Links to slides of 10+ talks at R Users Groups in Australia are provided below. Slides of the talks are downloadable at the links, including R codes if any. MelbURN: Melbourne Users of R Network: Experiences with using R in … Continue reading →

R news and tutorials contributed by (552) R bloggers

In early May I had the opportunity to attend a workshop on using high performance computing in R hosted at Nimbios. I’ve been meaning to write a summary of the meeting ever since but got sidetracked by various other projects. Since a collaborator recently asked for meeting notes I finally took the time to write

In my last post, I described three situations where the average of a sequence of numbers is not representative enough to be useful: in the presence of severe outliers, in the face of multimodal data distributions, and in the face of infinite-variance distributions. The post generated three interesting comments that I want to respond to here.First and foremost, I...

Hong Ooi talks about some of the more interesting projects that he has used R for in the last year. These include fitting models for mortgage loss given default, a Monte Carlo application for stress-testing loan portfolios (in combination with Excel an...

Experimenting with a tty Connection for R I presented twice at this years useR!. The first was a regular talk on the tty connection patch for R. The talk went smoothly, despite a live demonstration using the DLP-232PC data acquisition module (datasheet). The slides for this presentation are here: shotwell-tty-useR-2011.pdf The image above is a

Handling Large Data with R The following experiments are inspired from this excellent presentation by Ryan Rosario: http://statistics.org.il/wp-content/uploads/2010/04/Big_Memory%20V0.pdf. R presents many I/O functions to the users for reading/writing data such as ‘read.table’ , ‘write.table’ -> http://cran.r-project.org/doc/manuals/R-intro.html#Reading-data-from-files. With data growing larger by the day many new methodologies are available in order to achieve faster I/O operations.

Wilem Ligtenberg – GPU computing and R Why GPU computing – theoretical GFLOPs for a GPU is three times greater than a CPU. Use GPUs for same instruction multiple data problems (SIMD). Initially GPUs were developed for texture problems. For example, a wall smashed into lots of pieces. Each core handled a single piece. CUDA