Re: [R] making chains from pairs
On Nov 8, 2013, at 10:56 AM, Hermann Norpois wrote: Hello, having a data frame like test with pairs of characters I would like to create chains. For instance from the pairs A/B and B/I you get the vector A B I. It is like jumping from one pair to the next related pair. So for my example test you should get: A B F G H I C F I K D L M N O P second - with(test, tapply(V2, V1, FUN=function(x) test[test$V2==x, ] ) ) Warning messages: 1: In test$V2 == x : longer object length is not a multiple of shorter object length 2: In test$V2 == x : longer object length is not a multiple of shorter object length 3: In test$V2 == x : longer object length is not a multiple of shorter object length third - sapply(names(second) , function(df) c(df, second[[df]][ , V2 ]) ) third $A [1] A B F G H $B [1] B F I F I $C [1] C F I K $D [1] D L M N $L [1] L O P fourth - sapply(names(third), function(d) unique( c(third[[d]], unlist(third[ sapply( third[[d]], [ ) ]) ) ) ) fourth $A [1] A B F G H I $B [1] B F I $C [1] C F I K $D [1] D L M N O P $L [1] L O P test V1 V2 1 A B 2 A F 3 A G 4 A H 5 B F 6 B I 7 C F 8 C I 9 C K 10 D L 11 D M 12 D N 13 L O 14 L P Thanks Hermann dput (test) structure(list(V1 = c(A, A, A, A, B, B, C, C, C, D, D, D, L, L), V2 = c(B, F, G, H, F, I, F, I, K, L, M, N, O, P)), .Names = c(V1, V2), row.names = c(NA, -14L), class = data.frame) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Standard errors in regression models with interactions terms
In a rather simple regression, I’d like to ask the question, for high trees, whether it makes a difference (for volume) whether a three is thick. If my interpretation is correct, for low trees, i.e. for which trees$isHigh == FALSE, the answer is yes. The problem is how to merge the standard errors. Code follows. data(trees) trees$isHigh - trees$Height 76 trees$isThick - trees$Girth 13 m - lm(trees$Volume ~ trees$isHigh + trees$isThick + trees$isHigh:trees$isThick) summary(m) I might be mistaken, but a workaround is to rewrite the model as follows, which shows that the answer is yes. However, I would very much like to know how to answer the question with the original model. data(trees) trees$isLow - trees$Height = 76 trees$isThick - trees$Girth 13 m - lm(trees$Volume ~ trees$isLow + trees$isThick + trees$isLow:trees$isThick) summary(m) -- View this message in context: http://r.789695.n4.nabble.com/Standard-errors-in-regression-models-with-interactions-terms-tp4680104.html Sent from the R help mailing list archive at Nabble.com. __ R-help@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.
[R] `level' definition in `computeContour3d' (misc3d package)
I'd very much appreciate some help here: I'm in the process of clarifying
whether I can use `computeContour3d' to derive estimates of the surface
area of a single closed isosurface (and prospectively the enclosed
volume). getting the surface area from the list of triangles returned by
`computeContour3d' is straightforward but I've stumbled over the precise
meaning of `level' here. looking into the package, ultimately the level is
used in the namespace function `faceType' which reads:
function (v, nx, ny, level, maxvol)
{
if (level == maxvol)
p - v = level
else p - v level
v[p] - 1
v[!p] - 0
v[-nx, -ny] + 2 * v[-1, -ny] + 4 * v[-1, -1] + 8 * v[-nx,
-1]
}
my question: is the discrimination of the special case `level == maxvol'
(or rather of everything else) really desirable? I would argue
that always testing for `v = level' would be better. if I feed data with
discrete values (e.g. integer-valued) defined
on a coarse grid into `computeContour3d' it presently makes a big
difference whether there is a single data point (e.g.) with a value larger
than `level' or not. consider the 1D example:
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
data2 - c(0, 0, 1, 2, 1, 1, 1, 0, 0)
and level = 1
this defines the isocontour `level = 1' to lie at pos 3 and 7 in for data1
but as lying at pos 4 in data2. actually I would like (and expect) to get
the same isosurface for `data2' with this `level' setting. in short: the
meaning/definition of `level' changes depending on whether or not it is
equal to `maxvol'. this is neither stated in the manpage nor is this
desirable in my view. but maybe I miss something here. any clarification
would be appreciated.
j.
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[R] S4 vs S3. New Package
Hi, I am working on a new credit portfolio optimization package. My question is if it is more recommended to develop in S4 object oriented or S3. It would be more naturally to develop in object oriented paradigm, but there is many concerns regarding S4. 1) Performance of S4 could be an issue as a setter function, actually changes the whole object behind the scenes. 2) Documentation. It has been really hard to find examples in S4. Most books and articles consider straightforward S3 examples. Thanks, [[alternative HTML version deleted]] __ R-help@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.
[R] S4; Setter function is not chaning slot value as expected
It is my first time programming with S4 and I can't get the setter fuction
to actually change the value of the slot created by the constructor.
I guess it has to do with local copy, global copy, etc. of the variable -
but, I could't find anything relevant in documentation.
Tried to copy examples from the internet, but they had the same problem.
# The code
setClass (Account ,
representation (
customer_id = character,
transactions = matrix)
)
Account - function(id, t) {
new(Account, customer_id = id, transactions = t)
}
setGeneric (CustomerID-, function(obj,
id){standardGeneric(CustomerID-)})
setReplaceMethod(CustomerID, Account, function(obj, id){
obj@customer_id - id
obj
})
ac - Account(12345, matrix(c(1,2,3,4,5,6), ncol=2))
ac
CustomerID - 54321
ac
#Output
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
# CustomerID is value has changed to 54321, but as you can see it does't
CustomerID - 54321
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
Help!
[[alternative HTML version deleted]]
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] `level' definition in `computeContour3d' (misc3d package)
On 13-11-09 8:50 AM, j. van den hoff wrote:
I'd very much appreciate some help here: I'm in the process of clarifying
whether I can use `computeContour3d' to derive estimates of the surface
area of a single closed isosurface (and prospectively the enclosed
volume). getting the surface area from the list of triangles returned by
`computeContour3d' is straightforward but I've stumbled over the precise
meaning of `level' here. looking into the package, ultimately the level is
used in the namespace function `faceType' which reads:
function (v, nx, ny, level, maxvol)
{
if (level == maxvol)
p - v = level
else p - v level
v[p] - 1
v[!p] - 0
v[-nx, -ny] + 2 * v[-1, -ny] + 4 * v[-1, -1] + 8 * v[-nx,
-1]
}
my question: is the discrimination of the special case `level == maxvol'
(or rather of everything else) really desirable? I would argue
that always testing for `v = level' would be better. if I feed data with
discrete values (e.g. integer-valued) defined
on a coarse grid into `computeContour3d' it presently makes a big
difference whether there is a single data point (e.g.) with a value larger
than `level' or not. consider the 1D example:
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
data2 - c(0, 0, 1, 2, 1, 1, 1, 0, 0)
and level = 1
this defines the isocontour `level = 1' to lie at pos 3 and 7 in for data1
but as lying at pos 4 in data2. actually I would like (and expect) to get
the same isosurface for `data2' with this `level' setting. in short: the
meaning/definition of `level' changes depending on whether or not it is
equal to `maxvol'. this is neither stated in the manpage nor is this
desirable in my view. but maybe I miss something here. any clarification
would be appreciated.
I don't see why you'd expect the same output from those vectors, but
since they aren't legal input to computeContour3d, maybe I don't know
what you mean by them. Could you put together a reproducible example
that shows bad contours?
Duncan Murdoch
j.
--
__
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] S4; Setter function is not chaning slot value as expected
If you want to set a slot you have to refer to it:
ac@CustomerID - “54321”
or you use your setter:
ac - CustomerID(ac, “54321”)
What you did was creating a new symbol CustomerID referring to the String
“54321”
CustomerID - “54321”
CustomerID
[1] “54321”
Best
Simon
On 09 Nov 2013, at 15:31, daniel schnaider dschnai...@gmail.com wrote:
It is my first time programming with S4 and I can't get the setter fuction
to actually change the value of the slot created by the constructor.
I guess it has to do with local copy, global copy, etc. of the variable -
but, I could't find anything relevant in documentation.
