Hi Bruce
It sounds like the aggregate function is what you need. Here's an example
using the ChickWeight dataset in R:
chick.agg<-aggregate(ChickWeight[,c('weight', 'Time')], by =
ChickWeight[c('Chick','Diet')], sum)
weight.time<-chick.agg$weight/chick.agg$Time
chick.agg.df<-data.frame(chick.agg, weight.time)
As you can see above, once you have the aggregated dataframe, you can just
create another vector (weight.time), which is chick weight divided by time
and add this to a new database using the data.frame function. Take a look
at the ChickWeight function so you'll understand what I've done.
cheers,
Danny
On Saturday, April 20, 2013 2:55:03 PM UTC+1, Bruce Miller wrote:
>
> H all,
>
> I have relative abundance data from >100 sites. This is from acoustic
> monitoring and usually the data is for 2-3 nights but in some cases my
> be longer like months or years for each location..
> The data output from my management data base is proved by species by
> night for each location so data frame would look like this below. What I
> need to do is sum the Survey_time by Spec_Code for each location name
> and divide summed AI values for each Spec_code by the summed Survey time
> to adjust for unit effort then standardize it all by *10 to represent
> the relative abundance by survey hour to 10 hours. How best to do this?
> Using Plyr or reshape?
>
> Location name SPEC_CODE Start_Day Survey_Time
> AI Std AI
> 079-f2p1-Acetuna Buzz 2/14/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Buzz 2/14/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Eumspp 2/14/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Frag 2/14/2012 12.1 18
> 14.87603
> 079-f2p1-Acetuna Molspp 2/14/2012 12.1 5
> 4.132231
> 079-f2p1-Acetuna Molspp 2/14/2012 12.1 5
> 4.132231
> 079-f2p1-Acetuna Phyllo 2/14/2012 12.1 2
> 1.652893
> 079-f2p1-Acetuna Ptedav 2/14/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Ptegym 2/14/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Ptepar 2/14/2012 12.1 2
> 1.652893
> 079-f2p1-Acetuna Rhotum 2/14/2012 12.1 6
> 4.958678
> 079-f2p1-Acetuna Sacbil 2/14/2012 12.1 6
> 4.958678
> 079-f2p1-Acetuna Saclep 2/14/2012 12.1 11
> 9.090909
> 079-f2p1-Acetuna Buzz 2/15/2012 12.1 2
> 1.652893
> 079-f2p1-Acetuna Buzz 2/15/2012 12.1 2
> 1.652893
> 079-f2p1-Acetuna Molmol 2/15/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Molspp 2/15/2012 12.1 7
> 5.785124
> 079-f2p1-Acetuna Molspp 2/15/2012 12.1 7
> 5.785124
> 079-f2p1-Acetuna Nocalb 2/15/2012 12.1 6
> 4.958678
> 079-f2p1-Acetuna Phyllo 2/15/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Ptedav 2/15/2012 12.1 1
> 0.8264463
> 079-f2p1-Acetuna Ptegym 2/15/2012 12.1 4
> 3.305785
> 079-f2p1-Acetuna Ptepar 2/15/2012 12.1 4
> 3.305785
> 079-f2p1-Acetuna Pteper 2/15/2012 12.1 3
> 2.479339
> 079-f2p1-Acetuna Rhotum 2/15/2012 12.1 7
> 5.785124
> 079-f2p1-Acetuna Sacbil 2/15/2012 12.1 2
> 1.652893
> 079-f2p1-Acetuna Saclep 2/15/2012 12.1 6
> 4.958678
> 079-f2p1-Acetuna Buzz 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Buzz 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Eumspp 2/16/2012 12.2 4
> 3.278688
> 079-f2p1-Acetuna Molspp 2/16/2012 12.2 2
> 1.639344
> 079-f2p1-Acetuna Molspp 2/16/2012 12.2 2
> 1.639344
> 079-f2p1-Acetuna Myokea 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Nocalb 2/16/2012 12.2 4
> 3.278688
> 079-f2p1-Acetuna Phyllo 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Ptedav 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Ptegym 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Ptepar 2/16/2012 12.2 3
> 2.459016
> 079-f2p1-Acetuna Pteper 2/16/2012 12.2 1
> 0.8196721
> 079-f2p1-Acetuna Rhotum 2/16/2012 12.2 17
> 13.93443
> 079-f2p1-Acetuna Sacbil 2/16/2012 12.2 3
> 2.459016
> 079-f2p1-Acetuna Saclep 2/16/2012 12.2 15
> 12.29508
>
>
> Thanks for any suggestions. Excel will be a mess to try to do that.
>
> Bruce
>
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>
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