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.
| 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()| 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 | data | NA | text | record_id | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| cpr | data | NA | text | cpr | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| inclusion | data | NA | text | inclusion | NA | NA | date_dmy | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| inclusion_time | data | NA | text | inclusion_time | NA | NA | time_hh_mm_ss | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| dob | data | NA | text | dob | NA | NA | date_dmy | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| age | data | NA | text | age | NA | NA | number | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| age_integer | data | NA | text | age_integer | NA | NA | number | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| sex | data | NA | text | sex | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| cohabitation | data | NA | text | cohabitation | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| hypertension | data | NA | text | hypertension | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| diabetes | data | NA | text | diabetes | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| region | data | NA | radio | region | 1, East | 2, North | 3, South | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| mrs_assessed | data | NA | text | mrs_assessed | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| mrs_date | data | NA | text | mrs_date | NA | NA | date_dmy | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| mrs_score | data | NA | text | mrs_score | NA | NA | number | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| event_datetime | data | NA | text | event_datetime | NA | NA | datetime_dmy | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| event_age | data | NA | text | event_age | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| event_type | data | NA | text | event_type | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
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.
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")
)