dill

load("best_model.Rdata")
library(dsm)
library(raster)

# lazily get the plot data for the rug plot
plotdat <- plot(M)

# load the raster and mudge it into the format I want
dists <- stack("NA_Shore_Dist_10km_mean_10km.img")
dists <- as.data.frame(dists)

dill

# get EXIF and make a map

library(leaflet)
library(lubridate)
library(plyr)

# all my photos are in a directory pre with subdirectories
pre <- "~/Dropbox/Photos/"
paths_to_photos <- c("some_sub_directory"
                    )

dill

#273649 #647184 #B1B2C8 #A7755D #5D2E1C #38201C
#0F2B5F #5991C7 #8EC1E7 #B9DBF1 #D5A370 #7B4F37
#5A7362 #6B867C #A1A897 #9A8D6B #8E6341 #432B21
#1F150D #2B190C #513B2C #9B4D44 #893D37 #3E1B17
#9C8A45 #CABE85 #678B88 #9CADAF #CCCCCC #EFEFEF
#1C3333 #226060 #639CA4 #D2AD7C #BE7245 #46211C
#0D1723 #112040 #204D88 #96ABC6 #D1DDE2 #EFEFEF
#000000 #350E16 #5E1521 #A72C29 #C44221 #EC702E
#A56B47 #C79982 #0D8EDA #23ADED #6BC6F5 #EFEFEF
#2A2432 #4F3855 #846D86 #EFEFCF #D5B77D #A89E5E

dill

### analysis of ribbon seal data using a frequentist
### approach to a GAM

### David L Miller dave@ninepointeightone.net
### License: GNU GPL v3


# load data from
# https://github.com/pconn/SpatPred/blob/master/SpatPred/data/Ribbon_data.rda
load("Ribbon_data.rda")

dill

Thanks to wikipedia for the data :)

dill

#!/bin/bash
# alarm clock script
#
# requires: get-iplayer
#   git clone git@github.com:dinkypumpkin/get_iplayer.git

# get_iplayer --prefs-add --rtmp-tv-opts="--swfVfy=http://www.bbc.co.uk/emp/releases/iplayer/revisions/617463_618125_4/617463_618125_4_emp.swf"

# need to install rtmpdump

dill

# cyclic-random effects tensor for Noam

library(mgcv)


# code adapted from ?gam.model
dat <- gamSim(1,n=400,scale=2) ## simulate 4 term additive truth
## Now add some random effects to the simulation. Response is
## grouped into one of 20 groups by `fac' and each groups has a
## random effect added....

dill

### blah
library(exif)

# get the files
files <- dir(".", full.names=TRUE)
files <- files[grepl(".JPG", files)]

# get the data
exifs <- read_exif(files)

dill

Integrating data from multiple sources to improve species distribution models

Increasing quantities of and access to both wildlife survey data and non-designed incidental or citizen science data have left us with a rather big problem: how to we put all of these disparate pieces together and build species distribution models that use as much of the available data as possible? This leads us to a series of sub-questions that I will address in this talk: should we combine data then model it all at once or, build multiple models and figure out how to combine their outputs (or couple their fitting)? How can we find equivalences in recorded effort (and what can we do when no effort is recorded)? I'll illustrate these issues and offer some solutions using example data from aerial and shipboard surveys of seabirds in New England, as well as from large-scale surveys of marine mammals in the North Atlantic.

dill

# overlap of:
# - US state 2 letter codes from state.abb in R
# - 2-letter Scrabble words https://en.wiktionary.org/wiki/Appendix:Official_English_Scrabble_2-letter_words
# - chemical element symbols https://en.wikipedia.org/wiki/Symbol_%28chemistry%29

# get data
states <- tolower(state.abb)
scrabble <- c('aa','ab','ad','ae','ag','ah','ai','al','am','an','ar','as','at','aw','ax','ay','ba','be','bi','bo','by','ch','da','de','di','do','ea','ed','ee','ef','eh','el','em','en','er','es','et','ex','fa','fe','fy','gi','go','gu','ha','he','hi','hm','ho','id','if','in','io','is','it','ja','jo','ka','ki','ko','ky','la','li','lo','ma','me','mi','mm','mo','mu','my','na','ne','no','nu','ny','ob','od','oe','of','oh','oi','om','on','oo','op','or','os','ou','ow','ox','oy','pa','pe','pi','po','qi','re','sh','si','so','st','ta','te','ti','to','ug','uh','um','un','up','ur','us','ut','we','wo','xi','xu','ya','ye','yo','yu','za','zo')
elements <- tolower(c('Ac','Ag','Al','Am','Ar','As','At','Au','B','Ba','Be','Bh','Bi','Bk','Br'