stat_decomp: Classical seasonal adjustment Stat

Description Usage Arguments Details See Also Examples

View source: R/decompose-seasonal.R

Description

Conducts seasonal adjustment on the fly for ggplot2, from classical seasonal decomposition by moving averages

Usage

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stat_decomp(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  frequency = NULL, type = c("additive", "multiplicative"),
  index.ref = NULL, index.basis = 100, ...)

Arguments

mapping

Set of aesthetic mappings created by aes() or aes_(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.

data

The data to be displayed in this layer. There are three options:

If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot().

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created.

A function will be called with a single argument, the plot data. The return value must be a data.frame., and will be used as the layer data.

geom

The geometric object to use display the data

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

show.legend

logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders().

frequency

The frequency for the time series

type

The type of seasonal component

index.ref

if not NULL, a vector of integers indicating which elements of the beginning of each series to use as a reference point for converting to an index. If NULL, no conversion takes place and the data are presented on the original scale.

index.basis

if index.ref is not NULL, the basis point for converting to an index, most commonly 100 or 1000. See examples.

...

other arguments for the geom

Details

Classical decomposition is a very basic way of performing seasonal adjustment and is not recommended if you have access to X13-SEATS-ARIMA (stat_seas). stat_decomp cannot allow the seasonality to vary over time, or take outliers into account in calculating seasonality.

See Also

decompose

Other time series stats for ggplot2: stat_index, stat_rollapplyr, stat_seas, stat_stl

Examples

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ap_df <- tsdf(AirPassengers)

# Default additive decomposition (doesn't work well in this case!):
ggplot(ap_df, aes(x = x, y = y)) +
   stat_decomp()

# Multiplicative decomposition, more appropriate:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_decomp(type = "multiplicative")

# Multiple time series example:
ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
  geom_point() +
  facet_wrap(~sex) +
  stat_decomp() +
  ggtitle("Seasonally adjusted lung deaths")

# Example using index:
ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
  facet_wrap(~sex) +
  stat_decomp(index.ref = 1:12, index.basis = 1000) +
  ggtitle("Rolling annual median lung deaths, indexed (average month in 1974 = 1000)")

ggseas documentation built on June 12, 2018, 5:04 p.m.