Gets the effort for each deployment and a specific time interval such as day,
week, month or year. A custom time window can also be set up. This function
calls get_cam_op() internally.
Usage
get_custom_effort(
package = NULL,
...,
start = NULL,
end = NULL,
group_by = NULL,
unit = "hour",
datapkg = lifecycle::deprecated()
)Arguments
- package
Camera trap data package object, as returned by
read_camtrap_dp().- ...
filter predicates
- start
Start date. Default:
NULL. IfNULLthe earliest start date among all deployments is used. Ifgroup_byunit is notNULL, the lowest start value allowed is one group by unit before the start date of the earliest deployment. If this condition doesn't hold true, a warning is returned and the earliest start date among all deployments is used. Ifgroup_byunit isNULLthe start must be later than or equal to the start date among all deployments.- end
End date. Default:
NULL. IfNULLthe latest end date among all deployments is used. Ifgroup_byunit is notNULL, the latest end value allowed is one group by unit after the end date of the latest deployment. If this condition doesn't hold true, a warning is returned and the latest end date among all deployments is used. Ifgroup_byunit isNULLthe end must be earlier than or equal to the end date among all deployments.- group_by
Character, one of
"day","week","month","year". The effort is calculated at the interval rate defined ingroup_by. Default:NULL: no grouping, i.e. the entire interval fromstarttoendis taken into account as a whole. Calendar values are used, i.e. grouping by year will calculate the effort from Jan 1st up to Dec 31st for each year.- unit
Character, the time unit to use while returning custom effort. One of:
hour(default),day.- datapkg
Deprecated. Use
packageinstead.
Value
A tibble data frame with following columns:
deploymentID: Deployment unique identifier.locationName: Location name of the deployments.begin: Begin date of the interval the effort is calculated over.effort: The effort as number.unit: Character specifying the effort unit.
See also
Other exploration functions:
get_cam_op(),
get_effort(),
get_n_individuals(),
get_n_obs(),
get_n_species(),
get_rai(),
get_rai_individuals(),
get_record_table(),
get_scientific_name(),
get_species()
Examples
# Effort for each deployment over the entire duration of the project
# (datapackage) measured in hours (default)
get_custom_effort(mica)
#> # A tibble: 4 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek … 2019-10-09 239. hour
#> 2 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikke… 2019-10-09 219. hour
#> 3 62c200a9-0e03-4495-bcd8-032944f6f5a1 B_DM_val 4_'t WAD 2019-10-09 529. hour
#> 4 7ca633fa-64f8-4cfc-a628-6b0c419056d7 Mica Viane 2019-10-09 335. hour
# Effort for each deployment expressed in days
get_custom_effort(mica, unit = "day")
#> # A tibble: 4 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek … 2019-10-09 9.95 day
#> 2 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikke… 2019-10-09 9.11 day
#> 3 62c200a9-0e03-4495-bcd8-032944f6f5a1 B_DM_val 4_'t WAD 2019-10-09 22.0 day
#> 4 7ca633fa-64f8-4cfc-a628-6b0c419056d7 Mica Viane 2019-10-09 13.9 day
# Effort for each deployment from a specific start to a specific end
get_custom_effort(
mica,
start = as.Date("2019-12-15"), # or lubridate::as_date("2019-12-15")
end = as.Date("2021-01-10")
)
#> # A tibble: 4 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek … 2019-12-15 239. hour
#> 2 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikke… 2019-12-15 219. hour
#> 3 62c200a9-0e03-4495-bcd8-032944f6f5a1 B_DM_val 4_'t WAD 2019-12-15 0 hour
#> 4 7ca633fa-64f8-4cfc-a628-6b0c419056d7 Mica Viane 2019-12-15 0 hour
# Effort for each deployment at daily interval
get_custom_effort(
mica,
group_by = "day"
)
#> # A tibble: 2,232 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-09 0 hour
#> 2 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-10 0 hour
#> 3 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-11 0 hour
#> 4 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-12 0 hour
#> 5 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-13 0 hour
#> 6 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-14 0 hour
#> 7 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-15 0 hour
#> 8 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-16 0 hour
