search for: sdcols

Greetings, I couldn't find any existing function that would allow me to scan a matrix and eliminate invariant rows and columns so I have started to write a simple routine from scratch. The following code fails because the array index goes out of bounds for obvious reasons you'll see shortly. Start with some data x <- read.table("myex.dat",header=T) x v1 v2 v3 v4 v5 id 1

...which(colnames(colData) %in% colIDs) lockBinding('colIDs', internals) # Assemble the pseudo row and column names for the LongTable .pasteColons <- function(...) paste(..., collapse=':') rowData[, `:=`(.rownames=mapply(.pasteColons, transpose(.SD))), .SDcols=internals$rowIDs] colData[, `:=`(.colnames=mapply(.pasteColons, transpose(.SD))), .SDcols=internals$colIDs] return(.LongTable(rowData=rowData, colData=colData, assays=assays, metadata=metadata, .intern=internals)) } I have also defined a subset...

Hi everyone, I have a data.table called "data" with many columns which I want to group by column1 using data.table, given how fast it is. The problem with looping a data.table is that data.table does not like quotations to define the column names (e.g. "col2" instead of col2). I found a way around which is to use get("col2"), which works fine but the

I have a large data.frame Z (2,424,185,944 bytes, 10,256,441 rows, 17 columns). I want to get the result of table(aggregate(Z$V1, FUN = length, by = list(id=Z$V2))$x) alas, aggregate has been running for ~30 minute, RSS is 14G, VIRT is 24.3G, and no end in sight. both V1 and V2 are characters (not factors). Is there anything I could do to speed this up? Thanks. -- Sam Steingold