R Markdown

Interactive map of detections, from IMOS data + collaborators from NSW and QLD.

library(tidyverse)
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setwd("~/Documents/USC/Honours/R/data")
IMOS <- read_csv("Inputs/241116_step2.csv")
## Rows: 16265 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (15): filename, transmitter_id, tagging_project_name, species_common_na...
## dbl  (17): tag_id, transmitter_deployment_id, CAAB_species_id, WORMS_species...
## dttm  (2): detection_corrected_datetime, transmitter_deployment_datetime
## date  (1): detection_datetime
## 
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#

IMOSxy <- IMOS %>%
  group_by(station_name, receiver_deployment_latitude, receiver_deployment_longitude) %>%
  summarise(num_det = n(), .groups = 'drop')

IMOSxy_sf <- sf::st_as_sf(IMOSxy, coords = c("receiver_deployment_longitude",
                                             "receiver_deployment_latitude"), crs= 4326, agr = "constant")

 mapview::mapview(IMOSxy_sf, cex = "num_det", zcol = "station_name", fbg = F)

Hopefully you can zoom in on each receiver - to see just how many receivers have detections. Lots of places have detections I would have never known, for example, Kiama under Batemans Bay (-150.86, -34.67) has only 2 receivers, yet one has 90 detections, the other 78.