# Subsampling recordings

This vignette will walk through the workflow of subsampling recordings. This can be particularily useful if you have many more recordings than you can interpret manually.

## Create data

We will generate some data from the sites in `example_sites`. Click on the triangle below to see the details of this method.

Details of simulation

To simulate the file names we generate a series of recordings at each site. Here the schedule is every 30 minutes between 5:30 and 8:00 AM every second day between 1 May and 10 July.

Normally you would want to schedule based around local sunrise if targeting the dawn chorus, but this will work for our purposes.

Code

    library(dplyr)
    library(stringr)
    library(lubridate)
    #>
    #> Attaching package: 'lubridate'
    #> The following objects are masked from 'package:base':
    #>
    #>     date, intersect, setdiff, union

    simple_deploy <-
      tidyr::expand_grid(
        site_id = unique(example_sites$Sites),
        doy = seq(121, 191, by = 2),
        times = seq(-30, 120, by = 30)
      ) |>
      tidyr::separate(site_id, into = c("plot", "site"), sep = "_", remove = F) |>
      left_join(example_sites, join_by(site_id == Sites)) |>
      mutate(
        # aru_id = glue::glue("BARLT-000{as.numeric(as.factor(site_id))}"),
        date = ymd("2028-01-01") + doy,
        date_time = ymd_hm(glue::glue("{date} 06:00")) + minutes(times),
        date_time_chr = str_replace(as.character(date_time), "\\s", "T"),
        file_name = glue::glue("{plot}/{site_id}/{ARU}_{date_time_chr}.wav")
      )

    simple_deploy
    #> # A tibble: 2,160 × 16
    #>   site_id plot  site    doy times Date_set_out Date_removed ARU        lon   lat
    #>   <chr>   <chr> <chr> <dbl> <dbl> <chr>        <chr>        <chr>    <dbl> <dbl>
    #> 1 P01_1   P01   1       121   -30 2020-05-01   2020-05-03   BARLT10… -85.0  50.0
    #> 2 P01_1   P01   1       121     0 2020-05-01   2020-05-03   BARLT10… -85.0  50.0
    #> 3 P01_1   P01   1       121    30 2020-05-01   2020-05-03   BARLT10… -85.0  50.0
    #> 4 P01_1   P01   1       121    60 2020-05-01   2020-05-03   BARLT10… -85.0  50.0
    #> # ℹ 2,156 more rows
    #> # ℹ 6 more variables: Plots <chr>, Subplot <chr>, date <date>,
    #> #   date_time <dttm>, date_time_chr <chr>, file_name <glue>

Our site info will be that used in `example_sites`.

Code

    site_info <- simple_deploy |>
      slice_min(order_by = date_time, n = 1, by = site_id) |>
      dplyr::select(site_id, ARU, lon, lat, date_time)

## Clean metadata

To clean the metadata we can use the same code found in the Getting Started vignette. I have used a pipe to save space here, but you can see the details in the linked article (`vignette("ARUtools")`).

Code

    sites <- clean_site_index(site_info,
      name_aru_id = "ARU",
      name_site_id = "site_id",
      name_date_time = c("date_time"),
      name_coords = c("lon", "lat")
    )
    metadata <- clean_metadata(project_files = simple_deploy$file_name) |>
      add_sites(sites) |>
      calc_sun() |>
      dplyr::mutate(doy = lubridate::yday(date))
    #> Extracting ARU info...
    #> Extracting Dates and Times...
    #> Joining by column `date_time` using buffers
    dplyr::glimpse(metadata)
    #> Rows: 2,160
    #> Columns: 17
    #> $ file_name    <chr> "BARLT10962_2028-05-01T05:30:00.wav", "BARLT10962_2028-05…
    #> $ type         <chr> "wav", "wav", "wav", "wav", "wav", "wav", "wav", "wav", "…
    #> $ path         <chr> "P01/P01_1/BARLT10962_2028-05-01T05:30:00.wav", "P01/P01_…
    #> $ aru_id       <chr> "BARLT10962", "BARLT10962", "BARLT10962", "BARLT10962", "…
    #> $ manufacturer <chr> "Frontier Labs", "Frontier Labs", "Frontier Labs", "Front…
    #> $ model        <chr> "BAR-LT", "BAR-LT", "BAR-LT", "BAR-LT", "BAR-LT", "BAR-LT…
    #> $ aru_type     <chr> "BARLT", "BARLT", "BARLT", "BARLT", "BARLT", "BARLT", "BA…
    #> $ site_id      <chr> "P01_1", "P01_1", "P01_1", "P01_1", "P01_1", "P01_1", "P0…
    #> $ tz_offset    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
    #> $ date_time    <dttm> 2028-05-01 05:30:00, 2028-05-01 06:00:00, 2028-05-01 06:…
    #> $ date         <date> 2028-05-01, 2028-05-01, 2028-05-01, 2028-05-01, 2028-05-…
    #> $ longitude    <dbl> -85.03, -85.03, -85.03, -85.03, -85.03, -85.03, -85.03, -…
    #> $ latitude     <dbl> 50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 5…
    #> $ tz           <chr> "America/Toronto", "America/Toronto", "America/Toronto", …
    #> $ t2sr         <dbl> -46.80, -16.80, 13.20, 43.20, 73.20, 103.20, -43.35, -13.…
    #> $ t2ss         <dbl> 511.6167, 541.6167, 571.6167, 601.6167, 631.6167, 661.616…
    #> $ doy          <dbl> 122, 122, 122, 122, 122, 122, 124, 124, 124, 124, 124, 12…