Tried to copy examples from the internet, but they had the same problem.
# The code
setClass (Account ,
representation (
customer_id = character,
transactions = matrix)
)
Account - function(id, t) {
new(Account, customer_id = id, transactions = t)
}
setGeneric (CustomerID-, function(obj,
id){standardGeneric(CustomerID-)})
setReplaceMethod(CustomerID, Account, function(obj, id){
obj@customer_id - id
obj
})
ac - Account(12345, matrix(c(1,2,3,4,5,6), ncol=2))
ac
CustomerID - 54321
ac
#Output
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
# CustomerID is value has changed to 54321, but as you can see it does't
CustomerID - 54321
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
Help!
[[alternative HTML version deleted]]
__
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
__
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] S4 vs S3. New Package
This depends very often of on the developer and what he is comfortable with. I like S4 classes, as I come from C++ and S4 classes approximate C++ classes at least more than S3 classes do (Reference Classes would do so even more and I know very good R programmers liking these most). 1) I wrote a package for MCMC simulation with S4 classes carrying all simulated values - fast enough for me: in less than 1.5 secs I have my sample of 100.000 values together with several other 100T values like log-likelihoods, posterior hyper parameters, etc. I watch out for not copying too often an object but sometimes it is not avoidable. 2) That is not true: Books: http://www.amazon.de/Software-Data-Analysis-Programming-Statistics/dp/0387759352/ref=sr_1_1?ie=UTF8qid=1384014486sr=8-1keywords=John+chambers+data http://www.amazon.de/Programming-Data-Language-John-Chambers/dp/0387985034/ref=sr_1_4?ie=UTF8qid=1384014486sr=8-4keywords=John+chambers+data Online: https://www.rmetrics.org/files/Meielisalp2009/Presentations/Chalabi1.pdf https://www.stat.auckland.ac.nz/S-Workshop/Gentleman/S4Objects.pdf And for a bunch of packages look into the Bioconductor packages. Best Simon On 09 Nov 2013, at 16:22, daniel schnaider dschnai...@gmail.com wrote: Hi, I am working on a new credit portfolio optimization package. My question is if it is more recommended to develop in S4 object oriented or S3. It would be more naturally to develop in object oriented paradigm, but there is many concerns regarding S4. 1) Performance of S4 could be an issue as a setter function, actually changes the whole object behind the scenes. 2) Documentation. It has been really hard to find examples in S4. Most books and articles consider straightforward S3 examples. Thanks, [[alternative HTML version deleted]] __ R-help@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. __ R-help@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.
Re: [R] `level' definition in `computeContour3d' (misc3d package)
On Sat, 09 Nov 2013 17:16:28 +0100, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 13-11-09 8:50 AM, j. van den hoff wrote:
I'd very much appreciate some help here: I'm in the process of
clarifying
whether I can use `computeContour3d' to derive estimates of the surface
area of a single closed isosurface (and prospectively the enclosed
volume). getting the surface area from the list of triangles returned by
`computeContour3d' is straightforward but I've stumbled over the precise
meaning of `level' here. looking into the package, ultimately the level
is
used in the namespace function `faceType' which reads:
function (v, nx, ny, level, maxvol)
{
if (level == maxvol)
p - v = level
else p - v level
v[p] - 1
v[!p] - 0
v[-nx, -ny] + 2 * v[-1, -ny] + 4 * v[-1, -1] + 8 * v[-nx,
-1]
}
my question: is the discrimination of the special case `level == maxvol'
(or rather of everything else) really desirable? I would argue
that always testing for `v = level' would be better. if I feed data
with
discrete values (e.g. integer-valued) defined
on a coarse grid into `computeContour3d' it presently makes a big
difference whether there is a single data point (e.g.) with a value
larger
than `level' or not. consider the 1D example:
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
data2 - c(0, 0, 1, 2, 1, 1, 1, 0, 0)
and level = 1
this defines the isocontour `level = 1' to lie at pos 3 and 7 in for
data1
but as lying at pos 4 in data2. actually I would like (and expect) to
get
the same isosurface for `data2' with this `level' setting. in short: the
meaning/definition of `level' changes depending on whether or not it is
equal to `maxvol'. this is neither stated in the manpage nor is this
desirable in my view. but maybe I miss something here. any clarification
would be appreciated.
I don't see why you'd expect the same output from those vectors, but
since they aren't legal input to computeContour3d, maybe I don't know
what you mean by them. Could you put together a reproducible example
that shows bad contours?
it's not bad contours, actually. my question only concerns the different
meaning
of `level' depending on whether `level = maxvol' or not.
here is a real example:
8
library(misc3d)
dim - 21
cnt - (dim+1)/2
wid1 - 5
wid2 - 1
rng1 - (cnt-wid1):(cnt+wid1)
rng2 - (cnt-wid2):(cnt+wid2)
v - array(0, rep (dim, 3))
#put 11x11x11 box of ones at center
v[rng1, rng1, rng1] - 1
con1 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con1))
dum - readline(CR for next plot)
#put an additional 3x3x3 box of twos at center
v[rng2, rng2, rng2] - 2
con2 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con2))
8
this first puts a 11x11x11 box one Ones at the center of the
zero-initalized array and computes `con1' for `level=1'. in the 2. step
it puts a further, 3x3x3 box of Twos at the center and computes the
`level=1' contour again which this time does not delineate
the box of Ones but lies somewhere between the two non-zero boxes since
now the test in `faceType' is for ` level'. this is not immediately
obvious from the plots (no scale) but obvious from looking at `con1' and
`con2': the `con2' isosurface is shrunk by 3 voxels at each
side relative to `con1' (so my initial mail was wrong here: `con2' does
not jump to the next discrete isocontour but rather to
a point about halfway between both plateaus ). I also (for my own problem
at hand) computed the total surface area which is
(not surprisingly...) 600 for `con1' and 64.87 for `con2'. so if one is
interested in such surfaces (I am) this makes a big difference in such
data.
the present behavior is not wrong per se but I would much prefer if the
test where always for `= level' (so that in the present example the
resulting isosurface would in both cases delineate the box of Ones -- as
is the case when using `level = 1-e-6' instead of `level=1').
I believe the isosurface for a given value of `level' should have an
unambiguous meaning independent of what the data further inside are
looking like.
is this clearer now?
Duncan Murdoch
j.
--
__
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and provide commented, minimal, self-contained, reproducible code.
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] `level' definition in `computeContour3d' (misc3d package)
On 13-11-09 11:57 AM, j. van den hoff wrote:
On Sat, 09 Nov 2013 17:16:28 +0100, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 13-11-09 8:50 AM, j. van den hoff wrote:
I'd very much appreciate some help here: I'm in the process of
clarifying
whether I can use `computeContour3d' to derive estimates of the surface
area of a single closed isosurface (and prospectively the enclosed
volume). getting the surface area from the list of triangles returned by
`computeContour3d' is straightforward but I've stumbled over the precise
meaning of `level' here. looking into the package, ultimately the level
is
used in the namespace function `faceType' which reads:
function (v, nx, ny, level, maxvol)
{
if (level == maxvol)
p - v = level
else p - v level
v[p] - 1
v[!p] - 0
v[-nx, -ny] + 2 * v[-1, -ny] + 4 * v[-1, -1] + 8 * v[-nx,
-1]
}
my question: is the discrimination of the special case `level == maxvol'
(or rather of everything else) really desirable? I would argue
that always testing for `v = level' would be better. if I feed data
with
discrete values (e.g. integer-valued) defined
on a coarse grid into `computeContour3d' it presently makes a big
difference whether there is a single data point (e.g.) with a value
larger
than `level' or not. consider the 1D example:
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
data2 - c(0, 0, 1, 2, 1, 1, 1, 0, 0)
and level = 1
this defines the isocontour `level = 1' to lie at pos 3 and 7 in for
data1
but as lying at pos 4 in data2. actually I would like (and expect) to
get
the same isosurface for `data2' with this `level' setting. in short: the
meaning/definition of `level' changes depending on whether or not it is
equal to `maxvol'. this is neither stated in the manpage nor is this
desirable in my view. but maybe I miss something here. any clarification
would be appreciated.
