{JDCruncheR}

CRAN status Linting code R-CMD-check

🇫🇷 README en français | 🇬🇧 README in english

Présentation

Le but premier du package {JDCruncheR} est de fournir un accès rapide et facile au cruncher (JWSACruncher) depuis R. Le cruncher est un outil de mise à jour des workspaces de JDemetra+ sans avoir à ouvrir la GUI (Graphical User Interface). La dernière version peut être téléchargée ici : https://github.com/jdemetra/jwsacruncher/releases. Pour plus d’information, vous pouvez visiter la page wiki.

Avec {JDCruncheR}, vous pouvez aussi générer des bilans qualité utilisant l’output du cruncher. Ce bilan est un résumé des diagnostiques de la désaisonnalisation. Il peut être utilisé pour repérer les séries les plus problématiques qui nécessitent une analyse plus fine. Cela est très utile lorsqu’on a beaucoup de séries à désaisonnaliser.

Installation

🎉 {JDCruncheR} est maintenant disponible sur le CRAN ! 🎉

Pour installer, il suffit de lancer la ligne de code suivante :

install.packages("JDCruncheR")

Pour obtenir la version en cours de développement depuis GitHub :

# Si le package remotes n'est pas installé
# install.packages("remotes")

# Installer la version en cours de développement depuis GitHub
remotes::install_github("InseeFr/JDCruncheR")

Usage

Chargement du package

library("JDCruncheR")

Changer les seuils des tests statistiques

Les seuils des tests du bilan qualité sont personnalisables. Pour cela, il faut modifier l’option "jdc_thresholds".

Pour récupérer les valeurs des tests par défault, il faut appeler la fonction get_thresholds() :

get_thresholds("m7", default = TRUE)
#>   Good    Bad Severe 
#>      1      2    Inf
get_thresholds(default = TRUE)
#> $qs_residual_sa_on_sa
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $qs_residual_sa_on_i
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_sa_on_sa
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_sa_on_i
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_td_on_sa
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_td_on_i
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $residuals_independency
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_homoskedasticity
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_skewness
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_kurtosis
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_normality
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $oos_mean
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $oos_mse
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $m7
#>   Good    Bad Severe 
#>      1      2    Inf 
#> 
#> $q
#> Good  Bad 
#>    1  Inf 
#> 
#> $q_m2
#> Good  Bad 
#>    1  Inf 
#> 
#> $pct_outliers
#>      Good Uncertain       Bad 
#>         3         5       Inf 
#> 
#> $grade
#>      Good Uncertain       Bad    Severe 
#>         0         1         3         5

Pour changer la valeur de l’option, on peut utiliser la fonction set_thresholds() :

# Fixer les seuils à une certaine valeur
set_thresholds(test_name = "m7", thresholds = c(Good = 0.8, Bad = 1.4, Severe = Inf))
get_thresholds(test_name = "m7", default = FALSE)
#>   Good    Bad Severe 
#>    0.8    1.4    Inf

# Remettre tous les seuils à leur valeur par défaut
set_thresholds()
get_thresholds(test_name = "m7", default = FALSE)
#>   Good    Bad Severe 
#>      1      2    Inf

Changer les notes des modalités Good, Uncertain, Bad et Severe

Le mécanisme est le même que pour les seuils des tests statistiques avec la valeur "grade" :

Pour récupérer la valeur par défault des notes, il faut appeler la fonction get_thresholds() :

get_thresholds("grade", default = TRUE)
#>      Good Uncertain       Bad    Severe 
#>         0         1         3         5

Pour changer la valeur de la note, on peut utiliser la fonction set_thresholds() :

# Fixer les notes à une certaine valeur
set_thresholds(test_name = "grade", thresholds = c(Good = 0, Uncertain = 0.1, Bad = 1, Severe = 10))
get_thresholds(test_name = "grade", default = FALSE)
#>      Good Uncertain       Bad    Severe 
#>       0.0       0.1       1.0      10.0

