mirror of
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110 lines
3.2 KiB
Plaintext
110 lines
3.2 KiB
Plaintext
---
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title: "Database-creation"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Database-creation}
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%\VignetteEngine{knitr::rmarkdown}
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%\VignetteEncoding{UTF-8}
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---
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```{r, include = FALSE}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#>"
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)
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```
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```{r setup}
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library(REDCapCAST)
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```
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# Two different ways to create a data base
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`REDCapCAST` provides two approaches to creating a data dictionary aimed at helping out in two different cases:
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1. Easily create a REDCap data base from an existing data set.
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2. Create a table in Word describing a variables in a data base and use this to create a data base.
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In the following I will try to come with a few suggestions on how to use these approaches.
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## Easy data set to data base workflow
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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.
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```{r eval=TRUE}
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d1 <- mtcars |>
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dplyr::mutate(record_id = seq_len(dplyr::n())) |>
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ds2dd()
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d1 |>
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gt::gt()
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```
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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.
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The dataset should be correctly formatted for the data dictionary to preserve as much information as possible.
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```{r eval=TRUE}
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d2 <- REDCapCAST::redcapcast_data |>
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dplyr::mutate(record_id = seq_len(dplyr::n()),
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region=factor(region)) |>
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dplyr::select(record_id, dplyr::everything()) |>
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(\(.x){
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.x[!grepl("_complete$",names(.x))]
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})() |>
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(\(.x){
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.x[!grepl("^redcap",names(.x))]
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})() |>
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ds2dd_detailed() |>
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purrr::pluck("meta")
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d2 |>
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gt::gt()
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```
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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.
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## Data base from table
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...instructions and examples are coming...
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## Meta data and data upload
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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).
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Use one of the two approaches below:
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### Manual upload
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```{r eval=FALSE}
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write.csv(dd_ls$meta, "datadictionary.csv")
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```
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### Upload with `REDCapR`
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```{r eval=FALSE}
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REDCapR::redcap_metadata_write(
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dd_ls$meta,
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redcap_uri = keyring::key_get("DB_URI"),
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token = keyring::key_get("DB_TOKEN")
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)
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```
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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).
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## Step 4 - Data upload
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The same two options are available for data upload as meta data upload: manual or through `REDCapR`.
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Only the latter is shown here.
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```{r eval=FALSE}
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REDCapR::redcap_write(
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dd_ls$data,
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redcap_uri = keyring::key_get("DB_URI"),
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token = keyring::key_get("DB_TOKEN")
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)
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```
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