FlukeAndFeather

library(raster)
#> Loading required package: sp
# Create a random raster
set.seed(10)
foo <- raster(matrix(round(runif(100)), 10, 10))
# Add up neighbors
# NOTE: won't do anything about cells on the raster edge
foo_focal <- focal(foo, w = matrix(1, 3, 3), fun = sum)
# Set an isolation threshold

FlukeAndFeather

find0cross <- function(x) {
  # x must be numeric
  stopifnot(is.numeric(x))

  # Length 0 and 1 can't have zero crossings
  if (length(x) < 2) {
    return(rep(FALSE, length(x)))
  }

  # Recursively find zero-crossing values

FlukeAndFeather

# Takes a grayscale tiff (with alpha) and converts all dark points (by threshold) to the new color
colorize_tiff <- function(input, threshold, newcolor) {
  # Read original tiff
  orig_tiff <- tiff::readTIFF(input)
  # Convert grayscale to black and white
  orig_tiff[, , 1] <- ifelse(orig_tiff[, , 1] > threshold, 1, 0)
  
  # Create a colorized tiff
  new_tiff <- array(0, dim = c(dim(orig_tiff)[1:2], 4))
  # Set RGB

FlukeAndFeather

---
title: "Supporting Information"
subtitle: "Modeling short-term energetic costs of sonar disturbance to cetaceans using high resolution foraging data"
author: 
 - "Max F. Czapanskiy"
 - "Matthew S. Savoca"
 - "William T. Gough"
 - "Paolo S. Segre"
 - "Danuta M. Wisniewska"
 - "David E. Cade"

FlukeAndFeather

library(tidyverse)
set.seed(1024)
d <- tibble(
  a = letters[1:5],
  b = LETTERS[6:10],
  x = 1:5,
  y = (1:5)^2,
  z = runif(5)
)

FlukeAndFeather

library(raster)
library(scales)
library(tidyverse)

powpow <- function(n, a, b) {
  # based on answers at: https://dsp.stackexchange.com/questions/47640/generating-a-timeseries-with-an-arbitrary-power-spectrum
  
  # Thank you Sam!
  
  if (n %% 2 != 0)

FlukeAndFeather

library(tidyverse)

# Start with some dummy data
whale_data <- tribble(
  ~species,          ~prey,   ~order,
  "B. musculus",     "krill", 5,
  "B. physalus",     "fish",  3,
  "B. physalus",     "krill", 4,
  "M. novaeangliae", "fish",  1,
  "M. novaeangliae", "krill", 2

FlukeAndFeather

library(ggrepel)
library(tidyverse)
# These were my measurements for mussel area and quadrat area respectively
musselfrac <- 0.043 / 0.097
# dbinom() is the probability of n successes out of x trials with probability p
# So dbinom(0:25, 25, musselfrac) is the chance of: [0 points in mussels, 1
# point in mussels, 2 points in mussels, ..., 25 points in mussels]
musselprobs <- tibble(n = 0:25,
                      p = dbinom(0:25, 25, musselfrac))
# This is the probability of 24/25 points in mussels

FlukeAndFeather

pred_mr <- function(
  mass_kg, 
  mr_method = c("kleiber", "kolokotronesetal", "nagy", "savageetal", "whiteseymour"),
  unit = c("kcal_day", "kJ_day", "mlO2_hr", "W"),
  multiplier = 1
) {
  #' Predicted metabolic rate
  #' 
  #' Predict mammalian metabolic rates from various scaling equations.
  #'

FlukeAndFeather

library(dplyr)
library(geosphere)
library(glue)
library(ggplot2)
library(mapproj)

# Example data frame
set.seed(1)
tracks <- tibble(
  time = 1:5,