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new vignettes

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This commit is contained in:
Andreas Gammelgaard Damsbo 2024-02-27 12:42:58 +01:00
parent 767d03f45f
commit 409d53a6be
2 changed files with 8 additions and 8 deletions
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10
vignettes/Database creation.Rmd → vignettes/Database-creation.Rmd
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@ -1,8 +1,8 @@
--- ---
title: "Database casting" title: "Database-creation"
output: rmarkdown::html_vignette output: rmarkdown::html_vignette
vignette: > vignette: >
%\VignetteIndexEntry{Database casting} %\VignetteIndexEntry{Database-creation}
%\VignetteEngine{knitr::rmarkdown} %\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8} %\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. 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 |> mtcars |>
dplyr::mutate(record_id = seq_len(dplyr::n())) |> 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 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. The dataset should be correctly formatted for the data dictionary to preserve as much information as possible.
```{r} ```{r eval=FALSE}
dd_ls <- mtcars |> dd_ls <- mtcars |>
dplyr::mutate(record_id = seq_len(dplyr::n())) |> dplyr::mutate(record_id = seq_len(dplyr::n())) |>
dplyr::select(record_id, dplyr::everything()) |> dplyr::select(record_id, dplyr::everything()) |>

6
vignettes/Shiny casting.Rmd → vignettes/Shiny-app.Rmd
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@ -1,8 +1,8 @@
--- ---
title: "Introduction" title: "Shiny-app"
output: rmarkdown::html_vignette output: rmarkdown::html_vignette
vignette: > vignette: >
%\VignetteIndexEntry{Introduction} %\VignetteIndexEntry{Shiny-app}
%\VignetteEngine{knitr::rmarkdown} %\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8} %\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: 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) require(REDCapCAST)
shiny_cast() shiny_cast()
``` ```