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Category Archives: History Through Spreadsheets
I was reading an article that stated that names of Hurricane Harvey and Irma could be the eighth pair of back-to-back hurricane whose names have been retired since the 1954. I have never thought about which hurricane names have been retired, so I started to search around the web. As usual, the Wikipedia provided an excellent data source. Continue reading
I was watching the weather reports on Hurricane Erma and the discussions on how powerful it is. The most cited metrics for hurricanes and typhoons appears to be wind speed and eye barometric pressure (see Figure 1). I decided to look around for hurricane and typhoon strength data and the Wikipedia turned out to have a page containing large number of tables for all the most intense typhoons and hurricanes in different regions of the world. I used Power Query to (1) import the tables, (2) clean them up, (3) combine them, and (4) rank the storms by air pressure. Continue reading
You may have noticed that many of my recent posts are focused on data processing and analysis. These recent posts reflect the fact that I am preparing for a career change as I near "retirement," and I plan on working in the data analysis arena. This means that I have been in serious Python, R, and statistics training. While I love working with Python and R, I keep finding myself drawn back to Excel and Power Query (aka Get and Transform) for quick, ad hoc analysis work. While this blog will look at the frequency over time of the name Mark, you can use the tool to generate the same chart for any name. Continue reading
I heard some discussion on television about all the Confederate monuments around the country and when they were erected. I decided to look for the data and plot it for myself. I very quickly found a document from the Southern Poverty Law Center that looked interesting and provided me some interesting data tidying and charting challenges. My focus here is on duplicating their chart of monuments dedications dates. This chart type is not a standard Excel type and I wanted to see how I could duplicate it. This workbook will be used in a charting seminar that I plan to present in a month or so. Continue reading
While answering a recent question about the tonnage sank by the top US submarine skippers during WW2, I realized that I had not made available my conversion of the JANAC data for vessels sunk by US submarines. The JANAC records are considered the official records because they were cross-checked with information from Japanese records. Continue reading
I watched an interesting lecture on American History TV this weekend called Japanese Perspective on the Battle of Midway by Anthony Tully. The most interesting part of the discussion occurred when Tully began showing how the US production of aircraft carriers eventually overwhelmed the Japanese ability to build carriers. He used some simple graphs to show the relative carrier strength of the US Navy versus the Imperial Japanese Navy (IJN) over time. In this post, I will come up with my own graphics to visualize this information. Continue reading
I have been doing quite a bit of reading lately on WW2 naval actions, and I have been putting together tables that show me ship losses by year. This information gives me a feel for the tempo of battle during the war. I first looked at US naval losses (link) and am now looking at the Royal Navy losses (Figure 1). Continue reading
I have been putting together some information on US naval actions during WW2. Specifically, I wanted to look at US Naval losses by year during WW2 in order to get a feel for the change in battle tempo over time. The Wikipedia has an excellent page on all the US naval losses during WW2, so I simply downloaded this page, cleaned it, up and generated an Excel pivot table (Figure 1). The breakdown by combatant type is my own, everything else is from the Wikipedia. Continue reading
I recently decided to take some classes in data analysis at Datacamp, an online training site. My first classes were in dplyr and tidyr – two excellent R-based tools for manipulating files that not amenable to analysis because of inconsistencies and structure: tidyr provides many tools for cleaning up messy data, dplyr provides many tools for restructuring data. After completing the two classes, I decided that I needed to firm up my knowledge of these tools by applying them to a concrete example. Continue reading
During my recent seminar on Excel's Power Query feature, I showed my team how to grab data executive order data from the web and generate a simple plot (Figure 1). After generating the plot, I asked the audience what we could learn from this graph. I was expecting to hear that the early 1900s – the time between Teddy Roosevelt and Franklin Roosevelt – was a time of massive use of executive orders. Continue reading