similar to: Extracting column name in apply/lapply

The menu directory won't compile with gcc 3.4.0 because gcc complains that "ebp" cannot be used as a constraint. If preserving ebp is necessary then it will have to be done in the assembler code. Gcc 3.3.3 just seems to ignore the constraint. It didn't do anything special to preserve the register. I also noticed that getnumrows returns the contents of 0x484 which is the

Hello, I'm working with R version 1.5.0 in Windows. I've written a function (SummaryMat, segment below) which uses a loop to repeatedly call another function (PercentsMat, segment below). PercentsMat creates a matrix and adds rows to it each time it is called. I use deparse(substitute(...)) to get the names of the lists sent to PercentsMat to use them as row names in the generated

I am using the library(bigmemory) to handle large datasets, say 1 GB, and facing following problems. Any hints from anybody can be helpful. _Problem-1: _ I am using "read.big.matrix" function to create a filebacked big matrix of my data and get the following warning: > x = read.big.matrix("/home/utkarsh.s/data.csv",header=T,type="double",shared=T,backingfile

Hi, running the following code by itself runs as expected. ---------------------------------------------------------------------------- k <- 1 i <- 2 j <- 3 NumName <- varnames[num.cols[k]] FacNames <- varnames[fac.cols[c(i,j)]] tmp <- paste(FacNames[1],NumName,sep="~") fml <- formula(paste(tmp,FacNames[2],sep="|")) bwplot(fml, data

Dear All, I have the following code which works fine but takes 16+ hours to run and I was hoping someone could give me a hint as to how I could make it more efficient. Let me explain what the code is supposed to do. Each single row of the m.o.f matrix (n*p dim) has to go through the following operation Dm*m.o.f[i]^alpha1m where both Dm and alpha1m have n*1 dimensions. Each row of the m.o.f

On Fri, 22 Oct 2004 13:39:06 -0700, Bob Sidebotham <bob@windsong.bc.ca> wrote: > In 1.2 FXTable, numColumns is documented as numCols, but should be > numColumns, I think. Also the examples/table.rb app uses numCols, and > should use numColumns. Yes, you''re right. I''ve added both of these bugs to the FXRuby bug list, found here:

Hi, I still have little ability to predict how these functions will treat the columns of data frames: > # Here's a data frame with a column "a" of integers, > # and a column "b" of characters: > df <- data.frame( + a = 1:2, + b = c("a","b") + ) > df a b 1 1 a 2 2 b > > # Except -- both columns are characters: >

An alternative solution that allows you to break it down by categories is in the functions below. Comments/suggestions welcome and encouraged. Note that much thanks is due to those who responded to an earlier post of mine on a similar topic. To use (assuming you have a data.frame falled pathDist with columns distances (your numeric data of interest), Capacity, Surface, and SITE (vectors of

Help with this much appreciated I have a large dataframe that I would like to subset where the constraint Test1 <- subset(df, date == uniques[[1]]), where uniques is a list of dates that must be matched to create Test1. I would like to perform an operation on Test1 that results in a single column of data. So far so good. How do loop through all values in the uniques list (say

Hi, I'm a bit puzzled. I uses exactly the same code in RcppExamples package to try adding RcppFrame object to RcppResultSet. When running it gives me segmentation fault problem. I'm using gcc 4.1.2 on redhat 64bit. I'm not sure if this is the cause of the problem. Any advice would be greatly appreciated. Thank you. Rob. int numCol=4; std::vector<std::string>

Hi, I've had this problem for a while and tackled it is a quite dirty way so I'm wondering is a better solution exists: If we have two vectors: v1 = c(0,1,2,3,4) v2 = c(5,3,2,1,0) How to remove one instance of the "3,1" / "1,3" double? At the moment I'm using the following solution, which is quite horrible: v1 = c(0,1,2,3,4) v2 = c(5,3,2,1,0) ft <-

Dear Sir / Madam, I am new for R coding. Kindly help me out in sorting out the following problem. There are 50 rows with six coloumns(you could see in the attached .txt file). I wish to go for filtering this 50 rows for any one of the six coloumns satisfying the value >= 64. I need to have a final table with rows having >= 64 value in any one of the six coloumns and the rest could be

Hi, First, thank you all for your help. Here is my problem (simplified): Say I have a list: a=list(matrix(50,nrow=5,ncol=5),matrix(25,nrow=5,ncol=5),matrix(10,nrow=5,ncol=5)) I'd like to use rbinom with a different probability for each matrix. I tried: b=c(.8,.1,.9) brep=rep(b,each=25) lapply(a,function(a) rbinom(25,a,brep)) but that doesn't work-- it just uses the first value of b

Dear all, I would like to ask your help understand the subsequent steps for making my program faster. The following code: Gauslist<-array(data=NA,dim=c(dimx,dimy,dimz)) for (i in c(1:dimz)){ print(sprintf('Creating the %d map',i)); Gauslist[,,i]<-f <- GaussRF(x=x, y=y, model=model, grid=TRUE,param=c(mean,variance,nugget,scale,Whit.alpha)) } creates 100 GaussMaps (each

Dear Isabelle, for most of my work I have switched from S-Plus to R, because it is easier to make scripts with due to the better documentation and some other more reasonable implementations. And there was also a cost issue with the S-Plus Server, which is not for free at all. In R I use the its package for time series, which is quite sufficient for my needs. The only problem I encountered was

I need to develop a simple list manipulation. Although it seems easier to do it in matrix form, but I need it in list form. I have a matrix x <- matrix(c(12.1, 3.44, 0.1, 3, 12, 33.1, 1.1, 23), nrow=2) for list form example, the conversion is x.list <- lapply(seq_len(nrow(x)), function(i) x[i,]) ### list version calcnorm=function(a, b){ diff <- mapply("-", a, b)

Hi all, I have a matrix with 10000 rows and 10 columns. The last columns contains another identifiers but the values are not uniques so that I want to generate another matrix with rows with unique values in the last column. If I did tmp<-unique(my_mat$col10) this will give me 8560 unique entries so the ideal matrix will be 8560X10 columns now then. I tried sub_mat<-my_mat[tmp,] but

Hello everyone, A recent (in 2.5 I suspect) change in R is giving me trouble. I want to apply a function (tolower) to all the columns of a data.frame and get a data.frame in return. Currently, on a data.frame, both apply (for arrays) and lapply (for lists) work, but each returns its native class (resp. matrix and list): apply(mydat,2,tolower) # gives a matrix lapply(mydat,tolower) # gives

I wrote something to check for duplicate rows in a data frame, but it is too inefficient. Is there a way to do this without the nested loops? This code correctly indicates rows 1-7, 1-8, 2-9 and 7-8 are duplicates. > m <- matrix(c(1,1,1,1,1, 2,2,2,2,2, 6,6,6,6,6, 3,3,3,3,3, 4,4,4,4,4, 5,5,5,5,5, 1,1,1,1,1, 1,1,1,1,1, 2,2,2,2,2, 7,7,7,7,7), ncol=5, byrow=TRUE) > df <- data.frame(m)

We can do "add %x, undef" => "undef" because for any value of %x, we can always find a value that when added to %x produces any value in the domain of integers. This is not the case with floats since with some inputs, e.g., NaNs, we are not able to produce some values in the domain (e.g., there's no value of %x that makes "fadd NaN, %x" return 42.0). In