Version 7.0.0 of drake just arrived on CRAN, and it is faster and easier to use than previous releases. install.packages("drake") Recap Data analysis can be slow. A round of scientific computation can take several minutes, hours, or even days to complete. After it finishes, if you update your code or data, your hard-earned results may no longer be valid. How much of that valuable output can you keep, and how much do you need to update?
A few months ago, I wasn’t sure what to expect when looking at fluorescence microscopy images in published papers. I looked at the accompanying graph to understand the data or the point the authors were trying to make. Often, the graph represents one or more measures of the so-called co-localization, but I couldn’t figure out how to interpret them. It turned out; reading the images is simple.
Sharing data sets for collaboration or publication has always been challenging, but it’s become increasingly problematic as complex and high dimensional data sets have become ubiquitous in the life sciences. Studies are large and time consuming; data collection takes time, data analysis is a moving target, as is the software used to carry it out.
The drake R package is not only a reproducible research solution, but also a serious high-performance computing engine. The package website introduces drake, and this technical note draws from the guides on high-performance computing and timing in the drake manual. You can help! Some of these features are brand new, and others are newly refactored.
Version 2.0 of my data set validation package assertr hit CRAN just this weekend. It has some pretty great improvements over version 1. For those new to the package, what follows is a short and new introduction. For those who are already using assertr, the text below will point out the improvements. I can (and have) go on and on about the treachery of messy/bad datasets.
