library("rjd3production")library("rjd3production")The {rjd3production} package is useful for setting up a production pipeline for seasonally adjusted time series.
Before creating our production pipeline, we need to set up our working environment – our project. The init_env() function creates the following structure:
data/ folder: our raw dataWorkspaces/ folder: our workspacesoutput/ folder: the output time series, tables and graphsspecs/ folder: workspace-specific specifications (calendar regressors, outliers, etc.)BQ/ folder: quality reports and decision files.lintr file for static code analysis (formatting best practices)path_project <- tempfile(pattern = "my_sa_project")
init_env(path = path_project)In this tutorial, we will create a production pipeline using the ABS dataset from the {rjd3toolkit} package. The dataset is also available as the file ABS.csv at /usr/local/lib/R/site-library/rjd3providers/extdata/ABS.csv in the {rjd3providers} package.
library("rjd3toolkit")
path_ABS <- system.file("extdata", "ABS.csv", package = "rjd3providers")
my_data <- ABS[, seq_len(3L)]
colnames(my_data) <- substr(colnames(my_data), start = 2L, stop = 12L)If our time series are sensitive to calendar effects, we can correct for these effects using calendar regressors.
To do this, refer to the td-selection vignette for guidance on how to handle these effects and how to generate the td table containing our selected custom calendar regressors.
td <- select_td(my_data)
#>
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#> Computing spec No_TD ...Done !
#> Computing spec REG1 ...Done !
#> Computing spec REG2 ...Done !
#> Computing spec REG3 ...Done !
#> Computing spec REG5 ...Done !
#> Computing spec REG6 ...Done !
#> Computing spec LY ...Done !
#> Computing spec REG1_LY ...Done !
#> Computing spec REG2_LY ...Done !
#> Computing spec REG3_LY ...Done !
#> Computing spec REG5_LY ...Done !
#> Computing spec REG6_LY ...Done !
#>
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#> Computing spec No_TD ...Done !
#> Computing spec REG1 ...Done !
#> Computing spec REG2 ...Done !
#> Computing spec REG3 ...Done !
#> Computing spec REG5 ...Done !
#> Computing spec REG6 ...Done !
#> Computing spec LY ...Done !
#> Computing spec REG1_LY ...Done !
#> Computing spec REG2_LY ...Done !
#> Computing spec REG3_LY ...Done !
#> Computing spec REG5_LY ...Done !
#> Computing spec REG6_LY ...Done !
#>
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#> Computing spec No_TD ...Done !
#> Computing spec REG1 ...Done !
#> Computing spec REG2 ...Done !
#> Computing spec REG3 ...Done !
#> Computing spec REG5 ...Done !
#> Computing spec REG6 ...Done !
#> Computing spec LY ...Done !
#> Computing spec REG1_LY ...Done !
#> Computing spec REG2_LY ...Done !
#> Computing spec REG3_LY ...Done !
#> Computing spec REG5_LY ...Done !
#> Computing spec REG6_LY ...Done !We will use the {rjd3workspace} package for functions relating to the creation and manipulation of workspaces.
library("rjd3workspace")To create a new workspace, you can either create it manually from scratch or from a dataset. If you are using external variables, calendar regressors or a custom calendar, don’t forget to place all of these within a modelling context.
At INSEE, we use the create_insee_context() function to create our contexts:
my_context <- create_insee_context(s = my_data[, 1L])We will use the {rjd3x13} package to create the X13 specs.
library("rjd3x13")jws <- jws_new(modelling_context = my_context)
jsap <- jws_sap_new(jws, "Nouveau SAP")
add_sa_item(jsap = jsap, name = "Première série", x = my_data[, 1L], spec = x13_spec())
add_sa_item(jsap = jsap, name = "Seconde série", x = my_data[, 2L], spec = x13_spec())
#... avec autant de commande que de sériesjws <- create_ws_from_data(my_data)
set_context(jws, create_insee_context(s = my_data))If we have any, we need to assign calendar regressors to each series:
jws_compute(jws)
assign_td(td = td, jws = jws)
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#> Série 0.2.07.10.M, 3/3Don’t forget to update the metadata for our workspace with the path to our raw data:
add_raw_data_path(jws, path_ABS, delimiter = "COMMA")At last, we can save our workspace!
path_ws <- file.path(path_project, "Workspaces", "workspace_travail", "my_ws.xml")
save_workspace(jws, path_ws, replace = TRUE)In an R production pipeline, the cruncher plays a vital role as it enables: