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Database-creation

Source: vignettes/Database-creation.Rmd
Database-creation.Rmd

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.

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.

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 

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

Upload with REDCapR 

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” 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.

REDCapR::redcap_write(
  dd_ls$data,
  redcap_uri = keyring::key_get("DB_URI"),
  token = keyring::key_get("DB_TOKEN")
)