dataframes/matrices for
>>>>> each unique i in a for loop function...
>>>>>
>>>>> Thanks,
>>>>> Lanna
>>>>>
>>>>>
>>>>>
>>>>>> On Wed, Mar 10, 2010 at 12:07 PM, Lanna Jin <lanna...@gmail.com
>>>>>>>> > wrote:
>>>>>>>>
>>>>>>>>> Hi All,
>>>>>>>>>
>>>>>>>>> If given a dataframe (long form) with Year, Species, and Location,
>>>>>>>>> How would I write a function that would create a unique matrix of
>>>>>>>>> Species &
>>>>>>>>> Location for each Year?
>>>>>>>>>
>>>>>>>>> What I've tried doing is the following:
>>>>>>>>>
>>>>>>>>> data #dataframe
>>>>>>>>> dataT<-table(data$Species,data$Location,data$Year) #creates tables
>>>>>>>>> of
>>>>>>>>> Species vs Location for each Year
>>>>>>>>>
>>>>>>>>> But I'm encountering issues individually indexing the tables and
>>>>>>>>> converting
>>>>>>>>> them into matrices in one fell swoop.
>>>>>>>>>
>>>>>>>>> r1997<-as.matrix(tableT[,,"1997"]) #How I would do it individually;
>>>>>>>>> but I
>>>>>>>>> can I make matrices for each year as a function?
>>>>>>>>>
>>>>>>>>> Thanks in advance for your suggestions!
>>>>>>>>>
>>>>>>>>> --
>>>>>>>>> Lanna Jin
>>>>>>>>>
>>>>>>>>> lanna...@gmail.com
>>>>>>>>> 510-898-8525
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
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