major update with new functions and renv is out! see NEWS section

vignettes/Database-creation.Rmd

@@ -18,33 +18,59 @@ knitr::opts_chunk$set(
 library(REDCapCAST)
 ```
 
-# Easy data set to data base workflow
+# Two different ways to create a data base
 
-THe first iteration of a dataset to data dictionary function is the `ds2dd()`, which creates a very basic data dictionary with all variables stored as text. This is sufficient for just storing old datasets/spreadsheets securely in REDCap.
+`REDCapCAST` provides two approaches to creating a data dictionary aimed at helping out in two different cases:
 
-```{r eval=FALSE}
-mtcars |>
+1.   Easily create a REDCap data base from an existing data set.
+
+2.   Create a table in Word describing a variables in a data base and use this to create a data base.
+
+In the following I will try to come with a few suggestions on how to use these approaches.
+
+## Easy data set to data base workflow
+
+The first iteration of a dataset to data dictionary function is the `ds2dd()`, which creates a very basic data dictionary with all variables stored as text. This is sufficient for just storing old datasets/spreadsheets securely in REDCap.
+
+```{r eval=TRUE}
+d1 <- mtcars |>
   dplyr::mutate(record_id = seq_len(dplyr::n())) |>
-  ds2dd() |>
-  str()
+  ds2dd() 
+
+d1 |>
+  gt::gt()
 ```
 
 The more advanced `ds2dd_detailed()` is a natural development. It will try to apply the most common data classes for data validation and will assume that the first column is the id number. It outputs a list with the dataset with modified variable names to comply with REDCap naming conventions and a data dictionary. 
 
 The dataset should be correctly formatted for the data dictionary to preserve as much information as possible.
 
-```{r eval=FALSE}
-dd_ls <- mtcars |>
-  dplyr::mutate(record_id = seq_len(dplyr::n())) |>
+```{r eval=TRUE}
+d2 <- REDCapCAST::redcapcast_data |> 
+  dplyr::mutate(record_id = seq_len(dplyr::n()),
+                region=factor(region)) |>
   dplyr::select(record_id, dplyr::everything()) |>
-  ds2dd_detailed()
-dd_ls |> 
-  str()
+  (\(.x){
+    .x[!grepl("_complete$",names(.x))]
+  })() |> 
+  (\(.x){
+    .x[!grepl("^redcap",names(.x))]
+  })() |>  
+  ds2dd_detailed() |> 
+  purrr::pluck("meta") 
+
+d2 |> 
+  gt::gt()
 ```
 
-Additional specifications to the DataDictionary can be made manually, or it can be uploaded and modified manually in the graphical user interface on the web page.
+Additional specifications to the DataDictionary can be made manually, or it can be uploaded and modified manually in the graphical user interface on the REDCap server.
 
-## Step 3 - Meta data upload
+## Data base from table
+
+
+
+
+## Meta data and data upload
 
 Now the DataDictionary can be exported as a spreadsheet and uploaded or it can be uploaded using the `REDCapR` package (only projects with "Development" status).
 

vignettes/Shiny-app.Rmd

@@ -14,10 +14,6 @@ knitr::opts_chunk$set(
 )
 ```
 
-```{r setup}
-library(REDCapCAST)
-```
-
 To make the easiest possible transition from spreadsheet/dataset to REDCap, I have created a small Shiny app, which adds a graphical interface to the casting of a data dictionary and data upload. Install the package and run the app as follows:
 
 ```{r eval=FALSE}