similar to: lapply and aggregate function

Hello everybody, Data is myd <- data.frame(id1=rep(c("a","b","c"),each=3),id2=rep(1:3,3),val=rnorm(9)) I want to get a cumulative sum over each of id1. trying aggregate does not work myd$pcum <- aggregate(myd[,c("val")],list(orig=myd$id1),cumsum) Please suggest a solution. In real the dataframe is huge so looping with for and subsetting is not a

Using: # rsync --version rsync version 3.0.7 protocol version 30 Copyright (C) 1996-2009 by Andrew Tridgell, Wayne Davison, and others. Web site: http://rsync.samba.org/ Capabilities: 64-bit files, 64-bit inums, 32-bit timestamps, 64-bit long ints, socketpairs, hardlinks, symlinks, IPv6, batchfiles, inplace, append, ACLs, xattrs, iconv, symtimes rsync comes with ABSOLUTELY NO

Hi all. I would like to highlight overlapping regions of two densities and I could not find a way to do it. Here is the sample code: myd <- c(2,4,5, 4,3,2,2,3,3,3,2,3,3,4,2,4,3,3,3,2,2.5, 2, 3,3, 2.3, 3, 3, 2, 3) myd1 <- myd-2 plot(range(density(myd)$x, density(myd1)$x), range(density(myd)$y, density(myd1)$y), type = "n") lines(density(myd), col=1, lwd=4)

Dear All, I have a database of 200 observations named myD. In the dataframe there are a column named code (with codes varying from 1 to 77), a column named "prevalence" with some quantitative measurements are given and an column named Pr_mean, with no values. I would like to set a cycle to compute the average of prevalence values for each different code and store the averages under the

Hi all, ok, i know i can cut a dendrogram, which i did. all i get is three objects that a dendrograms itself. for example: myd$upper, myd$lower[[1]], myd$lower[[2]] and so on. of course i can plot them seperately now. but the lower parts still have hundreds of branches. i?ll need a 30 " widescreen to watch the whole picture. what i?d like to is group the lower branches , so that i get a

I have 2 dataframes. "mydata" contains numerical data. "mybys" contains information on the "group" each row of the data is in. I wish to aggregate each column in mydata using the corresponding column in mybys. Please see the example below. What is a more elegant or "better" way to accomplish this task? Thanks! mydata =

Hi, I must be missing something here...Essentially, a short piece of code works if it's standalone, but doesn't work if it's divided into two functions. The code that works is: ################### WORKS ############### library(pamr) set.seed(120) x <- matrix(rnorm(1000*20),ncol=20) y <- sample(c(1:4),size=20,replace=TRUE) mydata <- list(x=x,y=y)

Hi all, I'm working on a target whose registers have equal-sized subregisters and all of those subregisters can be named (or the other way round: registers can be grouped into super registers). So for instance we've got 16 registers W (as in wide) W0..W15 and 32 registers N (as in narrow) N0..N31. This way, W0 is made by grouping N0 and N1, W1 is N2 and N3, W2 is N4 and N5, ..., W15 is

ComRades: I am geeting the error message Error in ldots[[which(MET)]] : attempt to select less than one element when I try to fit the geostatistical model with the likfit() function of geoR. I have tried with old data for which likfit() successfully maximised the likelihood in previous versions of geoR, and yet the current version fails. I have tried in Windows Vista and Windows XP (I haven't

I have a bit of an issue with rsync. I am using to keep directories in sync via another server for backup. Here is the server config [w1] path = /w1 comment = w1 web dir [w2] path = /w2 comment = w2 web dir Now on the client i run this command rsync -avv --delete --force domain.com::w1/ /w1/ It will NOT delete anything that is no on the server anymore.. for example on the server/client there

Hi Everyone, This example code results in R 'crashing'; that is the R application closes with no warnings or error messages. #----------------------- myD <- read.table(stdin(), header=TRUE, nrows=20) Broth Salt pH Temp N Y Growth 1 310 9.0 2.92 10 90.0 NA 0 2 615 6.0 7.82 30 1.0 2 1 3 217 2.0 7.34 10 7.0 8

At office, using the internal LAN at my disposal, I'm having a go at parallel computing - to begin with - with pvm, rpvm & snow. The two boxes are as follows Remote machine uffbsd: CPU: Intel(R) Pentium(R) 4 CPU 2.00GHz (1994.13-MHz 686-class CPU) Origin = "GenuineIntel" Id = 0xf24 Stepping = 4 real memory = 260046848 (248 MB) This machine NbBSD: CPU: Mobile Intel(R)

Dear R Help, I read through the archives pretty extensively before sending this email, as it seemed there were several threads on using predict with GLM. However, while my issue is similar to previous posts (cannot get it to predict using new data), none of the suggested fixes are working. The important bits of my code: set.seed(644) n0=200 #number of observations

Hi, > tmpdata<-subset(myd, TYPE=="A") > levels(tmpdata$TYPE) > [1] "A" "B" "C" I'd like to get only "A" as output... Thanks for your help Marc

I keep getting the 'memory.size' error message when I run a program I have been writing. It always it cannot allocate a vector of a certain size. I believe the error comes in the code fragement below where I have multiple arrays that could be taking up space. Does anyone know a good way around this? w1 <- outer(xk$xk1, data[,x1], function(y,z) abs(z-y)) w2 <- outer(xk$xk2,

HI Jim and all, I want to put one more condition. Include col2 and col3 if they are not in col1. Here is the data mydat <- read.table(textConnection("Col1 Col2 col3 K2 X1 NA Z1 K1 K2 Z2 NA NA Z3 X1 NA Z4 Y1 W1"),header = TRUE,stringsAsFactors=FALSE) The desired out put would be Col1 Col2 col3 1 X1 0 0 2 K1 0 0 3 Y1 0 0 4 W1 0 0 6 K2 X1

Hello, I am facing a problem with lapply which I ''''think''' may be a bug. This is the most basic function in which I can reproduce it: myfun <- function() { foo = data.frame(1:10,10:1) foos = list(foo) fooCollumn=2 cFoo = lapply(foos,subset,select=fooCollumn) return(cFoo) } I am building a list of dataframes, in each of which I want to keep only column

Jim has been exceedingly patient (and may well continue to be so), but this smells like "failure to launch". At what point will you start showing your (failed) attempts at solving your own problems so we can help you work on your specific weaknesses and become self-sufficient? -- Sent from my phone. Please excuse my brevity. On February 25, 2018 7:55:55 AM PST, Val <valkremk at

Hi Val, My fault - I assumed that the NA would be first in the result produced by "unique": mydat <- read.table(textConnection("Col1 Col2 col3 Z1 K1 K2 Z2 NA NA Z3 X1 NA Z4 Y1 W1"),header = TRUE,stringsAsFactors=FALSE) val23<-unique(unlist(mydat[,c("Col2","col3")])) napos<-which(is.na(val23)) preval<-data.frame(Col1=val23[-napos],

Dear R-Help List, I'm trying to simulate data from a conditional distribution, and haven't been able to modify my existing code to do so. I searched the archives, but didn't find any previous post that matched my question. n=10000 pop = data.frame(W1 = rbinom(n, 1, .2), W2 = runif(n, min = 3, max = 8), W3 = rnorm(n, mean=0, sd=2)) pop = transform(pop, A = rbinom(n, 1,