similar to: how to generate pairwise plots with data frames - tia

Hi - I'm curious if there is a way to get access to the location of the calling script within R. I found one way of accessing it from this thread, https://stat.ethz.ch/pipermail/r-devel/2008-April/048914.html, which recommends using either: parent.frame(2)$ofile Or FILE <- (function() { attr(body(sys.function()), "srcfile") })()$filename However, those suggestions only

I have a list of file names, and a list of data frames contained in those files. mynames <- list.files() mydata <- lapply(mynames, read.delim) Every file contains two columns. > colnames(mydata[[1]]) [1] "Name" "NumReads" > colnames(mydata[[2]]) [1] "Name" "NumReads" I can set the colnames easily enough with a for loop. for (i in

Dear list I am comparing the results of several different experimental setups. With kruskal.test() I can test if there is any difference at all in any of them, if I understand it correctly. But now, when there is a difference, how do I generate a (half-) table of pairwise comparisons, using e.g. wilcox.test(), to find the ones where the difference actually occurs. I guess I don't have to

Hi, Given names <- c("a", "b", "c", "d"). I would like to generate "a vs. b", "a vs. c", "a vs. d", "b vs. c", "b vs. d", "c vs. d" Thanks [[alternative HTML version deleted]]

I'm working with a very large matrix ( 22k rows x 2k cols) of RNA expression data with R v.2.5.0 on a RedHat Enterprise machine, x86_64 architecture. The relevant code is below, but I call a function that takes a cluster of this data ( a list structure that contains a $rows elt which lists the rows (genes ) in the cluster by ID, but not the actual data itself ). The function creates two

Hello, everybody: I'm back to share a long piece of example code. Because I had trouble understanding the role of par() and margins when customizing barplots, I have struggled with this example. In case you are a student wanting to place legends or textual markers in the outer regions of a barplot, you might run these examples. There are a few little things I don't quite understand.

Let's say I have the data frame 'dd' below. I'd like to select one column from this data frame (say 'a') and keep its name in the resulting data frame. That can be done as in #2. However, what if I want to make my selection based on a vector of names (and again keep those names in the resulting data frame). My attempt is #4 but doesn't work. dd <- data.frame(a =

Hi - When I'm trying to read in a text file into a labeled character array, the memory stamp/footprint of R will exceed 4 gigs or more. I've seen this behavior on Mac OS X, Linux for AMD_64 and X86_64., and the R versions are 2.4, 2.4 and 2.2, respectively. So, it would seem that this is platform and R version independant. The file that I'm reading contains the upstream regions

Hello,   I could not find a clear solution for the follow question. please allow me to ask. thanks   mynames=cbind(c('a','b'),c(11,22)) lst=list(a=c(1,2), b=5) now I try to combine mynames and lst: a  1  11   a  2  11 b  5  22   thanks jian   [[alternative HTML version deleted]]

Hi - I saw your posting on the R-help mailing list. Were you ever able to get this working? did you end up switching to use the rsge library? I'm trying to do the same, and not having very much luck getting it going. Thanks! Peter Waltman [[alternative HTML version deleted]]

specifically, imagine we have: fooStack <- list() for ( i in 1:5 ) fooStack[[i]] <- list() and we have a function: fooModifier <- function( foo ) { foo$bar <- "bar" } then, if we invoke fooModifier, i.e.: fooModifier( fooStack[[ 1 ]] ) the $bar elt is only set in the scope of the function, and if we use the

Hi, #make example data dat <- data.frame(matrix(rnorm(15),ncol=5)) colnames(dat) <- c("ab","cd","ef","gh","ij") If I want to get a subset of the data for the middle 3 columns, and I know the names of the start column and the end column, I can do this: mysub <- subset(dat,select=c(cd:gh)) If I wanted to do this just on the column names,

Hi - Not a mission critical issue, but still highly annoying. I just upgraded R to 2.10.0 (the binary for Ubuntu karmic) and the tab completion facility now inserts a space after every completed term (something it didn't do in 2.9.0 or 2.9.2). It wouldn't be an issue so much if it weren't for the fact that it screws up tab-completing the next term, i.e. if I have a list of lists of

Hi - Any help would be greatly appreciated. I'm loading a list variable that's stored in an .RData file and would like attach it. I've used attach( <file_name> ), but that only lets me see the variable that's stored in the file. As the variable name is of the form "comp.x.x", I've tried using attach( ls( pat="comp" ) ), but get an error as ls()

Hi! I have been working several years with R but it's my first public question. I hope I'll be clear :) . This question is related to obtaining letter-based representation of non-parametric pairwise comparisons. I have a dataframe with this structure (but with quite more rows and cols): A B C factor 1 2 2 one 2 1 2 one 2 2 3 two 2 3 2 two 1 4 2 three 9 8 1 three I have no normality,

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Dear R-help, I have a question about pairwise.t.test and adjustment for multiple comparisons for paired data points. I have the following data: n=c("x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "y", "y", "y", "y", "y", "y",

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000

Dear list, When I use VennDiagram package, I got a error as follow: venn.plot <- draw.pairwise.venn( area1 = 3186, area2 = 325, cross.area = 5880); Error in draw.pairwise.venn(area1 = 3186, area2 = 325, cross.area = 588) : Impossible: cross section area too large. Does anyone have suggestion? Thank you.

I am familiar with pairwise t-tests, corrections for multiple testing, etc. however I have a problem whose answer I have not found after extensive R-help archive and Google searching. What I have done in the past: I have N items which are measured, exposed to a condition, and then measured again. I wish to know if the condition changes the items so I can perform a t-test. Better yet I can