[R] Summary information by groups programming assitance
Gabor Grothendieck
ggrothendieck at gmail.com
Tue Dec 23 03:29:13 CET 2008
The sorting should have been by Lake, psd and vol (not what I had)
so it should be revised to:
DFo <- DF[order(DF$Lake, DF$psd, DF$vol), ]
aggregate(DFo[c("Length", "vol")], DFo[c("Lake", "psd")], tail, 1)
This is the same as before except DF$psd is used in place of DF$Length
in the first line.
On Mon, Dec 22, 2008 at 9:14 PM, Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:
> Just sort the data first and then apply any of the solutions but with tail(x, 1)
> instead of max, e.g.
>
> DFo <- DF[order(DF$Lake, DF$Length, DF$vol), ]
> aggregate(DFo[c("Length", "vol")], DFo[c("Lake", "psd")], tail, 1)
>
>
> On Mon, Dec 22, 2008 at 8:15 PM, Ranney, Steven
> <steven.ranney at montana.edu> wrote:
>> Thank you all for your help. I appreciate the assistance. I'm thinking I should have been more specific in my original question.
>>
>> Unless I'm mistaken, all of the suggestions so far have been for maximum vol and maximum Length by Lake and psd. I'm trying to extract the max vol by Lake and psd along with the corresponding value of Length. So, instead of maximum vol and maximum Length, I'd like to find the max vol and the Length associated with that value.
>>
>> Sorry for any confusion,
>>
>> SR
>>
>> Steven H. Ranney
>> Graduate Research Assistant (Ph.D)
>> USGS Montana Cooperative Fishery Research Unit
>> Montana State University
>> P.O. Box 173460
>> Bozeman, MT 59717-3460
>>
>> phone: (406) 994-6643
>> fax: (406) 994-7479
>>
>> http://studentweb.montana.edu/steven.ranney
>> ________________________________
>>
>> From: Gabor Grothendieck [mailto:ggrothendieck at gmail.com]
>> Sent: Mon 12/22/2008 5:15 PM
>> To: Ranney, Steven
>> Cc: r-help at r-project.org
>> Subject: Re: [R] Summary information by groups programming assitance
>>
>>
>> Here are two solutions assuming DF is your data frame:
>>
>> # 1. aggregate is in the base of R
>>
>> aggregate(DF[c("Length", "vol")], DF[c("Lake", "psd")], max)
>>
>> or the following which is the same except it labels psd as Category:
>>
>> aggregate(DF[c("Length", "vol")], with(DF, list(Lake = Lake, Category
>> = psd)), max)
>>
>>
>> # 2. sqldf. The sqldf package allows specification using SQL notation:
>>
>> library|(sqldf)
>> sqldf("select Lake, psd as Category, max(Length), max(vol) from DF
>> group by Lake, psd")
>>
>> There are many other good solutions too using various packages which
>> have already
>> been mentioned on this thread.
>>
>> On Mon, Dec 22, 2008 at 4:51 PM, Ranney, Steven
>> <steven.ranney at montana.edu> wrote:
>>> All -
>>>
>>> I have data that looks like
>>>
>>> psd Species Lake Length Weight St.weight Wr
>>> Wr.1 vol
>>> 432 substock SMB Clear 150 41.00 0.01 95.12438
>>> 95.10118 0.0105
>>> 433 substock SMB Clear 152 39.00 0.01 86.72916
>>> 86.70692 0.0105
>>> 434 substock SMB Clear 152 40.00 3.11 88.95298
>>> 82.03689 3.2655
>>> 435 substock SMB Clear 159 48.00 0.04 92.42095
>>> 92.34393 0.0420
>>> 436 substock SMB Clear 159 48.00 0.01 92.42095
>>> 92.40170 0.0105
>>> 437 substock SMB Clear 165 47.00 0.03 80.38023
>>> 80.32892 0.0315
>>> 438 substock SMB Clear 171 62.00 0.21 94.58105
>>> 94.26070 0.2205
>>> 439 substock SMB Clear 178 70.00 0.01 93.91912
>>> 93.90571 0.0105
>>> 440 substock SMB Clear 179 76.00 1.38 100.15760
>>> 98.33895 1.4490
>>> 441 S-Q SMB Clear 180 75.00 0.01 97.09330
>>> 97.08035 0.0105
>>> 442 S-Q SMB Clear 180 92.00 0.02 119.10111
>>> 119.07522 0.0210
>>> ...
>>> [truncated]
>>>
>>> where psd and lake are categorical variables, with five and four
>>> categories, respectively. I'd like to find the maximum vol and the
>>> lengths associated with each maximum vol by each category by each lake.
>>> In other words, I'd like to have a data frame that looks something like
>>>
>>> Lake Category Length vol
>>> Clear substock 152 3.2655
>>> Clear S-Q 266 11.73
>>> Clear Q-P 330 14.89
>>> ...
>>> Pickerel substock 170 3.4965
>>> Pickerel S-Q 248 10.69
>>> Pickerel Q-P 335 25.62
>>> Pickerel P-M 415 32.62
>>> Pickerel M-T 442 17.25
>>>
>>>
>>> In order to originally get this, I used
>>>
>>> with(smb[Lake=="Clear",], tapply(vol, list(Length, psd),max))
>>> with(smb[Lake=="Enemy.Swim",], tapply(vol, list(Length, psd),max))
>>> with(smb[Lake=="Pickerel",], tapply(vol, list(Length, psd),max))
>>> with(smb[Lake=="Roy",], tapply(vol, list(Length, psd),max))
>>>
>>> and pulled the values I needed out by hand and put them into a .csv.
>>> Unfortunately, I've got a number of other data sets upon which I'll need
>>> to do the same analysis. Finding a programmable alternative would
>>> provide a much easier (and likely less error prone) method to achieve
>>> the same results. Ideally, the "Length" and "vol" data would be in a
>>> data frame such that I could then analyze with nls.
>>>
>>> Does anyone have any thoughts as to how I might accomplish this?
>>>
>>> Thanks in advance,
>>>
>>> Steven Ranney
>>>
>>> ______________________________________________
>>> R-help at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/r-help
>>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>> and provide commented, minimal, self-contained, reproducible code.
>>>
>>
>>
>>
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
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