Record table

This vignette shows how to get a species record table as returned by camtrapR’s function recordTable starting from a camera trap data package.

Setup

Load packages:

library(camtraptor)
library(lubridate)
library(dplyr)

By loading package camtraptor, a camera trap data package called camtraptor is made available. This data package contains camera trap data of musk rats and coypus. We will use this variable from now on.

Species record table

The camtrapR’s function recordTable() generates:

a record table from camera trap images or videos

At a certain extent the aggregation of media (e.g. images) into observations is already done in a camera trap data package.

If we consider that all observations are independent, then, it will be sufficient to run the following:

get_record_table(mica)
#> # A tibble: 17 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 B_DL_val … Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 B_DL_val … Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 B_DL_val … Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 B_DL_val … Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 B_DL_val … Anas s…     1 2020-07-30 04:29:31 2020-07-30 04:2…           81763
#>  7 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          520350
#>  8 B_DM_val … Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  9 B_DM_val … Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#> 10 B_DM_val … Ardea …     1 2021-03-27 20:38:18 2021-03-27 20:3…               0
#> 11 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#> 12 Mica Viane Homo s…     2 2019-10-23 10:19:44 2019-10-23 10:1…               0
#> 13 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 14 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 15 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 16 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 17 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

The function returns the same columns as the camtrapR’s function recordTable() except for column n. The following mapping is applied:

column name output	description
`Station`	the station name as provided by argument `stationCol` (default: `locationName`). It has to be a column of `deployments`
`Species`	`scientific_name` column in `observations`
`n`	`count` column in `observations` (number of individuals)
`DateTimeOriginal`	the `timestamp` column in `observations`
`Date`	the date from `timestamp`
`Time`	the time part from `timestamp`
`delta.time.secs`	the elapsed time in seconds between two (independent) observations
`delta.time.mins`	the elapsed time in minutes between two (independent) observations
`delta.time.hours`	the elapsed time in hours between two (independent) observations
`delta.time.days`	the elapsed time in days between two (independent) observations
`Directory`	a list with file paths as stored in column `file_path` of `media`
`FileName`	a list with file paths as stored in column `file_path` of `media`

The following remarks are both valid for camtrapR’s function recordTable() and the function get_record_table() of this package: 1. observations are grouped by station and species 2. observations of unidentified animals are removed 2. the elapsed time of the first observation (record) of a species at a certain station is set to 0 by default

Temporal independence

As described in Chapter 3 of camtrapR documentation, we could filter observations using an adjustable criterion for temporal independence between subsequent records of the same species in an attempt to remove non-independent records. As for recordTable(), this is achieved via argument minDeltaTime, defined as the minimum time difference (in minutes) between two records of the same species at the same station which are to be considered independent. As shown above, the default is 0, causing the function to return all records.

Again, as for recordTable(), we provide an argument, deltaTimeComparedTo, to further control how independence between records is assessed. Setting it to “lastRecord” returns only records taken minDeltaTime minutes after the last record, i.e. minDeltaTime minutes after timestamp of the last recorded media file. Example with minDeltaTime = 60 (1 hour):

mica_dependent <- mica
mica_dependent$data$observations[4,"timestamp"] <- lubridate::as_datetime("2020-07-29 05:55:00")
get_record_table(mica_dependent, 
                 minDeltaTime = 10, 
                 deltaTimeComparedTo = "lastRecord")
#> Number of not independent observations to be removed: 1
#> # A tibble: 16 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 B_DL_val … Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 B_DL_val … Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 B_DL_val … Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 B_DL_val … Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          602113
#>  7 B_DM_val … Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  8 B_DM_val … Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#>  9 B_DM_val … Ardea …     1 2021-03-27 20:38:18 2021-03-27 20:3…               0
#> 10 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#> 11 Mica Viane Homo s…     2 2019-10-23 10:19:44 2019-10-23 10:1…               0
#> 12 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 13 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 14 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 15 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 16 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Setting deltaTimeComparedTo to “lastIndependentRecord” returns only records taken minDeltaTime minutes after the last independent record, i.e. minDeltaTime minutes after timestamp of the last observation. Example with minDeltaTime = 60 (1 hour):

get_record_table(mica_dependent, 
                 minDeltaTime = 10, 
                 deltaTimeComparedTo = "lastIndependentRecord")
#> Number of not independent observations to be removed: 1
#> # A tibble: 16 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 B_DL_val … Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 B_DL_val … Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 B_DL_val … Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 B_DL_val … Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          602113
#>  7 B_DM_val … Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  8 B_DM_val … Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#>  9 B_DM_val … Ardea …     1 2021-03-27 20:38:18 2021-03-27 20:3…               0
#> 10 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#> 11 Mica Viane Homo s…     2 2019-10-23 10:19:44 2019-10-23 10:1…               0
#> 12 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 13 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 14 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 15 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 16 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Exclude some species

Similar to recordTable(), the function get_record_table() allows you also to exclude some species. Both vernacular names and scientific names are allowed (case insensitive):

get_record_table(mica, exclude = c("grey heron", "Anas platyrhynchos", "mens"))
#> Scientific name of grey heron: Ardea cinerea
#> Scientific name of mens: Homo sapiens
#> # A tibble: 11 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  2 B_DL_val … Anas s…     1 2020-07-30 04:29:31 2020-07-30 04:2…           81763
#>  3 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          520350
#>  4 B_DM_val … Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  5 B_DM_val … Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#>  6 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#>  7 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#>  8 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#>  9 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 10 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 11 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Station names

