If df is the data.frame with values and you want nn samples, then this
is a slightly different approach:
# example data.frame:
df = data.frame(a1 = sample(1:20,50, replace = TRUE),
a2 = sample(seq(0.1,10,length.out =
30),50, replace = TRUE),
a3 = sample(seq(0.3, 20,length.out =
20),50,replace = TRUE))
nrow = dim(df)[1] # 50
ncol = dim(df)[2] # 3
# start by randomizing the order in your data.frame
randomOrder = sample(1:nrow, nrow, replace = FALSE)
dff = df[randomOrder,]
# find and remove all duplicates from all columns. With this you will
only keep the first instance of any unique value:
rem = NULL
for (ic in 1:ncol) rem = c(rem, which(duplicated(dff[, ic])))
if (length(rem) > 0) dff = dff[-unique(rem),]
# Reduce to the length you need
if (dim(dff)[1] > nn) res = dff[1:nn,] else res = dff
I am not sure how this scales if you have a really big data, and whether
you could get some FAQ 7.31 problems depending on how you fill your
data.frame.
Cheers,
Jon
On 6/23/2015 12:13 AM, C W wrote:
Hi Jean,
Thanks!
Daniel,
Yes, you are absolutely right. I want sampled vectors to be as different
as possible.
I added a little more to the earlier data set.
x1 x2 x3
[1,] 1 3.7 2.1
[2,] 2 3.7 5.3
[3,] 3 3.7 6.2
[4,] 4 3.7 8.9
[5,] 5 3.7 4.1
[6,] 1 2.9 2.1
[7,] 2 2.9 5.3
[8,] 3 2.9 6.2
[9,] 4 2.9 8.9
[10,] 5 2.9 4.1
[11,] 1 5.2 2.1
[12,] 2 5.2 5.3
[13,] 3 5.2 6.2
[14,] 4 5.2 8.9
[15,] 5 5.2 4.1
If I sampled row, 1, 6, 11, solving the system of equations will not be
possible. So, I am avoiding "similar vectors".
Thanks,
Mike
On Mon, Jun 22, 2015 at 2:19 PM, Daniel Nordlund <djnordl...@frontier.com>
wrote:
On 6/22/2015 9:42 AM, C W wrote:
Hello R list,
I am have question about sampling unique coordinate values.
Here's how my data looks like
dat <- cbind(x1 = rep(1:5, 3), x2 = rep(c(3.7, 2.9, 5.2), each=5))
dat
x1 x2
[1,] 1 3.7
[2,] 2 3.7
[3,] 3 3.7
[4,] 4 3.7
[5,] 5 3.7
[6,] 1 2.9
[7,] 2 2.9
[8,] 3 2.9
[9,] 4 2.9
[10,] 5 2.9
[11,] 1 5.2
[12,] 2 5.2
[13,] 3 5.2
[14,] 4 5.2
[15,] 5 5.2
If I sampled (1, 3.7), then, I don't want (1, 2.9) or (2, 3.7).
I want to avoid either the first or second coordinate repeated. It leads
to undefined matrix inversion.
I thought of using sampling(), but not sure about applying it to a data
frame.
Thanks in advance,
Mike
I am not sure you gave us enough information to solve your real world
problem. But I have a few comments and a potential solution.
1. In your example the unique values in in x1 are completely crossed with
the unique values in x2.
2. since you don't want duplicates of either number, then the maximum
number of samples that you can take is the minimum number of unique values
in either vector, x1 or x2 (in this case x2 with 3 unique values).
3. Sample without replace from the smallest set of unique values first.
4. Sample without replacement from the larger set second.
x <- 1:5
xx <- c(3.7, 2.9, 5.2)
s2 <- sample(xx,2, replace=FALSE)
s1 <- sample(x,2, replace=FALSE)
samp <- cbind(s1,s2)
samp
s1 s2
[1,] 5 3.7
[2,] 1 5.2
Your actual data is probably larger, and the unique values in each vector
may not be completely crossed, in which case the task is a little harder.
In that case, you could remove values from your data as you sample. This
may not be efficient, but it will work.
smpl <- function(dat, size){
mysamp <- numeric(0)
for(i in 1:size) {
s <- dat[sample(nrow(dat),1),]
mysamp <- rbind(mysamp,s, deparse.level=0)
dat <- dat[!(dat[,1]==s[1] | dat[,2]==s[2]),]
}
mysamp
}
This is just an example of how you might approach your real world
problem. There is no error checking, and for large samples it may not
scale well.
Hope this is helpful,
Dan
--
Daniel Nordlund
Bothell, WA USA
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--
Jon Olav Skøien
Joint Research Centre - European Commission
Institute for Environment and Sustainability (IES)
Climate Risk Management Unit
Via Fermi 2749, TP 100-01, I-21027 Ispra (VA), ITALY
jon.sko...@jrc.ec.europa.eu
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