Here is a repo of a bug I've discovered. I use ABMI data to show that the wt_ind_det() fn creates an incorrect max_animals estimate.
I struggled to decompose the following section in wt_ind_det(): mutate(ungroup(mutate(mutate(group_by(arrange(ungroup(distinct(mutate(group_by(mutate( but I think somewhere in the group_by() image_id needs to be added.
library(wildrtrax)
library(tidyverse)
wt_auth()
##download some ABMI data
test_raw <- wt_download_report(
project_id = 2073,
sensor_id = "CAM",
report = "main",
weather_cols = FALSE
)
##isolate an example where there are duplicated date/times
##all are unique images, just rapidfire sometimes has 2 images in same date/time down to the second
## here a bull moose is detected over a period of about 8 minutes, individual count==1 across all tags
isolate_detection <- test_raw%>%
filter(location=="ST-430-3-SW" &
species_common_name=="Moose")%>%
filter(date(image_date_time)==ymd("2023-03-01"))
##create individual detections
test_detections <- wildrtrax::wt_ind_detect(
x = isolate_detection,
threshold = 30,
units = "minutes",
remove_human = TRUE,
remove_domestic = TRUE
)
##fails-- it calculates 2, due to wt_ind_detect() grouping over image_date_time and summing instead of over image_id
test_detections$max_animals==1
Here is a repo of a bug I've discovered. I use ABMI data to show that the
wt_ind_det()
fn creates an incorrect max_animals estimate.I struggled to decompose the following section in
wt_ind_det()
:mutate(ungroup(mutate(mutate(group_by(arrange(ungroup(distinct(mutate(group_by(mutate(
but I think somewhere in thegroup_by()
image_id needs to be added.