I write about applications of data and analytical techniques like statistical modelling and simulation to real-world situations. I show how to access and use data, and provide examples of analytical products and the code that produced them.
Beware that the direction of a principal component can vary depending on the sequence of the original data.
I make a SQLite database of daily observations of Australian security prices, volumes and short positions.
After some experimenting with how to show stock price and volume at the same time, I conclude unsuprisingly that the charts commonly used in finance are pretty much fit for purpose, but alternative presentations have their place too.
Shiny can be an effective platform to quickly build data-intensive web applications that otherwise would not be commercially viable. The rationale for using Shiny at the right time is convenience, cost, and statistical and graphics power.
An animated map of UK Royal Navy ship locations during World War I.
I reproduce the analysis of data from a recently published experiment on the impact on Australians' and New Zealanders' attitudes to overseas aid of being exposed to writing about Chinese aid in the Pacific. Along the way I muse about the Table 2 fallacy, and try to avoid it while still using multiple imputation, bootstrap and adjusting for covariates to slightly improve the original analysis.
I play around with Hamlet's text to set it up for easy data analysis. Hamlet is awesome, this post is really just an excuse to spend time with it; but it does perhaps start to put together something useful about data models for text.
Two huge surveys of Facebook users seem to provide valuable new information on how the world is responding to Covid-19, but I am very unsure about whether they have potential to enable earlier detection of outbreaks.
I try out biterm topic modelling on a free text question in the 2017 New Zealand Election Study about the most important issue in the election.