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[newbie] aggregating table() results and simplifying code with loop

13 messages · Davide Rizzo, John Kane, Rui Barradas

Davide Rizzo
# 
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/
Rui Barradas
# 
Hello,

You would get more and better help if you were to break your problem 
into smaller sub-problems.
I am not really sure if this is what you want but here it goes.


inxList <- function(DF, logcover, nYears){
     f <- function(x, n){
         if(any(x)){
             r <- rle(x)
             any(r$lengths[!r$values] == n)
         }else FALSE
     }
     yrs <- lapply(0:5, `+`, seq_len(5))
     inx.list <- lapply(yrs, function(i){apply(logcover[, i], 1, f, 
nYears)})
     inx.list
}

makeCovers <- function(Cover, nYears){
     lcover <- T80[, ycols] == Cover
     inx.list <- inxList(T80[, ycols], lcover, nYears)
     tmp <- rep(NA, length(ws))
     names(tmp) <- ws
     xtb <- do.call(rbind, lapply(seq_along(inx.list), function(ix){
         xt <- xtabs(~ WS, T80[ inx.list[[ix]], c(2, 1 + ix + 1:5) ])
         tmp[names(xt)] <- xt; tmp}))
     colnames(xtb) <- paste("WS", seq_along(ws), sep = "")
     data.frame(xtb, Cover = Cover, Period = seq_along(inx.list))
}

T80 <- read.table("sample.txt", header=TRUE, sep = ";")

ycols <- grep("y", names(T80))
ws <- unique(T80$WS)
covers <- as.character(unique(unlist(T80[ycols])))

result <- lapply( 4:2, function(.n)
         do.call(rbind, lapply(covers, makeCovers, .n)) )
names(result) <- paste("nYears", 4:2, sep = ".")

str(result)
# See the results for 4 years
result[[ "nYears.4" ]]  # or any other number of years


Hope this helps,

Rui Barradas
Em 13-09-2012 14:36, Davide Rizzo escreveu:
1 day later
John Kane
# 
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
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Davide Rizzo
# 
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:
1 day later
John Kane
# 
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
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Davide Rizzo
# 
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

==========================================================

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 ?...

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:
John Kane
# 
Comments in-line below.

Sorry to be so long getting back to you but sleep intervened.
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
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")

##
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Rui Barradas
# 
Hello,

Now I believe I've got it. (So-so believe.)
ddply is a good idea, in my first post I was using xtabs to the same 
effect, but ddply makes it a lot easier.
I had made a big mistake, though. I thought that only sequences of FALSE 
were valid, hence rle(). In a follow-up post, Davide listed all 
combinations of T/F that matter. The code below uses them to create a df 
of cases to count with ddply. The creation is done by merge, on the T/F 
columns (function f1) and the rest is just to apply to 6 periods of 5 
years each, after applying to covers, after applying to years of crop type.
Sounds confusing, and it probably is but I've separated all of it in 
small functions.
Each of the functions is in fact very simple, and all one needs is to 
remember that the inner most function is reached after a series of 
*apply statements, doing one thing at a time.


#------------------------------------------------------------------------------
# data.frame of target sequences of T/F per crop type and years
# (from an earlier post)

wanted <-
structure(list(V2 = c(TRUE, FALSE, TRUE, FALSE, FALSE, TRUE,
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE,
TRUE, FALSE, TRUE), V3 = c(FALSE, FALSE, FALSE, TRUE, FALSE,
FALSE, TRUE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE,
FALSE, TRUE, TRUE, TRUE), V4 = c(FALSE, FALSE, FALSE, FALSE,
FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE,
TRUE, TRUE, TRUE, TRUE, FALSE), V5 = c(FALSE, FALSE, FALSE, FALSE,
TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE,
FALSE, TRUE, FALSE, TRUE, TRUE), V6 = c(FALSE, TRUE, TRUE, FALSE,
FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE,
TRUE, TRUE, FALSE, FALSE, TRUE), V7 = c(" return", " return",
" return", " return", " return", " return", " return", " return",
" return", " return", " mono-succession", " mono-succession",
" mono-succession", " mono-succession", " mono-succession", " mono-succession",
" mono-succession", " mono-succession", " crops"), V8 = c(5L,
5L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 2L, 5L, 4L, 4L, 3L, 3L, 3L, 3L,
3L, 2L)), .Names = c("V2", "V3", "V4", "V5", "V6", "V7", "V8"
), row.names = c(NA, -19L), class = "data.frame")

want <- split(wanted, wanted$V7)

