mirror of
https://github.com/agdamsbo/REDCapCAST.git
synced 2024-10-30 03:21:53 +01:00
82 lines
2.4 KiB
Plaintext
82 lines
2.4 KiB
Plaintext
|
---
|
||
|
|
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")
|
||
|
|
)
|
||
|
|
```
|