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)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, union
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionBy 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!