similar to: why this function does not run correctly?

hi All, There is a distributed cluster with 5 bricks: gl0 Filesystem Size Used Avail Use% Mounted on /dev/sda4 5.5T 4.1T 1.5T 75% /mnt/brick1 gl1 Filesystem Size Used Avail Use% Mounted on /dev/sda4 5.5T 4.3T 1.3T 78% /mnt/brick1 gl2 Filesystem Size Used Avail Use% Mounted on /dev/sda4 5.5T 4.1T 1.4T 76% /mnt/brick1 gl3 Filesystem Size Used

I?ve never had such situation and I don?t recall someone sharing something similar. Most probably it?s easier to remove the node from the TSP and re-add it.Of course , test the case in VMs just to validate that it?s possible to add a mode to a cluster with snapshots. I have a vague feeling that you will need to delete all snapshots. Best Regards,Strahil Nikolov? On Thursday, August 10, 2023, 4:36

Based on a discussion on SO I ran some tests and found that converting to a factor is best done early in the process. Hence, I propose to rewrite the gl() function as : gl2 <- function(n, k, length = n * k, labels = 1:n, ordered = FALSE){ rep( rep( factor(1:n,levels=1:n,labels=labels, ordered=ordered),rep.int(k,n) ),length.out=length ) } Some test results : >

Given this data frame (a simplified, essential reproducible example) A<-c(8,7,10,1,5) A_flag<-c(10,0,1,0,2) B<-c(5,6,2,1,0) B_flag<-c(12,9,0,5,0) mydf<-data.frame(A, A_flag, B, B_flag) # this is my initial df mydf I want to get to this final situation i<-which(mydf$A_flag==0) mydf$A[i]<-NA ii<-which(mydf$B_flag==0) mydf$B[ii]<-NA

Hello an update about my question: I worked out the following solution (with the package "dplyr") library(dplyr) mydf%>% mutate(speed_vehicles=n_vehicles*mydf$speed) %>% group_by(date_time,type) %>% summarise( sum_n_times_speed=sum(speed_vehicles), n_vehicles=sum(n_vehicles), vel=sum(speed_vehicles)/sum(n_vehicles) ) In fact I was hoping to manage everything in a

hi all I have this dataframe (created as a reproducible example) mydf<-structure(list(date_time = structure(c(1508238000, 1508238000, 1508238000, 1508238000, 1508238000, 1508238000, 1508238000), class = c("POSIXct", "POSIXt"), tzone = ""), direction = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("A", "B"), class =

Hi there, I am currently working on a R programming project and got stuck. I am supposed to generate a set of possibilities of 1296 different combinations of 4 numbers, ie. 1111, 1234, 2361, (only contain 1 to 6) in a matrix form here is what I got which has not been working as it keeps coming out with the same number on the row.. The code: gl1<- gl(1296,1,length=1296, labels=1:1296,

yes, it works, even if I do not really get how and why it's working the combination of logical results (could you provide some insights for that?) moreover, and most of all, I was hoping for a compact solution because I need to deal with MANY columns (more than 40) in data frame with the same basic structure as the simplified example I posted thanks m ----- Messaggio originale ----- Da:

Hello, Try the following. icol <- which(grepl("flag", names(mydf))) mydf[icol] <- lapply(mydf[icol], function(x){ is.na(x) <- x == 0 x }) mydf # A A_flag B B_flag #1 8 10 5 12 #2 7 NA 6 9 #3 10 1 2 NA #4 1 NA 1 5 #5 5 2 0 NA Hope this helps, Rui Barradas On 11/22/2017 10:34 AM, Massimo Bressan

...well, I don't think this is exactly the expected result (see my post) to be noted that the columns affected should be "A" and "B" thanks for the help max ----- Messaggio originale ----- Da: "Rui Barradas" <ruipbarradas at sapo.pt> A: "Massimo Bressan" <massimo.bressan at arpa.veneto.it>, "r-help" <r-help at