#--------------------------------------------------------------------
# The functions go from f5 down to f1 (f5 calls f4, f4 calls f3, etc.)
#
f1 <- function(DF, Mat, currcols, WS){
	mcols <- names(T80[, ycols])[currcols]  # names for 'merge'
	names(DF)[1:5] <- mcols
	m <- merge(DF, data.frame(Mat[, mcols], WS = WS))
	ddply(m, .(WS), summarize, length(V8))
}
f2 <- function(w, tf, WS){
	l1 <- lapply(yrs, function(yy) t(f1(w, tf, yy, WS)))
	l2 <- lapply(l1, function(x){
		nms <- as.character(x["WS",])
		resline[ nms ] <- x[2, ]
		resline})
	res <- do.call(rbind, l2)
	data.frame(res)
}
f3 <- function(Cover, w){
	tf <- T80[, ycols] == Cover
	inx <- as.vector(apply(tf, 1, function(.x) any(.x)))
	tf <- tf[inx, ]
	WS <- T80$WS[inx]
	# Special care with empty sets
	if(sum(tf) > 0){
		res <- f2(w, tf, WS)
		na <- as.vector(apply(res, 1, function(.x) all(is.na(.x))))
		res <- res[!na, ]
		if(!is.null(res) && nrow(res) > 0){
			res$Cover <- Cover
			res$Period <- (1:6)[!na]
		}else res <- NULL
	}else res <- NULL
	res
}
f4 <- function(w){
	res <- do.call(rbind, lapply(covers, f3, w))
	# Special care with empty sets
	if(!is.null(nrow(res)) && nrow(res) > 0){
		res <- data.frame(res)
		res$Years <- w$V8[1]
	}else res <- NULL
	res
}
f5 <- function(w){
	sp <- split(w, w$V8)
	res <- do.call( rbind, lapply(sp, f4) )
	res <- data.frame(res)
	res
}

T80 <- read.table("sample.txt", header = TRUE, sep = ";")

ycols <- grep("y", names(T80))
ws <- unique(T80$WS)
covers <- as.character(unique(unlist(T80[ycols])))
yrs <- lapply(0:5, `+`, 1:5)
resline <- rep(NA, length(ws))
names(resline) <- ws

result <- lapply(want, f5)


(Ok, maybe two or three things at a time.)

I hope this is it!
Have fun.

Rui Barradas

Em 17-09-2012 14:19, John Kane escreveu:
John Kane
# 
Looks very interesting and as I said you're a much better progamer than I am. I get headaches using lapply()!

I am going to have to spend some time stepping through the functions to really see what you are doing.   However if I understand the output, for example one can simply aggreate the information by Crop and Period in each element of the list to get the corresponding sums for each of the desired patterns?

Thus for example in result[[1]] we get all the results for condition 19 (crops) from wanted?  


John Kane
Kingston ON Canada
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Rui Barradas
# 
Hello,
Em 18-09-2012 15:29, John Kane escreveu:
Thanks!
As for lapply, if you understand apply you'll understand lapply. With 
some differences, the principle is the same.
I've tried to keep each function simple, but it can become complicated 
to follow the call tree down to f1.
Yes, or at least it's supposed to. And I've kept in separate list 
elements precisely to have simplified access to each combination of Crop 
and Period.
(Condition 19, the last one, comes first because split sorts its output 
by list element name, and 'crop' is the first.)

Rui Barradas
Davide Rizzo
# 
Hi dear R-helpers,

I really appreciate your "coding the answers" to (my) questions.
I am now in the situation to get results in a very shorter time and
with a proper code.

+++++ the study (summary) +++++++++++++++++++++

In (short) response to the question by John, the global target of the
analysis is to identify the dynamics of crop sequences in a given
region. The spatial grain is the elementary watershed (as defined by
the local water agency). The temporal grain is a 5 year period (it
allows, according to previous analyses, to grasp the major crop
sequences for the study area). The input data is a grid of points
where the land cover has been monitored since the 80's up to now (with
a couple of interruptions). The first part of the analysis was aimed
at characterizing crop sequences and their occurrences in the
different watersheds.
The step of the analysis for what I'm asking your help is aimed at
calculating the return time of the most relevant crops (from an
agronomic point of view), on which operate then a clustering. Raw
attempts I've tried so far with other methods appeared to give
promising results, so I've decided to improve the approach using R.
Here is where you come in.
+++++++++++++++++++++++++++++++++++++++++++

++++++ (unsolved) questions +++++++++++++++++++

The proposal by John is clear to me, probably because his way of
coding is closer to my capability to understand. The solution by Rui
looks interesting, complete and effective (few elementary functions
and reduced time to have the results to be used in the other steps of
the analysis).

