Package 'ggseas' reference manual

Title:	'stats' for Seasonal Adjustment on the Fly with 'ggplot2'
Description:	Provides 'ggplot2' 'stats' that estimate seasonally adjusted series and rolling summaries such as rolling average on the fly for time series.
Authors:	Peter Ellis [aut, cre], Christophe Sax [ctb]
Maintainer:	Peter Ellis <[email protected]>
License:	GPL-3
Version:	0.5.4
Built:	2024-10-31 05:36:30 UTC
Source:	https://github.com/ellisp/ggseas

Seasonal decomposition on the fly

Description

ggseas allows you to perform seasonal decomposition on the fly as part of a 'ggplot2' pipeline.

Details

Two main sets of functions are provided:

stat_seas(), stat_stl() and friends do seasonal adjustment, indexing, rolling averages and by default render a line geom. You can consider these as taking the place of geom_line() in the ggplot2 pipeline
ggsdc() goes where ggplot() normally does (ie at the beginning of the graphics part of the pipeline) and creates a graphic with four facets for the original data, trend, seasonal and random components

Author(s)

Maintainer: Peter Ellis [email protected]

Other contributors:

Christophe Sax [contributor]

Visualise seasonal decomposition

Description

Creates a four-facet plot of seasonal decomposition showing observed, trend, seasonal and random components

Usage

ggsdc(data, mapping, frequency = NULL, method = c("stl", "decompose",
  "seas"), start = NULL, s.window, type = c("additive", "multiplicative"),
  index.ref = NULL, index.basis = 100, facet.titles = c("observed",
  "trend", "seasonal", "irregular"))
ggsdc(data, mapping, frequency = NULL, method = c("stl", "decompose",
  "seas"), start = NULL, s.window, type = c("additive", "multiplicative"),
  index.ref = NULL, index.basis = 100, facet.titles = c("observed",
  "trend", "seasonal", "irregular"))

Arguments

`data`	dataset to use for plot.
`mapping`	List of aesthetic mappings. Must include x and y, and optionally can include colour/color
`frequency`	frequency of the period of the time series eg 12 = monthly
`method`	function to use for performing the seasonal decomposition. stl and decompose are functions in the `stats` package; `seas` is access to the `seats` program from X-13-SEATS-ARIMA via the `seasonal` package
`start`	starting time for the data; only needed if `method = 'seas'`.
`s.window`	parameter to pass to `stl()`
`type`	parameter to pass to `decompose()`
`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.
`facet.titles`	a vector in the order of `observed`, `trend`, `seasonal` and `irregular` for the titles of the four facets of the decomposition. Make sure you get the order right...

Details

This function takes a data frame and performs seasonal decomposition on the variable mapped to the y aesthetic, grouped by the variable (if any) mapped to the colour or color aesthetic. This allows the user to perform the equivalent of plot(stats::decompose(x)) but in the ggplot2 environment for themes, polishing etc; and to overlay decompositions on the same graphic; and with the X13-SEATS-ARIMA seasonal decomposition (so far only with default settings).

The "seasonal" component can be either multiplicative (in which case it will in a small range of values around one) or additive (in which case it will be on the scale of the original data), depending on the settings.

Value

an object of class ggplot with four facets

Examples

# sample time series data in data frame
ap_df <- tsdf(AirPassengers)

ggsdc(ap_df, aes(x = x, y = y), method = "decompose") +
   geom_line()
   
ggsdc(ap_df, aes(x = x, y = y), method = "decompose", 
      type = "multiplicative") +
   geom_line(colour = "blue", size = 2) +
   theme_light(8)

ggsdc(ap_df, aes(x = x, y = y), method = "stl", s.window = 7) +
   labs(x = "", y = "Air passenger numbers") +
   geom_point()
   
## Not run:       
ggsdc(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex), method = "seas") +
      geom_line()
      
serv <- subset(nzbop, Account == "Current account" & 
            Category %in% c("Services; Exports total", "Services; Imports total"))
ggsdc(serv, aes(x = TimePeriod, y = Value, colour = Category),
      method = "seas", start = c(1971, 2), frequency = 4) +
   geom_line()

