`gg_tsdisplay.Rd`

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)

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
( |

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. |

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)