I don't see why you'd expect the same output from those vectors, but
since they aren't legal input to computeContour3d, maybe I don't know
what you mean by them. Could you put together a reproducible example
that shows bad contours?
it's not bad contours, actually. my question only concerns the different
meaning
of `level' depending on whether `level = maxvol' or not.
here is a real example:
8
library(misc3d)
dim - 21
cnt - (dim+1)/2
wid1 - 5
wid2 - 1
rng1 - (cnt-wid1):(cnt+wid1)
rng2 - (cnt-wid2):(cnt+wid2)
v - array(0, rep (dim, 3))
#put 11x11x11 box of ones at center
v[rng1, rng1, rng1] - 1
con1 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con1))
dum - readline(CR for next plot)
#put an additional 3x3x3 box of twos at center
v[rng2, rng2, rng2] - 2
con2 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con2))
8
this first puts a 11x11x11 box one Ones at the center of the
zero-initalized array and computes `con1' for `level=1'. in the 2. step
it puts a further, 3x3x3 box of Twos at the center and computes the
`level=1' contour again which this time does not delineate
the box of Ones but lies somewhere between the two non-zero boxes since
now the test in `faceType' is for ` level'. this is not immediately
obvious from the plots (no scale) but obvious from looking at `con1' and
`con2': the `con2' isosurface is shrunk by 3 voxels at each
side relative to `con1' (so my initial mail was wrong here: `con2' does
not jump to the next discrete isocontour but rather to
a point about halfway between both plateaus ). I also (for my own problem
at hand) computed the total surface area which is
(not surprisingly...) 600 for `con1' and 64.87 for `con2'. so if one is
interested in such surfaces (I am) this makes a big difference in such
data.
the present behavior is not wrong per se but I would much prefer if the
test where always for `= level' (so that in the present example the
resulting isosurface would in both cases delineate the box of Ones -- as
is the case when using `level = 1-e-6' instead of `level=1').
I believe the isosurface for a given value of `level' should have an
unambiguous meaning independent of what the data further inside are
looking like.
I think it does, but your data make the determination of its location
ambiguous.
The definition is the estimated location where the continuous field
sampled at v crosses level.
You have a field with a discontinuity (or two). You have whole volumes
of space where the field is equal to the level. The marching cubes
algorithm is designed to detect crossings, not solid regions.
For example, going back to one dimension, if your data looked like your
original vector
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
then it is ambiguous where it crosses 1: it could be at 3 and 7, or
there could be multiple crossings in that range. I believe the
analogous situation in misc3d would treat this as a crossing at 3 and 7.
Duncan Murdoch
Re: [R] `level' definition in `computeContour3d' (misc3d package)
On Sat, 09 Nov 2013 18:18:23 +0100, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 13-11-09 11:57 AM, j. van den hoff wrote:
On Sat, 09 Nov 2013 17:16:28 +0100, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 13-11-09 8:50 AM, j. van den hoff wrote:
I'd very much appreciate some help here: I'm in the process of
clarifying
whether I can use `computeContour3d' to derive estimates of the
surface
area of a single closed isosurface (and prospectively the enclosed
volume). getting the surface area from the list of triangles returned
by
`computeContour3d' is straightforward but I've stumbled over the
precise
meaning of `level' here. looking into the package, ultimately the
level
is
used in the namespace function `faceType' which reads:
function (v, nx, ny, level, maxvol)
{
if (level == maxvol)
p - v = level
else p - v level
v[p] - 1
v[!p] - 0
v[-nx, -ny] + 2 * v[-1, -ny] + 4 * v[-1, -1] + 8 * v[-nx,
-1]
}
my question: is the discrimination of the special case `level ==
maxvol'
(or rather of everything else) really desirable? I would argue
that always testing for `v = level' would be better. if I feed data
with
discrete values (e.g. integer-valued) defined
on a coarse grid into `computeContour3d' it presently makes a big
difference whether there is a single data point (e.g.) with a value
larger
than `level' or not. consider the 1D example:
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
data2 - c(0, 0, 1, 2, 1, 1, 1, 0, 0)
and level = 1
this defines the isocontour `level = 1' to lie at pos 3 and 7 in for
data1
but as lying at pos 4 in data2. actually I would like (and expect) to
get
the same isosurface for `data2' with this `level' setting. in short:
the
meaning/definition of `level' changes depending on whether or not it
is
equal to `maxvol'. this is neither stated in the manpage nor is this
desirable in my view. but maybe I miss something here. any
clarification
would be appreciated.
I don't see why you'd expect the same output from those vectors, but
since they aren't legal input to computeContour3d, maybe I don't know
what you mean by them. Could you put together a reproducible example
that shows bad contours?
it's not bad contours, actually. my question only concerns the
different
meaning
of `level' depending on whether `level = maxvol' or not.
here is a real example:
8
library(misc3d)
dim - 21
cnt - (dim+1)/2
wid1 - 5
wid2 - 1
rng1 - (cnt-wid1):(cnt+wid1)
rng2 - (cnt-wid2):(cnt+wid2)
v - array(0, rep (dim, 3))
#put 11x11x11 box of ones at center
v[rng1, rng1, rng1] - 1
con1 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con1))
dum - readline(CR for next plot)
#put an additional 3x3x3 box of twos at center
v[rng2, rng2, rng2] - 2
con2 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con2))
8
this first puts a 11x11x11 box one Ones at the center of the
zero-initalized array and computes `con1' for `level=1'. in the 2. step
it puts a further, 3x3x3 box of Twos at the center and computes the
`level=1' contour again which this time does not delineate
the box of Ones but lies somewhere between the two non-zero boxes since
now the test in `faceType' is for ` level'. this is not immediately
obvious from the plots (no scale) but obvious from looking at `con1' and
`con2': the `con2' isosurface is shrunk by 3 voxels at each
side relative to `con1' (so my initial mail was wrong here: `con2' does
not jump to the next discrete isocontour but rather to
a point about halfway between both plateaus ). I also (for my own
problem
at hand) computed the total surface area which is
(not surprisingly...) 600 for `con1' and 64.87 for `con2'. so if one is
interested in such surfaces (I am) this makes a big difference in such
data.
the present behavior is not wrong per se but I would much prefer if
the
test where always for `= level' (so that in the present example the
resulting isosurface would in both cases delineate the box of Ones -- as
is the case when using `level = 1-e-6' instead of `level=1').
I believe the isosurface for a given value of `level' should have an
unambiguous meaning independent of what the data further inside are
looking like.
I think it does, but your data make the determination of its location
ambiguous.
I was imprecise: what I meant is: the isosurface should not change in my
example between both cases.
The definition is the estimated location where the continuous field
sampled at v crosses level.
understood/agreed.
You have a field with a discontinuity (or two). You have whole volumes
of space where the field is equal to the level. The marching cubes
algorithm is designed to detect crossings, not solid regions.
For example, going back to one dimension, if your data looked like your
original vector
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
then it is ambiguous
Re: [R] Date handling in R is hard to understand
I agree w/ lubridate.I also would like to mention that date handling is amazingly difficult in ALL computer languages, not just R. Take a stroll through sites like thedailywtf.com to see how quickly people get into tarpits full of thorns when trying to deal with leap years, weeks vs month ends, etc. Bert Gunter wrote Have a look at the lubridate package. It claims to try to make dealing with dates easier. -- Bert On Fri, Nov 8, 2013 at 11:41 AM, Alemu Tadesse lt; alemu.tadesse@ gt; wrote: Dear All, I usually work with time series data. The data may come in AM/PM date format or on 24 hour time basis. R can not recognize the two differences automatically - at least for me. I have to specifically tell R in which time format the data is. It seems that Pandas knows how to handle date without being told the format. The problem arises when I try to shift time by a certain time. Say adding 3600 to shift it forward, that case I have to use something like: Measured_data$Date - as.POSIXct(as.character(Measured_data$Date), tz=,format = %m/%d/%Y %I:%M %p)+3600 or Measured_data$Date - as.POSIXct(as.character(Measured_data$Date), tz=,format = %m/%d/%Y %H:%M)+3600 depending on the format. The date also attaches MDT or MST and so on. When merging two data frames with dates of different format that may create a problem (I think). When I get data from excel it could be in any/random format and I needed to customize the date to use in R in one of the above formats. Any TIPS - for automatic processing with no need to specifically tell the data format ? Another problem I saw was that when using r bind to bind data frames, if one column of one of the data frames is a character data (say for example none - coming from mysql) format R doesn't know how to concatenate numeric column from the other data frame to it. I needed to change the numeric to character and later after binding takes place I had to re-convert it to numeric. But, this causes problem in an automated environment. Any suggestion ? Thanks Mihretu [[alternative HTML version deleted]] __ R-help@ 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. -- Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 __ R-help@ 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. -- View this message in context: http://r.789695.n4.nabble.com/Date-handling-in-R-is-hard-to-understand-tp4680070p4680125.html Sent from the R help mailing list archive at Nabble.com. __ R-help@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.