## Parameters

Generally for random sampling you may not want to have recordings selected with equal weight across time and dates. For example if you are sampling songbirds in the breeding season, most species will be most active for a couple hours from around sunrise. You will also want to limit the dates to ensure you are picking up breeding birds and not migrants.

To deal with this issue we allow the user to specify selection weights based on the time to sunrise (or sunset) as well as the day of year.

To visualize the selection parameters use `gen_dens_sel_simulation`. This will show you how the sample weights will change over time and time to sunrise/sunset.

Code

    p <- sim_selection_weights(
      min_range = c(-70, 240),
      day_range = c(120, lubridate::yday(lubridate::ymd("2021-07-20"))),
      min_mean = 30, min_sd = 60,
      day_mean = lubridate::yday(lubridate::ymd("2021-06-10")),
      day_sd = 20, offset = 0,
      return_log = TRUE, selection_fun = "norm"
    )

## Calculate selection weights

Once you have your parameters set up, you can use them to calculate the sampling weights from the metadata.

Code

    full_selection_probs <-
      metadata |>
      calc_selection_weights(
        col_site_id = site_id,
        col_min = t2sr,
        col_day = doy,
        params = p
      )

Below you can see the selection weights `psel_normalized`. The highest selection weights should match the output from `gen_dens_sel_simulation()`. Not that as our schedule is set to start based on time, the time to sunrise that the recordings occur differ by sites.

## Assign sample size

The next step is to assign sample sizes. This can be done on a per site basis or across the board. Here I use a 2% sampling rate, but your own subsampling intensity will depend on project goals. You can also set an oversample to draw extra samples in case some samples are unusable (e.g. due to wind).

Code

    sample_size <- count(full_selection_probs, site_id) |>
      transmute(site_id,
        n = floor(n * .02),
        n_os = ceiling(n * .3)
      )

The figure below can provide a coarse rule of thumb on how many minutes of recordings you might need or number of samples. It of course has many assumptions including knowledge of probability of observing per minute, that this probability will change by date, time of day, conspecific behaviour, weather, etc.

## Draw subsample

### GRTS

There are a few ways to subsample recordings. `ARUtools` has imported the grts() (Generalized Random Tessellation Stratified algorithm) function from spsurvey package. We developed a wrapper around the function to simplify it’s usage for our particular use case.

GRTS allows us to sample using a dispersed samples, while maintaining a stochastic element to sampling. In our case we are selecting samples dispersed across dates and time in the dates.

Code

    grts_res <- sample_recordings(full_selection_probs,
      n = sample_size,
      col_site_id = site_id,
      seed = 2024,
      col_sel_weights = psel_normalized
    )

