[R] [newbie] aggregating table() results and simplifying code with loop
John Kane
jrkrideau at inbox.com
Mon Sep 17 15:19:17 CEST 2012
Comments in-line below.
Sorry to be so long getting back to you but sleep intervened.
> -----Original Message-----
> From: ridavide at gmail.com
> Sent: Sun, 16 Sep 2012 21:24:15 +0200
> To: jrkrideau at inbox.com
> Subject: Re: [R] [newbie] aggregating table() results and simplifying
> code with loop
>
> Thank you John,
>
> you are giving me two precious tips (in addition, well explained!):
> 1. to use the package plyr (I didn't know it before, but it seems to
> make the deal!)
> 2. a smart and promising way to use it
>
> I can finally plot the partial results, to have a first glance and
> compare to them
>
> ==========================================================
>
> # once the sequences for a given crop have been assembled in a
> dataframe (e.g., maizedata)
> library(latticeExtra) # load the package for "improved" graphics
> dotplot(mzcount$crop_pattern) # to plot the occurrence of all the
> retrieved sequences
>
> #once the target pattern have been subset (e.g., m51)
> dotplot(m51$WS ~ m51$count) # to plot the occurrence of the target
> pattern(s) per watershed
Ah, so that's what WS stands for! Do you have an abstract or short summary of what you are doing in English or French? My Itallian is limited to about 3 words or what I can guess from French/Latin.
I think that you are misunderstanding what is happening in m51. It is just summarizing the total counts across those two conditions. I think either I made a logic error or was not understanding what was needed. In any case the dotplot is not plotting the actual occurances but the number of times the crop_pattern was found. The count is the actual number of occurances per WS per crop_pattern
Have a look at my new effort. it is still ugly but I think it more accurately supplies some of what you want.
Two caveats : 1. This is still very rough and an better programer may have a better approach.
2. I used the package ggplot2 to do the graphs. One more thing to learn but I am not used to lattice and I am used to ggplot2. Rather than spend a lot of time with lattice or
2. The last faceted plots are not intended to be of real use, just my quick look to see if anything looked like I expected. A bit of thinking probably would give somethimg much better
> ==========================================================
>
> This help me retrieving all the possible patterns for the different
> land covers. Hence, you made me able to improve the subset of
> patterns.
>
> Tomorrow morning I'm going to test the tips for all the land covers
> for all the 5years time-periods. Even if still labour-intensive, the
> solution you propose it's surely a steady improvement.
> Now only the second question remains: is there a way "to clean" this
> approach?... or probably not ?...
Yes I suspect that there are lots of ways to 'clean' the process and automate it so that it would apply to all three data sets quite easily. Some I think I can help with and some you may need other people's help.
For example, once we have a working model to handle one or two conditions It should be relatively easy to use an apply() or a loop to handle all of them and so on..
Well, I'm off to work now so I probably won't be able to get back to much before late evening my time ( probably after you are asleep) I think I'm 6 hours ahead of you.
##===================Revised approach====================
# load the various packages (plyr, latticeExtra, ggplot2, reshape2)
library(plyr)
library(latticeExtra)
library(ggplot2)
library(reshape2)
# sample data
T80<- read.csv("/home/john/rdata/sample.csv", header = TRUE, sep = ";")
# Davide's actual read statement
# T80<-read.table(file="C:/sample.txt", header=T, sep=";")
# Looking for Maize
pattern <- c("2Ma", "2Ma","2Ma", "2Ma","2Ma")
# one row examples to see that is happening
T80[1,3:7]
T80[1, 3:7] == pattern
T80[405, 3:7]
T80[405, 3:7] == pattern
T80[55, 3:7] == pattern
# now we apply the patterns to the entire data set.
pp1 <- T80[, 3:7] == pattern
# paste the TRUEs and FALSEs together to form a single variable
concatdat <- paste(pp1[, 1], pp1[, 2], pp1[, 3], pp1[, 4],pp1[,5] , sep = "+")
# Assmble new data frame.
maizedata <- data.frame(T80$WS, concatdat)
names(maizedata) <- c("WS", "crop_pattern")
str(maizedata)
maizedata$crop_pattern <- as.character(maizedata$crop_pattern)
pattern_count <- ddply(maizedata, .(crop_pattern), summarize, npattern = length(crop_pattern))
str(pattern_count)
head(pattern_count); dim(pattern_count) # quick look at data.frame and its size.