Re: [R] C50 Node Assignment
Just to clarify: I'm guessing the OP is referring to the CRAN package C50 here. A quick skim suggests the rules are a list element of a C5.0-class object, so maybe that's where to start? David Winsemius wrote In my role as a moderator I am attempting to bypass the automatic mail filters that are blocking this posting. Please reply to the list and to: = Kevin Shaney lt; kevin.shaney@ gt; C50 Node Assignment I am using C50 to classify individuals into 5 groups / categories (factor variable). The tree / set of rules has 10 rules for classification. I am trying to extract the RULE for which each individual qualifies (a number between 1 and 10), and cannot figure out how to do so. I can extract the predicted group and predicted group probability, but not the RULE to which an individual qualifies. Please let me know if you can help! Kevin = -- David Winsemius Alameda, CA, USA __ R-help@ 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. -- View this message in context: http://r.789695.n4.nabble.com/C50-Node-Assignment-tp4680071p4680127.html Sent from the R help mailing list archive at Nabble.com. __ R-help@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.
Re: [R] S4; Setter function is not chaning slot value as expected
On 11/09/2013 06:31 AM, daniel schnaider wrote:
It is my first time programming with S4 and I can't get the setter fuction
to actually change the value of the slot created by the constructor.
I guess it has to do with local copy, global copy, etc. of the variable -
but, I could't find anything relevant in documentation.
Tried to copy examples from the internet, but they had the same problem.
# The code
setClass (Account ,
representation (
customer_id = character,
transactions = matrix)
)
Account - function(id, t) {
new(Account, customer_id = id, transactions = t)
}
setGeneric (CustomerID-, function(obj,
id){standardGeneric(CustomerID-)})
Replacement methods (in R in general) require that the final argument (the
replacement value) be named 'value', so
setGeneric(CustomerID-,
function(x, ..., value) standardGeneric(CustomerID))
setReplaceMethod(CustomerID, c(Account, character),
function(x, , value)
{
x@customer_id - value
x
})
use this as
CustomerID(ac) - 54321
setReplaceMethod(CustomerID, Account, function(obj, id){
obj@customer_id - id
obj
})
ac - Account(12345, matrix(c(1,2,3,4,5,6), ncol=2))
ac
CustomerID - 54321
ac
#Output
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
# CustomerID is value has changed to 54321, but as you can see it does't
CustomerID - 54321
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
Help!
[[alternative HTML version deleted]]
__
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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.
--
Computational Biology / Fred Hutchinson Cancer Research Center
1100 Fairview Ave. N.
PO Box 19024 Seattle, WA 98109
Location: Arnold Building M1 B861
Phone: (206) 667-2793
__
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] Tables Package Grouping Factors
Visually, the elimination of duplicates in hierarchical tables in the tabular function from the tables package is very nice. I would like to do the same thing with non-crossed factors, but am perhaps missing some conceptual element of how this package is used. The following code illustrates my goal (I hope): library(tables) sampledf - data.frame( Sex=rep(c(M,F),each=6) , Name=rep(c(John,Joe,Mark,Alice,Beth,Jane),each=2) , When=rep(c(Before,After),times=6) , Weight=c(180,190,190,180,200,200,140,145,150,140,135,135) ) sampledf$SexName - factor( paste( sampledf$Sex, sampledf$Name ) ) # logically, this is the layout tabular( Name ~ Heading()* When * Weight * Heading()*identity, data=sampledf ) # but I want to augment the Name with the Sex but visually group the # Sex like # tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # would except that there really is no crossing between sexes. tabular( SexName ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # this repeats the Sex category excessively. --- Jeff NewmillerThe . . Go Live... DCN:jdnew...@dcn.davis.ca.usBasics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/BatteriesO.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k __ R-help@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.
Re: [R] `level' definition in `computeContour3d' (misc3d package)
On 13-11-09 12:53 PM, j. van den hoff wrote:
On Sat, 09 Nov 2013 18:18:23 +0100, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 13-11-09 11:57 AM, j. van den hoff wrote:
On Sat, 09 Nov 2013 17:16:28 +0100, Duncan Murdoch
murdoch.dun...@gmail.com wrote:
On 13-11-09 8:50 AM, j. van den hoff wrote:
I'd very much appreciate some help here: I'm in the process of
clarifying
whether I can use `computeContour3d' to derive estimates of the
surface
area of a single closed isosurface (and prospectively the enclosed
volume). getting the surface area from the list of triangles returned
by
`computeContour3d' is straightforward but I've stumbled over the
precise
meaning of `level' here. looking into the package, ultimately the
level
is
used in the namespace function `faceType' which reads:
function (v, nx, ny, level, maxvol)
{
if (level == maxvol)
p - v = level
else p - v level
v[p] - 1
v[!p] - 0
v[-nx, -ny] + 2 * v[-1, -ny] + 4 * v[-1, -1] + 8 * v[-nx,
-1]
}
my question: is the discrimination of the special case `level ==
maxvol'
(or rather of everything else) really desirable? I would argue
that always testing for `v = level' would be better. if I feed data
with
discrete values (e.g. integer-valued) defined
on a coarse grid into `computeContour3d' it presently makes a big
difference whether there is a single data point (e.g.) with a value
larger
than `level' or not. consider the 1D example:
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
data2 - c(0, 0, 1, 2, 1, 1, 1, 0, 0)
and level = 1
this defines the isocontour `level = 1' to lie at pos 3 and 7 in for
data1
but as lying at pos 4 in data2. actually I would like (and expect) to
get
the same isosurface for `data2' with this `level' setting. in short:
the
meaning/definition of `level' changes depending on whether or not it
is
equal to `maxvol'. this is neither stated in the manpage nor is this
desirable in my view. but maybe I miss something here. any
clarification
would be appreciated.
I don't see why you'd expect the same output from those vectors, but
since they aren't legal input to computeContour3d, maybe I don't know
what you mean by them. Could you put together a reproducible example
that shows bad contours?
it's not bad contours, actually. my question only concerns the
different
meaning
of `level' depending on whether `level = maxvol' or not.
here is a real example:
8
library(misc3d)
dim - 21
cnt - (dim+1)/2
wid1 - 5
wid2 - 1
rng1 - (cnt-wid1):(cnt+wid1)
rng2 - (cnt-wid2):(cnt+wid2)
v - array(0, rep (dim, 3))
#put 11x11x11 box of ones at center
v[rng1, rng1, rng1] - 1
con1 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con1))
dum - readline(CR for next plot)
#put an additional 3x3x3 box of twos at center
v[rng2, rng2, rng2] - 2
con2 - computeContour3d(v, level = 1)
drawScene(makeTriangles(con2))
8
this first puts a 11x11x11 box one Ones at the center of the
zero-initalized array and computes `con1' for `level=1'. in the 2. step
it puts a further, 3x3x3 box of Twos at the center and computes the
`level=1' contour again which this time does not delineate
the box of Ones but lies somewhere between the two non-zero boxes since
now the test in `faceType' is for ` level'. this is not immediately
obvious from the plots (no scale) but obvious from looking at `con1' and
`con2': the `con2' isosurface is shrunk by 3 voxels at each
side relative to `con1' (so my initial mail was wrong here: `con2' does
not jump to the next discrete isocontour but rather to
a point about halfway between both plateaus ). I also (for my own
problem
at hand) computed the total surface area which is
(not surprisingly...) 600 for `con1' and 64.87 for `con2'. so if one is
interested in such surfaces (I am) this makes a big difference in such
data.
the present behavior is not wrong per se but I would much prefer if
the
test where always for `= level' (so that in the present example the
resulting isosurface would in both cases delineate the box of Ones -- as
is the case when using `level = 1-e-6' instead of `level=1').
