Commit ff4d04c4 authored by LDalby's avatar LDalby
Browse files

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

parent 93f723de
......@@ -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)
```
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