Kinoko_Mori

Boat and Boat Related Activities

For about 5 to 10 years prior to this blog I'd been enamored with the idea of getting a boat and hanging out around the pacific. After going on a handful of outings with friends at the local sailing club I figured that was enough and headed to the pacific. So sometime around this time last year I got a boat that sings to me, Solace the Cape Dory 30, cutter This month, as sailing is such a holistically phenomenological ordeal for the individual, I've committed to sailing consistently for the next x-months. Every time I go out I expect to learn a little more about the natural world and…
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Practical Statistics – 6

It would appear sampling and bootstrapping are differentiated in r by: replace = TRUE/FALSE in the sample function. Given this super adorable repository of small cute little sets about fish. I've found a set with Fish weight and length. Two classic variables. Gotta love the classics. There's also three identifiers. This is great for what I'm doing - not being huge and unwieldy for my skillset - they'll work wonderfully for the small sections in the books. Here we have: "FSAdata::BGHRfish – view download documentation – Fish information from samples collected from Big Hill Reservoir, KS, 2014." Let's take a peak: General Exploration colnames(df) [1] "UID" "fishID" "specCode" "length" "weight"…
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Practical Statistics – 5

"….It is also useful to explore how the data is distributed…." So moving forward we get to make some boxes'n stuff, though done in 'boxes over time', this will be as the book decrees. Rather than re-work the same dataset I've gotten a new one to pour over for this and perhaps the next couple sections: "….Rivendell - Rainfall Chemistry, Stream Water Chemistry, Throughfall Chemistry, Groundwater Chemistry - Solute chemistry (2007-2015) Water samples from rainfall, stream, and wells. Major solutes (Ca, Mg, Na, K, and Si). Description: Significant solute flux from the weathered bedrock zone - which underlies soils and saprolite - has been suggested by many studies. However, controlling…
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Practical Statistics – 4

Truth of the matter is I'm burnt out on this set. I suppose there is more that can be done with it but I ain't gonna do it. Before I walk away from it I wanted to do one more distribution chart, soil type by area - Functionally. Before I had a big 'ol loop to do it for me. This time with subsets It's down to a 'handful' of lines, and graphed. SmallTreeData
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Boxes Over Time

Here's some sequential box plots for temperature over each year. #RStudio errors out on large graphs sizes, write it to file instead png("rplot.png", width = 4096, height = 4096) par(mfrow = c(6,5)) for(e in sort(unique(df$year))) { temper
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Graphs and Functional Programming

One of my concerns with my recent efforts was that it felt like I was doing more.....brute force coding than actual R coding. to much for, and if, and while whatever, nonsense. In this I hoped to do more functional programming: "....Functional programming is a programming paradigm where values are passed around into functions, and those functions are themselves values. Functional programmers often try to make large parts of their code referentially transparent...." I don't know what referentially transparent is and I ain't about to fall into any wiki holes at the moment but I did try to use more of R's built in functions rather than all the loops…
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Colloquial “Sugar Sand”

So I had a learning experience There's this stuff folks call "sugar sand" and if you drive a giant 2 wheel drive van in it, ultimately, you will need 3 trucks to pull you out after you spend 5~10 minutes digging yourself in. The main question I was asked was "Did you let air out of your tires?".... The first thing to do in any kind of vehicle is to let the air out of the tires. Drop the pressure to levels way, way too low to use on pavement. Twelve to 15 psi is good—less if you have narrow, high-profile tires. This increases the tire's footprint, allowing it to…
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Practical Statistics – 2

Estimates of Location and Variability A 'Typical Value', average, or estimate of where most of the data is located provides a central tendency for variables with measure data. Telling us where most values for a particular phenomenon will be found. These tendencies are the Mean and the Median in their various forms Average elevation for each area for (e in seq(1:4)) { assign("x", paste("SmallTreeData$Wilderness_Area", e, sep="")) z
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