Dear Massimo, It seems straightforward to use weighted.mean() in a dplyr context library(dplyr) mydf %>% group_by(date_time, type) %>% summarise(vel = weighted.mean(speed, n_vehicles)) Best regards, ir. Thierry Onkelinx Statisticus / Statistician Vlaamse Overheid / Government of Flanders INSTITUUT VOOR NATUUR- EN BOSONDERZOEK / RESEARCH INSTITUTE FOR NATURE AND FOREST Team

Do you mean like this: mydf <- within(mydf, { is.na(A)<- !A_flag is.na(B)<- !B_flag } ) > mydf A A_flag B B_flag 1 8 10 5 12 2 NA 0 6 9 3 10 1 NA 0 4 NA 0 1 5 5 5 2 NA 0 Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into

hello all R experts, how do I calculate the reliability between the two groups using the ICCs? I'll appreciate your reply, Thanks Sincerely, Supreet kaur, Biomedical research engineer, Nationwide Childrens Hospital, Columbus, OH (614)355-3509 [[alternative HTML version deleted]]

Hi Thanks for working example. you could use split/ lapply approach, however it is probably not much better than dplyr method. sapply(split(mydf, mydf$type), function(speed, n_vehicles) sum(mydf$speed*mydf$n_vehicles)/sum(mydf$n_vehicles)) gives you averages aggregate(mydf$n_vehicles, list(mydf$type), sum)$x gives you sums Cheers Petr > -----Original Message----- > From: R-help

Hi, I am having trouble plotting a series of dendrograms using lattice and grid code as found in Paul Murrells book R Graphics. This is the error message I recieve: Error in downViewport.vpPath(vpPathDirect(name), strict, recording = recording) : Viewport 'plot1.panel.1.1.off.vp' was not found I have attached the code and also my data file. Should anyone have any suggestions then

Hello, Using base R only, the following seems to do what you want. with(mydf, ave(speed, date_time, type, FUN = weighted.mean, w = n_vehicles)) Hope this helps, Rui Barradas Em 09-11-2017 13:16, Massimo Bressan escreveu: > Hello > > an update about my question: I worked out the following solution (with the package "dplyr") > > library(dplyr) > > mydf%>% >

Hello everybody, Please, help to fix me a problem. I have a distributed-replicated volume between two servers. On each server I have 2 RAID-10 arrays, that replicated between servers. Brick gl1:/mnt/brick1/gm0 49153 0 Y 13910 Brick gl0:/mnt/brick0/gm0 N/A N/A N N/A Brick gl0:/mnt/brick1/gm0 N/A

> On 9 Nov 2017, at 14:58, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > Thanks for working example. > > you could use split/ lapply approach, however it is probably not much better than dplyr method. > > sapply(split(mydf, mydf$type), function(speed, n_vehicles) sum(mydf$speed*mydf$n_vehicles)/sum(mydf$n_vehicles)) > gives you averages > The

Hi *Massimo,* *Try this.* *a <- mydf==0mydf[a] <- NAHTHEK* On Wed, Nov 22, 2017 at 5:34 AM, Massimo Bressan < massimo.bressan at arpa.veneto.it> wrote: > > > Given this data frame (a simplified, essential reproducible example) > > > > > A<-c(8,7,10,1,5) > > A_flag<-c(10,0,1,0,2) > > B<-c(5,6,2,1,0) > > B_flag<-c(12,9,0,5,0) >

Hi, community Please, help me with my trouble. I have 2 Gluster nodes, with 2 bricks on each. Configuration: Node1 brick1 replicated on Node0 brick0 Node0 brick1 replicated on Node1 brick0 Volume Name: gm0 Type: Distributed-Replicate Volume ID: 5e55f511-8a50-46e4-aa2f-5d4f73c859cf Status: Started Snapshot Count: 0 Number of Bricks: 2 x 2 = 4 Transport-type: tcp Bricks: Brick1: