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82 lines
2.4 KiB
Plaintext
82 lines
2.4 KiB
Plaintext
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---
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title: "Database casting"
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output: rmarkdown::html_vignette
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vignette: >
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%\VignetteIndexEntry{Database casting}
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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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# 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}
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mtcars |>
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dplyr::mutate(record_id = seq_len(dplyr::n())) |>
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ds2dd()
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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}
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dd_ls <- mtcars |>
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dplyr::mutate(record_id = seq_len(dplyr::n())) |>
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dplyr::select(record_id, dplyr::everything()) |>
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ds2dd_detailed()
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dd_ls |> str()
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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 web page.
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## Step 3 - Meta 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$meta,
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redcap_uri = keyring::key_get("DB_URI"),
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token = keyring::key_get("DB_TOKEN")
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)
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```
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In the ["REDCap R Handbook"](https://agdamsbo.github.io/redcap-r-handbook/) more is written on interfacing with REDCap in R using the `library(keyring)`to store credentials in [chapter 1.1](https://agdamsbo.github.io/redcap-r-handbook/access.html#sec-getting-access).
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## Step 4 - Data upload
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The same two options are available for data upload as meta data upload: manual or through `REDCapR`.
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Only the latter is shown here.
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```{r eval=FALSE}
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REDCapR::redcap_write(
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dd_ls$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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