dill

###


n <- 200

add_layer <- function(coords=data.frame(x=0, y=0), layer=0){
  so_rad <- seq(0, 2*pi-0.1, len=n)

  if(layer==0){
    lens0 <- 0.1*runif(1, 0.1, 1)

dill

## this code makes figure captions work in HTML output

# adapted from http://gforge.se/2014/01/fast-track-publishing-using-knitr-part-iii/
library(knitr)
options(figure_counter = TRUE)

# Evaluate the figure caption after the chunk,
# sometimes you want to calculate stuff inside the
# chunk that you want to include in the caption and
# it is therefore useful to evaluate it afterwards.

dill

#!/usr/bin/env bash

# David L Miller
# updated 28 February 2019

# taken from:
#   https://github.com/mathiasbynens/dotfiles/blob/master/.osx
#   https://twitter.com/chadwhitaker/status/1004441991415922688
#   https://www.idownloadblog.com/2014/08/03/how-to-remove-the-shadow-window-screenshots-on-mac-os-x/
# and elsewhere

dill

### species discovery curve from eBird


library(lubridate)

# life list
life <- read.csv("ebird_world_life_list.csv")
life$date <- lubridate::dmy(as.character(life$Date))
#sort
life <- life[order(life$date),]

dill

# how far did I go in 2017?

# read in the list of places I went to
txt <- readLines("howfar2017.txt")

# decode the place names to lat/long
library(ggmap)
gg <- geocode(txt, source="dsk")

dill

library(XML)

# pull in the xml file
# can export this from the iPhone and e-mail it to youself
xx <- xmlParse("export.xml")
xx <- xmlToList(xx)

# now do some data mudging
library(plyr)

dill

# for LJT

# this wasn't as simple as I thought, it was the summary() method that does the smart thing
# so there is a bit of split() nonsense to deal with...

library(Distance)
library(dsm)
library(ggplot2)

dill

# make a boundary

library(mapdata)
library(maptools)
library(rgeos)
library(ggplot2)
library(sp)

# build the coastline
coastline <- map("world", c("USA", "Canada"), plot=FALSE)

dill

library(gridBase)
library(grid)
library(mgcv)
library(statmod)
## nicer version of gam.check/rqgam.chack
# - deviance residuals for the Q-Q and histogram
# - RQR for resids vs linear pred
# - response vs fitted
# hist.p gives the quantiles of the residuals to show
better_check <- function(b, k.sample = 5000, k.rep = 200, rep = 0, level = 0.9,

dill

# try to understand measurements from the past...

# possible input types:
# - 5
# - 5'
# - 5'5
# - 5'7 1/2
# - 5'7\"
# - 5'8.5
# - 5'61/2\"