Creates a matrix that shows number of captured animals between multiple seasons.
Arguments
- animal_id
A column in the dataframe of all samples that stores individual animal identifier code.
- capture_date
A column in the dataframe of all samples that stores the date of sample collection. Must be in
Dateformat.- start_dates
Vector of dates in
Dateformat that define the start of each season.- end_dates
Vector of dates in
Dateformat that define the end of each season.
Value
A matrix with 1 + no. seasons rows and columns.
diagonal: number of new captures in each session,
above diagonal: number of recaptures from season x to season y,
below diagonal: number of animals from season y that skipped season x.
Season x is defined in first row, season y in first column.
Column Tot. Capts gives all detected individuals in season y.
Row Tot. Skipped gives all individuals skipped in season x but detected
later.
Examples
# Define start and end dates for sampling seasons.
seasons <- data.frame(
start = c(
as.Date("2017-01-01"),
as.Date("2018-01-01"),
as.Date("2019-01-01")
),
end = c(
as.Date("2017-12-31"),
as.Date("2018-12-31"),
as.Date("2019-12-31")
)
)
# Create a dynamics matrix for animal captures.
dyn_matrix(
animal_id = wolf_samples$AnimalRef,
capture_date = wolf_samples$Date,
start_dates = seasons$start,
end_dates = seasons$end
)
#> 2017-01-01 - 2017-12-31 2018-01-01 - 2018-12-31
#> 2017-01-01 - 2017-12-31 13 8
#> 2018-01-01 - 2018-12-31 2 4
#> 2019-01-01 - 2019-12-31 0 0
#> Tot. Skipped 0 2
#> 2019-01-01 - 2019-12-31 Tot. Capts
#> 2017-01-01 - 2017-12-31 6 13
#> 2018-01-01 - 2018-12-31 6 12
#> 2019-01-01 - 2019-12-31 17 25
#> Tot. Skipped 0 NA