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Category Archives: History Through Spreadsheets
I recently have seen pictures on the news of a line of people preparing to summit Mount Everest (Figure 1), which got me thinking about the difficulty of waiting in line under low-pressure conditions.The vast majority of the people who climb Everest use supplemental oxygen. The air pressure at the summit of Everest is about 0.3 atmosphere, which is not enough to support human life for an extended period of time. But a relatively small number of people have climbed Everest with No Supplemental Oxygen (NSO). In this post, I will look at this very select group of people. Continue reading
My vacation/retirement cabin is in the iron mining region of Minnesota. The rock throughout the area shows the reddish hue of iron. I recently heard some old-timers talking about how the intensity of mining operations during WW2 took the last of the high-grade iron ore (hematite –Figure 1) and left only low-grade ore (taconite). This comment made me curious about mining during WW2. Continue reading
I was listening to a political pundit mention that both US political parties want to confirm young Supreme Court justices to ensure that their judicial philosophies endure. I was curious as to whether that was true over time. I went to the Wikipedia and saw that they had a list of all the justices since the founding of the US and web pages for each justice. Sounds like a perfect opportunity for a bit of web scraping! Continue reading
I was listening to news the other night when I heard a reporter mention that Jimmy Carter just became the oldest US president in history. I thought verifying this fact would be a good Power Query exercise. He had just surpassed George H.W. Bush, the previous record holder. Continue reading
I have been working through the book Collect, Combine and Transform Data Using Power Query in Excel and Power BI by Gil Raviv – it is an excellent Power Query (PQ) resource. I particularly like the methods discussed in Chapter 10, which focused on how to make your queries robust, that is, insensitive to minor deviations in the input data. Chapter 10 spoke to me, and I immediately began looking for some practice data that suffered from common inconsistencies: headings in different cases, minor spelling errors in the data body, and inconsistent wording (example, "Co." instead of "Company"). I found that data in the Wikipedia's information on US WW2 cruisers. In this post, I will look at the production of cruisers by the US during WW2. See Figure 1 for a typical example of a WW2 US light cruiser. Continue reading
One WW2 topic that continues to intrigue me was how US war planners kept the Imperial Japanese Navy (IJN) at bay long enough to build a large naval force. The key was the use of submarines for commerce raiding to disrupt the war material supply chain and tie down Japanese surface forces with convoy defense duty. This post will use Power Query to scrape the Wikipedia for this data. The Wikipedia is becoming a wonderful source for WW2 information. Continue reading
While looking for some good summer history reading, I found the book America's Hundred Thousand: U.S. Production Fighters of World War II. This book covers the production miracle associated with scaling up up the US aircraft industry to supply planes for every front during WW2. Its title refers to the fact that the US produced ~100K fighter aircraft during WW2, which lasted for 44 months for the US (Figure 2). I decided that I would look at the numbers for all forms of aircraft produced by the US during WW2. Fortunately, the Hyperwar website has put the Army Air Forces Statistical Digest online, which gives me easy access to the data. The Digest contains aircraft production data for both the US and Canada. Figure 1 shows the production numbers for the 11 categories of aircraft production listed in the Statistical Digest. In addition to 100K fighter aircraft, there were nearly 200K of other aircraft manufactured as well. Continue reading
I have been working on improving my web scraping abilities by analyzing WW2 data. I have focused on topics related to how the US took the 14th largest military in the world and in roughly 18 months turned it into a military that could fight anywhere in the world. In this post, I want to look in detail at how war materials were delivered to beaches around the world using a vessel called a Landing Ship Tank (LST). I have wanted to write about the LST for a while, but the web data was distributed on about 1200 separate pages – one for each ship. While a Python script would have worked nicely, I wanted to try gathering the data without doing any programming. I found some software that did a good job automating this task, which I will discuss later in this post. Continue reading
It has been 50 years since 1968, and I have been seeing quite a few retrospectives on television about that tumultuous year. I was in 6th-grade in 1968 and the chaos of that year is still very clear in my memory – I remember spending quite a bit of class time on the Paris peace talks. One lesson was about how the Paris Peace negotiators argued about the shape of the table at which they would sit. Arguing about the shape of a table while people were dying seemed ridiculous to a 12-year boy. After hearing all these recent discussions about 1968, I decided to look at the US Vietnam casualty data (Figure 1) to see what insights I could gain on that year. All my work is done in Excel and my workbook is here. Continue reading
My first engineering manager was named Marl Godfrey. He was an excellent manager who also had keen insights into the human condition. These insights made quite an impression on my 22-year old self – I actually kept a notebook of his comments. Some of his most insightful comments were about the US military and the Vietnam War. Marl has grown up in Oklahoma and he had served in Vietnam. He once commented that Oklahoma had very aggressive draft boards, which resulted in Oklahoma having a relatively high death rate during the conflict. I was reminded of this statement when I recently reviewed my quote database. I thought that I should be able to determine how death rates varied by state during the Vietnam War, which is the subject of this post. Continue reading