similar to: How can I optimize this piece of code ?

For example, strings <- c("aaaa", "bbbb","ccba"). How to get "aaaa", "bbbb" that do not contain "ba" ? _________________________________________________________________ [[alternative HTML version deleted]]

For example, c("dog.is.an.animal", "cat.is.an.animal", "rat.is.an.animal"). How can I identify the common prefix is ".is.an.animal" and delete it to give c("dog", "cat", "rat") ? Thanks _________________________________________________________________ [[alternative HTML version deleted]]

Any better solution than this ? sum(strsplit("TCGGGGGACAATCGGTAACCCGTCT", "")[[1]] == "G") _________________________________________________________________ [[alternative HTML version deleted]]

I have a folder full of pngs and jpgs, and would like to consolidate them into a pdf with appropriate title and labels. Can this be done via R ? _________________________________________________________________ Easily publish your photos to your Spaces with Photo Gallery. [[alternative HTML version deleted]]

I am generating a report containing several R scripts in the appendix. Is there any way to "beautify" the R source codes in microsoft word, similar to what we see in tinn-R ? Thanks _________________________________________________________________ [[alternative HTML version deleted]]

Hi, I have developed some scripts that basically ask for input tab-limited format files, do some processing, and output several pictures or csv. Now I need to have some gui to wrap on top of the scripts, so that end-users can select their input files, adjust some parameters for processing, and select output folder or filenames. Please advice me if there is any tools or project suitable for

How to optimize the for-loop to be reasonably fast for sample.size=100000000 ? You may want to change sample.size=1000 to have an idea what I am achieving. set.seed(143) A <- matrix(sample(0:1, sample.size, TRUE), ncol=10, dimnames=list(NULL, LETTERS[1:10])) B <- list() for(i in 1:10) { B[[i]] <- apply(combn(LETTERS[1:10], i), 2, function(x) { sum(apply(data.frame(A[,x]), 1,

Hi, I am casting a dataframe from long to wide format. The same codes that works for a smaller dataframe would take a long time (more than two hours and still running) for a longer dataframe of 2495227 rows and ten different predictors. How to make it more efficient ? wer <- data.frame(Name=c(1:5, 4:5), Type=c(letters[1:5], letters[4:5]), Predictor=c("A", "A",

I tried aggregate, apply etc, but can't get the right result. For example, m <- cbind(c(LETTERS[1:5]), c("aa", "bb", "cc", "aa", "cc")) [,1] [,2][1,] "A" "aa"[2,] "B" "bb"[3,] "C" "cc"[4,] "D" "aa"[5,] "E" "cc" how to obtain

Given a dataframe m > m X Y V3 V4 1 1 A 0.5 1.2 2 1 B 0.2 1.4 3 2 A 0.1 0.9 How do I convert m to this with V4 as the cell values ? A B 1 1.2 1.4 2 0.9 NA

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value

unique(c(1:10,1)) gives 1:10 (i.e. unique values), is there any method to get only 2:10 (i.e. values that are unique) ? _________________________________________________________________ Easily edit your photos like a pro with Photo Gallery. [[alternative HTML version deleted]]

Hi, How do I collapse (average in the simplest case) the values of those duplicated ids (i.e., 2, 5, 6, 9) to give a table of unique ids ? t <- cbind(id=c(1:10, 2,5,6,9), value=rnorm(14)) _________________________________________________________________ [[alternative HTML version deleted]]

The aggregate function does "almost" all that I need to summarize a datasets, except that I can't specify exclusion of NAs without a little bit of hassle. > set.seed(143) > m <- data.frame(A=sample(LETTERS[1:5], 20, T), B=sample(LETTERS[1:10], 20, T), C=sample(c(NA, 1:4), 20, T), D=sample(c(NA,1:4), 20, T)) > m A B C D 1 E I 1 NA 2 A C NA NA 3 D I NA 3 4 C I

I have successfully installed ADaCGH package, and trying the example in SegmentPlotWrite did produce alot of pngs and html. I tried again the same example this morning (after a long night of installation), ADaCGH crashes at mpiInit() showing the error: Loading required package: Rmpi ELAN_EXCEPTION @ --: 6 (Initialisation error) elan_init: Can't get capability from environment Aborted I

Instead of prepend or append new columns to a matrix, how to insert them to a matrix ? For example, I would like to insert 3 new columns after the 5th column of matrix m. _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]

Instead of > m <- c(400000000, 50000000000) > paste("A", m, "B", sep="") [1] "A4e+08B" "A5e+10B" I want "A400000000" and "A50000000000"

I am using read.table("data.txt", sep="\t") to read in a tab-limited text file. However, two columns of data were read wrongly. read.table converts "+" and "-" in the two columns to 0. I have tried setting other parameters but to no avail. TIA _________________________________________________________________ Get in touch with your inner athlete. Take the

I forgot the reshape equivalent for converting from wide to long format. Can someone help as my matrix is very big. The followin is just an example. > m <- matrix(1:20, nrow=4, dimnames=list(LETTERS[1:4], letters[1:5])) > m a b c d e A 1 5 9 13 17 B 2 6 10 14 18 C 3 7 11 15 19 D 4 8 12 16 20 > as.data.frame(cbind(rep(rownames(m), ncol(m)), rep(colnames(m), each=nrow(m)),

I have problems converting my dataset from long to wide format. Previous attempts using reshape package and aggregate function were unsuccessful as they took too long. Apparently, my simplified solution also lasted as long. My complete codes is given below. When sample.size = 10000, the execution takes about 20 seconds. But sample.size = 100000 seems to take eternity. My actual sample.size is