# Visualization

### Markov Transition (Animated) Plots

This is a quick post intended for animating how the transition matrix of a Markov chain changes between larger time steps, as well as showing the probability of the chain being in any specified state over time. This post uses the tidyverse, along with `gganimate`.

``````> library(tidyverse)
> library(magrittr)  ## using some aliases not loaded by default
> library(gganimate)``````

### Spotify Cross-Playlist Predictions, Part 1

This is the first of probably two posts detailing the construction of an RShiny app. The app in question is meant to take data from two Spotify playlists, make recommendations for tracks from one – which I’ll call the “target” playlist – based on the contents of another – the “reference” playlist. I don’t expect this to be comparable in ability to Spotify’s own system (or anything else, really), but it seems like it should be interesting.

My code is here.

### Formatting With ggtext Example

This is a quick example regarding the `ggtext` package. It’s one of the many packages that extends `ggplot2`, with this one having a focus on adding and formatting text in graphs. The particularly interesting thing for me is that it allows Markdown and other formatting of the labels in a graph.

### Looking Normal(ly Distributed)

Among all probability distributions, the normal distribution is probably the most well-established and well-characterized. The importance of things like the central limit theorem and the normality assumptions in linear regression highlight it well.

One of the more interesting ones is the fact that you can approximate a binomial distribution with a normal one. Using a continuous distribution to approximate a discrete one feels a little weird, and there are certain assumptions needed for it to work, but it raises an interesting question – how normal can other distributions look?

### An (Animated) Example of Bayesian Updating

Bayesian statistics is centered on constructing certain assumptions about how the probability of an event is distributed, and then adjusting that belief as new information comes in. It can be more involved to construct a Bayesian model as opposed to the “look at many things in aggregate” approach used in frequentist statistics. But it has nice properties, and we’ll take a look at them in a real albeit fairly unimportant context: the Pokemon video games.