---
title: "Database-creation"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Database-creation}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(REDCapCAST)
```

# Two different ways to create a data base

`REDCapCAST` provides two approaches to creating a data dictionary aimed at helping out in two different cases:

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() 

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}
d2 <- REDCapCAST::redcapcast_data |> 
  dplyr::mutate(record_id = seq_len(dplyr::n()),
                region=factor(region)) |>
  dplyr::select(record_id, dplyr::everything()) |>
  (\(.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 REDCap server.

## Data base from table

...instructions and examples are coming...


## 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).

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/doc/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")
)
```