search for: visualise

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on motion charts at LondonR in early October. Since then we have developed the package further to include also interfaces to Google Maps, Geo Maps, Tables, Tree Maps and Anno...

Hi all, Version 0.2.2 of the googVis package has been released on CRAN and will be available from your local CRAN mirror in due course. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google. We presented our initial ideas on motion charts at LondonR in early October. Since then we have developed the package further to include also interfaces to Google Maps, Geo Maps, Tables, Tree Maps and Anno...

Hi all, Version 0.2.4 of the googVis package has been released on CRAN and will be available from your local CRAN mirror soon. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google Since the last version a lot of work has been carried out under the bonnet to make googleVis more flexible and easier to use. The new version no longer requires to install the package into a folder with...

Hi all, Version 0.2.4 of the googVis package has been released on CRAN and will be available from your local CRAN mirror soon. googleVis provides an interface between R and the Google Visualisation API. The functions of the package allow users to visualise data stored in R with the Google Visualisation API without uploading their data to Google Since the last version a lot of work has been carried out under the bonnet to make googleVis more flexible and easier to use. The new version no longer requires to install the package into a folder with...

Hello my friends, I have put together some visualisations of the LLVM source code because I think it is great to see how much work has gone into LLVM to date. Code Visualisation - Orion Transfer Ltd http://www.oriontransfer.co.nz/research/code-visualisation These are based of the 2.8 source code up until a few days ago. I hope this is of enjoyment for some of you. Kind regards, Samuel

Dear list, I wish to visualise some 4D data as a kind of colour / translucent cloud in 3D. I haven't seen such plots in R (but perhaps I missed a feature of rgl). The easiest option I found would be to export the data in povray's df3 (density file) format and visualise it with povray. The format specification baffles me...

...n addition XLA does not have an effect on the model p=0.88. These results are not logic when I look at my data and therefore I think I am missing something in the model? It would be very helpful if someone has some tips on this? In addition, I was wondering if somebody knows what is the best way to visualise this kind of data (nested data)? Thank you very much for any help and time. [[alternative HTML version deleted]]

...n. Reply from Jeffery Horner contained links to the revolution-computing.com webpage which had information pertaining to the Bay Users R group Meetup on Web Dashboards with R. I have a very specific project that I need to implement. I wish to use the graphics capabilities provided by Adobe FLEX to visualise outputs from R. For example: I would like to fit a regression model to a dataset in R and provide a FLEX interface wherein, a user may manipulate a slider which would increase or decrease a parameter estimate (between the confidence intervals of the estimate) of a variable to see the effect on the...

Hi, this may sound like a very stupid question and perhaps it is, so I apologise in advance if anyone feels bored by it. I have done some principal component analysis with prcomp and what I would like to do is to visualise the effect of a principal component, i.e. show a graph of my data and then show how that graph would change if I go, let's say, one standard deviation, in either direction. Kind of original graph plus graph with +1 s.d. plus graph with -1 s.d. I know how to plot several graphs together but...

Hi all R-helpers!!! I have only one small question: Which visualisation method would you suggest for results of function cmeans {e1071}? Thanks! Branimir K. Hackenberger

...h. Have a look and see if that's what you want. > -----Original Message----- > From: llvmdev-bounces at cs.uiuc.edu > [mailto:llvmdev-bounces at cs.uiuc.edu] On Behalf Of Edmund Grimley-Evans > Sent: 16 July 2010 11:25 > To: llvmdev at cs.uiuc.edu > Subject: [LLVMdev] How to visualise Clang optimisation phases > > When I'm trying to understand how or why the back end does > something I've found the -view-* options to llc to be useful. > It's not ideal, because "clang" doesn't take those options, > so I have to invoke first "clang...

Is someone working on a 3d vizualisation set of functions ? I read dream dream dream in PROJECTS about 3D GRAPHICS. When I want to visualize 3d data, I use Geomview but with a dataset greater than 50x50 that's too hard for my poor computer. I know a little R but nothing with S+, are the plan to emulate S+ or to have R functions to visualize 3d data ? -- Hervé Dréau Interne en Médecine hdreau

I am wanting to plot a series of wind vectors onto a contoured area map for a series of weather stations (eg arrows showing wind speed/direction for a particular time snapshot), can someone please advise me how best to approach this? My desired end point is to be able to link a time series of such data together so that I will in effect have a "movie" displaying the evolution of these

I sent a mail last week asking for some advise in relation to displaying wind vectors on a contour map of a region. Whilst I have had some useful feedback relating to the second part of this question (namely how to animate a time series of still frames), I haven't recieved any advise on how I might create the still images of the spatially distributed wind vector data at any given time point.

Hello R users, I'm using SOM() to cluster a gene expression data set the syntax i used was dataGrid <- c(somgrid(xdim = 3, ydim = 3, topo = c("rectangular","hexagonal"))) dataClusters <- SOM(dataMatrix, grid = dataGrid) plot(dataClusters) it seems that this works just fine but the thing i can't figure out is how to determine where each data point has been

Dear List, A student in the Department where I work would like to produce a graphic similar to this one: http://image.guardian.co.uk/sys-files/Guardian/documents/2009/09/16/Public_spending_160909.pdf Does anyone know if the figure in the pdf can be generated in a specific software application for example? Any suggestions would be most gratefully received by the student concerned. Many thanks,

When I'm trying to understand how or why the back end does something I've found the -view-* options to llc to be useful. It's not ideal, because "clang" doesn't take those options, so I have to invoke first "clang" and then "llc", and sometimes that combination gives different final code from invoking just "clang", but usually I can construct

> Having a look at clang's source, you can find in > "lib/CodeGen/BackendUtil.cpp" the functions where clang builds the > passes to emit code. The optimization passes used are there and you can > simulate them via the "opt" utility, by running each pass one at a time. > LLVM also declares standard module/function passes on >

...t understand how to transform my equation to a generalised equation. Is there an option for nls to choose family = poisson? Lower in the mail the code with the model and visualisations I use to check my results. I also copied the test dataset from my txt file. I am using R 2.15 and Rstudio to visualise it. plot(FR~N0) x <- nls(FR~(exp(i+j*N0)/(1+exp(i+j*N0)))*(k*N0/(k+N0)),start=list(i=0.02,j=0.002,k=6)) summary(x) hatx <- predict(x) lines(spline(N0,hatx)) N0 FR 10 2 10 3 10 2 10 4 10 2 10 2 10 1 10 2 10 2 10 2 20 2 20 3...

Dear list users How is it possible to visualise both a linear trend line and a quadratic trend line on a plot of two variables? Here my almost working exsample. data(Duncan) attach(Duncan) plot(prestige ~ income) abline(lm(prestige ~ income), col=2, lwd=2) Now I would like to add yet another trend line, but this time a quadratic one....