f_by_year <- f %>%
filter(!(site == "kakamega" & year_of < 1997)) %>%
group_by(site) %>%
mutate(sum_female_years = cumsum(female_years))
f_by_year$site <- revalue(f_by_year$site, site_map)
temp <- f_by_year %>%
filter(year_of == 2013)
p3 <- ggplot(f_by_year,
# p3 <- ggplot(filter(f_by_year, site %in% c("Gorilla", "Chimpanzee")),
aes(x = year_of, y = female_years)) +
geom_line(aes(color = site, group = site)) +
ggrepel::geom_text_repel(data = temp,
# ggrepel::geom_text_repel(data = filter(temp, site %in% c("Gorilla", "Chimpanzee")),
aes(x = year_of, y = female_years,
label = site, color = site),
nudge_x = 6, segment.size = NA, show.legend = FALSE) +
theme_journal_x2() +
coord_cartesian(xlim = c(1963, 2022)) +
scale_color_brewer(palette = "Dark2", guide = FALSE) +
labs(x = "Year", y = "Annual individual-years\nof fertility data")
p1 <- ggplot(f_by_year,
# p1 <- ggplot(filter(f_by_year, site %in% c("Gorilla", "Chimpanzee")),
aes(x = year_of, y = sum_female_years)) +
geom_line(aes(color = site, group = site)) +
ggrepel::geom_text_repel(data = temp,
# ggrepel::geom_text_repel(data = filter(temp, site %in% c("Gorilla", "Chimpanzee")),
aes(x = year_of, y = sum_female_years,
label = site, color = site),
nudge_x = 6, segment.size = NA, show.legend = FALSE) +
theme_journal_x2() +
coord_cartesian(xlim = c(1963, 2022)) +
scale_color_brewer(palette = "Dark2", guide = FALSE) +
labs(x = "Year", y = "Cummulative individual-years")
f1 <- f
f1$site <- revalue(f1$site, site_map)
temp <- f1 %>%
filter(year_of == 2013)
p2 <- ggplot(f1,
# p2 <- ggplot(filter(f1, site %in% c("Gorilla", "Chimpanzee")),
aes(x = year_of, y = n_animals)) +
geom_line(aes(color = site, group = site)) +
ggrepel::geom_text_repel(data = temp,
# ggrepel::geom_text_repel(data = filter(temp, site %in% c("Gorilla", "Chimpanzee")),
aes(x = year_of, y = n_animals,
label = site, color = site),
nudge_x = 6, segment.size = NA, show.legend = FALSE) +
theme_journal_x2() +
labs(x = "Year", y = "# Adult Females") +
scale_color_brewer(palette = "Dark2", guide = FALSE) +
coord_cartesian(xlim = c(1963, 2022))
cowplot::plot_grid(p2, p3, p1, ncol = 1, align = "hv")
apes <- filter(fert, Study.Id %in% c("karisoke", "gombe"))
apes <- apes %>%
group_by(Study.Id, Animal.Id) %>%
summarise(first_start = min(Start.Date)) %>%
inner_join(apes)
apes$is_break <- 1
apes <- apes %>%
group_by(Study.Id, Animal.Id) %>%
mutate(is_break = lead(is_break))
apes <- apes %>%
ungroup() %>%
group_by(Study.Id) %>%
mutate(Animal.Id = fct_reorder(Animal.Id, first_start))
ggplot() +
geom_segment(data = apes, aes(x = Animal.Id,
xend = Animal.Id,
y = Start.Date, yend = Stop.Date),
position = position_jitter(height = 0.1, width = 0)) +
geom_point(data = filter(apes, !is.na(is_break)),
aes(x = fct_reorder(Animal.Id, first_start),
y = Stop.Date), color = "red",
size = 1) +
theme_journal() +
facet_wrap(~Study.Id, scales = "free") +
coord_flip()
# ---- break_time ---------------------------------------------------------
brks <- apes %>%
group_by(Study.Id, Animal.Id) %>%
mutate(d_diff = as.numeric(lead(Start.Date) - Stop.Date)) %>%
ungroup() %>%
filter(!is.na(d_diff)) %>%
group_by(Study.Id) %>%
summarise(n = n(),
total_time = sum(d_diff) / 365.25)
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