The column containing the station names can also be defined by the user if the default value, "locationName", is not the correct one. It has to be a valid column of deployments. Here below, locationID is used:

get_record_table(mica, stationCol = "locationID")
#> # A tibble: 17 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 2df5259b-… Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 2df5259b-… Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 2df5259b-… Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 2df5259b-… Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 2df5259b-… Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 2df5259b-… Anas s…     1 2020-07-30 04:29:31 2020-07-30 04:2…           81763
#>  7 2df5259b-… Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          520350
#>  8 ce943ced-… Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  9 ce943ced-… Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#> 10 ce943ced-… Ardea …     1 2021-03-27 20:38:18 2021-03-27 20:3…               0
#> 11 ff1535c0-… Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#> 12 3232bcfd-… Homo s…     2 2019-10-23 10:19:44 2019-10-23 10:1…               0
#> 13 ff1535c0-… Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 14 ff1535c0-… Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 15 ff1535c0-… Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 16 ff1535c0-… Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 17 ff1535c0-… Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Remove duplicates

It can happen that “duplicates” occur, e.g. when two distinct observations of the same species are made based on the same sequence of images, e.g. same species but different lifeStage or sex. You can decide what to do with these duplicates by using the argument removeDuplicateRecords: by default it is equal to TRUE. The duplicates are therefore removed. To not remove them, set removeDuplicateRecords equal to FALSE.

Let’s create an easy example with a duplicate based on mica datapackage:

mica_dup <- mica
# create a duplicate at 2020-07-29 05:46:48, location: B_DL_val 5_beek kleine vijver
mica_dup$data$observations[4,"sequenceID"] <- mica_dup$data$observations$sequenceID[3]
mica_dup$data$observations[4, "deploymentID"] <- mica_dup$data$observations$deploymentID[3]
mica_dup$data$observations[4, "timestamp"] <- mica_dup$data$observations$timestamp[3]

Record table without duplicates:

get_record_table(mica_dup)
#> # A tibble: 16 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 B_DL_val … Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 B_DL_val … Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 B_DL_val … Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 B_DL_val … Anas s…     1 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          602113
#>  7 B_DM_val … Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  8 B_DM_val … Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#>  9 B_DM_val … Ardea …     1 2021-03-27 20:38:18 2021-03-27 20:3…               0
#> 10 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#> 11 Mica Viane Homo s…     2 2019-10-23 10:19:44 2019-10-23 10:1…               0
#> 12 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 13 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 14 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 15 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 16 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Record table with duplicates:

get_record_table(mica_dup, removeDuplicateRecords = FALSE)
#> # A tibble: 17 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 B_DL_val … Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 B_DL_val … Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 B_DL_val … Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 B_DL_val … Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 B_DL_val … Anas s…     1 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  7 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          602113
#>  8 B_DM_val … Ardea       1 2021-04-05 19:08:33 2021-04-05 19:0…               0
#>  9 B_DM_val … Ardea       1 2021-04-11 19:43:09 2021-04-11 19:4…          520476
#> 10 B_DM_val … Ardea …     1 2021-03-27 20:38:18 2021-03-27 20:3…               0
#> 11 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#> 12 Mica Viane Homo s…     2 2019-10-23 10:19:44 2019-10-23 10:1…               0
#> 13 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 14 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 15 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 16 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 17 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Use filter predicates

As for visualization and all other functions, you can select a subset of deployments by using filter predicates. E.g. to get the record table of observations for the deployments with latitude equal or higher than 51.18:

get_record_table(mica, pred_gt("latitude", 51.18))
#> df %>% dplyr::filter((latitude > 51.18))
#> # A tibble: 13 × 12
#>    Station    Species     n DateTimeOriginal    Date       Time  delta.time.secs
#>    <chr>      <chr>   <dbl> <dttm>              <date>     <chr>           <dbl>
#>  1 B_DL_val … Anas p…     2 2020-07-31 04:43:33 2020-07-31 04:4…               0
#>  2 B_DL_val … Anas p…     5 2020-08-02 05:00:14 2020-08-02 05:0…          173801
#>  3 B_DL_val … Anas p…     3 2020-08-03 05:09:12 2020-08-03 05:0…           86938
#>  4 B_DL_val … Anas p…     3 2020-08-04 05:04:09 2020-08-04 05:0…           86097
#>  5 B_DL_val … Anas s…     4 2020-07-29 05:46:48 2020-07-29 05:4…               0
#>  6 B_DL_val … Anas s…     1 2020-07-30 04:29:31 2020-07-30 04:2…           81763
#>  7 B_DL_val … Anas s…     1 2020-08-05 05:02:01 2020-08-05 05:0…          520350
#>  8 B_DL_val … Castor…     1 2020-06-19 22:05:55 2020-06-19 22:0…               0
#>  9 B_DL_val … Martes…     1 2020-06-28 22:01:12 2020-06-28 22:0…               0
#> 10 B_DL_val … Mustel…     1 2020-06-19 22:31:51 2020-06-19 22:3…               0
#> 11 B_DL_val … Mustel…     1 2020-06-23 23:33:53 2020-06-23 23:3…          349322
#> 12 B_DL_val … Mustel…     1 2020-06-28 23:33:16 2020-06-28 23:3…          431963
#> 13 B_DL_val … Vulpes…     1 2020-06-26 02:09:25 2020-06-26 02:0…               0
#> # ℹ 5 more variables: delta.time.mins <dbl>, delta.time.hours <dbl>,
#> #   delta.time.days <dbl>, Directory <list>, FileName <list>

Other arguments needed?

Are there other arguments of camtrapR’s function recordTable() you think should be relevant to add to get_camera_record(), please let us know by posting an issue!

- Setup
- Species record table