Bryce Mecum amoeba

2015 Bristol Bay "Family" Size

In the Bristol Bay, Alaska sockeye gillnet fishery, permits are attached to an individual person instead of a vessel. While you can apply to have a second permit, it's also possible for someone you know, including a family member, to hold a permit for you. Looking at the most recent 2015 data, how many permits are attached to a given last name? I used last name because it's the only way I could attempt to get at how many permits a family holds. Of course, there are popular last names and, for that situation, this analysis lumps numerous families together.

Uses d3.js to visualize the most recent 2015 permit data from the Alaska CFEC.

2015 Bristol Bay "Family" Size

In the Bristol Bay, Alaska sockeye gillnet fishery, permits are attached to an individual person instead of a vessel. While you can apply to have a second permit, it's also possible for someone you know, including a family member, to hold a permit for you. Looking at the most recent 2015 data, how many permits are attached to a given last name? I used last name because it's the only way I could attempt to get at how many permits a family holds. Of course, there are popular last names and, for that situation, this analysis lumps numerous families together.

Uses d3.js to visualize the most recent 2015 permit data from the Alaska CFEC.

	#function that extracts a depth (in meters) from a given lon lat from bathymetric data downloaded from online data using analyzepsat package. The file bathy.rds has already been downloaded by Cindy and is used below for BATH in the function

	# General comment: Try to use underscores instead of periods in names, like depth.df => depth_df because
	# periods in variable names has special meaning in R and I think it makes code harder to read. This is
	# very much personal preference though as it won't make your code not work.

	bathy = readRDS(file="bathy.rds")

	depth_df <- btrack #this can be any data frame that has lon lat, in this case, column headings are "Lon_E" and "Lat_N"
	depth_df$bottom_depth <- NA #setup empty column for bottom depth to be filled by for loop below

	# animation-2.r
	# author: Bryce Mecum ([email protected])
	#
	# Animations in R Tutorial
	# Part 2 of 6
	#
	# Extend part 1 by plotting a random (uniform) walk starting in the bottom
	# right-hand corner and moving North and West.

	library(maps)

	* using log directory ‘/Users/bryce/src/iptools/..Rcheck’
	* using R version 3.2.0 (2015-04-16)
	* using platform: x86_64-apple-darwin14.3.0 (64-bit)
	* using session charset: UTF-8
	* using option ‘--as-cran’
	* checking for file ‘./DESCRIPTION’ ... OK
	* checking extension type ... Package
	* this is package ‘iptools’ version ‘0.2.0’
	* checking CRAN incoming feasibility ... NOTE
	Maintainer: ‘Bob Rudis <[email protected]>’

	def mymethd():
	if True == True
	return True
	else
	return None

	# Example from http://www.w3.org/2007/OWL/wiki/PrimerExampleTurtle

	f:Marriage rdf:type owl:Class .

	_:x14 rdf:type owl:Class ;
	owl:unionOf (f:CivilMarriage f:ReligiousMarriage) .

	f:Marriage owl:equivalentClass _:x14 .

	library(dplyr)

	# Set up an example data frame
	stations = c("A", "B", "C")
	species = c("M", "N", "O", "P", "Q")
	baseline = c(5, 50, 500)

	n <- 25
	mydf <- data.frame(station=sample(stations, n, replace=TRUE), species=sample(species, n, replace=TRUE))
	mydf$baseline <- baseline[match(mydf$station, stations)]

	library(rvest)

	url <- "http://access.afsc.noaa.gov/REEM/WebDietData/DietTable.php?NODC=8857041501&Year=2007&Region=GOA"
	page <- read_html(url)

	column_names <- page %>% html_nodes(".afscbody table tr:first-child td ") %>% html_text()
	column_names

	table_data <- page %>% html_nodes(".afscbody table tr:not(:first-child) td ") %>% html_text()
	table_data