---
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")
)
```
