Auto detect number of seasons. Compile with the new run.

Grendel/scenarios/numbers_report.Rmd

@@ -21,7 +21,7 @@ library(fs)
 library(colorblindr)
 library(glue)
 
-date_stamp <- "2019-01-04"
+date_stamp <- "2019-01-05"
 ```
 
 Document compiled: `r Sys.time()`
@@ -40,6 +40,8 @@ path(current, jobs, grain_dist_file) %>%
   fst::read_fst() %>% 
   as_tibble() -> grain_dists
 
+n_seasons <- max(grain_dists$season)
+
 goose_numbers_file <- glue("{jobs}_scenario-goose-numbers_{date_stamp}.fst")
 path(current, jobs, goose_numbers_file) %>%
   fst::read_fst() %>% 
@@ -60,7 +62,6 @@ goose_numbers %>%
   group_by(day, species, grain_dist) %>% 
   summarise(numbers = mean(numbers)) -> default_numbers
 
-# Summarize field forage data ----  
 # Mean numbers per day
 goose_numbers %>% 
   select(grain_dist, param, value, season, day, pinkfoot, barnacle, greylag) %>% 
@@ -77,116 +78,21 @@ left_join(numbers_per_day, .,
           by = c("grain_dist", "day", "species"),
           suffix = c("", "_default")) %>% 
   mutate(standarized = numbers/numbers_default) -> numbers_per_day
+```
 
-goose_sp <- "pinkfoot"
-goose_sp <- "greylag_daily_avg"
+```{r daily-avg-plot, echo=FALSE}
 numbers_per_day %>% 
   filter(param == "GOOSE_BN_STARTNO_SCALER",
-         species == goose_sp) %>% 
+         species == "pinkfoot") %>% 
   ggplot(aes(day, standarized)) +
   geom_line(aes(color = factor(value))) + 
   scale_color_brewer(palette = "Set3", name = "Barnacle scaler") +
   hrbrthemes::theme_ipsum_rc(axis_title_size = 12) +
   labs(title = "Daily average numbers",
        subtitle = "Pinkfeet numbers as a function of barnacle numbers",
-       caption = "Numbers are average of 3 consecutive seasons",
+       caption = glue("Numbers are average of {n_seasons} consecutive seasons"),
        y = "proportion of default numbers") +
   facet_grid(~grain_dist)
-
-# Works to down to here  
-
-goose_numbers %>% 
-  select(replicate, grain_dist, param, value, season, pinkfoot, barnacle, greylag) %>% 
-  group_by(replicate, grain_dist, param, value, season) %>% 
-  summarise(pinkfoot = sum(pinkfoot, na.rm = TRUE),
-            barnacle = sum(barnacle, na.rm = TRUE),
-            greylag = sum(greylag, na.rm = TRUE)) %>% 
-  group_by(grain_dist, param, value, season) %>%  
-  summarise(pinkfoot = mean(pinkfoot, na.rm = TRUE),
-            barnacle = mean(barnacle, na.rm = TRUE),
-            greylag = mean(greylag, na.rm = TRUE)) %>% 
-  gather(key = species, value = numbers, -grain_dist, -param, -value, -season) -> numbers_per_season
-
-
-numbers_per_season %>% 
-  filter(param == "GOOSE_BN_STARTNO_SCALER") %>% 
-  ggplot(aes(value, numbers/1e6)) +
-  geom_point(alpha = 0.5) +
-  geom_smooth() + 
-  facet_grid(species ~ grain_dist, scales = "free_y") + 
-  hrbrthemes::theme_ipsum_rc(axis_title_size = 12) + 
-  labs(title = "Goose days as a function of barnacle goose input numbers",
-       y = expression("Goose days" %*% 1e-6),
-       x = "Baseline input number scaler") + 
-  scale_x_continuous(breaks = c(2,4,6,8,10))
 ```
 
-```{r}
-default_numbers_file <- glue("{jobs}_scenario-goose-numbers_{date_stamp}.fst")
-path_desktop("goose", date_stamp, jobs, default_numbers_file) %>%
-  fst::read_fst() %>% 
-  as_tibble() %>% 
-  mutate(pinkfoot = pf_families + pf_non_breeders,
-         greylag = gl_families + gl_non_breeders,
-         barnacle = bn_families + bn_non_breeders,
-         day = day - 365L*season) -> goose_numbers
-
-left_join(goose_numbers, grain_dists, by = c("param", "value", "season")) -> goose_numbers
-
-
-goose_numbers %>% 
-  mutate(day = day - 365L*season) %>% 
-  select(grain_dist, param, value, season, day, pinkfoot, barnacle, greylag) %>% 
-  group_by(grain_dist, param, value, season, day) %>% 
-  summarise_at(.vars = vars(pinkfoot, barnacle, greylag),
-               .funs = funs(daily = sum)) %>% 
-  group_by(grain_dist, param, value, day) %>% 
-  summarise_at(.vars = vars(ends_with("daily")),
-               .funs = funs(avg = mean, min, max)) %>% 
-  ungroup() %>% 
-  gather(key = species, value = numbers, -grain_dist, -param, -value, -day) %>% 
-  filter(numbers > 0) -> numbers_per_day
-
-
-
-
-goose_numbers %>% 
-  filter(value == 1) %>% 
-  select(grain_dist, day, pinkfoot, greylag, barnacle) %>% 
-  gather(key = species, value = numbers, -day, -grain_dist) %>% 
-  filter(numbers > 0) %>% 
-  group_by(day, species, grain_dist) %>% 
-  summarise(default_numbers = round(mean(numbers))) -> default_numbers
-
-goose_numbers %>% 
-  select(season, param, value, grain_dist, day, pinkfoot, greylag, barnacle) %>% 
-  gather(key = species, value = numbers, -day, -season, -param, -value, -grain_dist) %>% 
-  filter(numbers > 0) %>% 
-  left_join(default_numbers, by = c("day", "species", "grain_dist")) %>% 
-  mutate(standarized = numbers/default_numbers) -> foo
-
-
-goose_sp <- "pinkfoot"
-goose_sp <- "greylag_daily_avg"
-foo %>% 
-  filter(param == "GOOSE_BN_STARTNO_SCALER",
-         species == goose_sp) %>% 
-  ggplot(aes(day, standarized)) +
-  geom_line(aes(color = factor(value))) + 
-  scale_color_brewer(palette = "Set3", name = "Barnacle scaler") +
-  hrbrthemes::theme_ipsum_rc(axis_title_size = 12) +
-  labs(title = "Daily average numbers",
-       subtitle = "Pinkfeet numbers as a function of barnacle numbers",
-       caption = "Numbers are average of 3 consecutive seasons",
-       y = "proportion of default numbers") +
-  facet_grid(~grain_dist)
-
-
-
-
-
-
-
-
-```