I couldn’t find much of an overview of how the COVID19 situation looks like in the Americas, so I gathered some numbers from Google/Wikipedia about deaths in the countries in North and South America. For the moment I left out a handful of Caribbean Islands, I’ll add them later on.
So which countries are doing well in terms of number of deaths in relation to the total number of inhabitants, and especially, where do the United States stand in comparison. The chart was made with R, and data from Google/Wikipedia, current as of Sep. 22, 2020:
This is to be taken with a grain of salt, however. I am pretty certain that the reporting standards and data quality differ vastly from one country to another (something to be researched). However, this is the picture you get, when you take the publicly available data from Google/Wikipedia.
I thought it would be nice to have a nice and quick visual presentation that shows, how the different states of the United States compare in terms of of COVID19 hospitalisations and deaths numbers, including D.C., Puerto Rico and US Territories. It’s been done with R and with data from covidtracking.com. The data is going up to Sep. 24, 2020:
There are larger version of these charts (4096x4096px) for viewing and download :
The Swiss Federal Statistical Office (SFSO) has some nice data with which you can play around. To hone my R programming skills, I grabbed a recently updated dataset for Swiss female and male surnames for babies in 2019. You can find the datasets here.
The first challenge is to find out how to work with px-files. Thankfully, this is easy, the pxR package takes care of that. It imports a file in px-format and produces a data-frame that you can use like any other data-frame.
The second challenge was with one of the original column names “Sprachregion / Kanton”. This did not want to filter and kept me giving either a column name not found or an empty data-set. So I change this column name in the original file to read “Kanton” and it worked.
I thought I start with a density plot to see if this tells me anything about the names:
The names to the left are the ones that are not chosen by many, but there are an awful lot of these, lets call them rare, names. The names to the right are the ones that are chosen by many, but there are not a lot of these, lets call them common, names.
A first look would seem to suggest that 2019 was a year in which the diversity of baby names chosen was the highest in this period (2000-2019) for both male and female baby names.
Some number crunching: Total number of (unique) names in dataset are (for 2019) 2765 (female) and 2702 (male). You can read the SFSO press release (no English version) to find out more on the most common names in 2019 and more.
If you want to have a look at the code I wrote, you can find it on github.
Mostly missed this Morning: An earthquake of magnitude 6.6 on the Mid-Atlantic Ridge on Sep. 6, 2020 06:51UTC.
The depth was given as 10 km by both EMSC and USGS, so it was quite shallow. Obviously, this directly affects no one, BUT since it was in the middle of the ocean there always is a risk of some underwater events causing a tsunami.
Luckily, this time the risk was assessed by USGS on Tsunami.gov as non-existent.
However, this is a warning for US East Coast, Candad East Coast and Gulf of Mexico States. Unfortunately, there doesn’t seem to be such a unique single source of information for Europa (or Africa or South America for that matter).