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, a spectrum plot will be shown for non-seasonal data without missing values, and a PACF will be shown otherwise.

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.com/fpp3/

plot.ts, ACF, spec.ar

## Author

Rob J Hyndman & Mitchell O'Hara-Wild

## Examples

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