As a testament to my obsession with progress bars in R, here is a quick investigation about the overhead cost of drawing a progress bar during computations in R. I compared several approaches including my pbapply and Hadley Wickham’s plyr.
As of today, there are 20 R packages that reverse depend/import/suggest (3/14/3) the pbapply package. Current and future package developers who decide to incorporate the progress bar using pbapply might want to customize the type and style of the progress bar in their packages to better suit the needs of certain functions or to create a distinctive look. Here is a quick guide to help in setting up and customizing the progress bar.
The pbapply R package adds progress bar to vectorized functions, like
lapply. A feature request regarding progress bar for parallel functions has been sitting at the development GitHub repository for a few months. More recently, the author of the pbmcapply package dropped a note about his implementation of forking functionality with progress bar for Unix/Linux computers, which got me thinking. How should we add progress bar to snow type clusters? Which led to more important questions: what is the real cost of the progress bar and how can we reduce overhead on process times?
The title says it all. I wrote this piece about Publication Viability Analysis pondering about a pattern that I observed while looking at Hungarian ecologists publication output through time using the Web of Science database (the original post is in Hungarian).
This post was prompted by this blog about using the cranlogs package by Gabor Csardi. But my own interest as long time package developer dates back to this post by Ben Bolker. I like to see that my packages are being used. So I thought why stop at counting downloads and plotting the past. Why not predict into the future?
I was invited to represent ABMI at the Multi-taxa Monitoring in North America symposium, North American Congress for Conservation Biology, Madison, Wisconsin, July 18, 2016. The symposium was organized by Michael Lucid (Idaho Department of Fish and Game). It was great to see all the good work happening in North America, and the commitment to push the agenda of multi-taxa monitoring against critics and scarce funding (of course Alberta ‘has all the oil money’).
As I was preparing for an R intro course I came up with the idea of creating a fake data set that is stuffed full of all the conceivable errors one can imagine. Just in case my imagination falls short, I’d appreciate all the suggestions in the comments so that I can incorporate more errors.
Adding progress bar to '*apply' functions
Community ecology package
Resource selection (probability) functions for use-availability data
Multivariate data handling with S4 classes and sparse matrices
Analyzing wildlife data with detection error
Data cloning and MCMC tools for maximum likelihood methods
Hierarchical models made easy with data cloning
Population viability analysis with data cloning
Models and data sets for the study of species-area relationships
Multivariate data handling in ecology and biogeography
Dénes, F., Sólymos, P., Lele, S. R., Silveira, L., and Beissinger, S., 2016. Biome scale signatures of land use change on raptor abundance: insights from single-visit detection-based models. Journal of Applied Ecology, xx:xx–xx. — journal website.
Nordell, C. J., Haché, S., Bayne, E. M., Sólymos, P., Foster, K., Godwin, C. Krikun, R., Pyle, P., and Hobson, K. A., 2016. Within-site variation in feather stable hydrogen isotope (δ2Hf) values of boreal songbirds: implications for assignment to molt origin. PLoS ONE, xx:xx–xx. — journal website.
Mahon, C. L., Holloway, G., Sólymos, P., Cumming, S. G., Bayne, E. M., Schmiegelow, F. K. A., and Song, S. J., 2016. Community structure and niche characteristics of upland and lowland western boreal birds at multiple spatial scales. Forest Ecology and Management, 361:99–116. — journal website.
Bayne, E., Leston, L., Mahon, C. L., Sólymos, P., Machtans, C., Lankau, H., Ball, J., Van Wilgenburg, S., Cumming, S. G., Fontaine, T., Schmiegelow, F. K. A., and Song, S. J., 2016. Boreal bird abundance estimates within different energy sector disturbances vary with point count radius. Condor, 118:376–390. — journal website — fulltext PDF.
Sólymos, P., and Lele, S. R., 2016. Revisiting resource selection probability functions and single-visit methods: clarification and extensions. Methods in Ecology and Evolution, 7:196–205. — journal website — fulltext PDF —
detect R package — GitHub site.