ggdemetra: extending ggplot2 to perform
seasonal adjustment with RJDemetra
Alain Quartier-la-Tente
2024-02-04
ggdemetra is an extension of ggplot2 to add
seasonal adjustment statistics to your plots. The seasonal adjustment
process is done with RJDemetra that is an R
interface to JDemetra+, the
seasonal adjustment software officially recommended to the members of
the European Statistical System (ESS) and the European System of Central
Banks. RJDemetra implements the two leading seasonal
adjustment methods TRAMO/SEATS+
and X-12ARIMA/X-13ARIMA-SEATS.
There are 4 main functionalities in ggdemetra depending
of what you want to add in the graphic:
geom_sa(): to add a time series compute during the
seasonal adjustment (the trend, the seasonal adjusted time series,
etc.).
geom_outliers(): to add the outliers used in the
pre-adjustment process of the seasonal adjustment.geom_arima(): to add the ARIMA model used in the
pre-adjustment process of the seasonal adjustment.geom_diagnostics(): to add a table containing some
diagnostics on the seasonal adjustment process.
Note ts objects cannot be directly used in
ggplot2. To convert ts or mts
object to data.frame, you can use the ts2df()
function. For example, the data ipi_c_eu_df used in this
package is obtained by applying the ts2df()function to the
ipi_c_eu data available in RJDemetra:
ipi_c_eu_df <- ts2df(ipi_c_eu)
Seasonal adjustment specification
All the functions have some common parameters and especially those to
defined the seasonal adjustment method:
method is the method used for the seasonal adjustment:
X-13ARIMA (method = "x13", the default) or TRAMO-SEATS
(method = "tramoseats").
spec is the seasonal adjustment specification. It can
be the name of pre-defined specification (see
?RJDemetra::x13 or ?RJDemetra::tramoseats) or
a user-defined specification created by RJDemetra (by
RJDemetra::x13_spec or
RJDemetra::tramoseats_spec).
frequency is the frequency of the input time series. By
default, the frequency is computed and a message is printed with the one
chosen (use message = FALSE to suppress this message).
In the following examples, the data used is the French industrial
production index. By default, the seasonal adjustment will then be
processed with X-13ARIMA with a pre-defined specification
"RSA5c (automatic log detection, automatic ARIMA and
outliers detection and trading day and easter adjustment). However, in
the industrial production the working day effect has more economic sense
than the trading day effect and a gradual effect for easter does not
make economic sense for the aggregated series. The specification that
should be used with X-13ARIMA is
spec = RJDemetra::x13_spec("RSA3", tradingdays.option = "WorkingDays").
If no new data or seasonal adjustment specification is specified (method
or specification), these parameters is inherited from the previous
defined: therefore you only need to specify this parameter once.
library(ggplot2)
library(ggdemetra)
p_ipi_fr <- ggplot(data = ipi_c_eu_df, mapping = aes(x = date, y = FR)) +
geom_line() +
labs(title = "Seasonal adjustment of the French industrial production index",
x = NULL, y = NULL)
p_ipi_fr
Add the result of the seasonal adjusment
By default geom_sa() adds the seasonal adjusted time
series:
spec <- RJDemetra::x13_spec("RSA3", tradingdays.option = "WorkingDays")
p_ipi_fr +
geom_sa(color = "#155692",
spec = spec)
#> Frequency used: 12
To add other components of the seasonal adjustment, use the
component parameter of geom_sa() (see
?RJDemetra::user_defined_variables() for the availables
parameters). For example, to add the forecasts of the input data and of
the seasonal adjusted series:
p_sa <- p_ipi_fr +
geom_sa(component = "y_f", linetype = 2, message = FALSE,
spec = spec) +
geom_sa(component = "sa", color = "#155692") +
geom_sa(component = "sa_f", color = "#155692", linetype = 2)
p_sa
Add the outliers to the plot
There are four differents geometrics to add to the plot the names of
the outliers used in the pre-adjustment process:
geom = "text" (the default) adds directly the names of
the outliers and geom = "label" draws a rectangle behind
the names, making them easier to read.
geom = "text_repel" and
geom = "label_repel" do the same but text labels repel away
from each other and away from the data points (see
?ggrepel::geom_label_repel).
In our example, there are 3 outliers:
p_sa + geom_outlier(geom = "label")
They can be plotted in more readable way using the parameters of
ggrepel::geom_label_repel:
p_sa +
geom_outlier(geom = "label_repel",
ylim = c(NA, 65),
arrow = arrow(length = unit(0.03, "npc"),
type = "closed", ends = "last"))
Use the parameters first_date and last_date
to only have the outliers in a precise time interval. For example, to
only plot the outliers from 2009 use first_date = 2009:
p_sa +
geom_outlier(geom = "label_repel",
first_date = 2009,
ylim = c(NA, 65),
arrow = arrow(length = unit(0.03, "npc"),
type = "closed", ends = "last"))