Calculer un bilan qualité

Par exemple, en partant d’une matrice demetra_m.csv :

n start end mean skewness kurtosis lb2 p d q bp bd bq m7 q q.m2
France 88 2012-10-01 2020-01-01 0.6 0.0 0.9 2.9 0.8 36.1 0.0 0 1 1 0 1 1 0.2 0.5 2.0
Spain 78 2015-10-01 2022-03-01 0.4 -0.4 0.0 4.6 0.0 17.3 0.7 0 0 1 0 1 1 0.8 1.5 1.3
Greece 112 2010-10-01 2020-01-01 0.5 -0.3 0.0 3.7 0.0 46.9 0.0 3 1 1 0 1 1 0.3 0.4 0.8

On peut générer un bilan qualité :

BQ <- extract_QR(x = demetra_m)
print(BQ$modalities)
#>   series residuals_homoskedasticity residuals_skewness residuals_kurtosis
#> 1 France                       Good               Good               Good
#> 2  Spain                        Bad                Bad                Bad
#> 3 Greece                        Bad                Bad                Bad
#>   oos_mean oos_mse   m7    q q_m2 pct_outliers
#> 1     Good    <NA> Good Good  Bad         <NA>
#> 2     Good    <NA> Good  Bad  Bad         <NA>
#> 3     Good    <NA> Good  Bad Good         <NA>

Calculer un score

Il est possible maintenant de calculer un score à partir du bilan qualité

BQ_score <- compute_score(
    x = BQ,
    score_pond = c(
        oos_mean = 15L, 
        residuals_kurtosis = 15L, 
        residuals_homoskedasticity = 5L, 
        residuals_skewness = 5L, 
        m7 = 5L, 
        q_m2 = 5L
    )
)
extract_score(x = BQ_score)
#>   series score
#> 1 France    60
#> 2  Spain   110
#> 3 Greece   100

Exporter un bilan qualité

Enfin il est possible d’exporter un bilan qualité via la fonction export_xlsx.

Autres informations

Pour plus d’informations sur l’installation et la configuration du package {JDCruncheR}, vous pouvez visiter la page wiki

Pour une description plus complète des packages R pour JDemetra+ voir le document de travail Insee Les packages R pour JDemetra+ : une aide à la désaisonnalisation

Overview

The primary objective of the {JDCruncheR} package is to provide a quick and easy access to the JDemetra+ cruncher (JWSACruncher) from R. The cruncher is a tool for updating JDemetra+ workspaces, without having to open the graphical user interface. The latest version can be downloaded here: https://github.com/jdemetra/jwsacruncher/releases. For more information, please refer to the wiki page.

With {JDCruncheR}, you can also generate a quality report based on the cruncher’s output. This report is a formatted summary of the seasonal adjustment process master diagnostics and parameters. It can be used to spot the most problematic series which will require a finer analysis. This is most useful when dealing with a large number of series.

Installation

🎉 {JDCruncheR} is now available on CRAN! 🎉

To install it, you have to launch the following command line:

install.packages("JDCruncheR")

To get the current development version from GitHub:

# If remotes packages is not installed
# install.packages("remotes")

# Install development version from GitHub
remotes::install_github("InseeFr/JDCruncheR")

Usage

Loading the package

library("JDCruncheR")

Changing statistical test thresholds

The thresholds of the QR tests can be customised You have to modify the option "jdc_thresholds".

