v 0.3.0.9000

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ggfacto
 Title: Graphs for Correspondence Analysis
-Version: 0.3.0
+Version: 0.3.0.9000
 Authors@R: 
     person(given = "Brice",
            family = "Nocenti",

NEWS.md

@@ -1,3 +1,8 @@
+# ggfacto (development version)
+
+## Bug correction
+* `HCPC_tab()` was not working well with numeric variables.
+
 
 # ggfacto 0.3.0
 

R/geometrical_data_analysis.R

@@ -5961,14 +5961,17 @@ HCPC_tab <- function(data, row_vars = character(), clust, wt,
                                         pct = dplyr::if_else(pct == "row", "col", "row"),
                                         wt = !!wt,
                                         na = "drop", cleannames = TRUE, color = color,
-                                        levels = first_lvs) |>
+                                        levels = first_lvs,
+                                        ...) |>
     dplyr::rename_with(~ dplyr::if_else(stringr::str_detect(., "Total_", ), "Total", .)) |>
+    dplyr::relocate(.data$Total, .after = tidyselect::last_col()) |>
     dplyr::mutate(
       Total = dplyr::mutate(.data$Total,
                             wn = dplyr::if_else(is.na(.data$wn), as.double(.data$n), .data$wn)),
       Total = vctrs::`field<-`(.data$Total, "pct",
                                vctrs::field(.data$Total, "wn") /
-                                 dplyr::last(vctrs::field(.data$Total, "wn")))
+                                 dplyr::last(vctrs::field(.data$Total, "wn"))) |>
+        tabxplor::set_col_var("Total")
     )
 
   col_var <- tabxplor::get_col_var(cah_actives_tab)[tabxplor::get_col_var(cah_actives_tab) != ""]
@@ -5986,17 +5989,11 @@ HCPC_tab <- function(data, row_vars = character(), clust, wt,
     dplyr::filter(!stringr::str_detect(.data$lvs, "Remove levels")) |>
     dplyr::mutate(
       lvs = forcats::fct_recode(.data$lvs, !!!purrr::set_names("Total", row_tot)),
-      lvs = forcats::fct_relabel(.data$lvs,
-                                 ~ stringr::str_replace_all(., " ", unbrk))
-    )
 
-  cah_actives_tab <- cah_actives_tab |>
-    dplyr::mutate(dplyr::across(
-      where(tabxplor::is_fmt), ~ dplyr::if_else(.$display == "mean",
-                               true  = dplyr::mutate(., diff = 0) |>
-                                 tabxplor::as_totrow(),
-                               false = .)
-    ))
+      ## not a good idea : unbreakable spaces should be used at the end, in tab_kable()
+      # lvs = forcats::fct_relabel(.data$lvs,
+      #                            ~ stringr::str_replace_all(., " ", unbrk))
+    )
 
   n_rows <- dplyr::filter(cah_actives_tab, tabxplor::is_totrow(cah_actives_tab)) |>
     dplyr::mutate(
@@ -6008,6 +6005,17 @@ HCPC_tab <- function(data, row_vars = character(), clust, wt,
   cah_actives_tab <- dplyr::bind_rows(cah_actives_tab, n_rows) |>
     dplyr::group_by(.data$variables)
 
+
+  cah_actives_tab <- cah_actives_tab |>
+    dplyr::mutate(dplyr::across(
+      where(tabxplor::is_fmt), ~ dplyr::if_else(.$display == "mean",
+                               true  = dplyr::mutate(., diff = 0, digits = 2L) |>
+                                 tabxplor::as_totrow(),
+                               false = .)
+    ))
+
+
+
   cah_actives_tab
 }
 

README.Rmd

@@ -123,7 +123,7 @@ ggca(res.ca,
 
 ## Personnalize plots
 
-Step-by-step functions can be used to create a database with all the necessary data, modify it, then use it to draw the plot :
+Step-by-step functions can be used to create a database with all the necessary data, modify it, then use it to draw the plot:
 ``` {r, fig.width = 6, fig.height = 6, results = "hide", message = FALSE}
 library(dplyr)
 library(ggplot2)

README.md

@@ -143,13 +143,11 @@ ggca(res.ca,
 ## Personnalize plots
 
 Step-by-step functions can be used to create a database with all the
-necessary data, modify it, then use it to draw the plot :
+necessary data, modify it, then use it to draw the plot:
 
 ``` r
 library(dplyr)
-#> Warning: package 'dplyr' was built under R version 4.3.2
 library(ggplot2)
-#> Warning: package 'ggplot2' was built under R version 4.3.2
 
 plot_data <- ggmca_data(res.mca, tea, sup_vars = "SPC")
 

cran-comments.md

@@ -1,7 +1,5 @@
-## ggfacto new version 0.2.3 : first submission 
+## ggfacto new version 0.3.0 : first submission 
 
-ggfacto wasn't on CRAN since september 2022, because a dependancy also maintained by me,
- tabxplor, was not anymore. I will now assure they both stay on CRAN in the future.
 
 
 ## Test environments
@@ -14,31 +12,21 @@ ggfacto wasn't on CRAN since september 2022, because a dependancy also maintaine
    - Ubuntu Linux 22.04.3 LTS, R-release
    - Ubuntu Linux 22.04.3 LTS, R-oldrel-1
 
-* https://builder.r-hub.io :
-   - Windows Server 2022, R-devel, 64 bit
-   https://builder.r-hub.io/status/ggfacto_0.2.3.tar.gz-796a7101a9db42f59630de9b3e278f49
-   - Ubuntu Linux 20.04.1 LTS, R-release, GCC
-   https://builder.r-hub.io/status/ggfacto_0.2.3.tar.gz-def6afe088a6445ca2b5ef806a8ba52d
-   - Fedora Linux, R-devel, clang, gfortran
-   https://builder.r-hub.io/status/ggfacto_0.2.3.tar.gz-5149876d6b3742a385b0d4f4dbe6e398
-
-* https://win-builder.r-project.org/uA4df9irRZ02 : 
+* https://win-builder.r-project.org/Ys22fzQBwi4F : 
    - Windows x86_64-w64-mingw32 (64-bit), R-devel
 
 ## R CMD check results
 * local Windows 10:
     No ERRORs, no WARNINGs, no NOTEs. 
 
 * github Actions : 
-    No ERRORs, no WARNINGs, no NOTEs. 
-
-* https://builder.r-hub.io : 
-    No ERRORs, no WARNINGs, 1 NOTE : 
-        New submission/Package was archived on CRAN.
+    No ERRORs, no WARNINGs, no NOTEs except on : 
+
+    - Ubuntu Linux 22.04.3 LTS, R-oldrel-1 : 
+    could not install dependency `FactoMineR::`
 
 * https://win-builder.r-project.org : 
-    No ERRORs, no WARNINGs, 1 NOTE : 
-        New submission/Package was archived on CRAN.
+    No ERRORs, no WARNINGs, no NOTE. 
 
 
 ## Downstream dependencies