vignettes/ggdemetra.Rmd
ggdemetra.Rmd
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)
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.
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:
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"))
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)
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))
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)