Merge pull request #20 from TanguyBarthelemy/master

resolve a bug
InseeFr · Sep 8, 2023 · 5f31ff8 · 5f31ff8
2 parents bff4c1d + a881abd
commit 5f31ff8
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diff --git a/.Rbuildignore b/.Rbuildignore
@@ -8,3 +8,8 @@
 ^pkgdown$
 ^doc$
 ^Meta$
+^NEWS.html$
+^TO_DO$
+^LICENCE$
+^LICENSE$
+^.github$
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -22,7 +22,7 @@ Suggests:
     knitr,
     kableExtra,
     rmarkdown
-License: file LICENSE
+License: EUPL
 VignetteBuilder: knitr
 Encoding: UTF-8
 LazyData: true

diff --git a/JDcruncheR_en.Rproj → JDCruncheR.Rproj b/JDcruncheR_en.Rproj → JDCruncheR.Rproj
diff --git a/R/Cruncher.R b/R/Cruncher.R
@@ -218,7 +218,9 @@ multiprocessing_names <- function(workspace) {
         stop("The workspace doesn't exist")
 
     xml_workspace <- suppressWarnings(XML::xmlParse(workspace, error = function(...) {}))
-    noms_objets <- XML::xmlToDataFrame(nodes = XML::getNodeSet(doc = xml_workspace, path = "//ns2:demetraGenericWorkspace/ns2:items/ns2:item"))
+    noms_objets <- XML::xmlToDataFrame(nodes = XML::getNodeSet(
+        doc = xml_workspace,
+        path = "//ns2:demetraGenericWorkspace/ns2:items/ns2:item"))
     noms_multi_documents <- noms_objets[grep("multi-documents", noms_objets$family), ]
     noms_multi_documents <- noms_multi_documents[, c("name", "file")]
 

diff --git a/R/QR_export.R b/R/QR_export.R
@@ -61,8 +61,7 @@ export_xlsx.QR_matrix <- function(x, layout = c("all", "modalities", "values", "
         XLConnect::clearSheet(wb, sheets)
     }
 
-    XLConnect::setStyleAction(wb,
-                              XLConnect::XLC$STYLE_ACTION.DATA_FORMAT_ONLY)
+    XLConnect::setStyleAction(wb, XLConnect::XLC$STYLE_ACTION.DATA_FORMAT_ONLY)
     if (auto_format) {
         XLConnect::setDataFormatForType(wb,
                                         type = XLConnect::XLC$DATA_TYPE.NUMERIC,

diff --git a/R/QR_functions.R b/R/QR_functions.R
@@ -136,25 +136,26 @@ NULL
 #' @name compute_score
 #' @seealso [Traduction française][fr-compute_score()]
 #' @export
-compute_score.QR_matrix <- function(x,
-                                    score_pond = c(qs_residual_sa_on_sa = 30,
-                                                   f_residual_sa_on_sa = 30,
-                                                   qs_residual_sa_on_i = 20,
-                                                   f_residual_sa_on_i = 20,
-                                                   f_residual_td_on_sa = 30,
-                                                   f_residual_td_on_i = 20,
-                                                   oos_mean = 15,
-                                                   oos_mse = 10,
-                                                   residuals_independency = 15,
-                                                   residuals_homoskedasticity = 5,
-                                                   residuals_skewness = 5,
-                                                   m7 = 5,  q_m2 = 5),
-                                    modalities = c("Good", "Uncertain", "", "Bad", "Severe"),
-                                    normalize_score_value,
-                                    na.rm = FALSE,
-                                    n_contrib_score,
-                                    conditional_indicator,
-                                    ...) {
+compute_score.QR_matrix <- function(
+        x,
+        score_pond = c(qs_residual_sa_on_sa = 30,
+                       f_residual_sa_on_sa = 30,
+                       qs_residual_sa_on_i = 20,
+                       f_residual_sa_on_i = 20,
+                       f_residual_td_on_sa = 30,
+                       f_residual_td_on_i = 20,
+                       oos_mean = 15,
+                       oos_mse = 10,
+                       residuals_independency = 15,
+                       residuals_homoskedasticity = 5,
+                       residuals_skewness = 5,
+                       m7 = 5,  q_m2 = 5),
+        modalities = c("Good", "Uncertain", "", "Bad", "Severe"),
+        normalize_score_value,
+        na.rm = FALSE,
+        n_contrib_score,
+        conditional_indicator,
+        ...) {
     # score_formula_exp <- as.expression(substitute(score_formula))
 
