similar to: partial cumsum

I would like to cumulatively sum rows in a matrix, in which each row has 1 NA value, which I do NOT want to treat as 0s. The NAs are placeholders where there is actually no data, which is not the same as a 0. The usual "na.rm=TRUE" does not seem to work with the command cumsum. Is there another way to ignore the NAs or do I need to figure out a different way to do this? Here's an

Hi, I need help to find an efficient way to transform a vector like: a<-c(1,1,0,1,0,0,0,1,1,1,1,0,1,0,1,1) in a vector that counts only di 1 elements, like: b<-c(1,2,0,1,0,0,0,1,2,3,4,0,1,0,1,2) Thank you! -- View this message in context: http://r.789695.n4.nabble.com/counts-of-a-vector-tp2232047p2232047.html Sent from the R help mailing list archive at Nabble.com.

hey guys, I am working on a function to make a duplicated value unique. For example, the original vector would be like : a = c(2,1,1,3,3,3,4) I'll like to transform it into: a.nodup = 2, 1.01, 1.02, 3.01, 3.02, 3.03, 4 basically, find the duplicates and assign a unique value by adding a small amount and keep it in order. I come up with the following codes, but it runs slow if t is large. Is

Hello all - I have an example column in a dataFrame id.name 123.45 123.45 123.45 123.45 234.56 234.56 234.56 234.56 234.56 234.56 234.56 345.67 345.67 345.67 456.78 456.78 456.78 456.78 456.78 456.78 456.78 456.78 456.78 ... [truncated] And I'd like to create a second vector of sequential values (i.e., 1:N) for each unique id.name value. In other words, I need id.name x 123.45 1 123.45

Dear R users, I have this data frame, y samp 8 0.03060419 X 18 0.06120838 Y 10 0.23588374 X 3 0.32809965 X 1 0.36007100 X 7 0.36730571 X 20 0.47176748 Y 13 0.65619929 Y 11 0.72014201 Y 17 0.73461142 Y 6 0.76221313 X 2 0.77005691 X 4 0.92477243 X 9 0.93837591 X 5 0.98883581 X 16 1.52442626 Y 12 1.54011381 Y 14 1.84954487 Y

Dear R Community - I hope you might be able to provide some guidance regarding the use of the rle function. I have a set of time-series data where a measured value is recorded every 30 seconds after the start of an experiment. Many of the measured values repeat and I am interested only in the values when there is a change. If I turn the measured values into a vector, the rle function works

Hello! Is it posible to apply /cumsum()/ along the 3rd dimension of 3D array? Something like matrlab function - /cumsum (*A*,dim)/ which returns the cumulative sum of the elements along the dimension of *A* specified by scalar dim. Thanks in advance ?eljka -- View this message in context: http://r.789695.n4.nabble.com/cumsum-in-3d-arrays-tp4110470p4110470.html Sent from the R help mailing

Dear list members I have a rather large vector (part of a data frame) giving the time (date + time, POSIXct) of observations. The times are irregular (with both small and large jumps) but increasing, and there are several millions of them. I now wish to reduce my data set, so that I only have observations which are at least (for example) 60 seconds apart. Basically, I need (all) the indices

Dear R-users, Say that I have a sequence of zeroes and ones: x <- c(1,1,1,0,0,0,0,1,1,1,0,0,0,0,1,1,1,0,0,0,0) The sequences of ones represent segments and I want to report the starting and endpoints of these segments. For example, in 'x', the first segment starts at location 1 and ends at 3, and the second segment starts at location 8 and ends at location 10. Is there an efficient

