I'm not analyzing COVID data, though I'm impressed with Ontario's open data

I’m neither an epidemiologist nor a medical doctor. So, no one wants to see my amateur disease modelling.

That said, I’ve complained in the past about Ontario’s open data practices. So, I was very impressed with the usefulness of the data the Province is providing for COVID: a straightforward csv file that is regularly updated from a stable URL.

Using the data is easy. Here’s an example of creating a table of daily counts and cumulative totals:

data_source <- "[data.ontario.ca/dataset/f...](https://data.ontario.ca/dataset/f4112442-bdc8-45d2-be3c-12efae72fb27/resource/455fd63b-603d-4608-8216-7d8647f43350/download/conposcovidloc.csv)"
covid_data <- read_csv(data_source) %>% 
  select(ACCURATE_EPISODE_DATE) %>% 
  rename(date = ACCURATE_EPISODE_DATE) %>% 
  group_by(date) %>% 
  summarise(daily_count = n()) %>% 
  mutate(cumulative_count = cumsum(daily_count))

From there we can make some simple plots to get a sense of how the case load is changing.

A line graph shows the number of new daily COVID-19 cases in Ontario rising sharply from February to mid-April 2020, with a peak followed by a decline.

A line graph shows the cumulative COVID-19 cases in Ontario over time from January to April 2020 on a logarithmic scale.

And, I’ll leave it at that, at least for public posting πŸ€“

What is Real by Adam Becker is a great book on the measurement problem in quantum physics. Becker writes very clearly about the actual physics of the problem and the fascinating history. Such a clear example of how science is a human endeavour with politics and personalities πŸ”­πŸ“š

Simple brew tea shortcut

Since I’m mostly stuck inside these days, I find I’m drinking more tea than usual. So, as a modification of my brew coffee shortcut, I’ve created a brew tea shortcut.

This one is slightly more complicated, since I want to do different things depending on if the tea is caffeinated or not.

We start by making this choice:

A menu titled Brew Tea offers a choice between Caffeine and No caffeine.

Then, if we choose caffeine, we log this to the Health app:

A health log entry records a caffeine intake of 40 mg, with an optional date field.

Uncaffeinated tea counts as water (at least for me):

A health logging entry shows a sample of 250 mL of water with No caffeine indicated above it.

And, then, regardless of the type of tea, we set a timer for 7 minutes:

A digital interface displays options to end a menu and start a 7-minute timer.

Running this one requires more interactions with Siri, since she’ll ask which type we want. We can either reply by voice or by pressing the option we want on the screen.

A simple Shortcut for tracking workout time

I’ve been tracking my time at work for a while now, with the help of Toggl and Timery. Now that I’m working from home, work and home life are blending together, making it even more useful to track what I’m doing.

Physical exercise is essential to my sanity. So, I wanted to integrate my Apple Watch workouts into my time tracking. I thought I’d be able to leverage integration with the Health app through Shortcuts to add in workout times. Turns out you can’t access this kind of information and I had to take a more indirect route using the Automation features in Shortcuts.

I’ve setup two automations: one for when I start an Apple Watch workout and the other for when I stop the workout:

An automation setup shows personal triggers for actions when starting or ending workouts, with corresponding icons.

The starting automation just starts an entry in Timery:

A workflow automation is set to start a time entry when any workout begins, with the option to ask before running turned off. A Timery shortcut is used to start a time entry tagged as Fitness and Exercise.

The stopping automation, unsurprisingly, stops the running entry:

A notification from the Timery app asks to stop the current time entry, with options to Show More or close the notification.

As with most of my Shortcuts, this is a simple one. Developing a portfolio of these simple automations is really helpful for optimizing my processes and freeing up time for my priorities.

I enjoyed Star Trek Picard. A fun mix of nostalgia, new characters, and interesting story. Plus Patrick Stewart really is a great actor. I’m glad they had a chance to complete Data’s story, after the abrupt ending in Nemesis. πŸ“Ί

A sentence I never expected to write: A big thank you to my sister for delivering much needed toilet paper while I was in quarantine. A messy emergency was approaching 😬

A pack of twelve double rolls of MAX toilet paper is placed on a kitchen counter.