I believe the isosurface for a given value of `level' should have an
unambiguous meaning independent of what the data further inside are
looking like.
I think it does, but your data make the determination of its location
ambiguous.
I was imprecise: what I meant is: the isosurface should not change in my
example between both cases.
The definition is the estimated location where the continuous field
sampled at v crosses level.
understood/agreed.
You have a field with a discontinuity (or two). You have whole volumes
of space where the field is equal to the level. The marching cubes
algorithm is designed to detect crossings, not solid regions.
For example, going back to one dimension, if your data looked like your
original vector
data1 - c(0, 0, 1, 1, 1, 1, 1, 0, 0)
then it is
Re: [R] Tables Package Grouping Factors
On 13-11-09 1:23 PM, Jeff Newmiller wrote: Visually, the elimination of duplicates in hierarchical tables in the tabular function from the tables package is very nice. I would like to do the same thing with non-crossed factors, but am perhaps missing some conceptual element of how this package is used. The following code illustrates my goal (I hope): library(tables) sampledf - data.frame( Sex=rep(c(M,F),each=6) , Name=rep(c(John,Joe,Mark,Alice,Beth,Jane),each=2) , When=rep(c(Before,After),times=6) , Weight=c(180,190,190,180,200,200,140,145,150,140,135,135) ) sampledf$SexName - factor( paste( sampledf$Sex, sampledf$Name ) ) # logically, this is the layout tabular( Name ~ Heading()* When * Weight * Heading()*identity, data=sampledf ) # but I want to augment the Name with the Sex but visually group the # Sex like # tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # would except that there really is no crossing between sexes. tabular( SexName ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # this repeats the Sex category excessively. I don't think it's easy to get what you want. The basic assumption is that factors are crossed. One hack that would get you what you want in this case is to make up a new variable representing person within sex (running from 1 to 3), then treating the Name as a statistic. Of course, this won't work if you don't have equal numbers of each sex. A better solution is more cumbersome, and only works in LaTeX (and maybe HTML). Draw two tables, first for the female subset, then for the male subset. Put out the headers only on the first one and the footer only on the second, and it will be typeset as one big table. You'll have to fight with the fact that the factors Sex and Name remember their levels whether they are present or not, but it should work. For example, sampledf$Sex - as.character(sampledf$Sex) sampledf$Name - as.character(sampledf$Name) females - subset(sampledf, Sex == F) males - subset(sampledf, Sex == M) latex( tabular( Factor(Sex)*Factor(Name) ~ Heading()*When * Weight * Heading()*identity, data=females), options = list(doFooter=FALSE, doEnd=FALSE) ) latex( tabular( Factor(Sex)*Factor(Name) ~ Heading()*When * Weight * Heading()*identity, data=males), options = list(doBegin=FALSE, doHeader=FALSE) ) It would probably make sense to support nested factor notation using %in% to make this easier, but currently tables doesn't do that. Duncan Murdoch __ R-help@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.
[R] Custom Numeric type in R
Hi again, In R, there are various numerics like, NA, Inf, or simple integers etc. However I want to include one custom type: TBD, which R should treat as numeric, not character. That TBD should have same property like Inf, however except this: TBD - TBD = 0 In future, I am planning to add few more properties to TBD. Can somebody guide me if this is possible in R, ans also some pointer on how can be done in R Thanks and regards, [[alternative HTML version deleted]] __ R-help@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.
Re: [R] Tables Package Grouping Factors
On 13-11-09 1:23 PM, Jeff Newmiller wrote: Visually, the elimination of duplicates in hierarchical tables in the tabular function from the tables package is very nice. I would like to do the same thing with non-crossed factors, but am perhaps missing some conceptual element of how this package is used. The following code illustrates my goal (I hope): library(tables) sampledf - data.frame( Sex=rep(c(M,F),each=6) , Name=rep(c(John,Joe,Mark,Alice,Beth,Jane),each=2) , When=rep(c(Before,After),times=6) , Weight=c(180,190,190,180,200,200,140,145,150,140,135,135) ) sampledf$SexName - factor( paste( sampledf$Sex, sampledf$Name ) ) # logically, this is the layout tabular( Name ~ Heading()* When * Weight * Heading()*identity, data=sampledf ) # but I want to augment the Name with the Sex but visually group the # Sex like # tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # would except that there really is no crossing between sexes. tabular( SexName ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # this repeats the Sex category excessively. I forgot, there's a simpler way to do this. Build the full table with the junk values, then take a subset: full - tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) full[c(1:3, 10:12), ] Figuring out which rows you want to keep can be a little tricky, but doing something like this might be good: counts - tabular( Sex*Name ~ 1, data=sampledf ) full[ as.logical(counts), ] Duncan Murdoch __ R-help@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.
Re: [R] S4 vs S3. New Package
For my take on the issue see fortune(strait jacket). cheers, Rolf Turner P. S. I said that quite some time ago and I have seen nothing in the intervening years to change my views. R. T. On 11/10/13 04:22, daniel schnaider wrote: Hi, I am working on a new credit portfolio optimization package. My question is if it is more recommended to develop in S4 object oriented or S3. It would be more naturally to develop in object oriented paradigm, but there is many concerns regarding S4. 1) Performance of S4 could be an issue as a setter function, actually changes the whole object behind the scenes. 2) Documentation. It has been really hard to find examples in S4. Most books and articles consider straightforward S3 examples. __ R-help@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.
Re: [R] C50 Node Assignment
There is a sub-object called 'rules' that has the output of C5.0 for this model: library(C50) mod - C5.0(Species ~ ., data = iris, rules = TRUE) cat(mod$rules) id=See5/C5.0 2.07 GPL Edition 2013-11-09 entries=1 rules=4 default=setosa conds=1 cover=50 ok=50 lift=2.94231 class=setosa type=2 att=Petal.Length cut=1.9 result= conds=3 cover=48 ok=47 lift=2.88 class=versicolor type=2 att=Petal.Length cut=1.9 result= type=2 att=Petal.Length cut=4.901 result= type=2 att=Petal.Width cut=1.7 result= conds=1 cover=46 ok=45 lift=2.875 class=virginica type=2 att=Petal.Width cut=1.7 result= conds=1 cover=46 ok=44 lift=2.8125 class=virginica type=2 att=Petal.Length cut=4.901 result= You would either have to parse this or parse the summary results: summary(mod) Call: C5.0.formula(formula = Species ~ ., data = iris, rules = TRUE) snip Rules: Rule 1: (50, lift 2.9) Petal.Length = 1.9 - class setosa [0.981] Rule 2: (48/1, lift 2.9) Petal.Length 1.9 Petal.Length = 4.9 Petal.Width = 1.7 - class versicolor [0.960] snip Max On Sat, Nov 9, 2013 at 1:11 PM, Carl Witthoft c...@witthoft.com wrote: Just to clarify: I'm guessing the OP is referring to the CRAN package C50 here. A quick skim suggests the rules are a list element of a C5.0-class object, so maybe that's where to start? David Winsemius wrote In my role as a moderator I am attempting to bypass the automatic mail filters that are blocking this posting. Please reply to the list and to: = Kevin Shaney lt; kevin.shaney@ gt; C50 Node Assignment I am using C50 to classify individuals into 5 groups / categories (factor variable). The tree / set of rules has 10 rules for classification. I am trying to extract the RULE for which each individual qualifies (a number between 1 and 10), and cannot figure out how to do so. I can extract the predicted group and predicted group probability, but not the RULE to which an individual qualifies. Please let me know if you can help! Kevin = -- David Winsemius Alameda, CA, USA __ R-help@ 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. -- View this message in context: http://r.789695.n4.nabble.com/C50-Node-Assignment-tp4680071p4680127.html Sent from the R help mailing list archive at Nabble.com. __ R-help@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. -- Max __ R-help@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.
