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:

Easily create a REDCap data base from an existing data set.
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()

field_name	form_name	section_header	field_type	field_label	select_choices_or_calculations	field_note	text_validation_type_or_show_slider_number	text_validation_min	text_validation_max	identifier	branching_logic	required_field	custom_alignment	question_number	matrix_group_name	matrix_ranking	field_annotation
record_id	basis	NA	text	record_id	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
mpg	basis	NA	text	mpg	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
cyl	basis	NA	text	cyl	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
disp	basis	NA	text	disp	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
hp	basis	NA	text	hp	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
drat	basis	NA	text	drat	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
wt	basis	NA	text	wt	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
qsec	basis	NA	text	qsec	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
vs	basis	NA	text	vs	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
am	basis	NA	text	am	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
gear	basis	NA	text	gear	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
carb	basis	NA	text	carb	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

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")
)

Database-creation