To get the (default or not) values of the thresholds of the tests, you can call the fonction get_thresholds() :

get_thresholds("m7")
#>   Good    Bad Severe 
#>      1      2    Inf
get_thresholds(default = TRUE)
#> $qs_residual_sa_on_sa
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $qs_residual_sa_on_i
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_sa_on_sa
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_sa_on_i
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_td_on_sa
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $f_residual_td_on_i
#>    Severe       Bad Uncertain      Good 
#>     0.001     0.010     0.050       Inf 
#> 
#> $residuals_independency
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_homoskedasticity
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_skewness
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_kurtosis
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $residuals_normality
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $oos_mean
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $oos_mse
#>       Bad Uncertain      Good 
#>      0.01      0.10       Inf 
#> 
#> $m7
#>   Good    Bad Severe 
#>      1      2    Inf 
#> 
#> $q
#> Good  Bad 
#>    1  Inf 
#> 
#> $q_m2
#> Good  Bad 
#>    1  Inf 
#> 
#> $pct_outliers
#>      Good Uncertain       Bad 
#>         3         5       Inf 
#> 
#> $grade
#>      Good Uncertain       Bad    Severe 
#>         0         1         3         5

To change the value of the option, you can use the fonction set_thresholds():

# Set threshold to imposed value
set_thresholds(test_name = "m7", thresholds = c(Good = 0.8, Bad = 1.4, Severe = Inf))
get_thresholds(test_name = "m7", default = FALSE)
#>   Good    Bad Severe 
#>    0.8    1.4    Inf

# Reset all thresholds to default
set_thresholds()
get_thresholds(test_name = "m7", default = FALSE)
#>   Good    Bad Severe 
#>      1      2    Inf

Changing the scores for the Good, Uncertain, Bad and Severe modalities

The mechanism is the same as for the statistical test thresholds with the "grade" value:

To retrieve the default grade value, call the get_thresholds() function:

get_thresholds("grade", default = TRUE)
#>      Good Uncertain       Bad    Severe 
#>         0         1         3         5

To change the value of the grade, you can use the set_thresholds() function:

# Set grades to a certain value
set_thresholds(test_name = "grade", thresholds = c(Good = 0, Uncertain = 0.1, Bad = 1, Severe = 10))
get_thresholds(test_name = "grade", default = FALSE)
#>      Good Uncertain       Bad    Severe 
#>       0.0       0.1       1.0      10.0

Calculate a quality report

For example, starting from a matrix demetra_m.csv :

n start end mean skewness kurtosis lb2 p d q bp bd bq m7 q q.m2
France 88 2012-10-01 2020-01-01 0.6 0.0 0.9 2.9 0.8 36.1 0.0 0 1 1 0 1 1 0.2 0.5 2.0
Spain 78 2015-10-01 2022-03-01 0.4 -0.4 0.0 4.6 0.0 17.3 0.7 0 0 1 0 1 1 0.8 1.5 1.3
Greece 112 2010-10-01 2020-01-01 0.5 -0.3 0.0 3.7 0.0 46.9 0.0 3 1 1 0 1 1 0.3 0.4 0.8

A quality report can be generated:

BQ <- extract_QR(x = demetra_m)
print(BQ$modalities)
#>   series residuals_homoskedasticity residuals_skewness residuals_kurtosis
#> 1 France                       Good               Good               Good
#> 2  Spain                        Bad                Bad                Bad
#> 3 Greece                        Bad                Bad                Bad
#>   oos_mean oos_mse   m7    q q_m2 pct_outliers
#> 1     Good    <NA> Good Good  Bad         <NA>
#> 2     Good    <NA> Good  Bad  Bad         <NA>
#> 3     Good    <NA> Good  Bad Good         <NA>

Calculate a score

It is now possible to calculate a score from the quality report:

BQ_score <- compute_score(
    x = BQ,
    score_pond = c(
        oos_mean = 15L, 
        residuals_kurtosis = 15L, 
        residuals_homoskedasticity = 5L, 
        residuals_skewness = 5L, 
        m7 = 5L, 
        q_m2 = 5L
    )
)
extract_score(x = BQ_score)
#>   series score
#> 1 France    60
#> 2  Spain   110
#> 3 Greece   100

Exporting a quality report

Finally, you can export a quality report using the export_xlsx function.

Other informations

For more informations on installing and configuring the {JDCruncheR} package, you can visit the wiki page.

For a more comprehensive description of the R packages for JDemetra+ check the Insee working paper R Tools for JDemetra+: Seasonal adjustment made easier