Updated README and two new vignettes. A little basic for now.

README.md

@@ -12,11 +12,11 @@ experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](h
 
 # REDCapCAST package <img src="man/figures/logo.png" align="right" />
 
-REDCap Castellated data handling when using repeated instruments.
+REDCap database casting and handling of castellated data when using repeated instruments and longitudinal projects.
 
 This package is a fork of [pegeler/REDCapRITS](https://github.com/pegeler/REDCapRITS). The REDCapRITS represents great and extensive work to handle castellated REDCap data in different programming languages. This fork is purely minded on R usage and includes a few implementations of the main `REDCap_split` function.
 
-The main goal for this project was to allow for a "minimal data" approach by allowing to filter records, instruments and variables in the export to only download data needed. I think this approach is desireable for handling sensitive, clinical data. No similar functionality is available from similar tools (like `REDCapR` or `REDCapTidieR`). Please refer to [REDCap-Tools](https://redcap-tools.github.io/) for other great tools.
+THis package is very much to be seen as an attempt at a REDCap-R foundry for handling both the transition from dataset/variable list to database and the other way, from REDCap database to a tidy dataset. The goal was also to allow for a "minimal data" approach by allowing to filter records, instruments and variables in the export to only download data needed. I think this approach is desireable for handling sensitive, clinical data. No similar functionality is available from similar tools (like `REDCapR` or `REDCapTidieR`). Please refer to [REDCap-Tools](https://redcap-tools.github.io/) for other great tools.
 
 ## Use and immprovements
 
@@ -30,8 +30,7 @@ This package is primarily relevant for working with longitudinal projects and/or
 
 * `easy_redcap()`: combines secure API key storage with the `keyring`-package, focused data retrieval and optional widening. This is the recommended approach for easy data access and analysis.
 
-
+...
-Compared to the original `REDCapRITS`, all matching functions are improved to accept column naming of REDCap data from manual download or API export.
 
 ## Future
 

vignettes/Database creation.Rmd

@@ -0,0 +1,81 @@
+---
+title: "Database casting"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Database casting}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(REDCapCAST)
+```
+
+# Easy data set to data base workflow
+
+THe first iteration of a dataset to data dictionary function is the `ds2dd()`, which creates a very basic data dictionary with all variables stored as text. This is sufficient for just storing old datasets/spreadsheets securely in REDCap.
+
+```{r}
+mtcars |>
+  dplyr::mutate(record_id = seq_len(dplyr::n())) |>
+  ds2dd()
+```
+
+The more advanced `ds2dd_detailed()` is a natural development. It will try to apply the most common data classes for data validation and will assume that the first column is the id number. It outputs a list with the dataset with modified variable names to comply with REDCap naming conventions and a data dictionary. 
+
+The dataset should be correctly formatted for the data dictionary to preserve as much information as possible.
+
+```{r}
+dd_ls <- mtcars |>
+  dplyr::mutate(record_id = seq_len(dplyr::n())) |>
+  dplyr::select(record_id, dplyr::everything()) |>
+  ds2dd_detailed()
+dd_ls |> str()
+```
+
+Additional specifications to the DataDictionary can be made manually, or it can be uploaded and modified manually in the graphical user interface on the web page.
+
+## Step 3 - Meta data upload
+
+Now the DataDictionary can be exported as a spreadsheet and uploaded or it can be uploaded using the `REDCapR` package (only projects with "Development" status).
+
+Use one of the two approaches below:
+
+### Manual upload
+
+```{r eval=FALSE}
+write.csv(dd_ls$meta, "datadictionary.csv")
+```
+
+### Upload with `REDCapR`
+
+```{r eval=FALSE}
+REDCapR::redcap_metadata_write(
+  dd_ls$meta,
+  redcap_uri = keyring::key_get("DB_URI"),
+  token = keyring::key_get("DB_TOKEN")
+)
+```
+
+In the ["REDCap R Handbook"](https://agdamsbo.github.io/redcap-r-handbook/) more is written on interfacing with REDCap in R using the `library(keyring)`to store credentials in [chapter 1.1](https://agdamsbo.github.io/redcap-r-handbook/access.html#sec-getting-access).
+
+## Step 4 - Data upload
+
+The same two options are available for data upload as meta data upload: manual or through `REDCapR`.
+
+Only the latter is shown here.
+
+```{r eval=FALSE}
+REDCapR::redcap_write(
+  dd_ls$data,
+  redcap_uri = keyring::key_get("DB_URI"),
+  token = keyring::key_get("DB_TOKEN")
+)
+```

vignettes/Shiny casting.Rmd

@@ -0,0 +1,30 @@
+---
+title: "Introduction"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Introduction}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(REDCapCAST)
+```
+
+To make the easiest possible transistion 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}
+require(REDCapCAST)
+shiny_cast()
+```
+
+The app will launch in a new window and the interface should be fairly self-explanatory.
+The app only provides the most basic functionality, but might be extended in the future.
+