Re: [R] S4 vs S3. New Package
On 11/09/2013 11:59 AM, Rolf Turner wrote: For my take on the issue see fortune(strait jacket). cheers, Rolf Turner P. S. I said that quite some time ago and I have seen nothing in the intervening years to change my views. Mileage varies; the Bioconductor project attains a level of interoperability and re-use (http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html) that would be difficult with a less formal class system. R. T. On 11/10/13 04:22, daniel schnaider wrote: Hi, I am working on a new credit portfolio optimization package. My question is if it is more recommended to develop in S4 object oriented or S3. It would be more naturally to develop in object oriented paradigm, but there is many concerns regarding S4. 1) Performance of S4 could be an issue as a setter function, actually changes the whole object behind the scenes. Depending on implementation, updating S3 objects could as easily trigger copies; this is a fact of life in R. Mitigate by modelling objects in a vector (column)-oriented approach rather than the row-oriented paradigm of Java / C++ / etc. Martin Morgan 2) Documentation. It has been really hard to find examples in S4. Most books and articles consider straightforward S3 examples. __ R-help@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. -- Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793 __ R-help@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.
Re: [R] Tables Package Grouping Factors
The problem that prompted this question involved manufacturers and their model numbers, so I think the cross everything and throw away most of it will get out of hand quickly. The number of models per manufacturer definitely varies. I think I will work on the print segments of the table successively approach. Thanks for the ideas. --- Jeff NewmillerThe . . Go Live... DCN:jdnew...@dcn.davis.ca.usBasics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/BatteriesO.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --- Sent from my phone. Please excuse my brevity. Duncan Murdoch murdoch.dun...@gmail.com wrote: On 13-11-09 1:23 PM, Jeff Newmiller wrote: Visually, the elimination of duplicates in hierarchical tables in the tabular function from the tables package is very nice. I would like to do the same thing with non-crossed factors, but am perhaps missing some conceptual element of how this package is used. The following code illustrates my goal (I hope): library(tables) sampledf - data.frame( Sex=rep(c(M,F),each=6) , Name=rep(c(John,Joe,Mark,Alice,Beth,Jane),each=2) , When=rep(c(Before,After),times=6) , Weight=c(180,190,190,180,200,200,140,145,150,140,135,135) ) sampledf$SexName - factor( paste( sampledf$Sex, sampledf$Name ) ) # logically, this is the layout tabular( Name ~ Heading()* When * Weight * Heading()*identity, data=sampledf ) # but I want to augment the Name with the Sex but visually group the # Sex like # tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # would except that there really is no crossing between sexes. tabular( SexName ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # this repeats the Sex category excessively. I forgot, there's a simpler way to do this. Build the full table with the junk values, then take a subset: full - tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) full[c(1:3, 10:12), ] Figuring out which rows you want to keep can be a little tricky, but doing something like this might be good: counts - tabular( Sex*Name ~ 1, data=sampledf ) full[ as.logical(counts), ] Duncan Murdoch __ R-help@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.
Re: [R] S4 vs S3. New Package
Thank you! On Sat, Nov 9, 2013 at 2:30 PM, Simon Zehnder szehn...@uni-bonn.de wrote: This depends very often of on the developer and what he is comfortable with. I like S4 classes, as I come from C++ and S4 classes approximate C++ classes at least more than S3 classes do (Reference Classes would do so even more and I know very good R programmers liking these most). 1) I wrote a package for MCMC simulation with S4 classes carrying all simulated values - fast enough for me: in less than 1.5 secs I have my sample of 100.000 values together with several other 100T values like log-likelihoods, posterior hyper parameters, etc. I watch out for not copying too often an object but sometimes it is not avoidable. 2) That is not true: Books: http://www.amazon.de/Software-Data-Analysis-Programming-Statistics/dp/0387759352/ref=sr_1_1?ie=UTF8qid=1384014486sr=8-1keywords=John+chambers+data http://www.amazon.de/Programming-Data-Language-John-Chambers/dp/0387985034/ref=sr_1_4?ie=UTF8qid=1384014486sr=8-4keywords=John+chambers+data Online: https://www.rmetrics.org/files/Meielisalp2009/Presentations/Chalabi1.pdf https://www.stat.auckland.ac.nz/S-Workshop/Gentleman/S4Objects.pdf And for a bunch of packages look into the Bioconductor packages. Best Simon On 09 Nov 2013, at 16:22, daniel schnaider dschnai...@gmail.com wrote: Hi, I am working on a new credit portfolio optimization package. My question is if it is more recommended to develop in S4 object oriented or S3. It would be more naturally to develop in object oriented paradigm, but there is many concerns regarding S4. 1) Performance of S4 could be an issue as a setter function, actually changes the whole object behind the scenes. 2) Documentation. It has been really hard to find examples in S4. Most books and articles consider straightforward S3 examples. Thanks, [[alternative HTML version deleted]] __ R-help@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. -- Daniel Schnaider SP Phone: +55-11-9.7575.0822 d...@scaigroup.com skype dschnaider Linked In: http://www.linkedin.com/in/danielschnaider w http://www.arkiagroup.com/ww.scaigroup.com Depoimentos de clientes http://www.scaigroup.com/Projetos/depoimentos Casos de Sucesso Referências http://www.scaigroup.com/Projetos SCAI Group no Facebook http://facebook.scaigroup.com/ SCAI Group no Twitter http://twitter.scaigroup.com/ SCAI Group no Google Plus http://plus.scaigroup.com/ [[alternative HTML version deleted]] __ R-help@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.
[R] Problem while using searchTwitter function in TwitteR package
Hi, I am data mining from twitter using the twitteR package but I am facing a problem mining statuses. A lot of times the status does not return the desired number of statuses but returns only a specific number. Also, this number keeps on varying for exactly the same arguments ( No, its not that more statuses were put up during that time), it even decreases sometimes. Is there some logic to how this works ? This is the warning I am getting : In doRppAPICall(search/tweets, n, params = params, retryOnRateLimit = retryOnRateLimit, : 3000 tweets were requested but the API can only return 299 Thanks for the help in advance, Ameek Singh [[alternative HTML version deleted]] __ R-help@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.
[R] Using Unicode inside R's expression() command
I'm using 'expression()' in R plots in order to get italicized text. But it appears as if I cannot use Unicode symbols inside 'expression' outside of ASCII characters. Is there some way I can work around this? My goal is to get the 'fi' ligature in various labels in my R barplots (together with italicized text). I think I could work around this if there was another way of italicizing selected characters in a string than using 'expression()'. I'm using R for Windows version 3.0.2. CairoPDF(file = Ligature1.pdf) plot.new() text(x =.5, y = .5, labels = fi, family = Times New Roman) dev.off() CairoPDF(file = Ligature2.pdf) plot.new() text(x =.5, y = .5, labels = expression(paste(italic(m), u, fi, italic(m), sep = )), family = Times New Roman) dev.off() attachment: lig1.pngattachment: lig2.png__ R-help@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.
[R] union of list objects if objects intersect
Hello,
I have a list called ja and I wish to unify list objects if there is some
overlap.
For instance something like
if (length (intersect (ja[[1]], ja[[2]]) !=0) { union (ja[[1]], ja[[2]] }
but of course it should work cumulatively (for larger data sets).
Could you please give me a hint.