## End(Not run)

ggsdc(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex), s.window = 7, 
   index.ref = 1:12, index.basis = 1000) +
   geom_line() +
   ylab("Lung deaths index (average month in 1974 = 1000)")
      
bop <- subset(nzbop, Account == "Current account" & !Balance)
ggsdc(bop, aes(x = TimePeriod, y = Value, colour = Category), frequency = 4, 
   method = "decomp", type = "multiplicative") +
      geom_line() 
      
ggsdc(bop, aes(x = TimePeriod, y = Value, colour = Category), frequency = 4, s.window = 7) +
      geom_line() 
# sample time series data in data frame
ap_df <- tsdf(AirPassengers)

ggsdc(ap_df, aes(x = x, y = y), method = "decompose") +
   geom_line()
   
ggsdc(ap_df, aes(x = x, y = y), method = "decompose", 
      type = "multiplicative") +
   geom_line(colour = "blue", size = 2) +
   theme_light(8)

ggsdc(ap_df, aes(x = x, y = y), method = "stl", s.window = 7) +
   labs(x = "", y = "Air passenger numbers") +
   geom_point()
   
## Not run:       
ggsdc(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex), method = "seas") +
      geom_line()
      
serv <- subset(nzbop, Account == "Current account" & 
            Category %in% c("Services; Exports total", "Services; Imports total"))
ggsdc(serv, aes(x = TimePeriod, y = Value, colour = Category),
      method = "seas", start = c(1971, 2), frequency = 4) +
   geom_line()

## End(Not run)

ggsdc(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex), s.window = 7, 
   index.ref = 1:12, index.basis = 1000) +
   geom_line() +
   ylab("Lung deaths index (average month in 1974 = 1000)")
      
bop <- subset(nzbop, Account == "Current account" & !Balance)
ggsdc(bop, aes(x = TimePeriod, y = Value, colour = Category), frequency = 4, 
   method = "decomp", type = "multiplicative") +
      geom_line() 
      
ggsdc(bop, aes(x = TimePeriod, y = Value, colour = Category), frequency = 4, s.window = 7) +
      geom_line()

Monthly Deaths from Lung Diseases in the UK

Description

A long form combination of fdeaths and mdeaths from the datasets package.

Usage

ldeaths_df
ldeaths_df

Format

A data frame with 141 rows and 3 variables.

Details

YearMon. Approximate, regular decimal representation of the beginning of the period of measurement. January 1974 is 1974.000
sex.
deaths. Monthly deaths from bronchitis, emphysema and asthma. ...

Source

P. J. Diggle (1990) Time Series: A Biostatistical Introduction. Oxford, table A.3

New Zealand Balance of Payments major components 1971Q2 to 2015Q2

Description

New Zealand's "BPM6 Quarterly, Balance of payments major components (Qrtly-Mar/Jun/Sep/Dec)".

Usage

nzbop
nzbop

Format

An object of class data.frame with 3676 rows and 5 columns.

Details

"BPM6" refers to the sixth edition of the IMF's Balance of Payments and International Investment Position Manual, which is the method used by Statistics New Zealand to prepare these data.

Note:

'Value' is in millions of New Zealand dollars and is not adjusted for inflation.
'fob' means 'free on board'.
'inv.' stands for investment
TimePeriod is the last day of the quarterly reference period ie 1971-06-30 means the fourth, fifth and six months of 1971.

This dataset was downloaded from http://www.stats.govt.nz/infoshare/ and transformed in the following way:

missing values were filtered out (ie of series that started later than the longest series)
a 'Balance' indicator variable was added for easier manipulation and filtering
the single variable categorisation was split into two (Account and Category) to make it tidier.

Source

Statistics New Zealand http://www.stats.govt.nz/browse_for_stats/economic_indicators/balance_of_payments/info-releases.aspx

Classical seasonal adjustment Stat

Description

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

Usage

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, ...)
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.

