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.

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.

dd_ls <- mtcars |>
  dplyr::mutate(record_id = seq_len(dplyr::n())) |>
  dplyr::select(record_id, dplyr::everything()) |>
dd_ls |> 

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

write.csv(dd_ls$meta, "datadictionary.csv")

Upload with REDCapR

  redcap_uri = keyring::key_get("DB_URI"),
  token = keyring::key_get("DB_TOKEN")

In the “REDCap R Handbook” more is written on interfacing with REDCap in R using the library(keyring)to store credentials in chapter 1.1.

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.

  redcap_uri = keyring::key_get("DB_URI"),
  token = keyring::key_get("DB_TOKEN")