I need to graph categorical data like a or b in the the following figure. Could
anybody let me know what command line I should go with?
Thanks a lot!
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I'm trying to establish a connection to a pair of fifos in R, one represents
the input stream of a process and the other one the output of the same
process. The problem is that R behaves very different when running the
commands directly in the interpreter than when running via a script file.
He
On Fri, 2013-12-27 at 16:47 -0600, Hadley Wickham wrote:
> For your original case, you may find it more useful to do memory +
> line profiling (e.g. as visualised by
> https://github.com/hadley/lineprof) to figure out what's going on.
>
> Hadley
I've been trying memory and line profiling, but mem
Hi Ross,
It's not obvious how useful memory.profile() is here. I created the
following little experiment to help me understand what
memory.profile() is showing (and to make it easier to see the
changes), but it's left me more confused than enlightened:
m_delta <- function(expr) {
# Evaluate in
Dear Friends,
I'm using Matchit package for a Cohor Study.
After match the number provided by the summary output apparently don't
correspond to the true value:
Here the values provided by summary:>summary (test) Summary of Balance for
Matched Data Age: Means treated 44.89; Means control 45.47
How
Suppose that you have a dataframe myData, with columns A,B,C and you want
a new column D to have the counts for each item in column A, then try:
myData$D<-table(myData$A)[as.character(myData$A)]
The table creates the counts as a named vector by converting the column to
a factor, the names are th
I am trying to understand why a function causes my memory use to
explode. While doing that I noticed that my memory use as reported by
gc() is growing, but the results of memory.profile() are almost
unchanged (the count for raw grew by 3). How can the two functions
produce different results, and
Hi, I don’t really have a problem, but I’m trying to improve my R abilities and
I think I might use some help.
I’ve done this graph:
a<-.05; b<- 1; kr1<-70;kr2<-60;kr3<-50
K1<- kr1/a; K2<- kr2/a; K3<- kr3/a
curve(kr1*x*(b-a*x/kr1),col="blue",from=-50,to=1500,xlab="Nombre
d'individus",ylab="Cr
I just wanted to note that Arun's first approach, which uses matrix
indexing -- often a very useful way to do these things, btw -- can be
simplified a bit.
m <- do.call(rbind,xyz_indices) ## 4x3 matrix
## avoids repeated evaluation. lapply is not needed as it's a list already
sapply(seq(dim(edm)[
Thank you Uwe. I always forget about how environments are embedded within
each other.
On Fri, Dec 27, 2013 at 9:33 AM, Sébastien Bihorel wrote:
> Hi,
>
> I have a problem of variable scope illustrated by the following example
> (this examples greatly simplifies the actual code I am working on
Hi,
Not able to reproduce the problem when "A" is a matrix
A <- as.matrix( read.table(text="nazwa1 nazwa3 0,2531
nazwa7 nazwa5 0,562
nazwa2 nazwa6 0,65959",header=FALSE,sep=""))
cbind(A[,1],A[,3])
# [,1] [,2]
#[1,] "nazwa1" "0,2531"
#[2,] "nazwa7" "0,562"
#[3,] "nazwa2" "0,6595
You didn't save the output from the call to funb() within funa(). Try this.
funa <- function(x) {
# here is some code that defines the variables aa, bb, cc
aa <- 11
bb <- 21
cc <- 31
myabc <- c(aa, bb, cc)
# fun b uses some input variable of funa to modify aa, bb, and cc
myabc <- funb(x)
cat(sp
This does the trick.
apply(sapply(xyz_indices, function(xyz) edm[xyz[1], xyz[2], xyz[3], ]), 1,
mean)
Jean
On Fri, Dec 27, 2013 at 7:28 AM, Morway, Eric wrote:
> In the larger problem I'm attempting to solve, I read a 2Gb file
> into a 4D array, where the first 3 dimensions are related to spa
On 27.12.2013 15:33, Sébastien Bihorel wrote:
Hi,
I have a problem of variable scope illustrated by the following example
(this examples greatly simplifies the actual code I am working on but I
unfortunately cannot share it). I have two functions, funa and funb: funb
is called within funa. The
HI,
You could try:
res1 <- sapply(seq(dim(edm)[4]),function(i)
mean(edm[do.call(rbind,lapply(xyz_indices,function(x) c(x,i)))],na.rm=TRUE))
#or
indx <-
cbind(matrix(rep(unlist(xyz_indices),50),ncol=3,byrow=TRUE),rep(1:50,each=4))
res2 <-tapply(edm[indx],((seq(200)-1)%/%4)+1,mean)
dimnames(re
Hi,
I have a problem of variable scope illustrated by the following example
(this examples greatly simplifies the actual code I am working on but I
unfortunately cannot share it). I have two functions, funa and funb: funb
is called within funa. The goal of funb is to modify variables created
withi
In the larger problem I'm attempting to solve, I read a 2Gb file
into a 4D array, where the first 3 dimensions are related to space
(x, y, z), and the 4th dimension is time. My goal is to find the
average of specific x, y, z-indices for each time step.
A small, reproducible example starts like th
Dear Vinny Moriarty,
Vinny Moriarty gmail.com> writes:
>
> I've got an ecological data set that I've worked up to the point of having
> a relative abundance matrix I created with the decostand() command in Vegan.
>
> Here is the distance matrix data:
<-- clip: 8 x 4 data matrix giving distance
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