    dplyr::glimpse(grts_res$sites_base)
    #> Rows: 40
    #> Columns: 33
    #> $ siteID          <chr> "sample-01", "sample-02", "sample-03", "sample-04", "s…
    #> $ siteuse         <chr> "Base", "Base", "Base", "Base", "Base", "Base", "Base"…
    #> $ replsite        <chr> "None", "None", "None", "None", "None", "None", "None"…
    #> $ lon_WGS84       <dbl> 0.001365439, 0.001545102, 0.001203742, 0.001563069, 0.…
    #> $ lat_WGS84       <dbl> 4.723823e-04, 5.254387e-04, -2.502089e-04, -2.125268e-…
    #> $ stratum         <chr> "P01_1", "P01_1", "P01_1", "P01_1", "P02_1", "P02_1", …
    #> $ wgt             <dbl> 35.29300, 36.72486, 72.07435, 38.37921, 33.54426, 76.4…
    #> $ ip              <dbl> 0.028334233, 0.027229512, 0.013874562, 0.026055777, 0.…
    #> $ caty            <chr> "None", "None", "None", "None", "None", "None", "None"…
    #> $ aux             <dbl> 0.9307375, 0.8944490, 0.4557587, 0.8558936, 0.9678483,…
    #> $ file_name       <chr> "BARLT10962_2028-05-31T06:30:00.wav", "BARLT10962_2028…
    #> $ type            <chr> "wav", "wav", "wav", "wav", "wav", "wav", "wav", "wav"…
    #> $ path            <chr> "P01/P01_1/BARLT10962_2028-05-31T06:30:00.wav", "P01/P…
    #> $ aru_id          <chr> "BARLT10962", "BARLT10962", "BARLT10962", "BARLT10962"…
    #> $ manufacturer    <chr> "Frontier Labs", "Frontier Labs", "Frontier Labs", "Fr…
    #> $ model           <chr> "BAR-LT", "BAR-LT", "BAR-LT", "BAR-LT", "Song Meter 4"…
    #> $ aru_type        <chr> "BARLT", "BARLT", "BARLT", "BARLT", "SongMeter", "Song…
    #> $ site_id         <chr> "P01_1", "P01_1", "P01_1", "P01_1", "P02_1", "P02_1", …
    #> $ tz_offset       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
    #> $ date_time       <dttm> 2028-05-31 06:30:00, 2028-06-20 06:30:00, 2028-05-13 …
    #> $ date            <date> 2028-05-31, 2028-06-20, 2028-05-13, 2028-06-22, 2028-…
    #> $ longitude       <dbl> -85.03, -85.03, -85.03, -85.03, -87.45, -87.45, -87.45…
    #> $ latitude        <dbl> 50.01, 50.01, 50.01, 50.01, 52.68, 52.68, 52.68, 52.68…
    #> $ tz              <chr> "America/Toronto", "America/Toronto", "America/Toronto…
    #> $ t2ss            <dbl> 530.1500, 516.0667, 493.4667, 455.6000, 438.7000, 551.…
    #> $ psel_by         <chr> "t2sr", "t2sr", "t2sr", "t2sr", "t2sr", "t2sr", "t2sr"…
    #> $ psel_min        <dbl> -0.4561300, -0.4598709, -0.4920422, -0.4628636, -0.460…
    #> $ psel_doy        <dbl> -0.6675488, -0.6758601, -0.8021915, -0.6858336, -0.650…
    #> $ psel            <dbl> 0.3250817, 0.3211873, 0.2741078, 0.3170496, 0.3290624,…
    #> $ psel_scaled     <dbl> 0.9775970, 0.9658855, 0.8243066, 0.9534425, 0.9895679,…
    #> $ psel_std        <dbl> 0.9776457, 0.9659337, 0.8243477, 0.9534901, 0.9895803,…
    #> $ psel_normalized <dbl> 0.9307375, 0.8944490, 0.4557587, 0.8558936, 0.9678483,…
    #> $ geometry        <POINT [m]> POINT (152 52.23333), POINT (172 58.1), POINT (1…

### slice_sample

If you don’t want to sample using `grts()` you can just use `dplyr::slice_sample()`.

Code

    withr::with_seed(2024, {
      random_sample <-
        full_selection_probs |>
        dplyr::slice_sample(
          n = 4, by = site_id,
          weight_by = psel_normalized,
          replace = F
        )
    })

If you want to set site specific sample sizes, you can use some dplyr tricks to handle that:
Code

    withr::with_seed(2024, {
      random_sample_stratified <-
        full_selection_probs |>
        left_join(sample_size, by = join_by(site_id)) |>
        nest_by(site_id, n) |>
        rowwise() |>
        mutate(sample = list(dplyr::slice_sample(
          .data = data,
          n = .data$n,
          weight_by = psel_normalized,
          replace = F
        ))) |>
        dplyr::select(site_id, sample) |>
        tidyr::unnest(sample)
    })

Oversamples with random sampling, just involves dropping recordings you’ve selected and drawing some more.

Code

    oversample <- filter(
      full_selection_probs,
      !path %in% random_sample$path
    ) |>
      dplyr::slice_sample(
        n = 2, by = site_id,
        weight_by = psel_normalized,
        replace = F
      )

### Compare methods

Looking at the draws, you may notice the `grts` results are more spaced out, while the random sample, contains more clumping, which is characteristic of random sampling.

## Assigning recording lengths

While some surveys may include all full recordings, others may only interpret a portion of some recordings, allowing for more recordings to be at least partially interpreted.

To assign lengths, we can repeat the same process as above sequentially assigning lengths or more simply just assign them all at once.

