photo descrepencies and errors

[NewGraphEnvironment]

here are what the dupes look like after the first round of duplicates are removed which we can see by running fpr::fpr_photo_remove_dupes then moding the function by removing the call to distinct and file.remove from the function on a second run of the function

test_bfpr_photo_remove_dupes <- function(dir_target = NULL,
                                   col_time = date_time_original,
                                   col_photo_name = source_file,
                                   col_model = model,
                                   col_iso = iso,
                                   remove_renamed = FALSE,
                                   ...){
  dat1 <- exifr::read_exif(path = dir_target,recursive=T)  %>%
    janitor::clean_names()  %>%
    dplyr::group_by( {{ col_model }}, {{ col_iso }}, {{ col_time }}) %>%
    dplyr::filter(dplyr::n()>1) %>%
    dplyr::mutate(l = stringr::str_length( {{ col_photo_name }} ))

  if(remove_renamed){
    dat2 <- dat1 %>%
      dplyr::arrange( {{ col_time }}, {{ col_model }}, {{ col_iso }}, desc(l))
  }else if(identical(remove_renamed, FALSE))
    dat2 <- dat1 %>%
      dplyr::arrange( {{ col_time }},{{ col_model }}, {{ col_iso }}, l)

  # dat3 <- dat2 %>%
  #   dplyr::distinct( {{ col_time }}, .keep_all = T)

  # dat3 %>%
  #   dplyr::pull( {{ col_photo_name }}) %>%
  #   purrr::map(file.remove)

}

[NewGraphEnvironment]

improvements now made to fpr::fpr_photo_remove_dupes

to see the photos that have duplicates and triplicates and the difference before and after running actual removals we can load a .RDATA file and inspect all the objects it brings in. Restarting R (control + command + 0) on mac beforehand can be handy. We should be careful to not rerun fpr::fpr_photo_remove_dupes with the dry_run = F.