Trap day calculation when no daily timelapse image capture (Solution)

**Issue background:**

- Several cameras do not allow for the capture of motion detection and timelapse images.
- As a result, if cameras are set for motion detection, we cannot capture a daily timelapse image for environmental data.
- This daily timelapse image is also what is used to calculate trap days.
- Without trap day values we cannot calculate relative activity indices.

**Current solution (May 2024):**

- The following script will take a dataset with no records of timelapse trigger modes, and add a record for each day between the first and last images each camera takes. The assumption is being made that cameras were operational during this entire period.
- This script should be added the function for calculating relative activity index for sample sessions (_**sample_rai(tl_image_data)**_)

_##Create a dummy dataset of daily timelapse images that can be added to the dataset that has no timelapse images. This assumes the camera was active every day and so makes a timelapse record for each deployment label from the date of the first motion detection to the date of the final motion detection._

library(dplyr)
library(tidyr)

mock_tl <- group_by(image_data, study_area_name, sample_station_label, deployment_label) |>
summarise(
min_date = min(as.Date(date_time), na.rm = TRUE),
max_date = max(as.Date(date_time), na.rm = TRUE),
.groups = "keep"
) |>
expand(
date = as.Date(min_date:max_date)
) |>
mutate(
date_time = as.POSIXct(paste(date, "12:00:00"), tz = "UTC"),
trigger_mode = "Time Lapse",
lens_obscured = FALSE
) |>
select(-date)

_##Join the mock Time Lapse dataset onto the original dataset (with no timelapse) images_

tl_image_data <- bind_rows(image_data, mock_tl) |>
arrange(date_time)

**Long-term Solution:**

- While the above solution works, once there are enough outstanding improvements to warrant a more concerted effort on the entire package this should be more formally addressed.