docs: vignettes migrated see NEWS

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: FreesearchR
 Title: Easy data analysis for clinicians
-Version: 25.11.1
+Version: 25.11.2
 Authors@R: c(
     person("Andreas Gammelgaard", "Damsbo",email="agdamsbo@clin.au.dk", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-7559-1154")),

NEWS.md

@@ -1,3 +1,7 @@
+# FreesearchR 25.11.2
+
+*NEW* Vignettes were moved to the [FreesearchR project knowledge base](https://freesearchr.github.io/FreesearchR-knowledge/). This was mainly to ease rendering and allow quick and easy updates as well as future translations.
+
 # FreesearchR 25.11.1
 
 *NEW* Added option to select extensive baseline table selecting between "Minimal" (current) or "Extensive" which adds mean/sd and min/max as well as plots all levels also for dichotomous variables.

vignettes/data-types.Rmd

@@ -16,23 +16,4 @@ knitr::opts_chunk$set(
 options(rmarkdown.html_vignette.check_title = FALSE)
 ```
 
-```{r setup}
+Documentation on the data types used in FreesearchR can be found in the [FreesearchR project documentations (link)](https://freesearchr.github.io/FreesearchR-knowledge/app/data_types.html).
-library(FreesearchR)
-i18n_path <- system.file("translations", package = "FreesearchR")
-i18n <- shiny.i18n::Translator$new(translation_csvs_path = i18n_path)
-i18n$set_translation_language("en")
-```
-
-## A clinical data class
-
-Traditionally in *R*, data is identified by classes, like numeric, integer, double, logical, factor etc. These classes can be a little confusing from a clinical or operational standpoint. In the ***FreesearchR*** app, these classes has been simplified and modified to the following data types, that are assigned on a prioritised order like the following:
-
-```{r echo = FALSE}
-data_types() |> purrr::imap(\(.x,.i){
-  dplyr::bind_cols("type"=.i,.x,.name_repair = "unique_quiet")
-  }) |> dplyr::bind_rows() |> 
-  setNames(c("Data type","Description","Data classes included")) |> 
-  knitr::kable()
-```
-
-Categorising data in this way makes sense when making choices on how to evaluate and analyse data. This is used throughout the ***FreesearchR*** app to simplify data handling.

vignettes/missingness.Rmd

@@ -15,12 +15,4 @@ knitr::opts_chunk$set(
 options(rmarkdown.html_vignette.check_title = FALSE)
 ```
 
-```{r setup}
+Considerations of data missingness can be found in the [FreesearchR project documentations (link)](https://freesearchr.github.io/FreesearchR-knowledge/intro/missingness.html).
-library(FreesearchR)
-```
-
-## Considering missing observations
-
-### Further reading
-
-The authors behind the [{finalfit}-package](https://finalfit.org/index.html) have shared a very comprehensive article on what to do and think about missing observations in your data. Please [have a look here](https://finalfit.org/articles/missing.html).

vignettes/visuals.Rmd

@@ -16,44 +16,4 @@ knitr::opts_chunk$set(
 options(rmarkdown.html_vignette.check_title = FALSE)
 ```
 
-```{r setup}
+Documentation on visuals used in FreesearchR can be found in the [FreesearchR project documentations (link)](https://freesearchr.github.io/FreesearchR-knowledge/app/visuals.html).
-library(FreesearchR)
-i18n_path <- system.file("translations", package = "FreesearchR")
-i18n <- shiny.i18n::Translator$new(translation_csvs_path = i18n_path)
-i18n$set_translation_language("en")
-```
-
-## Basic visualisations
-
-The goal of ***FreesearchR*** is to keep things simple. Visuals can get very complicated. We provide a selection of plots, that helps visualise typical clinical and will be enough for most use cases, and for publishing to most journals.
-
-If you want to go further, have a look at these sites with suggestions and sample code for data plotting:
-
--   [*R* Charts](https://r-charts.com/): Extensive gallery with great plots
-
--   [*R* Graph gallery](https://r-graph-gallery.com/): Another gallery with great graphs
-
--   [graphics principles](https://graphicsprinciples.github.io/): Easy to follow recommendations for clear visuals. 
-
-
-### Available plots
-
-Below are the available plot types listed.
-
-```{r echo = FALSE}
-c("continuous", "dichotomous", "categorical") |> 
-  lapply(\(.x){
-    dplyr::bind_cols(
-      dplyr::tibble("Data type"=.x),
-      supported_plots() |> 
-      lapply(\(.y){
-        if (.x %in% .y$primary.type){
-          .y[c("descr","note")]|> dplyr::bind_cols()
-        }
-})|> 
-  dplyr::bind_rows() |> 
-  setNames(c("Plot type","Description")))
-  }) |> 
-  dplyr::bind_rows() |> 
-  knitr::kable()
-```