Nevertheless I'm still in trouble because I'm not used with function
(that's one of the reasons I'm here).
The following questions will sound naive to you, but I'm not able to
decrypt Rui's code without asking again your help. Basic manuals (like
"R Language Definition" ch. 4, or "The R Inferno" ch. 5) were not
sufficient for me to understand how "w", "DF", "Mat", "WS" and
"currcols" are defined.
In other words, I see that the five embedded functions Rui has
proposed call for the above mentioned arguments, but I'm not able to
understand at which moment these arguments are defined.
+++++++++++++++++++++++++++++++++++++++++++

Everything flows without warnings, so I have the bad feeling to find
myself less smart than R...
That not a big problem, but having not understood the arguments, I do
not understand the functions (so, the results).
Thanks in advance for meeting this call for some extra-help...

Take care,
Dd

***********************************************************
Davide Rizzo
website :: http://sites.google.com/site/ridavide/
On Tue, Sep 18, 2012 at 5:08 PM, Rui Barradas <ruipbarradas at sapo.pt> wrote:
Rui Barradas
# 
Hello,
Inline
Em 18-09-2012 23:42, Davide Rizzo escreveu:
A function's arguments are defined, surprise!, in the function's 
definition. See R-intro chap. 10 and R-Inferno chap. 6.
Example:

f <- function(x){
     y <- 2*x + 1
     y
}

x <- 1
f(2)  # returns 5

This is because the 'x' outside the function is not the same as the 'x' 
inside the function. In the function definition I can choose any name I 
want, and then use that name in the function's body.
(Actually it's a bit more complicated because if a function uses a 
variable not passed to nor defined in the function, R will look for it 
in the caller's environment, and goes up until the global environment. 
It throws an error only if it's nowhere to be found.)

Chap. 6 of the R-inferno is about global assignments, also saying that 
in R if a function changes its arguments the modified version has 
nothing to do with the argument's value outside the function. Uunless 
it's returned, on exit this new value is lost. This is the case of 
example 1, variable 'x'.

This behavior is very usefull because it means that side effects are 
unlikely to occur. That's what P. Burns is talking about when he says 
that "Surprise in movies and novels is good. Surprise in computer code 
is bad." To understand a function look at the way it is called, and to 
what happens in it. Forget almost all of the rest, the exception being 
what is passed to it.

Finally, like with everything, it takes some time to be comfortable with 
R. And practice.

Have fun.

Rui Barradas
John Kane
# 
That's because I'm much closer to you in knowledge about R.
Rui's approach is "very, very  nice but does seem a bit opaque to us newbies.  

I think the first thing is : Do you undrestand the 'result' list structure? It sounds like you do but basically he is processing each set of conditions that you supplied and storing them indiviually as data frames in each element of the list.  

Secondly, to work through the functions you should be able to step through each function without running the function.  


For example for F5 the working parts are
============================================================
sp <- split(w, w$V8)
	res <- do.call( rbind, lapply(sp, f4) )
	res <- data.frame(res)
	res
=============================================================

We see from the main program that 

result <- lapply(want, f5)

so for the above code want is the "w" value.  Just stick it into the code and see what happens. That is print out values as you go. 

Each call seems to go to another function but bit by bit you should be able to see what is happening.  I suspect it will be messy but it should help a bit.

Another trick might be to make up a very tiny data set that meets all conditions and run the program in, I think , debug mode and stick in print statements all along the way so that you can get a feel for what is happening at each step.  

In any case you should be able to extact each condition from result by something like crop  <-  result[[1]] (note [[1]]  rather than [1] and run ddply() or aggregate() on the resulting data frame to see if the output looks reasonable.

Thanks for the quick summary.  Once I realised that WS = Watershed I thought that was what it was likely to be about but it`s handy to know and sometimes even a slight bit of knowledge about the subject area can help alert one to something strange in the output that may indicate a programing or logic problem.

Good luck.   R is amazingly powerful and can often produce results much more quickly than other stats language but it requires a different mindset. I used to get very funny mind aches when I first started using it after years of SAS or SYSTAT or even SPSS.
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