Between travel and finishing up a couple of projects the past two semesters, I wasn’t able to blog as much as I’d like. Rather than jumping straing into research after the holidays, it seemed like a nice time to blog about my favorite figure from the past semester. It was the striking image associated with this paper, though I can’t seem to find a link for it online, so here it is:
Today I encountered an error in the tidyverse that took me a while to figure out, so I wanted to document it for others. I’m also not sure where this error originates, so I wasn’t positive which package to post it in (or with RStudio) - I’d be happy to submit a bug report if anyone can point me in the right direction :) The Scenario You have a data frame you want to alter using piping, something like this.
Introduction I had wanted to get into R package creation for a while now, but finally got a chance to do so for my lab’s recent Zika paper, Assessing Real-time Zika Risk in the United States. If you’re interested in learning about the content from that paper you can check out the blogpost hosted on the BMC Infectious Diseases blog. I’ve also given an introduction to the package on the rtZIKVrisk github.
Introduction I recently was doing model fitting on a ton of simulations, and needed to figure out a way to speed things up. My first instinct was to get out of the R environment and write CSnippets for the pomp package (more on this in a later blog), or to use RCpp, but I used the profvis package to help diagnose the speed issues, and found a really simple change that can save a ton of time depending on your model.
Introduction This demonstration is part of a requirement for my statistical consulting class at UT Austin. I will go through the basics of bootstrapping time series data using three different resampling methods. Fixed Block Sampling Stationary Block Sampling Model-based resampling Packages Used For this demonstration I will use the following packages: The boot package is the workhorse behind the bootstrapping methods, but the forecast method is used for the time series modeling.