Time-series plot

Plots one or more series over time, with an optional moving-average overlay. The input can be a ts object, a numeric vector, or a data frame with a time column, a value column and an optional grouping column.

Usage

timeseries_plot(
  x,
  time = NULL,
  value = NULL,
  group = NULL,
  rolling = NULL,
  forecast = NULL,
  frequency = NULL,
  level = 0.95,
  palette = NULL,
  point = FALSE,
  title = NULL,
  x_lab = NULL,
  y_lab = NULL
)

Arguments

x

A ts object, a numeric vector, or a data frame.

time

When x is a data frame, the time column (string or unquoted name); when x is a numeric vector, an optional vector of times.

value

When x is a data frame, the value column.

group

When x is a data frame, an optional grouping column mapped to colour.

rolling

Optional integer window for a centred moving-average overlay. The data are ordered by time (within each group) before the moving average is computed, so unsorted input is handled correctly.

forecast

Optional forecast overlay for a single (non-grouped) series. Either an integer horizon (number of future steps) to forecast with the built-in STL + seasonal-naive-with-drift method (see ts_forecast()), or a pre-computed forecast supplied as a data frame with columns time, fit and (optionally) lwr/upr, for instance from a fitted forecast::forecast() object. The point forecast continues the line and a shaded prediction-interval ribbon, which widens with the horizon, is drawn behind it.

frequency

Number of observations per period, used only to coerce a numeric x to a ts when an integer forecast horizon is requested.

level

Prediction-interval coverage for the built-in forecast (a single number strictly between 0 and 1, e.g. 0.95).

palette

Colours for the groups; defaults to depictr_palette().

point

Whether to add points as well as the line.

title, x_lab, y_lab

Title and axis labels.

Value

A ggplot2::ggplot object.

See also

ts_forecast(), decompose_plot(), seasonal_plot()

Examples

timeseries_plot(AirPassengers, rolling = 12,
                title = "Air passengers", y_lab = "Passengers")

# 24-month forecast with a 90% prediction interval
timeseries_plot(AirPassengers, forecast = 24, level = 0.9)