similar to: Function for describing segements in sequential data

List, Consider the following data. gender mygroup id 1 F A 1 2 F B 2 3 F B 2 4 F B 2 5 F C 2 6 F C 2 7 F C 2 8 F D 2 9 F D 2 10 F D 2 11 F D 2 12 F D 2 13 F D 2 14 M A 3 15 M A 3 16 M A 3 17

I need an analogue of "uniq -c" for a data frame. xtabs(), although dog slow, would have footed the bill nicely: --8<---------------cut here---------------start------------->8--- > x <- data.frame(a=1:32,b=1:32,c=1:32,d=1:32,e=1:32) > system.time(subset(as.data.frame(xtabs( ~. , x )), Freq != 0 )) user system elapsed 12.788 4.288 17.224 --8<---------------cut

Hello, I'm trying to use coxph() function to fit a very simple Cox proportional hazards regression model (only one covariate) but the parameter space is restricted to an open set (0, 1). Can I still obtain a valid estimate by using coxph function in this scenario? If yes, how? Any suggestion would be greatly appreciated. Thanks!!! [[alternative HTML version deleted]]

Dear Sir, I have been using R for a long time. But recently I have faced a problem when installing the Bioconductor package named "methyAnalysis". Firstly it was require to update my older R (R version 3.4.3 (2017-11-30)) in to newer version. That time I have also updated the RStudio software. After that when I have tried to install the package named "methyAnalysis". It

Thank you Michael Dewey. Can you please send me the email id for Bioconductor. regards Pijush On Fri, Dec 29, 2017 at 5:20 PM, Michael Dewey <lists at dewey.myzen.co.uk> wrote: > Dear Pijush > > You might do better to ask on the Bioconductor list as IRanges does not > seem to be on CRAN so I deduce it is a Bioconductor package too. > > Michael > > > On

I've got two tables.... first one(table1): ID chrom start end Ex1 2 152 180 Ex2 10 2000 2220 Ex3 15 3000 4000 second one ( table2): chrom location name 2 160 Alv 2 190 GNN 2 100

Hi, S4 method dispatch can be very slow. Would it be reasonable to cache the most recent dispatch, anticipating the next invocation will be on the same type? This would be very helpful in loops. fun0 <- function(x) sapply(x, paste, collapse="+") fun1 <- function(x) { paste <- selectMethod(paste, class(x[[1]])) sapply(x, paste,

Hi All, > act_2 Date Dtime Hour Min Second Rep 51 2006-02-22 14:52:18 14 52 18 useractivity_act 52 2006-02-22 14:52:18 14 52 18 4 55 2006-02-22 14:52:49 14 52 49 4 57 2006-02-22 14:52:51 14 52 51 4 58 2006-02-22 14:52:52 14 52 52 3 60 2006-02-22 14:54:42 14 54

Hi I deal with long vectors almost all of whose elements are zero. Typically, the length will be ~5e7 with ~100 nonzero elements. I want to deal with these objects using a sort of sparse vector. The problem is that I want to be able to 'add' two such vectors. Toy problem follows. Suppose I have two such objects, 'a' and 'b': > a $index [1] 20 30 100000000

Dear Pijush You might do better to ask on the Bioconductor list as IRanges does not seem to be on CRAN so I deduce it is a Bioconductor package too. Michael On 29/12/2017 07:29, Pijush Das wrote: > Dear Sir, > > > > > I have been using R for a long time. But recently I have faced a problem > when installing the Bioconductor package named "methyAnalysis".

Hello, I have start and end coordinates from different experiments (DNase hypersensitivity data) and now I would like to combine overlapping intervals. For instance (see my test data below) (2) 30-52 and (3) 49-101 are combined to 30-101. But 49-101 and 70-103 would not be combined because they are on different chromosomes (chr a and chr b). Does anybody have an idea? Thanks Hermann > df

Dear all, I have some very big data files that look something like this: id chr pos ihh1 ihh2 xpehh rs5748748 22 15795572 0.0230222 0.0268394 -0.153413 rs5748755 22 15806401 0.0186084 0.0268672 -0.367296 rs2385785 22 15807037 0.0198204 0.0186616 0.0602451 rs1981707 22 15809384 0.0299685 0.0176768 0.527892 rs1981708 22 15809434 0.0305465 0.0187227 0.489512 rs11914222 22 15810040 0.0307183

On 11/29/2017 04:15 PM, T?th D?nes wrote: > Hi, > > A benchmarking study with an additional (data.table-based) solution. I don't think speed is the right benchmark (I do agree that correctness is!). For the R-help list, maybe something about least specialized R knowledge required would be appropriate? I'd say there were some 'hard' solutions -- Michael (deep

Or with the Bioconductor IRanges package: df <- with(input, DataFrame(station, year=IRanges(from, to), record)) expand(df, "year") DataFrame with 24 rows and 3 columns station year record <character> <integer> <character> 1 07EA001 1960 QMS 2 07EA001 1961 QMC 3 07EA001 1962 QMC 4

Hi - I am trying to find and describe missing data in a time series. For instance, in the library openair, there is a data frame called "mydata": library(openair) head(mydata) date ws wd nox no2 o3 pm10 so2 co pm25 1 1998-01-01 00:00:00 0.60 280 285 39 1 29 4.7225 3.3725 NA 2 1998-01-01 01:00:00 2.16 230 NA NA NA 37 NA NA NA 3 1998-01-01 02:00:00

Hi, A benchmarking study with an additional (data.table-based) solution. Enjoy! ;) Cheers, Denes -------------------------- ## packages ########################## library(dplyr) library(data.table) library(IRanges) library(microbenchmark) ## prepare example dataset ########### ## use Bert's example, with 2000 stations instead of 2 d_df <- data.frame( station =

Hi! I would like to divide a 401 by 401 image into 20 equal blocks and then re-plot the image with the segments randomly distributed in it. How can I do this? ............. Regards, Kenduiywo Benson [[alternative HTML version deleted]]

Hi Martin, On 11/29/2017 10:46 PM, Martin Morgan wrote: > On 11/29/2017 04:15 PM, T?th D?nes wrote: >> Hi, >> >> A benchmarking study with an additional (data.table-based) solution. > > I don't think speed is the right benchmark (I do agree that correctness > is!). Well, agree, and sorry for the wording. It was really just an exercise and not a full

Previously, I posed the question pasted down below to the list and received some very helpful responses. While the code suggestions provided in response indeed work, they seem to only work with *very* small data sets and so I wanted to follow up and see if anyone had ideas for better efficiency. I was quite embarrased on this as our SAS programmers cranked out programs that did this in the blink

Dear r-helpers, I have a very simple question. Suppose my data is like id=c(rep(1,2),rep(2,2)) b=c(2,3,4,5) m=cbind(id,b) > m id b [1,] 1 2 [2,] 1 3 [3,] 2 4 [4,] 2 5 I wish to select the first observation for each id. That is, I want to quickly select two rows: id b 1 2 2 4 only. how should i do this? [[alternative HTML version deleted]]