# FALSE+FALSE+FALSE+FALSE+FALSE accounts for 21,493 values.
which(pattern_count$npattern == max(pattern_count$npattern)) # this does the same as looking at the data
# not needed here but useful for larger datasets.
# If we graph pattern_count as it stands we lose any useful detail because of that outlier.
p <- ggplot(pattern_count , aes(crop_pattern, npattern )) + geom_point() +
coord_flip()
p
(pattern1 <- pattern_count[-1,]) # Drop the offending FALSE+FALSE+FALSE+FALSE+FALSE
dim(pattern1) # Okay now we have the maize patterns, without the WS who had no maize at all.
p <- ggplot(pattern1 , aes(crop_pattern, npattern )) + geom_point() +
coord_flip()
p
newmaize <- subset(maizedata, maizedata$crop_pattern != "FALSE+FALSE+FALSE+FALSE+FALSE")
dim(newmaize) ; head(newmaize)
str(newmaize)
summaize_by_WS <- ddply(newmaize, .(WS), summarize, crop_pattern_ws = length(crop_pattern))
p <- ggplot( summaize_by_WS , aes(summaize_by_WS )) + geom_point +
coord_flip()
p
summaize_by_WS_and_crop <- ddply(newmaize, .(WS, crop_pattern), summarize, crop_pattern_ws = length(crop_pattern))
# crappy graph but just try to see what we might get. probablly need to subset or use better grid layout.
p <- ggplot( summaize_by_WS_and_crop , aes(crop_pattern, crop_pattern_ws )) + geom_point() +
coord_flip() + facet_grid(WS ~ . )
p
# save the last graph to look at it in a graphics package== still terrible
ggsave( "/home/john/Rjunk/crop.png")
##
>
> Thanks again for the help.
> Cheers,
> Dd
>
> ***********************************************************
> Davide Rizzo
> website :: http://sites.google.com/site/ridavide/
>
>
> On Sun, Sep 16, 2012 at 8:24 PM, John Kane <jrkrideau at inbox.com> wrote:
>> Hi Davide,
>>
>> I had some time this afternoon and I wonder if this approach is llkely
>> to get the results you want? As before it is not complete but I think
>> it holds promise.
>>
>> On the other hand Rui is a much better programer than I am so he may
>> have a much cleaner solution. My way still looks labour-intensive at
>> the moment.
>>
>> I am using the plyr package which you will probably have to install.
>> load.packages("plyr") should do it.
>> ==========================================================
>> # load the plyr package -
>> library(plyr)
>>
>> # sample data
>> T80<- read.csv("/home/john/rdata/sample.csv", header = TRUE, sep = ";")
>> # Davide's actual read statement
>> # T80<-read.table(file="C:/sample.txt", header=T, sep=";")
>>
>> # Looking for Maize
>> pattern <- c("2Ma", "2Ma","2Ma", "2Ma","2Ma")
>>
>> # one row examples to see that is happening
>> T80[1,3:7]
>> T80[1, 3:7] == pattern
>>
>> T80[405, 3:7]
>> T80[405, 3:7] == pattern
>>
>> T80[55, 3:7] == pattern
>>
>> # now we apply the patterns to the entire data set.
>> pp1 <- T80[, 3:7] == pattern
>>
>> # paste the TRUEs and FALSEs together to form a single variable
>> concatdat <- paste(pp1[, 1], pp1[, 2], pp1[, 3], pp1[, 4],pp1[,5] ,
>> sep = "+")
>>
>> # Assmble new data frame.
>> maizedata <- data.frame(T80$WS, concatdat)
>> names(maizedata) <- c("WS", "crop_pattern")
>>
>> mzcount <- ddply(maizedata, .(WS, crop_pattern), summarize, count =
>> length(crop_pattern))
>> mzcount # This is all the data not just the relevant maise patterns
>>
>> # This seems to be getting us somewhere though we are not not there yet
>> # Does this subset look like we are going in the right direction?
>> m51 <- subset(mzcount,
>> mzcount$crop_pattern == "FALSE+FALSE+FALSE+FALSE+TRUE"
>> | mzcount$crop_pattern == "TRUE+FALSE+FALSE+FALSE+FALSE")
>>
>> m51 <- ddply(m51, .(WS), summarize, count = sum(count))
>> m51
>> =================================================================
>>
>> John Kane
>> Kingston ON Canada
>>
>>
>>> -----Original Message-----
>>> From: ridavide at gmail.com
>>> Sent: Sat, 15 Sep 2012 19:00:29 +0200
>>> To: jrkrideau at inbox.com, ruipbarradas at sapo.pt
>>> Subject: Re: [R] [newbie] aggregating table() results and simplifying
>>> code with loop
>>>
>>> Thanks Rui, thanks John for your very different solutions.
>>>
>>> I'll try to break my questions into smaller steps following your tips.
