In dbca-wa/turtleviewer: WA Turtle Data Viewer

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

library(dplyr)
library(wastdr)
library(turtleviewer)
data("turtledata")

Context

This vignette demonstrates how to filter and analyse the supplied data.

Filter data

First, we want to filter data to a certain area.

area_names <- turtledata$sites$area_name %>% unique()
area_names

filter_cdo <- . %>% dplyr::filter(area_name == "Cape Dommett")
filter_pth <- . %>% dplyr::filter(area_name == "Port Hedland")

Turtle Tracks and Nests

A first example of using supplied data.

turtledata$tracks %>% 
  filter_cdo %>% 
  wastdr::map_tracks_odkc(sites=turtledata$sites)

turtledata$tracks %>% 
  filter_pth %>% 
  wastdr::map_tracks_odkc(sites=turtledata$sites)

turtledata$tracks %>% 
  filter_cdo() %>% 
  turtleviewer::sf_as_tbl() %>% 
  dplyr::group_by(season, site_name, nest_type) %>%
  dplyr::tally() %>% 
  dplyr::ungroup() %>%
  tidyr::spread(nest_type, n, fill = 0) %>% 
  turtleviewer::rtbl()

Nest disturbances

TODO

Marine Wildlife Incidents

turtledata$mwi %>% 
  # filter by any area_name %>% 
  wastdr::map_mwi_odkc(sites=turtledata$sites)


# tracks_cdo %>% sf_as_tbl() %>% rt() # TODO export helpers to wastdr
# tracks_pth %>% sf_as_tbl() %>% rt()