Sitting with a data set with too many variables? The SVD can be a valuable tool when you’re trying to sift through a large group of continuos variables. The image is CC by Jonas in China.

It can feel like a daunting task when you have a > 20 variables to find the few variables that you actually “need”. In this article I describe how the singular value decomposition (SVD) can be applied to this problem. While the traditional approach to using SVD:s isn’t that applicable in my research, I recently attended Jeff Leek’s Coursera class on Data analysis that introduced me to a new way of using the SVD. In this post I expand somewhat on his ideas, provide a simulation, and hopefully I’ll provide you a new additional tool for exploring data. Continue reading →

It’s not always easy getting the right size. The image is CC by Kristina Gill.

A vital part of statistics is producing nice plots, an area where R is outstanding. The graphical ablility of R is often listed as a major reason for choosing the language. It is therefore funny that exporting these plots is such an issue in Windows. This post is all about how to export anti-aliased, high resolution plots from R in Windows. Continue reading →

A good looking table matters!

This tutorial is on how to create a neat table in Word by combining knitr and R Markdown. I’ll be using my own function, htmlTable, from the Gmisc package.

Update: With the latest RStudio verions getting tables from R into Word is even easier, see my new post on the subject.

Background: Because most journals that I submit to want the documents in Word and not LaTeX, converting my output into Word is essential. I used to rely on converting LaTeX into Word but this was tricky, full of bugs and still needed tweaking at the end. With R Markdown and LibreOffice it’s actually rather smooth sailing, although I must admit that I’m disappointed at how bad Word handles html. Continue reading →

Offering – the backbone of open source. The image is CC by TenthMusePhotography

This is part II of the previous package creation post. I’ve created my own CRAN repository since I wanted a quick upload. Perhaps in the future I’ll post on the main server but right now it’s actually rather convenient, although it took me a while to figure out exactly how this works. Continue reading →

A nice package can be both beautiful and functional. The image is CC by MIAD Communication Design.

Inspired by this post by Szilard Pafka I decided to do a similar adventure in a Windows environment and see what problems I run into.

Start by installing Eclipse & StatET, the installation can sometimes cause some annoyances. I’ve covered a lot of them in my previous post. Continue reading →

The two IDE that I use for R are RStudio and Eclipse with StatET. They complement each other nicely, RStudio works out of the box while I previously shown how to get Eclipse & StatET going, you can find it here, which is slightly challanging.

RStudio

I use RStudio for all my statistics where I don’t want to create functions or more advanced programming. It’s great since it allows me to get immediate help, code completes the initiated variables. The settings are simple and you hardly need to do anything. Continue reading →