similar to: Array analogue of row and col

Dear List, I have a set of distributions recorded at an equal interval of time and I would like to plot them as series of horizontal histograms (with the x-axis representing time, and y-axis representing the bins) since the distribution shifts from unimodal to multimodal in several occasions. What I would like to see is something close to a violinplot, but I do not want a kernel density

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I am running this small program: x <- factor(c("A","B","A","C")) y <- c(1,2,3,4) w <-data.frame(x,y) if (w$x=="A"){ w$z=1 } w And I obtain: x y z 1 A 1 1 2 B 2 1 3 A 3 1 4 C 4 1 And not x y z 1 A 1 1 2 B 2 NA 3 A 3 1 4 C 4 NA Like I should obtain. What am I doing wrong? Please notice that I get a warning approximately saying -

Hi Im working on my master thesis, I need to get a plot currents distributed over a surface. I have the data listed in a dataframe with the x coordinates, y coordinates and the value. Im using the persp function but I’ve not found out how to put the data into the z1 variable es <-read.table("RDatalog_pass_dev_1_wafers_1_8V.txt",header=T,sep="\t") x1 <-

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Dear R-helpers, I am running metaMDS in the vegan package, which uses isoMDS in MASS, to perform Nonmetric Multidimentional Scaling (NMDS). I have seen some authors report a p-value for the NMDS ordination based on randomization of the dataset. As I understand it this is meant to compare the stress in your dataset to multiple runs of randomized data. I do not see a way to perform such a test in

Hi. Here is a piece of cpp code. It works, but I do not understand the rational for the use of "R::dpois" to call the function dpois since in the examples I have always found directly "dpois" or "Rcpp::dpois" that both do not work in my code. Could anyone be so patient to explain me why should it be like that? Thaks a lot, Enrico #include <Rcpp.h> using

I would like to write a fast version of a function which contains slow loops that I cannot vectorize. I would like to rewrite it in C, but in the loops I need to call a few (two) R functions. Which is the best way to do so? I have read that one can call a C library which import all the R functions (R.h, is it?). Is that efficient? I tried to have a look into the internal C code to find just the

Dear all, I have a function f(x) which return a list as result. $T1 [1] 0.03376190 $T2 [1] 0.04725 $T3 [1] 0.3796071 $T4 [1] 0.3713452 $T5 [1] 0.4523651 $T6 [1] 0.4575873 I now find the result for a vector of x values at one time. I want to store the reuslt for each xi value in a column of a matrix x <- seq(0,1, by=0.1) result <- matrix(0, nrow=6, ncol=length(x)) for (i in

Hello. I am creating an R package that I'd like to submit to CRAN (OS Windows XP). How do I distinguish among 'public' functions, e.g., those that are intended to be called by users of the package and for which I am providing documentation & examples, and 'private' functions, which are used internally by the 'public' functions, but for which I do not wish to

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Hello, I have build a syntax to find out if a given substring is included in a larger string that works like this: d1$V1[regexpr("some text = 9",d1$V2)>0] <- 9 and this works all right till "some text" contains standard ASCII set. However, it does not work when accents are included as the following: d1$V1[regexpr("some t?xt = 9",d1$V2)>0] <- 9 I have

Hi, I have the following dataset (simplified for example). __DATA__ 300.35 200.25 104.30 22.00 31.12 89.99 444.50 22.10 43.00 22.10 200.55 66.77 Now from that I wish to do the following: 1. Compute variance of each row 2. Pick top-2 row with highest variance 3. Store those selected rows for further processing To achieve this, I tried to: a) read the table and compute variance for each row,

hi, i'm trying to perform a clustering on a big dataframe the code is this: print("load required R packages") require(spgrass6) require(cluster) gmeta6 <- gmeta6() print("read in our 7 raster files from GRASS") x <- readFLOAT6sp(c ("er","crosc","longc","slope","profc","minic","maxic"))