#> 9 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-17 0 hour
#> 10 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-18 0 hour
#> # ℹ 2,222 more rows
# Effort for each deployment at weekly interval
get_custom_effort(
mica,
group_by = "week"
)
#> # A tibble: 324 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-06 0 hour
#> 2 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-13 0 hour
#> 3 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-20 0 hour
#> 4 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-27 0 hour
#> 5 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-11-03 0 hour
#> 6 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-11-10 0 hour
#> 7 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-11-17 0 hour
#> 8 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-11-24 0 hour
#> 9 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-12-01 0 hour
#> 10 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-12-08 0 hour
#> # ℹ 314 more rows
# Effort for each deployment at monthly interval
get_custom_effort(
mica,
group_by = "month"
)
#> # A tibble: 76 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-10-01 0 hour
#> 2 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-11-01 0 hour
#> 3 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-12-01 0 hour
#> 4 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-01-01 0 hour
#> 5 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-02-01 0 hour
#> 6 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-03-01 0 hour
#> 7 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-04-01 0 hour
#> 8 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-05-01 0 hour
#> 9 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-06-01 0 hour
#> 10 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-07-01 66.5 hour
#> # ℹ 66 more rows
# Effort for each deployment at yearly interval
get_custom_effort(
mica,
group_by = "year"
)
#> # A tibble: 12 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2019-01-01 0 hour
#> 2 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2020-01-01 239. hour
#> 3 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek… 2021-01-01 0 hour
#> 4 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikk… 2019-01-01 0 hour
#> 5 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikk… 2020-01-01 219. hour
#> 6 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikk… 2021-01-01 0 hour
#> 7 62c200a9-0e03-4495-bcd8-032944f6f5a1 B_DM_val 4_'t W… 2019-01-01 0 hour
#> 8 62c200a9-0e03-4495-bcd8-032944f6f5a1 B_DM_val 4_'t W… 2020-01-01 0 hour
#> 9 62c200a9-0e03-4495-bcd8-032944f6f5a1 B_DM_val 4_'t W… 2021-01-01 529. hour
#> 10 7ca633fa-64f8-4cfc-a628-6b0c419056d7 Mica Viane 2019-01-01 335. hour
#> 11 7ca633fa-64f8-4cfc-a628-6b0c419056d7 Mica Viane 2020-01-01 0 hour
#> 12 7ca633fa-64f8-4cfc-a628-6b0c419056d7 Mica Viane 2021-01-01 0 hour
# Applying filter(s), e.g. deployments with latitude >= 51.18, can be
# combined with other arguments
get_custom_effort(mica, pred_gte("latitude", 51.18), group_by = "month")
#> df %>% dplyr::filter((latitude >= 51.18))
#> # A tibble: 6 × 5
#> deploymentID locationName begin effort unit
#> <chr> <chr> <date> <dbl> <chr>
#> 1 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek … 2020-06-01 0 hour
#> 2 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek … 2020-07-01 66.5 hour
#> 3 29b7d356-4bb4-4ec4-b792-2af5cc32efa8 B_DL_val 5_beek … 2020-08-01 172. hour
#> 4 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikke… 2020-06-01 219. hour
#> 5 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikke… 2020-07-01 0 hour
#> 6 577b543a-2cf1-4b23-b6d2-cda7e2eac372 B_DL_val 3_dikke… 2020-08-01 0 hour
# You can afterwards calculate the total effort over all deployments
library(dplyr)
get_custom_effort(mica, group_by = "year", unit = "day") %>%
dplyr::filter(effort > 0) %>%
dplyr::group_by(begin) %>%
dplyr::summarise(
deploymentIDs = list(deploymentID),
locationNames = list(locationName),
ndep = length(unique(deploymentID)),
nloc = length(unique(locationName)),
effort = sum(effort),
unit = unique(unit)
)
#> # A tibble: 3 × 7
#> begin deploymentIDs locationNames ndep nloc effort unit
#> <date> <list> <list> <int> <int> <dbl> <chr>
#> 1 2019-01-01 <chr [1]> <chr [1]> 1 1 13.9 day
#> 2 2020-01-01 <chr [2]> <chr [2]> 2 2 19.1 day
#> 3 2021-01-01 <chr [1]> <chr [1]> 1 1 22.0 day