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Description

Prepare for Production of Seasonal Adjustment with 'JDemetra+'.

A comprehensive tool for setting up seasonal data pipelines using 'JDemetra+' (version 3) and 'rjdverse'. This includes setting up a new working environment, creating and selecting calendar regressors, managing specifications (trading-days regressors and outliers) at the workspace level, making a workspace usable by the 'cruncher', removing insignificant outliers, and comparing workspaces.

{rjd3production}

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Présentation

{rjd3production} aide les producteurs de données CVS-CJO à mettre en place des chaînes de production.

Il permet notamment de :

  • CrĂ©er des calendriers français et rĂ©gresseurs de calendrier compatibles JDemetra+

  • Identifier des SAI par leur nom

  • Selectionner les jeux de calendrier pour une ou plusieurs sĂ©ries

  • Manipuler les rĂ©gresseurs de calendrier et les outliers d’un Workspace selon la dynamique suivante:

    • Les fonctions import_XXX() et export_XXX() permettent de convertir les data.frame contenant les outliers et rĂ©gresseurs de calendrier en fichiers et inversement

    • Les fonctions retrieve_XXX() et assign_XXX() permettent d’extraire (resp. d’assigner) les outliers et rĂ©gresseurs de calendrier d’un workspace


flowchart LR
    %% Objects
    WS["WS<br/>(JDemetra+<br/>workspace)"]
    DF_OUT["outliers_df<br/>(data.frame)"]
    DF_TD["td_df<br/>(data.frame)"]
    YAML_OUT["outliers YAML<br/>(outliers_&lt;ws_name&gt;.yaml)"]
    YAML_TD["TD YAML<br/>(td_&lt;ws_name&gt;.yaml)"]
    SERIES["Series<br/>(time series data)"]

    %% Outliers workflow
    DF_OUT -->|"assign_outliers()"| WS
    WS -->|"retrieve_outliers()"| DF_OUT
    DF_OUT -->|"export_outliers()"| YAML_OUT
    YAML_OUT -->|"import_outliers()"| DF_OUT

    %% TD workflow
    SERIES -->|"select_td()"| DF_TD
    WS -->|"retrieve_td()"| DF_TD
    DF_TD -->|"export_td()"| YAML_TD
    YAML_TD -->|"import_td()"| DF_TD
    DF_TD -->|"assign_td()"| WS

    %% Styles
    classDef ws fill:#e6f2ff,stroke:#4a7ebb,stroke-width:1px;
    classDef df fill:#e9f7ef,stroke:#2e8b57,stroke-width:1px;
    classDef yaml fill:#fff3e0,stroke:#cc8400,stroke-width:1px;
    classDef series fill:#f5e6ff,stroke:#7a3db8,stroke-width:1px;

    class WS ws
    class DF_OUT,DF_TD df
    class YAML_OUT,YAML_TD yaml
    class SERIES series

Installation

{rjd3production} s’appuie sur le package {rJava}

L’exécution des packages rjd3 nécessite Java 21 ou plus. La manière de mettre en place une telle configuration dans R est expliquée ici.

Latest release

Pour obtenir la version stable actuelle (à partir de la dernière version) :

  • Depuis GitHub :
# install.packages("remotes")
remotes::install_github("InseeFr/rjd3production@*release")
install.packages("rjd3production", repos = c("https://TanguyBarthelemy.r-universe.dev", "https://cloud.r-project.org"))

Version de développement

Vous pouvez installer la version de développement de {rjd3production} depuis [GitHub] (https://github.com/) avec :

# install.packages("remotes")
remotes::install_github("InseeFr/rjd3production")

Overview

{rjd3production} helps producers of seasonal data to set up production lines.

In particular, it enables you to:

  • Create JDemetra+-compatible French calendars and calendar regressors

  • Identify SAIs by name

  • Select calendar sets for one or more series

  • Manipulate Workspace calendar regressors and outliers according to the following dynamics:

    • The import_XXX() and export_XXX() functions convert data.frames containing calendar outliers and regressors into files, and vice versa.
    • The retrieve_XXX() and assign_XXX() functions extract (resp. assign) calendar outliers and regressors from a workspace.

flowchart LR
    %% Objects
    WS["WS<br/>(JDemetra+<br/>workspace)"]
    DF_OUT["outliers_df<br/>(data.frame)"]
    DF_TD["td_df<br/>(data.frame)"]
    YAML_OUT["outliers YAML<br/>(outliers_&lt;ws_name&gt;.yaml)"]
    YAML_TD["TD YAML<br/>(td_&lt;ws_name&gt;.yaml)"]
    SERIES["Series<br/>(time series data)"]

    %% Outliers workflow
    DF_OUT -->|"assign_outliers()"| WS
    WS -->|"retrieve_outliers()"| DF_OUT
    DF_OUT -->|"export_outliers()"| YAML_OUT
    YAML_OUT -->|"import_outliers()"| DF_OUT

    %% TD workflow
    SERIES -->|"select_td()"| DF_TD
    WS -->|"retrieve_td()"| DF_TD
    DF_TD -->|"export_td()"| YAML_TD
    YAML_TD -->|"import_td()"| DF_TD
    DF_TD -->|"assign_td()"| WS

    %% Styles
    classDef ws fill:#e6f2ff,stroke:#4a7ebb,stroke-width:1px;
    classDef df fill:#e9f7ef,stroke:#2e8b57,stroke-width:1px;
    classDef yaml fill:#fff3e0,stroke:#cc8400,stroke-width:1px;
    classDef series fill:#f5e6ff,stroke:#7a3db8,stroke-width:1px;

    class WS ws
    class DF_OUT,DF_TD df
    class YAML_OUT,YAML_TD yaml
    class SERIES series

Installation

To get the current stable version (from the latest release):

  • From GitHub:
# install.packages("remotes")
remotes::install_github("InseeFr/rjd3production@*release")
install.packages("rjd3production", repos = c("https://TanguyBarthelemy.r-universe.dev", "https://cloud.r-project.org"))

Development version

You can install the development version of {rjd3production} from GitHub with:

# install.packages("remotes")
remotes::install_github("InseeFr/rjd3production")
Metadata

Version

1.1.0

License

Unknown

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