Thanks
Hermann
ja
$A
[1] A B F G H
$B
[1] B F I
$C
[1] C F I K
$D
[1] D L M N
$L
[1] L O P
dput (ja)
structure(list(A = c(A, B, F, G, H), B = c(B, F,
I), C = c(C, F, I, K), D = c(D, L, M, N), L = c(L,
O, P)), .Names = c(A, B, C, D, L))
[[alternative HTML version deleted]]
__
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] select .txt from .txt in a directory
Hi,
Try:
library(stringr)
# Created the selected files (98) in a separate working folder
(SubsetFiles1) (refer to my previous mail)
filelst - list.files()
#Sublst - filelst[1:2]
res - lapply(filelst,function(x) {con - file(x)
Lines1 - readLines(con) close(con)
Lines2 - Lines1[-1]
Lines3 - str_split(Lines2,-.9M)
Lines4 - str_trim(unlist(lapply(Lines3,function(x) {x[x==] - NA
paste(x,collapse= )})))
Lines5 - gsub((\\d+)[A-Za-z],\\1,Lines4)
res1 - read.table(text=Lines5,sep=,header=FALSE,fill=TRUE)
res1})
##Created another folder Modified to store the res files
lapply(seq_along(res),function(i)
write.table(res[[i]],paste(/home/arunksa111/Zl/Modified,paste0(Mod_,filelst[i]),sep=/),row.names=FALSE,quote=FALSE))
lstf1 - list.files(path=/home/arunksa111/Zl/Modified)
lst1 - lapply(lstf1,function(x)
readLines(paste(/home/arunksa111/Zl/Modified,x,sep=/)))
which(lapply(lst1,function(x) length(grep(\\d+-.9,x)))0 )
#[1] 7 11 14 15 30 32 39 40 42 45 46 53 60 65 66 68 69 70 73 74 75 78 80 82 83
#[26] 86 87 90 91 93
lst2 - lapply(lst1,function(x) gsub((\\d+)(-.9),\\1 \\2,x))
#lapply(lst2,function(x) x[grep(\\d+-.9,x)]) ##checking for the pattern
lst3 - lapply(lst2,function(x) {x-gsub((-.9)(-.9),\\1 \\2,x)})#
#lapply(lst3,function(x) x[grep(\\d+-.9,x)]) ##checking for the pattern
# lapply(lst3,function(x) x[grep(-.9,x)]) ###second check
lst4 - lapply(lst3,function(x) gsub((Day) (\\d+),\\1_\\2, x[-1]))
#removed the additional header V1, V2, etc.
#sapply(lst4,function(x) length(strsplit(x[1], )[[1]])) #checking the number
of columns that should be present
lst5 - lapply(lst4,function(x) unlist(lapply(x, function(y) word(y,1,33
lst6 - lapply(lst5,function(x)
read.table(text=x,header=TRUE,stringsAsFactors=FALSE,sep=,fill=TRUE))
# head(lst6[[94]],3)
lst7 - lapply(lst6,function(x) x[x$Year =1961 x$Year =2005,])
#head(lst7[[45]],3)
lst8 - lapply(lst7,function(x) x[!is.na(x$Year),])
lst9 - lapply(lst8,function(x) {
if((min(x$Year)1961)|(max(x$Year)2005)){
n1- (min(x$Year)-1961)*12
x1- as.data.frame(matrix(NA,ncol=ncol(x),nrow=n1))
n2- (2005-max(x$Year))*12
x2- as.data.frame(matrix(NA,ncol=ncol(x),nrow=n2))
colnames(x1) - colnames(x)
colnames(x2) - colnames(x)
x3- rbind(x1,x,x2)
}
else if((min(x$Year)==1961) (max(x$Year)==2005)) {
if((min(x$Mo[x$Year==1961])1)|(max(x$Mo[x$Year==2005])12)){
n1 - min(x$Mo[x$Year==1961])-1
x1 - as.data.frame(matrix(NA,ncol=ncol(x),nrow=n1))
n2 - (12-max(x$Mo[x$Year==2005]))
x2 - as.data.frame(matrix(NA,ncol=ncol(x),nrow=n2))
colnames(x1) - colnames(x)
colnames(x2) - colnames(x)
x3 - rbind(x1,x,x2)
}
else {
x
}
} })
which(sapply(lst9,nrow)!=540)
#[1] 45 46 54 64 65 66 70 75 97
lst10 - lapply(lst9,function(x) {x1 - x[!is.na(x$Year),]
hx1 - head(x1,1)
tx1 - tail(x1,1)
x2 - as.data.frame(matrix(NA, ncol=ncol(x), nrow=hx1$Mo-1))
x3 - as.data.frame(matrix(NA,ncol=ncol(x),nrow=12-tx1$Mo))
colnames(x2) - colnames(x)
colnames(x3) - colnames(x)
if(nrow(x) 540) rbind(x2,x,x3) else x })
which(sapply(lst10,nrow)!=540)
#integer(0)
lst11 -lapply(lst10,function(x)
data.frame(col1=unlist(data.frame(t(x)[-c(1:2),]),use.names=FALSE)))
lst12- lapply(seq_along(lst10),function(i){
x- lst11[[i]]
colnames(x)- lstf1[i]
row.names(x)- 1:nrow(x)
x
})
res2 - do.call(cbind,lst11)
dim(res2)
#[1] 16740 98
res2[res2==-.9]-NA # change missing value identifier as in your data set
which(res2==-.9)
#integer(0)
dates1-seq.Date(as.Date('1Jan1961',format=%d%b%Y),as.Date('31Dec2005',format=%d%b%Y),by=day)
dates2- as.character(dates1)
sldat- split(dates2,list(gsub(-.*,,dates2)))
lst12-lapply(sldat,function(x) lapply(split(x,gsub(.*-(.*)-.*,\\1,x)),
function(y){x1-as.numeric(gsub(.*-.*-(.*),\\1,y));if((31-max(x1))0)
{x2-seq(max(x1)+1,31,1);x3-paste0(unique(gsub((.*-.*-).*,\\1,y)),x2);c(y,x3)}
else y} ))
any(sapply(lst12,function(x) any(lapply(x,length)!=31)))
#[1] FALSE
lst22-lapply(lst12,function(x) unlist(x,use.names=FALSE))
sapply(lst22,length)
dates3-unlist(lst22,use.names=FALSE)
length(dates3)
res3 - data.frame(dates=dates3,res2,stringsAsFactors=FALSE)
str(res3)
res3$dates-as.Date(res3$dates)
res4 - res3[!is.na(res3$dates),]
res4[1:3,1:3]
dim(res4)
#[1] 16436 99
A.K.
On Friday, November 8, 2013 5:54 PM, Zilefac Elvis zilefacel...@yahoo.com
wrote:
Hi Ak,
I think I figured out how to do the sub-setting. All I needed was to use column
3 in Temperature_inventory and select matching .txt files in the .zip file. The
final result would be a subset of files whose IDs are in column 3 of
temp_inventory.
*
I also have this script which you developed
Re: [R] select .txt from .txt in a directory
HI,
The code could be shortened by using ?merge or ?join().
library(plyr)
##Using the output from `lst6`
lst7 - lapply(lst6,function(x) {x1 -
data.frame(Year=rep(1961:2005,each=12),Mo=rep(1:12,45)); x2
-join(x1,x,type=left,by=c(Year,Mo))})
##rest are the same (only change in object names)
sapply(lst7,nrow)
lst8 -lapply(lst7,function(x)
data.frame(col1=unlist(data.frame(t(x)[-c(1:2),]),use.names=FALSE)))
lst9- lapply(seq_along(lst8),function(i){
x- lst11[[i]]
colnames(x)- lstf1[i]
row.names(x)- 1:nrow(x)
x
})
sapply(lst9,nrow)
res2New - do.call(cbind,lst9)
dim(res2New)
#[1] 16740 98
res2New[res2New ==-.9]-NA # change missing value identifier as in your
data set
which(res2New==-.9)
#integer(0)
dates1-seq.Date(as.Date('1Jan1961',format=%d%b%Y),as.Date('31Dec2005',format=%d%b%Y),by=day)
dates2- as.character(dates1)
sldat- split(dates2,list(gsub(-.*,,dates2)))
lst12-lapply(sldat,function(x) lapply(split(x,gsub(.*-(.*)-.*,\\1,x)),
function(y){x1-as.numeric(gsub(.*-.*-(.*),\\1,y));if((31-max(x1))0)
{x2-seq(max(x1)+1,31,1);x3-paste0(unique(gsub((.*-.*-).*,\\1,y)),x2);c(y,x3)}
else y} ))
any(sapply(lst12,function(x) any(lapply(x,length)!=31)))
#[1] FALSE
lst22-lapply(lst12,function(x) unlist(x,use.names=FALSE))
sapply(lst22,length)
dates3-unlist(lst22,use.names=FALSE)
length(dates3)
res3New - data.frame(dates=dates3,res2New,stringsAsFactors=FALSE)
str(res3New)
res3New$dates-as.Date(res3New$dates)
res4New - res3New[!is.na(res3New$dates),]
res4New[1:3,1:3]
dim(res4New)
colnames(res4) - colnames(res4New)
identical(res4,res4New)
#[1] TRUE
A.K.
