In ComputationalMovementAnalysis/ComputationalMovementAnalysisData: Provides Data to use in the Course ComputationalMovementAnalysis

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  message=FALSE, 
  warning = FALSE
)

As the figure below illustrates, the wildboar GPS collars adhere to a complex sampling regime. Please consider this when doing your analysis!

knitr::include_graphics("images/sampling_regime.jpg")

The above figure was generated using the following code:

library(ComputationalMovementAnalysisData)
library(ggplot2)
library(dplyr)
library(forcats)

limits <- c(0,200)
breaks = seq(0,200,50)
labels = paste(c(rep("",length(breaks)-1),">"), breaks)

wildschwein_BE %>%
  mutate(TierName = fct_reorder(TierName, DatetimeUTC,min, .desc = TRUE)) %>%
  group_by(TierID, TierName, CollarID) %>%
  mutate(
    timelag = as.numeric(difftime(lead(DatetimeUTC),DatetimeUTC, units = "mins")),
    ) %>%
  ggplot(aes(DatetimeUTC, TierName, colour = timelag)) +
  geom_line(lwd = 10) +
  scale_color_gradientn(name = "Sampling interval", colours = RColorBrewer::brewer.pal(11, "Spectral"), limits = limits, na.value = NA, oob = scales::squish, breaks = seq(0,200,50), labels = labels) +
  theme_minimal() +
  theme(legend.position = "top") +
  guides(color = guide_colorbar(title.position = "top", title.hjust = .5, barwidth = unit(20, "lines"), barheight = unit(.5, "lines")))

ggsave("vignettes/images/sampling_regime.jpg", height = 8, width = 10, dpi = 300, units = "in")

ComputationalMovementAnalysis/ComputationalMovementAnalysisData documentation built on Dec. 17, 2021, 3:04 p.m.