Hi, First post here. Grateful for any help you can give. I have data which looks like this: id time x 1 12:01 5 1 12:02 14 1 12:03 6 1 12:04 3 2 12:01 98 2 12:02 23 2 12:03 1 2 12:04 4 3 12:01 5 3 12:02 65 3 12:03 23 3 12:04 23 But I want to add a column which is the cumulative sum of X, but only by id. I've used cumsum before, but not

hey, I can not find a function for the following problem, hopefully you can help me. I have a vactor like this one v = c(NA,NA,TRUE,TRUE,NA,TRUE,NA,TRUE,TRUE,TRUE) and I would like to the TRUE values by the their "local sequence number". This means, the result should look thike this: c(NA,NA,1,2,NA,1,NA,1,2,3) Of course I could solve the problems using a loop, but this would be

Hi all, I am working with a spatial data set for which I am only interested in high concentration values ("leaks"). The low values (< 90th percentile) have already been turned into NA's, leaving me with a matrix like this: < CH4_leak lon lat CH4 1 -71.11954 42.35068 2.595834 2 -71.11954 42.35068 2.595688 3 NA NA NA 4 NA

Hi, I don't understand the following. When I create a small artificial set of date information in class POSIXct, I can calculate the mean and the median: a = as.POSIXct(Sys.time()) a = a + 60*0:10; a [1] "2009-03-10 11:30:16 EDT" "2009-03-10 11:31:16 EDT" "2009-03-10 11:32:16 EDT" [4] "2009-03-10 11:33:16 EDT" "2009-03-10 11:34:16

There are a number of functions that are dispatched to from split(). > methods('split') [1] split.data.frame split.Date split.default split.POSIXct Is there a way to figure out which of these variants is actually dispatched to when I call split? I know that if the argument is of the type data.frame, split.data.frame will be called? Is it the case that if the argument is not

Hi all, Thanks in advance for any help. I have a vector "b": b=c(1,1,1,2,3,4,3,2,1,1,1,1,1,2,3,4,5,4,3.5,3,2,1,1,1) Imagine b is river flow throughout time. I would like some code that will generate the following information: number of individual 'periods' where b>1 (= 2 in this case) period 1 length = 5, max = 4 period 2 length = 8, max = 5 I can't figure anything

Hi All, A twitter user, Mike fc (@coolbutuseless) mentioned today that he was surprised that repeated NAs weren't treated as a run by the rle function. Now I know why they are not. NAs represent values which could be the same or different from eachother if they were known, so from a purely conceptual standpoint there is no way to tell whether they are the same and thus constitute a run or

I've a matrix like this: 1985 1.38 1.27 1.84 2.10 0.59 3.47 1986 1.05 1.13 1.21 1.54 0.21 2.14 1987 1.33 1.21 1.77 1.44 0.27 2.85 1988 1.86 1.06 2.33 2.14 0.55 1.40 1989 2.10 0.65 2.74 2.43 1.19 1.45 1990 1.55 0.00 1.59 1.94 0.99 2.14 1991 0.92

Dear all, Could you please help me how to get the output as I described in the following example? x<-c(543, 543, 543, 543, 551 , 551 ,1128 ,1197, 1197) diff<-x-lag(x) diff [1] NA 0 0 0 8 0 577 69 0 How to index the occasions in x repeatedly if the diff<15? if diff>=15, it will give a new index. I want the output be like y. y<-c(1,1,1,1,1,1,2,3,3) Thank you so

Dear all, Could you please help me how to get the output as I described in the following example? x<-c(543, 543, 543, 543, 551 , 551 ,1128 ,1197, 1197) diff<-x-lag(x) diff [1] NA 0 0 0 8 0 577 69 0 How to index the occasions in x repeatedly if the diff<15? if diff>=15, it will give a new index. I want the output be like y. y<-c(1,1,1,1,1,1,2,3,3) Thank you so

I would like to cumulatively sum rows in a matrix, in which each row has 1 NA value. The usual "na.rm=TRUE" does not seem to work with the command cumsum. Is there another way to ignore the NAs or do I need to figure out a different way to do this? Here's an example matrix of title "proportion": Ntrail Strail NFJD Baldy Onion Crane [1,]