     QR_modalities <- x$modalities
@@ -484,8 +485,14 @@ extract_score.QR_matrix <- function(x, format_output = c("data.frame", "vector")
     return(res)
 }
 #' @export
-extract_score.mQR_matrix <- function(x, format_output = c("data.frame", "vector"), weighted_score = FALSE) {
-    return(lapply(x, extract_score, format_output = format_output, weighted_score = weighted_score))
+extract_score.mQR_matrix <- function(
+        x,
+        format_output = c("data.frame", "vector"),
+        weighted_score = FALSE) {
+    return(lapply(X = x,
+                  FUN = extract_score,
+                  format_output = format_output,
+                  weighted_score = weighted_score))
 }
 
 
@@ -797,23 +804,25 @@ NULL
 #' @family var QR_matrix manipulation
 #' @seealso [Traduction française][fr-recode_indicator_num()]
 #' @export
-recode_indicator_num <- function(x,
-                                 variable_name,
-                                 breaks = c(0, .01, .05, .1, 1),
-                                 labels =  c("Good", "Uncertain", "Bad", "Severe"),
-                                 ...) {
+recode_indicator_num <- function(
+        x,
+        variable_name,
+        breaks = c(0, .01, .05, .1, 1),
+        labels =  c("Good", "Uncertain", "Bad", "Severe"),
+        ...) {
     UseMethod("recode_indicator_num", x)
 }
 #' @export
 recode_indicator_num.default <- function(x, variable_name, breaks, labels, ...) {
     stop("This function requires a QR_matrix or mQR_matrix object")
 }
 #' @export
-recode_indicator_num.QR_matrix <- function(x,
-                                           variable_name,
-                                           breaks = c(0, .01, .05, .1, 1),
-                                           labels =  c("Good", "Uncertain", "Bad", "Severe"),
-                                           ...) {
+recode_indicator_num.QR_matrix <- function(
+        x,
+        variable_name,
+        breaks = c(0, .01, .05, .1, 1),
+        labels =  c("Good", "Uncertain", "Bad", "Severe"),
+        ...) {
     modalities <- x$modalities
     values <- x$values
     for (var in variable_name) {
@@ -831,15 +840,13 @@ recode_indicator_num.QR_matrix <- function(x,
     return(x)
 }
 #' @export
-recode_indicator_num.mQR_matrix <- function(x,
-                                            variable_name,
-                                            breaks = c(0, .01, .05, .1, 1),
-                                            labels =  c("Good", "Uncertain", "Bad", "Severe"),
-                                            ...) {
-    return(mQR_matrix(lapply(x,
-                             recode_indicator_num,
-                             variable_name = variable_name,
-                             breaks = breaks,
-                             labels = labels,
-                             ...)))
+recode_indicator_num.mQR_matrix <- function(
+        x,
+        variable_name,
+        breaks = c(0, .01, .05, .1, 1),
+        labels =  c("Good", "Uncertain", "Bad", "Severe"),
+        ...) {
+    return(mQR_matrix(lapply(
+        X = x, FUN = recode_indicator_num,
+        variable_name = variable_name, breaks = breaks, labels = labels, ...)))
 }
diff --git a/R/extractQR.R b/R/extractQR.R
@@ -202,7 +202,7 @@ extractNormalityTests <- function(demetra_m) {
     if (length(tests_possibles) != 3)
         stop("At least one test is missing, among: skewness, kurtosis, lb2")
 
-    if (length(grep("^X\\.(\\d) {1,}$",
+    if (length(grep("^X\\.(\\d){1,}$",
                     colnames(demetra_m)[rep(tests_possibles, each = 2) + rep(1:2, 3)])) != 6)
         stop("Re-compute the cruncher export with the options: residuals.skewness:3, residuals.kurtosis:3 and residuals.lb2:3")
 

diff --git a/README.Rmd b/README.Rmd
@@ -13,20 +13,52 @@ knitr::opts_chunk$set(
 )
 ```
 
-# JDCruncheR
+# `JDCruncheR`
 
-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 to update a JDemetra+ workspace, without having to open the software itself. 
-The latest version can be downloaded here: <https://github.com/jdemetra/jwsacruncher/releases>. Fore more information, please refer to the wiki page: <https://github.com/jdemetra/jwsacruncher/wiki>.
+[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/JDCruncheR)](https://cran.r-project.org/package=JDCruncheR)
 
-With JDCruncheR, you can also generate a *quality report* based on the cruncher output. This report is a summary of the seasonal adjustment diagnostics. It can be used to spot the most problematic series which will require a finer analysis. This is most useful when dealing with a great number of series, which renders impossible the examination of every diagnostic for every series in a reasonable time. 
+## Français 🇫🇷
 
-There are two ways to install the JDCruncheR package: 
+### Présentation
 
-- using the devtools package: 
-```{r gh-installation, eval = FALSE}
-# install.packages("devtools")
-devtools::install_github("InseeFr/JDCruncheR", build_vignettes = TRUE)
+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](https://github.com/jdemetra/jwsacruncher/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
+
+Il y a 2 méthodes d'installation du package `JDCruncheR` :
+
+- utiliser le package `remotes` :
+
+``` r
+# install.packages("remotes")
+remotes::install_github("InseeFr/JDCruncheR", build_vignettes = TRUE)
+```
+
+- depuis le dossier compressé **.zip** ou **.tar.gz**, qui peuvent être trouvés ici : https://github.com/InseeFr/JDCruncheR/releases. Pour plus d'informations sur l'installation et la configuration du package `JDCruncheR`, vous pouvez visiter la page [wiki](https://github.com/jdemetra/jwsacruncher/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](https://www.insee.fr/fr/statistiques/5019786)
+
+## English 🇬🇧
+
+### 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](https://github.com/jdemetra/jwsacruncher/wiki).
+
+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 main 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
+
+There are two ways to install the `JDCruncheR` package:
+
+- using the `remotes` package:
+
+``` r
+# install.packages("remotes")
+remotes::install_github("InseeFr/JDCruncheR", build_vignettes = TRUE)
 ```
-- from the **.zip** or **.tar.gz** file, that can both be found here: <https://github.com/InseeFr/JDCruncheR/releases>. For more info on the package installation and the cruncher configuration, please refer to the [wiki](https://github.com/InseeFr/JDCruncheR/wiki).
 
