Tonight my wife came to me with the problem of Dollar Words. The idea is that you can assign a numerical value to each letter of the alphabet (a = 1, b = 2 … z = 26). If you replace each letter of a word with the value and add the values up, you get a sum. If the sum adds up to 100, that’s called a dollar word.
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Hawaii Counties School Reopening Metrics
There are a number of school reopening metrics around the country. They use some figures that are not so easy to gather on existing dashboards. To help visualize how Hawaii stacks up against the suggested metrics, I pulled the data and calculated the metrics for our state.
The first mainland metric that I used came from Minnesota’s Safe Learning Plan. They use a metric of total cases in the last 14 days per 10,000 population.
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Mobility in Hawaii
I was reviewing the coronavirus dashboard for the state of Hawaii. It has mobility data from anonymized cell phone records from Descartes Labs. The chart was so small that I couldn’t really read it, so I went to the source cited to see if I could see this better. At the source, they cite a Hawaii data project that looks at this data but something about the Hawaii data site isn’t working that great for the mobility visualization.
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Last Day Distance in GVRAT
As people start finishing the race it seems like they really push themselves on the last day. I’ve seen people running 20, 30, 50 miles to reach the final distance. I wondered if there was a difference between what people average and what they do on the last day of the race.
Data Loading suppressPackageStartupMessages(library(tidyverse)) suppressPackageStartupMessages(library(lubridate)) gv <- read_csv("../datasets/GVRAT_June_Summary.csv") ## Parsed with column specification: ## cols( ## .default = col_double(), ## Name = col_character(), ## `28-Jun` = col_logical(), ## `29-Jun` = col_logical(), ## `30-Jun` = col_logical() ## ) ## See spec(.
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GVRAT and Long Run Days
I do my long run on Saturdays. A couple of friends do theirs on Sundays. I thought with the number of people in the database, it wouldn’t be too hard to see what people use as their long run day. Conceptually it’s just figuring out what is the mean per day of the week and seeing which one is the longest day. There could be some issues with this approach but overall this should return what I’m looking for.
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Finisher Characteristics and Hawaii Update
I’m almost at the end of May, and I’m over 100 miles into this race. It’s been a bit tough so far because of the time trial I did last week, but I’m still trying to get some cushion on the August 31 deadline for the race.
On the Facebook group for this race they talk about the races between races. Some people look at their state standings. Others may do their name rankings (i.
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GVRAT Bib Numbers and Starting Date
On the Facebook group for the Great Virtural Race across Tennessee, there was a post where people were complaining about recent entries (with correspondingly high bib numbers) logging miles all the way back to the start of the race. This is against the rules of the race in that you’re only supposed to start logging from the time you sign up. It’s not like I hadn’t thought of doing this myself, but since I was confident I would make it anyway, I didn’t take that step.
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My GVRAT Stats
Some more data fun with the Great Virtual Race across Tennessee. I decided I would like to look at how I’ve been doing in comparison to the other racers. I also added in the finishers (who are running back across Tennessee to Arkansas virtually).
One note about the data source for the finishers is that it is only complete from 5/1-5/17 for some reason. I suspect that this is because it’s about when the first finishers crossed the finish line.
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Great Virtual Race across Tennessee First Look
I joined the Great Virtual Race across Tennessee this summer. It’s by the guys who produce the Barkley Marathons, the crazy nearly impossible ultramarathon series. The goal for this event is to run across Tennessee virtually, a little more than 1000 km. Each day the data table is published as a csv and so I thought I would take a look at what I could do with it.
Some basics:
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Hawaii Coronavirus Update 5/10/2020
It’s been a couple of weeks since I reran the graphs with new data. Since then there have been thankfully few cases of coronavirus in our state. It’s clearly still circulating at some small level but once we get to this type of number, it becomes possible to track down and isolate the contacts.
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(ggplot2) library(lubridate) ## ## Attaching package: 'lubridate' ## The following objects are masked from 'package:dplyr': ## ## intersect, setdiff, union ## The following objects are masked from 'package:base': ## ## date, intersect, setdiff, union library(scales) library(tidyr) library(zoo) ## ## Attaching package: 'zoo' ## The following objects are masked from 'package:base': ## ## as.
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