library(ggplot2)
library(plotly)
library(tsibble)
library(tsibbletalk)
library(feasts)
# nesting and crossing
tourism_shared <- tourism %>%
as_shared_tsibble(spec = (State / Region) * Purpose)
tourism_feat <- tourism_shared %>%
features(Trips, feat_stl) %>%
mutate(strong_trend = trend_strength > .5)
p0 <- plotly_key_tree(tourism_shared, height = 900, width = 600)
p1 <- tourism_shared %>%
ggplot(aes(x = Quarter, y = Trips)) +
geom_line(aes(group = Region), alpha = 0.5) +
facet_wrap(~ Purpose, scales = "free_y")
p2 <- tourism_feat %>%
ggplot(aes(x = trend_strength, y = seasonal_strength_year)) +
geom_point(aes(group = Region))
p4 <- tourism_feat %>%
plot_ly() %>%
add_histogram(x = ~ strong_trend)
subplot(p0,
subplot(
ggplotly(p1, tooltip = "Region", width = 900),
ggplotly(p2, tooltip = "Region", width = 900),
nrows = 2),
widths = c(.4, .6)) %>%
highlight(dynamic = TRUE)
subplot(p0,
subplot(
ggplotly(p1, tooltip = "Region", width = 900),
p4,
nrows = 2),
widths = c(.4, .6)) %>%
layout(barmode = "overlay") %>%
highlight(dynamic = TRUE)
# nesting only
library(dplyr)
library(crosstalk)
library(DT)
tourism_hier <- tourism %>%
group_by(State, Region) %>%
summarise(Trips = sum(Trips)) %>%
ungroup() %>%
as_shared_tsibble(spec = State / Region)
tourism_hier_feat <- tourism_hier %>%
features(Trips, feat_stl)
p0 <- plotly_key_tree(tourism_hier, height = 900, width = 600)
p1 <- tourism_hier %>%
ggplot(aes(x = Quarter, y = Trips, group = Region)) +
geom_line()
p2 <- tourism_hier_feat %>%
ggplot(aes(x = trend_strength, y = seasonal_strength_year)) +
geom_point()
t1 <- datatable(tourism_hier_feat)
bscols(
subplot(p0,
subplot(
ggplotly(p1, tooltip = "Region", width = 900),
ggplotly(p2, tooltip = "Region", width = 900),
nrows = 2),
widths = c(.4, .6)) %>%
highlight(dynamic = TRUE),
t1
)
# model()
tourism2 <- tourism %>%
filter(Region %in% c("Melbounre", "Sydney")) %>%
as_shared_tsibble()
dcmp <- tourism2 %>%
model(STL(Trips ~ season(window = Inf)))
components(dcmp)
library(fable)
tourism_f <- tourism2 %>%
model(ets = ETS(Trips)) %>%
forecast(h = "2 years")
tourism_f %>%
ggplot(aes(x = Quarter, y = Trips))
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