Checks that data is in the correct format and manipulates it for analysis.
Examples
bpt_manipulate_data_analysis(
event_data = event_data,
location_data = location_data,
census_data = census_data,
proportion_calf_data = proportion_calf_data
)
#> $data
#> # A tibble: 11 × 25
#> f0 f1 m2 m3 ma fa m0 m1 u0 u1 ua calf
#> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1 0 1 1 2 1 2 2 0 1 0 0 3
#> 2 2 1 1 0 1 3 1 2 0 0 1 3
#> 3 0 0 0 0 0 1 1 0 0 0 0 1
#> 4 1 3 2 0 1 2 3 3 1 1 0 5
#> 5 4 1 2 2 0 10 0 4 0 1 0 4
#> 6 0 1 0 1 1 2 2 0 0 1 0 2
#> 7 2 0 2 0 2 3 5 2 1 0 0 8
#> 8 1 0 0 0 0 1 0 0 0 1 0 1
#> 9 2 2 0 0 1 8 0 1 1 1 1 3
#> 10 4 2 2 0 2 10 1 2 0 0 0 5
#> 11 2 1 1 1 0 5 2 3 1 1 1 5
#> # ℹ 13 more variables: yearling <int>, adult <int>, groupsize_total <int>,
#> # annual <fct>, week <int>, weekfac <fct>, season <fct>, doy <int>,
#> # doy_fac <fct>, location <fct>, location_weekfac <fct>, season_annual <fct>,
#> # id <fct>
#>
#> $census_data
#> # A tibble: 2 × 4
#> census census_cv census_study_year census_doy
#> <int> <dbl> <chr> <int>
#> 1 250 0.05 2020-2021 365
#> 2 275 0.06 2021-2022 365
#>
#> $prop_calf_data
#> # A tibble: 2 × 4
#> prop_calf prop_calf_cv prop_calf_study_year prop_calf_doy
#> <dbl> <dbl> <chr> <int>
#> 1 0.2 0.05 2020-2021 365
#> 2 0.15 0.09 2021-2022 365
#>