library(forecast)
library(seasonal)
library(ggplot2)
library(gridExtra)

p1 <- autoplot(AirPassengers) + ggtitle("Air Passengers", "Original data")
ap_sa <- final(seas(AirPassengers))
p2 <- autoplot(ap_sa) + ggtitle("Air Passengers", "Seasonally adjusted")

BoxCox.lambda(AirPassengers) # -0.29
BoxCox.lambda(ap_sa) # 0.38

grid.arrange(p1, p2)

library(tidyverse)
library(seasonal)
china <- data.frame(exports = exp, imports = imp)

p3 <- ggplot(china, aes(x = imports, y = exports)) +
  geom_point() +
  geom_path() +
  scale_x_sqrt() +
  scale_y_sqrt() +
  coord_equal()

p4 <- china %>%
  map_df(function(x){
    x = ts(x, start = c(1983, 7), frequency = 12)
    return(final(seas(x)))
  }) %>%
  ggplot(aes(x = imports, y = exports)) +
  geom_point() +
  geom_path() +
  scale_x_sqrt() +
  scale_y_sqrt() +
  coord_equal()

grid.arrange(p3, p4, ncol = 2)