FlukeAndFeather

library(move2)
library(ritis)
library(sf)
library(tidyverse)

# Download movebank study-level information
movebank_studies <- movebank_download_study_info(
  # Filter to studies where data are *visible* (not necessarily downloadable)
  i_can_see_data = TRUE,
  # Specify the list of attributes we want to query

FlukeAndFeather

library(tidyverse)
set.seed(123)

ex_countries <- tibble(
  country = c("Canada", "USA", "Mexico", "Brazil", "Chile"),
  ruleoflaw = c(0.8, 0.7, 0.41, 0.5, 0.66),
  ruleoflawZ = (ruleoflaw - mean(ruleoflaw)) / sd(ruleoflaw)
) 
sharks <- tibble(
  commonname = c("Tiger", "Bull", "Blacktip", "Nurse"),

FlukeAndFeather

# Make the whole analysis
# The prequisite for all is the end of your pipeline (in this case, your model predictions)
.PHONY: all
all: models/predictions.pkl
# You can preview the sequence of commands in the pipeline with: make all -B --recon
# I recommend putting that in your README

# 1. Download raw data
### replace species.csv, species2.csv, etc with names of your raw data files
### the ampersand-colon &: tells make that this rule has multiple grouped targets

FlukeAndFeather

# Based on tutorial: https://gganimate.com/articles/gganimate.html
# Accessed 2021-10-01

library(gganimate)

# We'll start with a static plot
p <- ggplot(iris, aes(x = Petal.Width, y = Petal.Length)) + 
  geom_point()

plot(p)

FlukeAndFeather

# If you don't have devtools, install that first. If you have Windows and R version 4.0 (or greater), see: https://cran.r-project.org/bin/windows/Rtools/
devtools::install_github("lockedata/datasauRus")

# Load the datasaurus dozen and our favorite suite of data processing tools
library(datasauRus)
library(tidyverse)

# Quick look at the contents of the datasaurus_dozen data frame
summary(datasaurus_dozen)
unique(datasaurus_dozen$dataset)

FlukeAndFeather

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

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

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(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

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(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)