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)

Arguments

data

A tidy time series object (tsibble)

y

The variable to plot (a bare expression). If NULL, it will automatically selected from the data.

plot_type

type of plot to include in lower right corner. By default ("auto"), a season plot will be shown for seasonal data, and a spectrum plot will be shown for non-seasonal data.

lag_max

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.

Value

A list of ggplot objects showing useful plots of a time series.

References

Hyndman and Athanasopoulos (2019) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. https://OTexts.org/fpp3/

See also

Examples

library(tsibble) library(dplyr) tsibbledata::aus_retail %>% filter( State == "Victoria", Industry == "Cafes, restaurants and catering services" ) %>% gg_tsdisplay(Turnover)