7 PointBlue Penguin Study
Currently, this is the same as in the book, but will be expanded.
7.0.1 Interactive mapping of individual penguins abstracted from a big dataset
In a study of spatial foraging patterns by Adélie penguins (Pygoscelis adeliae), Ballard et al. (2019) collected data over five austral summer seasons (parts of December and January in 2005-06, 2006-07, 2007-08, 2008-09, 2012-13) period using SPLASH tags that combine Argos satellite tracking with a time-depth recorder, with 11,192 observations. The 24 SPLASH tags were re-used over the period of the project with a total of 162 individual breeding penguins, each active over a single season.
Our code takes advantage of the abstraction capabilities of base R and dplyr to select an individual penguin from the large data set and prepare its variables for useful display. Then the interactive mode of tmap works well for visualizing the penguin data – both the cloud of all observations and the focused individual – since we can zoom in to see the locations in context, and basemaps can be chosen. The tmap code colors the individual penguin’s locations based on a decimal day derived from day and time. While interactive view is more limited in options, it does at least support a legend and title (Figure ??).
library(sf); library(tmap); library(tidyverse); library(igisci);
sat_table <- read_csv(ex("penguins/sat_table_splash.csv"))
obs <- st_as_sf(filter(sat_table,include==1), coords=c("lon","lat"), crs=4326)
uniq_ids <- unique(obs$uniq_id) # uniq_id identifies an individual penguin
onebird <- obs %>%
filter(uniq_id==uniq_ids[sample.int(length(uniq_ids), size=1)]) %>%
mutate(decimalDay = as.numeric(day) + as.numeric(hour)/24)