Buried in the Toronto Economic Bulletin (warning: Excel document) is a column listing the number of passengers passing through Pearson International Airport (YYZ) each month, going back more than fifteen years. There’s a story to tell in the forecast, too:

Flight data are a great forecasting example because they display such clear seasonal patterns, in this case peaking in the summer months and falling off in the winter. R has excellent tools for working with time series data and whipping up simple forecasts like this one. But there’s some friction with the modern tidyverse tools, because the latter expect a data.frame as the common interchange format.

In this post, I’ll outline an approach to fitting many time series models using the tidyverse tools, including model selection for out-of-sample performance. To ease the transition between these two worlds I make extensive use of list columns and the broom package.

Last summer I landed a few patches in Emacs’s ledger-mode that make it easier to use with alternative implementations of Ledger, such as hledger. Since the competing hledger-mode garnered some attention last week on Hacker News, I thought these new features might be worth highlighting to those interested in plain-text accounting in Emacs.

A few years ago I published a post outlining how to make nice-looking choropleth maps in R, and this piece still draws a reasonable share of my hits each month. Unfortunately, some of the techniques I used at the time are now quite out of date, and I was starting to feel bad for anyone taking my advice.

As of today the post has received a makeover, and takes a more modern approach. For any returning readers, the changes are explained in a series of HTML <ins> tags — which I have only recently discovered.

Some time ago I discovered a very surprising performance issue while working with the Rcpp package, and thought I’d share the example I discovered.

If you’ve used the clocking features of org-mode, you’re no doubt familiar with the clock table, which allows you to summarise time spent on different tasks. This is great for getting an overview of projects, but it’s not a very flexible tool if you want to have a more detailed idea of how you spend your time.

At this point I’ve accumulated about a year’s worth of clocked work time in org, and while clock tables have served me well so far, eventually I just wanted to get my data into R or Python for more minute analysis, and charts like the following:

However, I haven’t come across a reliable¹ way to get individual clock entries out of org-mode files and into a more widely readable format. So I’ve written one.