On Saturday, November 9, 2013 5:46 PM, arun smartpink...@yahoo.com wrote:
Hi,
Try:
library(stringr)
# Created the selected files (98) in a separate working folder
(SubsetFiles1) (refer to my previous mail)
filelst - list.files()
#Sublst - filelst[1:2]
res - lapply(filelst,function(x) {con - file(x)
Lines1 - readLines(con) close(con)
Lines2 - Lines1[-1]
Lines3 - str_split(Lines2,-.9M)
Lines4 - str_trim(unlist(lapply(Lines3,function(x) {x[x==] - NA
paste(x,collapse= )})))
Lines5 - gsub((\\d+)[A-Za-z],\\1,Lines4)
res1 - read.table(text=Lines5,sep=,header=FALSE,fill=TRUE)
res1})
##Created another folder Modified to store the res files
lapply(seq_along(res),function(i)
write.table(res[[i]],paste(/home/arunksa111/Zl/Modified,paste0(Mod_,filelst[i]),sep=/),row.names=FALSE,quote=FALSE))
lstf1 - list.files(path=/home/arunksa111/Zl/Modified)
lst1 - lapply(lstf1,function(x)
readLines(paste(/home/arunksa111/Zl/Modified,x,sep=/)))
which(lapply(lst1,function(x) length(grep(\\d+-.9,x)))0 )
#[1] 7 11 14 15 30 32 39 40 42 45 46 53 60 65 66 68 69 70 73 74 75 78 80 82 83
#[26] 86 87 90 91 93
lst2 - lapply(lst1,function(x) gsub((\\d+)(-.9),\\1 \\2,x))
#lapply(lst2,function(x) x[grep(\\d+-.9,x)]) ##checking for the pattern
lst3 - lapply(lst2,function(x) {x-gsub((-.9)(-.9),\\1 \\2,x)})#
#lapply(lst3,function(x) x[grep(\\d+-.9,x)]) ##checking for the pattern
# lapply(lst3,function(x) x[grep(-.9,x)]) ###second check
lst4 - lapply(lst3,function(x) gsub((Day) (\\d+),\\1_\\2, x[-1]))
#removed the additional header V1, V2, etc.
#sapply(lst4,function(x) length(strsplit(x[1], )[[1]])) #checking the number
of columns that should be present
lst5 - lapply(lst4,function(x) unlist(lapply(x, function(y) word(y,1,33
lst6 - lapply(lst5,function(x)
read.table(text=x,header=TRUE,stringsAsFactors=FALSE,sep=,fill=TRUE))
# head(lst6[[94]],3)
lst7 - lapply(lst6,function(x) x[x$Year =1961 x$Year =2005,])
#head(lst7[[45]],3)
lst8 - lapply(lst7,function(x) x[!is.na(x$Year),])
lst9 - lapply(lst8,function(x) {
if((min(x$Year)1961)|(max(x$Year)2005)){
n1- (min(x$Year)-1961)*12
x1- as.data.frame(matrix(NA,ncol=ncol(x),nrow=n1))
n2- (2005-max(x$Year))*12
x2- as.data.frame(matrix(NA,ncol=ncol(x),nrow=n2))
colnames(x1) - colnames(x)
colnames(x2) - colnames(x)
x3- rbind(x1,x,x2)
}
else if((min(x$Year)==1961) (max(x$Year)==2005)) {
if((min(x$Mo[x$Year==1961])1)|(max(x$Mo[x$Year==2005])12)){
n1 - min(x$Mo[x$Year==1961])-1
x1 - as.data.frame(matrix(NA,ncol=ncol(x),nrow=n1))
n2 - (12-max(x$Mo[x$Year==2005]))
x2 - as.data.frame(matrix(NA,ncol=ncol(x),nrow=n2))
colnames(x1) - colnames(x)
colnames(x2) - colnames(x)
x3 - rbind(x1,x,x2)
}
else {
x
}
} })
which(sapply(lst9,nrow)!=540)
#[1] 45 46 54 64 65 66 70 75 97
lst10 - lapply(lst9,function(x) {x1 - x[!is.na(x$Year),]
hx1 - head(x1,1)
tx1 - tail(x1,1)
x2 - as.data.frame(matrix(NA, ncol=ncol(x), nrow=hx1$Mo-1))
x3 - as.data.frame(matrix(NA,ncol=ncol(x),nrow=12-tx1$Mo))
colnames(x2) - colnames(x)
colnames(x3) - colnames(x)
if(nrow(x) 540) rbind(x2,x,x3) else x })
Re: [R] Tables Package Grouping Factors
On 13-11-09 5:56 PM, Jeff Newmiller wrote: The problem that prompted this question involved manufacturers and their model numbers, so I think the cross everything and throw away most of it will get out of hand quickly. The number of models per manufacturer definitely varies. I think I will work on the print segments of the table successively approach. Thanks for the ideas. I've just added cbind() and rbind() methods for tabular objects, so that approach will be a lot easier. Just do the table of the first subset, then rbind on the subsets for the rest. Will commit to R-forge after a bit more testing and documentation. Duncan Murdoch --- Jeff NewmillerThe . . Go Live... DCN:jdnew...@dcn.davis.ca.usBasics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/BatteriesO.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --- Sent from my phone. Please excuse my brevity. Duncan Murdoch murdoch.dun...@gmail.com wrote: On 13-11-09 1:23 PM, Jeff Newmiller wrote: Visually, the elimination of duplicates in hierarchical tables in the tabular function from the tables package is very nice. I would like to do the same thing with non-crossed factors, but am perhaps missing some conceptual element of how this package is used. The following code illustrates my goal (I hope): library(tables) sampledf - data.frame( Sex=rep(c(M,F),each=6) , Name=rep(c(John,Joe,Mark,Alice,Beth,Jane),each=2) , When=rep(c(Before,After),times=6) , Weight=c(180,190,190,180,200,200,140,145,150,140,135,135) ) sampledf$SexName - factor( paste( sampledf$Sex, sampledf$Name ) ) # logically, this is the layout tabular( Name ~ Heading()* When * Weight * Heading()*identity, data=sampledf ) # but I want to augment the Name with the Sex but visually group the # Sex like # tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # would except that there really is no crossing between sexes. tabular( SexName ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) # this repeats the Sex category excessively. I forgot, there's a simpler way to do this. Build the full table with the junk values, then take a subset: full - tabular( Sex*Name ~ Heading()*When * Weight * Heading()*identity, data=sampledf ) full[c(1:3, 10:12), ] Figuring out which rows you want to keep can be a little tricky, but doing something like this might be good: counts - tabular( Sex*Name ~ 1, data=sampledf ) full[ as.logical(counts), ] Duncan Murdoch __ R-help@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.
Re: [R] S4; Setter function is not chaning slot value as expected
Modelling a mutable entity, i.e. an account, is really a perfect
example of when to use reference classes. You might find the examples
on http://adv-r.had.co.nz/OO-essentials.html give you a better feel
for the strengths and weaknesses of R's different OO systems.
Hadley
On Sat, Nov 9, 2013 at 9:31 AM, daniel schnaider dschnai...@gmail.com wrote:
It is my first time programming with S4 and I can't get the setter fuction
to actually change the value of the slot created by the constructor.
I guess it has to do with local copy, global copy, etc. of the variable -
but, I could't find anything relevant in documentation.
Tried to copy examples from the internet, but they had the same problem.
# The code
setClass (Account ,
representation (
customer_id = character,
transactions = matrix)
)
Account - function(id, t) {
new(Account, customer_id = id, transactions = t)
}
setGeneric (CustomerID-, function(obj,
id){standardGeneric(CustomerID-)})
setReplaceMethod(CustomerID, Account, function(obj, id){
obj@customer_id - id
obj
})
ac - Account(12345, matrix(c(1,2,3,4,5,6), ncol=2))
ac
CustomerID - 54321
ac
#Output
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
# CustomerID is value has changed to 54321, but as you can see it does't
CustomerID - 54321
ac
An object of class Account
Slot customer_id:
[1] 12345
Slot transactions:
[,1] [,2]
[1,]14
[2,]25
[3,]36
Help!
[[alternative HTML version deleted]]
__
R-help@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.
--
Chief Scientist, RStudio
http://had.co.nz/
__
R-help@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.
