Plots a time series along with its ACF along with an customisable third graphic of either a PACF, histogram, lagged scatterplot or spectral density.
gg_tsdisplay(data, y = NULL, plot_type = c("auto", "partial", "season", "histogram", "scatter", "spectrum"), lag_max = NULL)
A tidy time series object (tsibble)
The variable to plot (a bare expression). If NULL, it will automatically selected from the data.
type of plot to include in lower right corner. By default
maximum lag at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series. Will be automatically limited to one less than the number of observations in the series.
A ggplot2 plot.
Hyndman and Athanasopoulos (2019) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. https://OTexts.org/fpp3/
library(tsibble) library(dplyr) tsibbledata::aus_retail %>% filter( State == "Victoria", Industry == "Cafes, restaurants and catering services" ) %>% gg_tsdisplay(Turnover)