From 409d53a6bea0b63ca67e8e756463b4f8c185422c Mon Sep 17 00:00:00 2001
From: Andreas Gammelgaard Damsbo <andreas@gdamsbo.dk>
Date: Tue, 27 Feb 2024 12:42:58 +0100
Subject: [PATCH] new vignettes

---
 .../{Database creation.Rmd => Database-creation.Rmd}   | 10 +++++-----
 vignettes/{Shiny casting.Rmd => Shiny-app.Rmd}         |  6 +++---
 2 files changed, 8 insertions(+), 8 deletions(-)
 rename vignettes/{Database creation.Rmd => Database-creation.Rmd} (95%)
 rename vignettes/{Shiny casting.Rmd => Shiny-app.Rmd} (90%)

diff --git a/vignettes/Database creation.Rmd b/vignettes/Database-creation.Rmd
similarity index 95%
rename from vignettes/Database creation.Rmd
rename to vignettes/Database-creation.Rmd
index 088fa8b..c226385 100644
--- a/vignettes/Database creation.Rmd	
+++ b/vignettes/Database-creation.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "Database casting"
+title: "Database-creation"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Database casting}
+  %\VignetteIndexEntry{Database-creation}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -22,17 +22,17 @@ library(REDCapCAST)
 
 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}
+```{r eval=FALSE}
 mtcars |>
   dplyr::mutate(record_id = seq_len(dplyr::n())) |>
-  ds2dd()
+  ds2dd() |> str()
 ```
 
 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}
+```{r eval=FALSE}
 dd_ls <- mtcars |>
   dplyr::mutate(record_id = seq_len(dplyr::n())) |>
   dplyr::select(record_id, dplyr::everything()) |>
diff --git a/vignettes/Shiny casting.Rmd b/vignettes/Shiny-app.Rmd
similarity index 90%
rename from vignettes/Shiny casting.Rmd
rename to vignettes/Shiny-app.Rmd
index c2c1629..f379153 100644
--- a/vignettes/Shiny casting.Rmd	
+++ b/vignettes/Shiny-app.Rmd
@@ -1,8 +1,8 @@
 ---
-title: "Introduction"
+title: "Shiny-app"
 output: rmarkdown::html_vignette
 vignette: >
-  %\VignetteIndexEntry{Introduction}
+  %\VignetteIndexEntry{Shiny-app}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---
@@ -20,7 +20,7 @@ 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}
+```{r eval=FALSE}
 require(REDCapCAST)
 shiny_cast()
 ```