similar to: Coord_equal in ggplot2

Hi everyone, Hi Hadley, I am a heavy user of coord_equal() in ggplot2 since most of my data is spatial, on x,y coordinates. Everything works. However by enforcing an aspect ratio of 1 for the plotting region, coord_equal() usually wastes a lot of space if the region of interest is not a perfect square. For example: x=runif(10) a=data.frame(x=x*3,y=x) ggplot(data=a, aes(x=x,y=y)) +

Dear all, I'm not getting what I'm doing wrong. The line below from my read.fsa.bin function throws an error when just loading my AFLP package and disappears when I load the zoo package as well. #the line that throws the error Index <- which(Peak == rollmax(Peak, k = 1 + 2 * floor((min(diff(SizeStandard)) * Fs - 1) / 2), fill = -Inf)) #the error Error in UseMethod("rollmax")

Dear useRs, Recently I've discorved ggplot2 and I must say that I really like it, although the documentation still is a working in progress. My first question: How can I change the position of the labels and the text of the labels? With a basic plot I would use axis(2, at = position.of.the.ticks, labels = text.at.the.ticks). Could someone provide me with an example of how to do this with

Dear all, One of the functions that I wrote (ggsave.latex) extents the functionality of a function (ggsave) in another package (ggplot2). Instead of copying all the information I would like to create a link in the helpfile of ggsave.latex to the helpfile of ggsave. I tried \code{\link{ggsave}} and \code{\link{ggplot2::ggsave}}, but neither worked. Both cases gave a 'missing link' warning.

Dear all, I have a list of n matrices which all have the same dimension (r x s). What would be a fast/elegant way to calculate the element wise average? So result[1, 1] <- mean(c(raw[[1]][1, 1] , raw[[2]][1, 1], raw[[...]][1, 1], raw[[n]][1, 1])) Here is my attempt. #create a dummy dataset n <- 3 r <- 5 s <- 6 raw <- lapply(seq_len(n), function(i){ matrix(rnorm(r * s), ncol =

Consider the following situation: we have quantified algal concentrations for a variety of species using many samples at each of three years. It seems to make sense to generate a line plot (matplot-like), with each species plotted as a separate line, with the points connected to emphasize the temporal pattern. The problem: lots of overlapping error bars. The question: from both a

Hi all, I have managed to create a figure on the screen with multiple plots in it. Something like the example below. When I save that with ggsave(), only the last plot gets saved (pPath in the example) instead of the entire figure. Any suggestions how I can save this kind of figures automated? Thanks, Thierry library(ggplot2) pPoint <- qplot(unemploy/pop, psavert, data=economics) pPath

Dear all, I get a bug in the examples of my AFLP package on R-forge (https://r-forge.r-project.org/R/?group_id=1027) but only on the Linux version. The windows version compiles. The Mac version skips the examples and compiles. The strange thing is that the packages compiles on my Ubuntu 10.10 machine with R 2.15.0. Therefore I can't reproduce the error. I have traced the problem at

Dear all, I'm documenting an elaborate analysis as a private package. The package writes all required output to a database. I use a Sweave file to transform all the results from the database into a report. Now I would like to add this Sweave to my package. I'm a bit puzzled on how to do that. It is not a vignette: the data is not available when the package is build and I need to

Dear all, I'm having troubles migrating a large matrix from one system to another. #system 1: Ubuntu 12.04, 64-bit, running R 2.15.2 # do some simulations # save the simulations > save(Output, file = "Simulations.Rdata") #Output is a numeric matrix with 6 columns and about 2M rows. Use ftp to transfer the Simulations.Rdata file to system 2 #system2: Windows XP, 32-bit running

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

Dear all, Could someone point me to a function or algorithm to generate random bivariate binomial data? Some details about what I'm trying to do. I have a dataset of trees who were categorised as not damaged or damaged. Each tree is measured twice (once in two consecutive years). The trees can recover from the damage but the data is clearly correlated. As a (un)damaged tree is more likely

Dear all, My package has Depends: lme4.0 in the DESCRIPTION. I need to extract the fixed effect of a model and their standard errors. I use coef(summary(model)) inside a function to do that. Model is the output of a call to glmer() from the lme4.0 package. coef(summary(model)) throws an error: $ operator is invalid for atomic vectors I have tracked it down to a problem with summary(model)

Dear all, Is it possible to incorporate a nested design in GEE? I have measurements on trees that where measured in two years. The trees are nested in plots. Each plot contains 24 trees. The number of plots is 72. Hence we would expect 2 * 24 * 72 = 3456 data points. A few are missing, so we end up wih 3431 data points. This is what I have tried until now. #assuming independence between trees

Dear All, After having overcome the issue of legends (thanks, Thierry, once more), I am trying to use facetting, but here also I can not find how to do this. I do not want to use qplot, but rather the more flexible options. However, it seems I am doing still something pretty stupid, because I always get an error, even if it seems I am doing everything like the examples. My code is below.

Dear useRs, I'm trying to specify the colour of a factor with ggplot2. The example below gets me close to what I want, but it's missing a legend. Any ideas? Thanks, Thierry library(ggplot2) dataset <- data.frame(x = rnorm(40), y = runif(40), z = gl(4, 10, labels = LETTERS[1:4])) ggplot(data = dataset, aes(x = x, y = y, group = z)) + geom_point(colour = c("red",

Dear all, I'm puzzled by the error I get from R CMD check one of my packages. I'm running R CMD check with the --as-cran flag and it get the error both from running it from the command line as from within Rstudio. On the same machine R CMD check on my GRTS package (https://r-forge.r-project.org/R/?group_id=1027) works fine. Any suggestions on how to fix this? I can send the source code

Dear all, I'm trying to connect to an MSAccess database (ArcGIS personal geodatabase). I keep getting an error about the channel when using sqlQuery(). However, sqlTables() does not complain about the channel and lists all tables in the database. If I try sqlFetch(), then R crashes. I'm happy to hear suggestions on how to solve this. Best regards, Thierry > MDB <-

Hello, I think there is a bug in coord_equal when x s a factor : ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar() ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar() + coord_equal(1/2) David

Dear all, predict.lme() throws an error when the fixed part consists of only an intercept and using newdata. See the reproducible example below. I've tracked the error down to asOneFormula() which returns in this case NULL instead of a formula. Changing NULL instead of ~1 in that function (see below) solves the problem in the case of an intercept only model (m1). It does not solve the problem