Code

    withr::with_seed(6546, {
      random_sample$length <- sample(
        x = c("5min", "3min", "1min"),
        size = nrow(random_sample), replace = T
      )
    })

    #> # A tibble: 3 × 11
    #>   length P02_1 P03_1 P04_1 P05_1 P07_1 P08_1 P09_1 P10_1 P01_1 P06_1
    #>   <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    #> 1 1min       1     3     1     1     3     2     1     1     0     0
    #> 2 3min       1     1     1     2     0     1     3     2     3     3
    #> 3 5min       2     0     2     1     1     1     0     1     1     1

This however creates the issue that some sites will have less and some more of each length.

Code

    withr::with_seed(569, {
      sample5min <- slice_sample(random_sample,
        n = 1, by = site_id, weight_by = psel_normalized
      )

      sample3min <- slice_sample(
        random_sample |>
          filter(!path %in% sample5min$path),
        n = 1, by = site_id, weight_by = psel_normalized
      )

      random_sample_with_lengths <- random_sample |>
        mutate(
          Length_group =
            case_when(
              path %in% sample5min$path ~ "5min",
              path %in% sample3min$path ~ "3min",
              TRUE ~ "1min"
            ),
          length_clip = as.numeric(str_extract(Length_group, "^\\d")) * 60
        )
    })

Sampling by site and length creates an equal sample by length and site.

    #> # A tibble: 3 × 11
    #>   Length_group P01_1 P02_1 P03_1 P04_1 P05_1 P06_1 P07_1 P08_1 P09_1 P10_1
    #>   <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
    #> 1 1min             2     2     2     2     2     2     2     2     2     2
    #> 2 3min             1     1     1     1     1     1     1     1     1     1
    #> 3 5min             1     1     1     1     1     1     1     1     1     1

# Assign start times

ARUtools includes the function `get_wav_length`, which will return the length of a ‘wav’ file in seconds.

Code

    random_sample_with_lengths$length_clip <-
      purrr::map(
        1:nrow(random_sample_with_lengths),
        ~ get_wav_length(
          path = random_sample_with_lengths$path[[.x]],
          return_numeric = T
        )
      )

This can be used to safely add a random start time to your recordings if you don’t want to all start at zero.

For this document we will assume all recordings are 5 minutes.

Code

    random_sample_with_lengths$length <- 5 * 60

Code

    random_sample_with_lengths <-
      random_sample_with_lengths |>
      rowwise() |>
      mutate(StartTime = case_when(
        Length_group == "5min" ~ 0,
        TRUE ~ runif(
          1, 0,
          pmax(
            0,
            length - length_clip
          )
        )
      )) |>
      ungroup()

## Format output filename

Finally we can format the filenames for Wildtrax, which processes sites, dates, and times based on file name.

Code

    final_selection <- random_sample_with_lengths |>
      add_wildtrax()

    final_selection |>
      head() |>
      dplyr::select(path, wildtrax_file_name)
    #> # A tibble: 6 × 2
    #>   path                                         wildtrax_file_name
    #>   <chr>                                        <glue>
    #> 1 P01/P01_1/BARLT10962_2028-07-06T07:00:00.wav P01_1_20280706_070000
    #> 2 P01/P01_1/BARLT10962_2028-06-02T07:00:00.wav P01_1_20280602_070000
    #> 3 P01/P01_1/BARLT10962_2028-05-15T06:30:00.wav P01_1_20280515_063000
    #> 4 P01/P01_1/BARLT10962_2028-07-04T06:30:00.wav P01_1_20280704_063000
    #> # ℹ 2 more rows

## Clip and copy files for upload

The `format_clip_wave` will rename, clip and copy your file to a location that can then be used for uploading to Wildtrax

Setting up a subset of folders will allow you to copy files to an organized folder structure which can help with checking for errors prior to uploading.

Code

    out_directory <- "/path/to/upload/directory/"
    dir.create(out_directory, recursive = T)
    ul_tab <- expand_grid(
      period = c("Dawn"), # Add 'Dusk' if using more than one time period
      length = unique(selected_recordings$Length_group)
    )
    purrr::map(glue::glue("{out_directory}/{ul_tab$period}/{ul_tab$length}"),
      dir.create,
      recursive = T
    )

If you have a large number of files, you may want to run this separately for files that require clipping and those that do not as it will run much quicker for the files that do not need to be clipped.

You can use the option `use_job=T` to allow the use of the `job` package, which will launch a background job if the copying will take a long time.

Code

    log_output <-
      format_clip_wave(
        segment_df = final_selection,
        in_base_directory = "", out_base_directory = out_directory,
        length_clip_col = "length_clip",
        sub_dir_out_col = c("Time_period", "Length_group"),
        filepath_in_col = "path",
        out_filename_col = "wildtrax_file_name",
        use_job = F, filewarn = F
      )

That’s it, your output folder should now have your selected files, properly named for Wildtrax and ready to upload.