Total cases are all cases since the start of the epidemic, i.e. cumulative cases. For the number of people currently ill, see “Active cases”.
Cases per 100,000 indicator shows the overall prevalence per 100,000 population since the pandemic started. For the share of people who are currently ill, see “Active cases per 100,000”.
Total tested / Total tests, New tested / New tests, Tested per 1,000 / Tests per 1,000: You will see a very large jump in the number of tests on February 1st, 2021. As of that date, the Government of Canada apparently started using a different method of reporting the number of COVID tests in its official COVID19 dataset. Previously, test counts were being reported as a numtested variable, and since February 1st, 2021, COVID tests numbers have been reported as a numtests variable with much higher counts. Starting February 1st, the numtested variable contained only missing values. This likely means that previously the Government of Canada was reporting the number of people tested, and now it reports the number of tests performed. I decided to join these two variables into the same time series for the sake of continuity.
The data is downloaded from the Government of Canada official COVID-19 page at 6-hour intervals to minimize the load on the repository. Thus there may be a delay of up to 6 hours from the time canada.ca update their data to the moment it gets updated on my server.
To calculate “Cases per 100,000”, “Active cases per 100,000”, “New cases per 100,000”, “Mortality per 100,000”, and “Tests done per 1,000”, I use Statistics Canada population estimates for the corresponding quarter. If the estimates for the latest quarter are not yet available, estimates for the previous quarter are used. Source: Statistics Canada Data table 17-10-0009.
Geospatial data used to render the map was retrieved from Statistics Canada. The polygons were then simplified to ensure quick rendering of the map.
The data prior to March 21, 2020 was truncated due to being, for most indicators, highly incomplete or missing entirely, which resulted in various issues when computing and visualizing epidemiological indicators.
The dataset at some point contained very few negative numbers, which don't make sense in this context. These are/were almost certainly data entry errors on the part of the government, so I fixed this issue by converting all numbers in the dataset to their absolute values.
The map color palette may look different depending on the indicator and/or the date you have selected. This is due to some of the data being highly skewed, which causes leaflet::colorQuantile to fail. I had to program around this issue by creating a function that switches to leaflet::colorNumeric in such cases. colorNumeric doesn't use quantiles to break down the data, so the resulting color scheme doesn't look as good.
My name is Petr Baranovskiy, I am an R language enthusiast, and I specialize in economic policy analysis, economic and statistical modeling, energy policy, and the use of geospatial data for policy analysis. If you liked this app, please visit my blog at dataenthusiast.ca and follow me on Twitter.
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