In diazrenata/BBSstories: What the Package Does (One Line, Title Case)

library(ggplot2)
library(dplyr)

individual_rats <- portalr::summarise_individual_rodents(clean = TRUE, type = "Granivores", time = "date", length = "Longterm")

head(individual_rats)

individual_rats <- individual_rats %>%
  filter(year %in% c(1978:2002), !is.na(wgt), treatment == "control") %>%
  mutate(six_mo = ifelse(month > 6, 2, 1)) %>%
  mutate(year_six_mo = (year * 10) + six_mo) %>%
  mutate(bmr = 5.69 * (wgt ^ .75)) %>%
  group_by(year_six_mo) %>%
  summarize(n = dplyr::n(),
            biomass = sum(wgt),
            energy = sum(bmr)) %>%
  ungroup() %>%
  mutate(mean_m = biomass/n,
         mean_e = energy/n) %>%
  tidyr::pivot_longer(-year_six_mo, names_to = "currency")



ggplot(individual_rats, aes(year_six_mo, value)) +
  geom_point() +
  geom_line() +
  theme_bw() +
  facet_wrap(vars(currency), scales = "free_y")

wide_rats <- individual_rats %>%
  tidyr::pivot_wider(id_cols = year_six_mo, names_from = currency, values_from = value)

ggplot(wide_rats, aes(n, mean_e)) +
  geom_point() +
  theme_bw()

summary(lm(data =wide_rats, scale(mean_e) ~ scale(n)))


summary(lm(scale(energy) ~ year_six_mo, wide_rats))
summary(lm(scale(n) ~ year_six_mo, wide_rats))
summary(lm(scale(biomass) ~ year_six_mo, wide_rats))
summary(lm(scale(mean_e) ~ year_six_mo, wide_rats))