Examples

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)")

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)")

Index Stat

Description

Convert a time series from the original scale to an index for ggplot2

Usage

stat_index(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  index.ref = NULL, index.basis = 100, ...)
stat_index(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  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()`.
`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

Examples

ap_df <- tsdf(AirPassengers)

ggplot(ldeaths_df, aes(x = YearMon, y = deaths, color = sex)) +
   stat_index(index.ref = 1:12, index.basis = 1000) +
   ylab("Deaths index\n(average of first 12 months = 1000")
   
ap_df <- tsdf(AirPassengers)

ggplot(ldeaths_df, aes(x = YearMon, y = deaths, color = sex)) +
   stat_index(index.ref = 1:12, index.basis = 1000) +
   ylab("Deaths index\n(average of first 12 months = 1000")

Rolling summary Stat

Description

Calculates a rolling summary, usually rolling average, on the fly for ggplot2

Usage

stat_rollapplyr(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE, width,
  align = "right", FUN = mean, index.ref = NULL, index.basis = 100, ...)
stat_rollapplyr(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE, width,
  align = "right", FUN = mean, 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()`.
`width`	The width to which the rolling version of FUN is applied
`align`	specifies whether the transformed series should be left or right-aligned or centered compared to the rolling window of observations
`FUN`	summary function, usually some kind of average, to apply on a rolling basis
`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

Calculates a rolling summary (usually rolling average) on the fly for purposes of plotting with ggplot2.

Examples

ap_df <- tsdf(AirPassengers)

ggplot(ap_df, aes(x = x, y = y)) +
   stat_rollapplyr(width = 12)
   
# rolling average after converting to an index, 1000 = average value
# in the first 12 months.
ggplot(ap_df, aes(x = x, y = y)) +
   stat_rollapplyr(width = 12, index.ref = 1:12, index.basis = 1000)

ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
  geom_point() +
  facet_wrap(~sex) +
  stat_rollapplyr(width = 12, FUN = median) +
  ggtitle("Seasonally adjusted lung deaths")

ap_df <- tsdf(AirPassengers)

ggplot(ap_df, aes(x = x, y = y)) +
   stat_rollapplyr(width = 12)
   
# rolling average after converting to an index, 1000 = average value
# in the first 12 months.
ggplot(ap_df, aes(x = x, y = y)) +
   stat_rollapplyr(width = 12, index.ref = 1:12, index.basis = 1000)

ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
  geom_point() +
  facet_wrap(~sex) +
  stat_rollapplyr(width = 12, FUN = median) +
  ggtitle("Seasonally adjusted lung deaths")

X13 seasonal adjustment Stat

Description

Conducts X13-SEATS-ARIMA seasonal adjustment on the fly for ggplot2

Usage

stat_seas(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  x13_params = NULL, index.ref = NULL, index.basis = 100,
  frequency = NULL, start = NULL, ...)
stat_seas(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  x13_params = NULL, index.ref = NULL, index.basis = 100,
  frequency = NULL, start = NULL, ...)

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()`.
`x13_params`	a list of other parameters for `seas`
`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.
`frequency`	The frequency for the time series
`start`	The starting point for the time series, in a format suitable for `ts()`
`...`	other arguments for the geom

Examples

## Not run: 
ap_df <- tsdf(AirPassengers)

# SEATS with defaults:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_seas()
   
# X11 with no outlier treatment:
ggplot(ap_df, aes(x = x, y = y)) +
  stat_seas(x13_params = list(x11 = "", outlier = NULL))

# Multiple time series example:    
ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
  geom_point() +
  facet_wrap(~sex) +
  stat_seas() +
  ggtitle("Seasonally adjusted lung deaths")
  
# example use of index:  
ggplot(ap_df, aes(x = x, y = y)) +
  stat_seas(x13_params = list(x11 = "", outlier = NULL),
  index.ref = 1, index.basis = 1000) +
  labs(y = "Seasonally adjusted index\n(first observation = 1000)")
  
# if the x value is not a decimal eg not created with time(your_ts_object),
# you need to specify start and frequency by hand:
ggplot(subset(nzbop, Account == "Current account"), 
      aes(x = TimePeriod, y = Value)) +
   stat_seas(start = c(1971, 2), frequency = 12) +
   facet_wrap(~Category, scales = "free_y")
  
  
## End(Not run)
## Not run: 
ap_df <- tsdf(AirPassengers)

# SEATS with defaults:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_seas()
   
# X11 with no outlier treatment:
ggplot(ap_df, aes(x = x, y = y)) +
  stat_seas(x13_params = list(x11 = "", outlier = NULL))

