Introduction Setup The easy case (all eigenvalues are real) The hard case (complex eigenvalues) Demonstration Conclusions References Introduction Lately, I’ve been stuck in getting an intuition for exactly what is going on when a real matrix has complex eigenvalues (and complex eigenvectors) accordingly. After consulting various sources, and playing around with some examples, I think I have a grasp on what’s going on, and translating the math into an interpretation in the original space.
Introduction My Use Case Workflow Building the Docker image Uploading the docker image to Docker Hub Setting up Travis to use the Docker image References Introduction The below summarize the workflow I’ve converged on, after reading through various tutorials on Docker, examples, etc.
If you’re here, I presume you have some interest in R package development and/or using Docker, which is a tool for containerizing an environment for running software.
Overview Setup Initial data examination Injuries by date Injuries by cause Overview This is an exploration of the TidyTuesday dataset on “Amusement Park Injuries”, done as part of the Wednesday coding clinic for Research Bazaar Gainesville.
Setup ## load packages library(tidyverse) ## ── Attaching packages ─────────────────────── tidyverse 1.2.1 ── ## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2 ## ✔ tibble 2.1.3 ✔ dplyr 0.8.3 ## ✔ tidyr 1.
Agenda Resources Installation and Setup Backup option Data Formats Determine embedding dimension using Simplex Projection Identify nonlinearity using S-map Multivariate Models Convergent Cross Mapping Surrogate Analysis with CCM Extra topics These are the notes for the rEDM tutorial I gave at the November 13-15 Nonlinear Dynamics and Fisheries Workshop at the NMFS Southwest Fisheries Science Center in Santa Cruz.
Agenda Time 900-915 set up computers 915-930 data formats 930-945 simplex, plotting rho vs.
While on my visit to the University of Nebraska, Lincoln, I had the pleasure of taking over Chris Chizinski’s R class on Friday (2018-11-02).
I demo’d a few things about setting up RStudio, using RStudio packages and the here package, and then walked through a workflow of doing data analysis, converting code into functions, and writing scripts and functions to be more accessible for readers.
For reference, here are my slides.
For my rEDM package, I’ve been using the pkgdown package to build a website comprising all the documentation and vignettes, for easy reference from a web browser.
The normal workflow for this is something like:
Make updates to the package. Run pkgdown::build_site() to generate the website files into a docs folder. Commit changes and upload to GitHub. Use GitHub Pages, configured to source the files from the docs folder on the master branch.
Motivation What I used to do Why use an R package? How-to Guide Requirements Tutorial Setup Workflow Bonus steps Other Readings Motivation I’ve been wondering about the best way to organize (reproducible) research projects in R for a while now. I figured this might be a good spot to write up some thoughts.
What I used to do Initially my projects would consist of just a few R files that separate out functions from a main script that calls the functions.
Introduction Package Setup Data Forecasting Generate data to plot Figure Introduction Since I’m writing R code to make certain figures for this website, I thought I could go ahead and annotate some of it in R markdown to serve as blog posts.
Package Setup library(tidyverse) ## ── Attaching packages ─────────────────────── tidyverse 1.2.1 ── ## ✔ ggplot2 3.2.1 ✔ purrr 0.3.2 ## ✔ tibble 2.1.3 ✔ dplyr 0.