+- from the **.zip** or **.tar.gz** file, that can both be found here:     <https://github.com/InseeFr/JDCruncheR/releases>.
 
+For a more comprehensive description of the R packages for JDemetra+ check the Insee working paper [R Tools for JDemetra+: Seasonal adjustment made easier](https://www.insee.fr/en/statistiques/5019812)
diff --git a/README.md b/README.md
@@ -1,30 +1,48 @@
 
+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
 # `JDCruncheR`
 
-[![CRAN\_Status\_Badge](http://www.r-pkg.org/badges/version/JDCruncheR)](https://cran.r-project.org/package=JDCruncheR)
+[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/JDCruncheR)](https://cran.r-project.org/package=JDCruncheR)
 
 ## Français 🇫🇷
 
 ### 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](https://github.com/jdemetra/jwsacruncher/wiki).
+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](https://github.com/jdemetra/jwsacruncher/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.
+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
 
-Il y a 2 méthodes d'installation du package `JDCruncheR` :
-    
+Il y a 2 méthodes d’installation du package `JDCruncheR` :
+
 - utiliser le package `remotes` :
-    
+
 ``` r
 # install.packages("remotes")
 remotes::install_github("InseeFr/JDCruncheR", build_vignettes = TRUE)
 ```
 
-- depuis le dossier compressé **.zip** ou **.tar.gz**, qui peuvent être trouvés ici : https://github.com/InseeFr/JDCruncheR/releases. Pour plus d'informations sur l'installation et la configuration du package `JDCruncheR`, vous pouvez visiter la page [wiki](https://github.com/jdemetra/jwsacruncher/wiki)
+- depuis le dossier compressé **.zip** ou **.tar.gz**, qui peuvent être
+  trouvés ici : <https://github.com/InseeFr/JDCruncheR/releases>. Pour
+  plus d’informations sur l’installation et la configuration du package
+  `JDCruncheR`, vous pouvez visiter la page
+  [wiki](https://github.com/jdemetra/jwsacruncher/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](https://www.insee.fr/fr/statistiques/5019786)
+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](https://www.insee.fr/fr/statistiques/5019786)
 
 ## English 🇬🇧
 
@@ -33,29 +51,31 @@ Pour une description plus complète des packages R pour JDemetra+ voir le docume
 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](https://github.com/jdemetra/jwsacruncher/wiki).
+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](https://github.com/jdemetra/jwsacruncher/wiki).
 
 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
-main 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.
+cruncher’s output. This report is a formatted summary of the seasonal
+adjustment process main 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
 
 There are two ways to install the `JDCruncheR` package:
-    
+
 - using the `remotes` package:
-    
+
 ``` r
 # install.packages("remotes")
 remotes::install_github("InseeFr/JDCruncheR", build_vignettes = TRUE)
 ```
 
 - from the **.zip** or **.tar.gz** file, that can both be found here:
-    <https://github.com/InseeFr/JDCruncheR/releases>. For more info on
-the package installation and the cruncher configuration, please
-refer to the [wiki](https://github.com/InseeFr/JDCruncheR/wiki).
+  <https://github.com/InseeFr/JDCruncheR/releases>.
 
-For a more comprehensive description of the R packages for JDemetra+ check the Insee working paper [R Tools for JDemetra+: Seasonal adjustment made easier](https://www.insee.fr/en/statistiques/5019812)
+For a more comprehensive description of the R packages for JDemetra+
+check the Insee working paper [R Tools for JDemetra+: Seasonal
+adjustment made easier](https://www.insee.fr/en/statistiques/5019812)