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+ggdemetra: extending ggplot2 to perform seasonal adjustment with RJDemetra
+Alain +Quartier-la-Tente
+ +2024-08-13
+ + Source:vignettes/ggdemetra.Rmd
+ ggdemetra.Rmdggdemetra 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:
+-
+
-
+
methodis the method used for the seasonal adjustment: +X-13ARIMA (method = "x13", the default) or TRAMO-SEATS +(method = "tramoseats").
+
+ -
+
specis the seasonal adjustment specification. It can +be the name of pre-defined specification (see +?RJDemetra::x13or?RJDemetra::tramoseats) or +a user-defined specification created byRJDemetra(by +RJDemetra::x13_specor +RJDemetra::tramoseats_spec).
+
+ -
+
frequencyis the frequency of the input time series. By +default, the frequency is computed and a message is printed with the one +chosen (usemessage = FALSEto 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 andgeom = "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"))
+
+
+Add the ARIMA model +
+The ARIMA model used pre-adjustment process can be added to the plot
+with geom_arima(). The parameter
+geom = "label" draws a rectangle behind the ARIMA model,
+making it easier to read:
+p_sa +
+ geom_arima(geom = "label",
+ x_arima = -Inf, y_arima = -Inf,
+ vjust = -1, hjust = -0.1)
+
+
+
+
+Add a table with some diagnostics +
+A table with some diagnostics on the seasonal adjustment process can
+be added with geom_diagnostics(). The desired diagnostics
+have to be added to the diagnostics parameter (see
+?RJDemetra::user_defined_variables() for the availables
+diagnostics). For example, to add the result of the X-11 combined test
+and the p-values of the residual seasonality qs and f tests:
+diagnostics <- c("diagnostics.combined.all.summary", "diagnostics.qs", "diagnostics.ftest")
+p_sa +
+ geom_diagnostics(diagnostics = diagnostics,
+ ymin = 58, ymax = 72, xmin = 2010,
+ table_theme = gridExtra::ttheme_default(base_size = 8))
To customize the names of the diagnostics in the plot, pass a named
+vector to the diagnostics parameter:
+diagnostics <- c(`Combined test` = "diagnostics.combined.all.summary",
+ `Residual qs-test (p-value)` = "diagnostics.qs",
+ `Residual f-test (p-value)` = "diagnostics.ftest")
+p_sa +
+ geom_diagnostics(diagnostics = diagnostics,
+ ymin = 58, ymax = 72, xmin = 2010,
+ table_theme = gridExtra::ttheme_default(base_size = 8))
To add the table below the plot, you can for example use
+gridExtra::grid.arrange():
+p_diag <- ggplot(data = ipi_c_eu_df, mapping = aes(x = date, y = FR)) +
+ geom_diagnostics(diagnostics = diagnostics,
+ spec = spec, frequency = 12,
+ table_theme = gridExtra::ttheme_default(base_size = 8)) +
+ theme_void()
+
+gridExtra::grid.arrange(p_sa, p_diag,
+ nrow = 2, heights = c(4, 1.5))
+
+
+
+
+
+
+ Use existing model +
+ggdemetra offers several function that can be used to manipulate +existing models.
+
+mod <- RJDemetra::x13(ipi_c_eu[,"UK"], spec)The previous plots can be initialized with the
+init_ggplot() function:
+init_ggplot(mod) +
+ geom_line(color = "#F0B400") +
+ geom_sa(color = "#155692") +
+ geom_arima(geom = "label",
+ x_arima = -Inf, y_arima = -Inf,
+ vjust = -1, hjust = -0.1)
The different components of seasonal adjustment models can be
+extracted through calendar(), calendaradj(),
+irregular(), trendcycle(),
+seasonal(), seasonaladj(),
+trendcycle() and raw().
+data <- ts.union(raw(mod), raw(mod, forecast = TRUE),
+ trendcycle(mod), trendcycle(mod, forecast = TRUE),
+ seasonaladj(mod), seasonaladj(mod, forecast = TRUE))
+colnames(data) <- c("y", "y_f",
+ "t", "t_f",
+ "sa", "sa_f")
+ggplot(data = ts2df(data), mapping = aes(x = date)) +
+ geom_line(mapping = aes(y = y), color = "#F0B400", na.rm = TRUE) +
+ geom_line(mapping = aes(y = y_f), color = "#F0B400", na.rm = TRUE, linetype = 2) +
+ geom_line(mapping = aes(y = t), color = "#1E6C0B", na.rm = TRUE) +
+ geom_line(mapping = aes(y = t_f), color = "#1E6C0B", na.rm = TRUE, linetype = 2) +
+ geom_line(mapping = aes(y = sa), color = "#155692", na.rm = TRUE) +
+ geom_line(mapping = aes(y = sa_f), color = "#155692", na.rm = TRUE, linetype = 2) +
+ theme_bw()
SI-ratio plots can be plotted with siratioplot and
+ggsiratioplot:
+ggsiratioplot(mod)
And there is also an autoplot() function:
+autoplot(mod)