>>> However, not everything is clear for me... so before giving you a
>>> feed-back I need to study further your answers. For the moment I could
>>> specify that I'm looking for the following 19 patterns:
>>>
>>> 1. True, False, False, False, False # return period of 5 years (1/2)
>>> 2. False, False, False, False, True # return period of 5 years (2/2)
>>> 3. True, False, False, False, True # return period of 4 years (1/3)
>>> 4. False, True, False, False, False # return period of 4 years (2/3)
>>> 5. False, False, False, True, False # return period of 4 years (3/3)
>>> 6. True, False, False, True, False # return period of 3 years (1/3)
>>> 7. False, True, False, False, True # return period of 3 years (2/3)
>>> 8. False, False, True, False, False # return period of 3 years (3/3)
>>> 9. False, True, False, True, False # return period of 2 years (1/2)
>>> 10. True, False, True, False, True # return period of 2 years (1/2)
>>> 11. True, True, True, True, True # mono-succession of 5 years
>>> 12. False, True, True, True, True # mono-succession of 4 years (1/2)
>>> 13. True, True, True, True, False # mono-succession of 4 years (2/2)
>>> 14. True, False, True, True, True # mono-succession of 3 years (1/5)
>>> 15. True. True. True. False, True # mono-succession of 3 years (2/5)
>>> 16. False, False, True, True, True # mono-succession of 3 years (3/5)
>>> 17. True, True, True, False, False # mono-succession of 3 years (4/5)
>>> 18. False, True, True, True, False # mono-succession of 3 years (5/5)
>>> 19. True, True, False, True, True # crops repeated two years
>>>
>>> In particular, I want to apply all these 19 patterns to 7 (out of 11)
>>> land covers: 2BC, 2Co, 2Ma, 2We, 2MG, 2ML, 2PG. The pattern are so
>>> structured: True means presence of a given land cover (iteratively,
>>> one of the seven listed above), False means any other land-cover
>>> (amidst the remainder 10).
>>>
>>> Thanks again for any further help.
>>> Greetings,
>>> Dd
>>>
>>> ***********************************************************
>>> Davide Rizzo
>>> website :: http://sites.google.com/site/ridavide/
>>>
>>>
>>> On Sat, Sep 15, 2012 at 5:51 PM, John Kane <jrkrideau at inbox.com> wrote:
>>>> I have not seen any replies to your questions so I will suggest an
>>>> approach that may work if I can get a function to work.
>>>>
>>>> If I understand what you want, you have a pattern something like this:
>>>> pattern1 <- c("2Ma", "no2Ma","no2Ma", "no2Ma","no2Ma")
>>>> pattern2 <- c("no2Ma", 'no2Ma', "no2Ma", "no2Ma", "2Ma")
>>>>
>>>> for each five year period where 2Ma stands to Maize, one of 11
>>>> different
>>>> grains
>>>> 1AU 2BC 2Co 2Ma 2MG 2ML 2oc 2PG 2SA 2We 3sN
>>>>
>>>> and what you want to know is if each year gives a pattern like
>>>>
>>>> check1 <- c(TRUE, FALSE, FALSE, FALSE, FALSE)
>>>> check2 <- c(FALSE, FALSE, FALSE, FALSE, TRUE)
>>>>
>>>> If I understand the patterns you only care for the two above, is that
>>>> correct?
>>>>
>>>> I am running out of time today but I think that this approach will get
>>>> you started
>>>> ===========================================================
>>>>
>>>> T80<-read.table(file="C:/sample.txt", header=T, sep=";")
>>>>
>>>> # Reminder of just what we want to get as a final result.
>>>> check1 <- c(TRUE, FALSE, FALSE, FALSE, FALSE)
>>>> check2 <- c(FALSE, FALSE, FALSE, FALSE, TRUE)
>>>>
>>>> pattern1 <- c("2Ma", "2Ma","2Ma", "2Ma","2Ma")
>>>>
>>>> # one row examples to see that is happening
>>>> T80[1,3:7]
>>>> T80[1, 3:7] == pattern1
>>>>
>>>> T80[405, 3:7]
>>>> T80[405, 3:7] == pattern1
>>>>
>>>> # now we apply the patterns to the entire data set.
>>>> pp1 <- T80[, 3:7] == pattern1
>>>> pp2 <- T80[, 3:7] == pattern2
>>>>
>>>> # reassign the WS values so we know where the data is from
>>>> WSnames <- rep(T80$WS, 2)
>>>>
>>>> # Assmble new data frame.
>>>> maizedata <- data.frame(WSnames, rbind(pp1,pp2))
>>>> ========================================================
>>>>
>>>> Now, assuming this runs for you and I have not made a serious mistake
>>>> in
>>>> logic, kyou should be able to do some subsetting (?subset) to
>>>> extract
>>>> only the
>>>> check1 and check2 patterns above.
>>>>
>>>> This is where I ran into trouble as I don't have the time this morning
>>>> to work out the subsetting conditions. It looks tricking and you
>>>> probably need a couple of subsetting moves.
>>>>
>>>> It's not a pretty solutlion and, particularly, I expect someone could
>>>> clean it up to make the subsetting easier or even unnecessary but I
>>>> hope
>>>> it helps.
