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117 lines
3 KiB
Markdown
117 lines
3 KiB
Markdown
# Database-creation
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``` r
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library(REDCapCAST)
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```
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## Two different ways to create a data base
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`REDCapCAST` provides two approaches to creating a data dictionary aimed
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at helping out in two different cases:
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1. Easily create a REDCap data base from an existing data set.
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2. Create a table in Word describing a variables in a data base and use
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this to create a data base.
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In the following I will try to come with a few suggestions on how to use
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these approaches.
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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
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[`ds2dd()`](https://agdamsbo.github.io/REDCapCAST/reference/ds2dd.md),
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which creates a very basic data dictionary with all variables stored as
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text. This is sufficient for just storing old datasets/spreadsheets
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securely in REDCap.
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``` r
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d1 <- mtcars |>
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dplyr::mutate(record_id = seq_len(dplyr::n())) |>
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ds2dd()
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d1 |>
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gt::gt()
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```
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The more advanced
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[`ds2dd_detailed()`](https://agdamsbo.github.io/REDCapCAST/reference/ds2dd_detailed.md)
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is a natural development. It will try to apply the most common data
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classes for data validation and will assume that the first column is the
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id number. It outputs a list with the dataset with modified variable
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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
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preserve as much information as possible.
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``` r
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d2 <- REDCapCAST::redcapcast_data |>
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dplyr::mutate(record_id = seq_len(dplyr::n()),
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region=factor(region)) |>
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dplyr::select(record_id, dplyr::everything()) |>
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(\(.x){
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.x[!grepl("_complete$",names(.x))]
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})() |>
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(\(.x){
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.x[!grepl("^redcap",names(.x))]
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})() |>
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ds2dd_detailed() |>
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purrr::pluck("meta")
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d2 |>
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gt::gt()
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```
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Additional specifications to the DataDictionary can be made manually, or
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it can be uploaded and modified manually in the graphical user interface
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on the REDCap server.
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### Data base from table
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…instructions and examples are coming…
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### Meta data and data upload
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Now the DataDictionary can be exported as a spreadsheet and uploaded or
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it can be uploaded using the `REDCapR` package (only projects with
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“Development” status).
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Use one of the two approaches below:
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#### Manual upload
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``` r
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write.csv(dd_ls$meta, "datadictionary.csv")
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```
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#### Upload with `REDCapR`
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``` r
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REDCapR::redcap_metadata_write(
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dd_ls$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
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Handbook”](https://agdamsbo.github.io/redcap-r-handbook/) more is
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written on interfacing with REDCap in R using the
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[`library(keyring)`](https://keyring.r-lib.org/)to store credentials in
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[chapter
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1.1](https://agdamsbo.github.io/redcap-r-handbook/doc/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:
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manual or through `REDCapR`.
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Only the latter is shown here.
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``` r
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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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