knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width = "100%", fig.dim = c(8, 5), dpi = 300, warning = FALSE, message = FALSE )
The functions developped in rjdmarkdown
are:
print_preprocessing()
for the pre-processing model; print_decomposition()
for the decomposition; print_diagnostics()
to print diagnostics tests on the quality of the seasonal adjustment.The result is different between X-13ARIMA and TRAMO-SEATS models.
library(rjdmarkdown) library(RJDemetra) sa_x13 <- x13(ipi_c_eu[, "FR"]) sa_ts <- tramoseats(ipi_c_eu[, "FR"])
print_preprocessing(sa_x13) print_decomposition(sa_x13, caption = NULL) print_diagnostics(sa_x13)
Some others graphics can also be added with the ggdemetra
package, for example to add the seasonally adjusted series and its forecasts:
library(ggdemetra) ggplot(data = ipi_c_eu_df, mapping = aes(x = date, y = FR)) + geom_line() + labs(title = NULL, x = NULL, y = NULL) + geom_sa(component = "y_f", linetype = 2, frequency = 12, method = "tramoseats") + geom_sa(component = "sa", color = "red") + geom_sa(component = "sa_f", color = "red", linetype = 2) print_preprocessing(sa_ts) print_decomposition(sa_ts, caption = NULL) print_diagnostics(sa_ts)
A R Markdown can also directly be created and render with the create_rmd
function.
It can take as argument a SA
, jSA
, sa_item
, multiprocessing
(all the models of the multiprocessing
are printed) or workspace object (all the models of all the multiprocessing
of the workspace
are printed).
The print of the pre-processing, decomposition and diagnostics can also be customized with preprocessing_fun
, decomposition_fun
and diagnostics_fun
arguments.
For example, to reproduce the example of the previous section:
preprocessing_customized <- function(x){ library(ggdemetra) y <- get_ts(x) data_plot <- data.frame(date = time(y), y = y) p <- ggplot(data = data_plot, mapping = aes(x = date, y = y)) + geom_line() + labs(title = NULL, x = NULL, y = NULL) + geom_sa(component = "y_f", linetype = 2, frequency = 12, method = "tramoseats") + geom_sa(component = "sa", color = "red") + geom_sa(component = "sa_f", color = "red", linetype = 2) plot(p) cat("\n\n") print_preprocessing(sa_ts) } decomposition_customized <- function(x){ print_decomposition(x, caption = NULL) } output_file <- tempfile(fileext = ".Rmd") create_rmd(sa_ts, output_file, output_format = "html_document", preprocessing_fun = preprocessing_customized, decomposition_fun = decomposition_customized, knitr_chunk_opts = list( fig.pos = "h", results = "asis", fig.cap =c("Seasonal adjustment of the French industrial production index", "S-I Ratio"), warning = FALSE, message = FALSE, echo = FALSE) ) # To open the file: browseURL(sub(".Rmd",".html", output_file, fixed= TRUE))
Several models can also be printed creating a workspace:
wk <- new_workspace() new_multiprocessing(wk, "sa1") add_sa_item(wk, "sa1", sa_x13, "X13") add_sa_item(wk, "sa1", sa_ts, "TramoSeats") # It's important to compute the workspace to be able to import the models compute(wk) output_file <- tempfile(fileext = ".Rmd") create_rmd(wk, output_file, output_format = "html_document", output_options = list(toc = TRUE, number_sections = TRUE)) # To open the file: browseURL(sub(".Rmd",".html", output_file, fixed= TRUE))
To produce this document, the knitr
options were set as followed:
knitr::opts_chunk$set( collapse = TRUE, comment = "#>", out.width = "100%", fig.dim = c(7, 5), warning = FALSE, message = FALSE )
And the options results='asis', fig.cap = "S-I Ratio"
were used in the chunks.
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