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Convert camera trap data to occupancy format

mm_to_occupancy.Rd

This function transforms camera trap detection data into an occupancy format suitable for analysis. It aggregates detections into user-defined time windows and optionally converts counts into presence-absence (0/1) data.

Usage

mm_to_occupancy(
  data,
  date_column,
  format = "%Y-%m-%d",
  site_column,
  species_column,
  size_column,
  by_day = 7,
  presence_absence = TRUE
)

Arguments

data

A data frame containing camera trap detection records.

date_column

The name of the column containing detection dates.

format

a character string. If not specified when converting from a character representation, it will try c("%Y-%m-%d", "%Y/%m/%d") one by one, and give an error if none works. Otherwise, the processing is via strptime() whose help page describes available conversion specifications.

site_column

The name of the column identifying sampling sites.

species_column

The name of the column containing species names. Can be NULL if species information is not needed.

size_column

The name of the column representing detection counts.

by_day

An integer specifying the number of days per time window (default: 7).

presence_absence

Logical. If TRUE, converts counts to presence-absence data (1 = detected, 0 = not detected). Default is TRUE.

Value

A wide-format data frame where rows represent sites (and optionally species), and columns represent detection windows. Values indicate either detection counts or presence-absence (0/1).

See also

mm_to_community()

Examples


data <- data.frame(
  date = c("01-01-2023", "03-01-2023", "10-01-2023", "15-01-2023"),
  site = c("A", "A", "B", "B"),
  species = c("Tiger", "Tiger", "Deer", "Deer"),
  count = c(1, 2, 3, 1)
)

occupancy_data <- mm_to_occupancy(
  data,
  date_column = date,
  site_column = site,
  species_column = species,
  size_column = count,
  by_day = 7,
  presence_absence = TRUE
)

occupancy_data
#> # A tibble: 4 × 732
#>   site  species `1-01-20 to 1-01-26` `1-01-27 to 1-02-02` `1-02-03 to 1-02-09`
#>   <chr> <chr>                  <dbl>                <dbl>                <dbl>
#> 1 A     Deer                       0                    0                    0
#> 2 A     Tiger                      0                    0                    0
#> 3 B     Deer                       0                    0                    0
#> 4 B     Tiger                      0                    0                    0
#> # ℹ 727 more variables: `1-02-10 to 1-02-16` <dbl>, `1-02-17 to 1-02-23` <dbl>,
#> #   `1-02-24 to 1-03-02` <dbl>, `1-03-03 to 1-03-09` <dbl>,
#> #   `1-03-10 to 1-03-16` <dbl>, `1-03-17 to 1-03-23` <dbl>,
#> #   `1-03-24 to 1-03-30` <dbl>, `1-03-31 to 1-04-06` <dbl>,
#> #   `1-04-07 to 1-04-13` <dbl>, `1-04-14 to 1-04-20` <dbl>,
#> #   `1-04-21 to 1-04-27` <dbl>, `1-04-28 to 1-05-04` <dbl>,
#> #   `1-05-05 to 1-05-11` <dbl>, `1-05-12 to 1-05-18` <dbl>, …