# Multiple time series example:    
ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
  geom_point() +
  facet_wrap(~sex) +
  stat_seas() +
  ggtitle("Seasonally adjusted lung deaths")
  
# example use of index:  
ggplot(ap_df, aes(x = x, y = y)) +
  stat_seas(x13_params = list(x11 = "", outlier = NULL),
  index.ref = 1, index.basis = 1000) +
  labs(y = "Seasonally adjusted index\n(first observation = 1000)")
  
# if the x value is not a decimal eg not created with time(your_ts_object),
# you need to specify start and frequency by hand:
ggplot(subset(nzbop, Account == "Current account"), 
      aes(x = TimePeriod, y = Value)) +
   stat_seas(start = c(1971, 2), frequency = 12) +
   facet_wrap(~Category, scales = "free_y")
  
  
## End(Not run)

LOESS seasonal adjustment Stat

Description

Conducts seasonal adjustment on the fly for ggplot2, from LOESS seasonal decomposition

Usage

stat_stl(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  frequency = NULL, s.window, index.ref = NULL, index.basis = 100, ...)
stat_stl(mapping = NULL, data = NULL, geom = "line",
  position = "identity", show.legend = NA, inherit.aes = TRUE,
  frequency = NULL, s.window, 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
`s.window`	either the character string `"periodic"` or the span (in lags) of the loess window for seasonal extraction, which should be odd and at least 7, according to Cleveland et al. This has no default and must be chosen.
`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

Examples

ap_df <- tsdf(AirPassengers)

# periodic if fixed seasonality; doesn't work well:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_stl(s.window = "periodic")

# seasonality varies a bit over time, works better:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_stl(s.window = 7)

# Multiple time series example:
ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
   geom_point() +
   facet_wrap(~sex) +
   stat_stl(s.window = 7) +
   ggtitle("Seasonally adjusted lung deaths")

# Index so first value is 100:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_stl(s.window = 7, index.ref = 1)
ap_df <- tsdf(AirPassengers)

# periodic if fixed seasonality; doesn't work well:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_stl(s.window = "periodic")

# seasonality varies a bit over time, works better:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_stl(s.window = 7)

# Multiple time series example:
ggplot(ldeaths_df, aes(x = YearMon, y = deaths, colour = sex)) +
   geom_point() +
   facet_wrap(~sex) +
   stat_stl(s.window = 7) +
   ggtitle("Seasonally adjusted lung deaths")

# Index so first value is 100:
ggplot(ap_df, aes(x = x, y = y)) +
   stat_stl(s.window = 7, index.ref = 1)

Time series to data frame

Description

Convert a ts object to data.frame with columns for time period and the original data

Usage

tsdf(timeseries, colname = "x")
tsdf(timeseries, colname = "x")

Arguments

`timeseries`	an object of class ts or mts
`colname`	Column name to give to the time period column

Details

A convenience function to create a data frame from a time series or multiple time series object. The motivation is to make it easy to pass time series data to functions that need data frames such as ggplot2.

Value

a data.frame with the same number of rows as the original time series

Examples

head(tsdf(AirPassengers))

ld <- cbind(fdeaths, mdeaths)
head(tsdf(ld))
head(tsdf(AirPassengers))

ld <- cbind(fdeaths, mdeaths)
head(tsdf(ld))

Package 'ggseas'

Help Index

Seasonal decomposition on the fly

Description

Details

Author(s)

See Also

Visualise seasonal decomposition

Description

Usage

Arguments

Details

Value

See Also

Examples

Monthly Deaths from Lung Diseases in the UK

Description

Usage

Format

Details

Source

See Also

New Zealand Balance of Payments major components 1971Q2 to 2015Q2

Description

Usage

Format

Details

Source

Classical seasonal adjustment Stat

Description

Usage

Arguments

Details

See Also

Examples

Index Stat

Description

Usage

Arguments

See Also

Examples

Rolling summary Stat

Description

Usage

Arguments

Details

See Also

Examples

X13 seasonal adjustment Stat

Description

Usage

Arguments

See Also

Examples

LOESS seasonal adjustment Stat

Description

Usage

Arguments

See Also

Examples

Time series to data frame

Description

Usage

Arguments

Details

Value

Examples