>>>>
>>>> Once you have extracted what you want use apply() or perhaps the
>>>> plyr
>>>> package to aggregate the results.
>>>>
>>>> Repeat for all grains. Actually look into setting the whole thing up
>>>> as
>>>> a function. You should be able to write the program once as a function
>>>> and do a loop or an apply() to do all 11 grains in one go.
>>>>
>>>> Best of luck.
>>>>
>>>> John Kane
>>>> Kingston ON Canada
>>>>
>>>>
>>>>> -----Original Message-----
>>>>> From: ridavide at gmail.com
>>>>> Sent: Thu, 13 Sep 2012 15:36:28 +0200
>>>>> To: r-help at r-project.org
>>>>> Subject: [R] [newbie] aggregating table() results and simplifying
>>>>> code
>>>>> with loop
>>>>>
>>>>> Dear all,
>>>>> I'm looking for primary help at aggregating table() results and at
>>>>> writing a loop (if useful)
>>>>>
>>>>> My dataset ( http://goo.gl/gEPKW ) is composed of 23k rows, each one
>>>>> representing a point in the space of which we know the land cover
>>>>> over
>>>>> 10 years (column y01 to y10).
>>>>>
>>>>> I need to analyse it with a temporal sliding window of 5 years (y01
>>>>> to
>>>>> y05, y02 to y06 and so forth)
>>>>> For each period I'm looking for specific sequences (e.g., Maize,
>>>>> -noMaize, -noMaize, -noMaize, -noMaize) to calculate the "return
>>>>> time"
>>>>> of principal land covers: barley (2BC), colza (2Co), maize (2Ma),
>>>>> etc.
>>>>> I define the "return time" as the presence of a given land cover
>>>>> according to a given sequence. Hence, each return time could require
>>>>> the sum of different sequences (e.g., a return time of 5 years
>>>>> derives
>>>>> from the sum of [2Ma,no2Ma,no2Ma,no2Ma,no2Ma] +
>>>>> [no2Ma,no2Ma,no2Ma,no2Ma,2Ma]).
>>>>> I need to repeat the calculation for each land cover for each time
>>>>> window. In addition, I need to repeat the process over three datasets
>>>>> (the one I give is the first one, the second one is from year 12 to
>>>>> year 24, the third one from year 27 to year 31. So I have breaks in
>>>>> the monitoring of land cover that avoid me to create a continuous
>>>>> dataset). At the end I expect to aggregate the sum for each spatial
>>>>> entity (column WS)
>>>>>
>>>>> I've started writing the code for the first crop in the first 5yrs
>>>>> period (http://goo.gl/FhZNx) then copying and pasting it for each
>>>>> crop
>>>>> then for each time window...
>>>>> Moreover I do not know how to aggregate the results of table(). (NB
>>>>> sometimes I have a different number of WS per table because a given
>>>>> sequence could be absent in a given spatial entity... so I have the
>>>>> following warning msg: number of columns of result is not a multiple
>>>>> of vector length (arg 1)). Therefore, I'm "obliged" to copy&paste the
>>>>> table corresponding to each sequence....
>>>>>
>>>>> FIRST QUEST. How to aggregate the results of table() when the number
>>>>> of columns is different?
>>>>> Or the other way around: Is there a way to have a table where each
>>>>> row
>>>>> reports the number of points per time return per WS? something like
>>>>>
>>>>> WS1 WS2 WS3 WS4 ... WS16 crop period
>>>>> 23 15 18 43 ... 52 Ma5 01
>>>>> 18 11 25 84 ... 105 Ma2 01
>>>>> ... ... ... ... ... ... ... ...
>>>>> ... ... ... ... ... ... Co5 01
>>>>> ... ... ... ... ... ... ... ...
>>>>> ... ... ... ... ... ... Ma5 02
>>>>> ... ... ... ... ... ... ... ...
>>>>> In this table each row should represent a return time for a given
>>>>> land
>>>>> cover a given period (one of the 6 time window of 5 years)?
>>>>>
>>>>> SECOND QUEST. Could a loop (instead of a modular copy/paste code)
>>>>> improve the time/reliability of the calculation? If yes, could you
>>>>> please indicate me some entry-level references to write it?
>>>>>
>>>>> I am aware this are newbie's questions, but I have not be able to
>>>>> solve them using manuals and available sources.
>>>>> Thank you in advance for your help.
>>>>>
>>>>> Greetings,
>>>>> Dd
>>>>>
>>>>> PS
>>>>> R: version 2.14.2 (2012-02-29)
>>>>> OS: MS Windows XP Home 32-bit SP3
>>>>>
>>>>> *****************************
>>>>> Davide Rizzo
>>>>> post-doc researcher
>>>>> INRA UR055 SAD-ASTER
>>>>> website :: http://sites.google.com/site/ridavide/
>>>>
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