At the College of Idaho, there’s been discussion about visualizing the curriculum as well as understanding the curriculum. Naturally this interests me as a chance to wallow in some complicated data (students are required to complete a major and 3 minors across 4 “peaks” rather than complete courses from a traditional “core”). I thought using R and a Neo4j graph database would be useful (something to look forward to) - but first I needed to get data from the catalogue!
This semester I’m teaching Applied Databases for the first time and have been struggling with some notes and handouts for students; as well as simple, easy to use database interfaces that work well across platforms. I love RMarkdown, and today realized that knitr has an SQL code engine!
Basic Syntax I often give handouts on SQL statements as we learn about them, so I need a nice way to show commands.
When I lived in Tacoma, I was running quite a bit. Since I moved away my training has become much more irregular, but I thought it would be interesting to take the Tacoma data from my current Garmin Forerunner 220 a take a look.
Data Prep The Garmin stores data in .fit format, but gpsbabel can translate to a nicely structured GPX file, which is what I’ll start with here.
Recently some diversity stats have been circulated around the College of Idaho, and as new Idahoan I wondered about the general diversity (or lack thereof) in Idaho. I remembered seeing this post a while back about mapping in R, so I went to work.
Shapefiles First, we need shapefiles for both the Idaho country boundaries and census tracts, which will give finer detail for data. These can be downloaded from the [US Census Bureau] (https://www.
Introduction Recently a student in another course came to my office looking for someone “who could explain the Monte Carlo simulation” to her. I was caught a bit off-guard since (a) it was 10 minutes before my geometry class and (b) there is no single Monte Carlo simulation.
After a brief discussion, I found out she wanted to predict stock prices using Monte Carlo simulation, but she thought that the Monte Carlo simulation provided the prediction - she couldn’t say how the actual predictions were being made which is the crucial part.
This is the second part of two posts about using ggplot to visualize geometric transformations in the complex plane.
Inversions For this post we’ll focus on inversions, having already covered rotations, translations, and reflections. An inversion can be thought of as a reflection across a circle, the the inside of the circle gets flipped to fill the plane outside the circle and the outside is flipped into the circle. This is a more complicated transformation, both to visualize and to perform mathematically, but is essential to geometry.
I’m currently teaching a Geometry course, and wished there was an easy way to illustrate geometric transformations for my students. I’m sure they’ll agree I’m not a great artist.
Since R is my preferred way to draw any picture, I thought “Let’s use GGPlot to show transformations!”
For those not versed in geometry, we would like to easily visualize translations (shifts along a vector), rotations, and dilations of points (or collections of points) in the complex plane.
Besides lots of family time and the creation of this blog/website, this is what I’ve been thinking about over the winter break.
Background As part of my research in emergent reducibility, I’ve had to face a binary classification situation with severe class imbalance. Among brute-force searches, it seems that there’s roughly 1 case of emergent reducibility (what I’m looking for) for every 1 million irreducible cubic polynomials. It is known that there are infinitely many cubic polynomials with emergent reducibility.