library(tekkamaki)
library(conflicted)
library(dplyr)
library(ggplot2)
grDevices::palette("Okabe-Ito")
options(
ggplot2.continuous.colour = "viridis",
ggplot2.continuous.fill = "viridis",
ggplot2.discrete.colour = grDevices::palette()[-1],
ggplot2.discrete.fill = grDevices::palette()[-1]
)Run simulation:
## # A tibble: 1 × 13
## carrying_capacity infile last origin outdir overdispersion recruitment
## <dbl> <chr> <int> <dbl> <chr> <dbl> <dbl>
## 1 1000 "" 3 0.2 /var/folders… -1 2
## # ℹ 6 more variables: sample_size_adult <list>, sample_size_juvenile <list>,
## # seed <int>, years <int>, sample_family <list>, demography <list>A result is a nested tibble with two columns:
sample_family and demography:
sample_family = result$sample_family[[1L]] |> print()## # A tibble: 7,045 × 6
## id father_id mother_id birth_year location capture_year
## <int> <int> <int> <int> <int> <int>
## 1 95 0 0 -4 NA NA
## 2 96 0 0 -4 NA NA
## 3 94 95 96 1 NA NA
## 4 98 0 0 -4 NA NA
## 5 99 0 0 -4 NA NA
## 6 97 98 99 1 NA NA
## 7 93 94 97 6 NA NA
## 8 102 0 0 -4 NA NA
## 9 103 0 0 -4 NA NA
## 10 101 102 103 1 NA NA
## # ℹ 7,035 more rows
demography = result$demography[[1L]] |> print()## # A tibble: 8,862 × 5
## year season location age count
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 3 0 4 200
## 2 1 0 0 0 13515
## 3 1 1 0 0 8388
## 4 1 2 0 0 5343
## 5 1 3 0 0 3243
## 6 2 0 2 1 1459
## 7 2 1 2 1 1050
## 8 2 2 2 1 765
## 9 2 3 2 1 653
## 10 3 0 2 2 266
## # ℹ 8,852 more rowsHere is an example of visualizing demography:
df = demography |>
dplyr::mutate(time = year + 0.25 * season) |>
dplyr::mutate(age = ifelse(age > 4, "adult", "juvenile")) |>
dplyr::summarize(count = sum(count), .by = c(time, location, age))
ggplot(df) +
aes(time, count) +
geom_path(aes(colour = age, group = age)) +
facet_grid(vars(location)) +
theme_bw() +
theme(legend.position = "top")