---
title: "Writing to a REDCap Project"
author: Will Beasley [Biomedical & Behavior Methodology Core](https://www.ouhsc.edu/bbmc/team/), OUHSC Pediatrics;;<br>Raymond Balise, University of Miami School of Medicine<br>Stephan Kadauke, Children's Hospital of Philadelphia
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Writing to a REDCap Project}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  markdown: 
    wrap: 72
---

```{r}
#| include = FALSE
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  tidy    = FALSE
)
```

Writing data *to* REDCap is more difficult than reading data *from*
REDCap. When you read, you receive data in the structure that the REDCap
provides you. You have some control about the columns, rows, and data
types, but there is not a lot you have to be concerned.

In contrast, the structure of the dataset you send to the REDCap server
must be precise. You need to pass special variables so that the REDCap
server understands the hierarchical structure of the data points. This
vignette walks you through that process.

If you are new to REDCap and its API, please first understand the
concepts described in these two
[vignettes](https://ouhscbbmc.github.io/REDCapR/articles/):

-   [Typical REDCap Workflow for a Data
    Analyst](https://ouhscbbmc.github.io/REDCapR/articles/workflow-read.html)
-   [Retrieving Longitudinal and Repeating
    Structures](https://ouhscbbmc.github.io/REDCapR/articles/longitudinal-and-repeating.html)

# Part 1 - Intro

## Strategy

As described in the [Retrieving Longitudinal and Repeating
Structures](https://ouhscbbmc.github.io/REDCapR/articles/longitudinal-and-repeating.html)
vignette, the best way to read and write data from projects with
longitudinal/repeating elements is to break up the "block matrix"
dataset into individual datasets. Each rectangle should have a coherent
grain.

Following this strategy, we'll write to the REDCap server in two
distinct steps:

1.  Upload the patient-level instrument(s)
2.  Upload the each repeating instrument separately.

The actual upload phase is pretty straight-forward --it's just a call to
`REDCapR::redcap_write()`. Most of the vignette's code prepares the
dataset so that the upload will run smoothly.

## Pre-requisites

See the [Typical REDCap Workflow for a Data
Analyst](https://ouhscbbmc.github.io/REDCapR/articles/workflow-read.html)
vignette and

1.  [Verify REDCapR is
    installed](https://ouhscbbmc.github.io/REDCapR/articles/workflow-read.html#verify-redcapr-is-installed)
2.  [Verify REDCap
    Access](https://ouhscbbmc.github.io/REDCapR/articles/workflow-read.html#verify-redcap-access)
3.  [Review
    Codebook](https://ouhscbbmc.github.io/REDCapR/articles/workflow-read.html#review-codebook)

## Retrieve Token

Please closely read the [Retrieve Protected
Token](https://ouhscbbmc.github.io/REDCapR/articles/workflow-read.html#part-2---retrieve-protected-token)
section, which has important security implications. The current vignette
imports a fake dataset into REDCap, and we'll use a token stored in a
local file.

``` r
# retrieve-credential
path_credential <- system.file("misc/example.credentials", package = "REDCapR")
credential  <- REDCapR::retrieve_credential_local(
  path_credential = path_credential,
  project_id      = 3748
)

c(credential$redcap_uri, credential$token)
```

## Datasets to Write to Server

To keep this vignette focused on writing/importing/uploading to the
server, we'll start with the data that needs to be written. These
example tables were prepared by [Raymond
Balise](https://github.com/RaymondBalise) for our 2023
[R/Medicine](https://events.linuxfoundation.org/r-medicine/) workshop,
"Using REDCap and R to Rapidly Produce Biomedical Publications".

There are two tables, each with a different
[granularity](https://www.1keydata.com/datawarehousing/fact-table-granularity.html):

-   `ds_patient`: each row represents one patient,
-   `ds_daily`: each row represents one daily measurement per patient.

``` r
# load-patient
ds_patient <-
  "test-data/vignette-repeating-write/data-patient.rds" |>
  system.file(package = "REDCapR") |>
  readr::read_rds()

ds_patient
```

``` r
# load-repeating
ds_daily <-
  "test-data/vignette-repeating-write/data-daily.rds" |>
  system.file(package = "REDCapR") |>
  readr::read_rds()

ds_daily
```

# Part 2 - Write Data: One row per patient

Besides the
[`data.frame`](https://stat.ethz.ch/R-manual/R-devel/library/base/html/data.frame.html)
to write to REDCap, the only required arguments of the
[`REDCapR::redcap_write()`](https://ouhscbbmc.github.io/REDCapR/reference/redcap_write.html)
function are `redcap_uri` and `token`; both are contained in the
credential object created in the previous section.

As discussed in the [Troubleshooting
vignette](https://ouhscbbmc.github.io/REDCapR/articles/TroubleshootingApiCalls.html#writing),
we recommend running these two preliminary checks before trying to write
the dataset to the server for the very first time.

## Prep: Stoplight Fields

If the REDCap project isn't longitudinal and doesn't have arms,
uploading a patient-level data.frame to REDCap doesn't require adding
variables. However we typically populate the `*_complete` variables to
communicate the record's status.

If the row is needs a human to add more values or inspect the existing
values consider [marking the
instrument](https://ouhscbbmc.github.io/REDCapR/reference/constant.html)
"incomplete" or "unverified"; the patient's instrument record will
appear red or yellow in REDCap's Record Dashboard. Otherwise consider
marking the instrument "complete" so it will appear green.

With this example project, the only patient-level instrument is
"enrollment", so the corresponding variable is `enrollment_complete`.

``` r
# patient-complete
ds_patient <-
  ds_patient |>
  dplyr::mutate(
    enrollment_complete   = REDCapR::constant("form_complete"),
  )
```

## Prep: `REDCapR::validate_for_write()`

`REDCapR::validate_for_write()` inspects a data frame to anticipate
potential problems before writing with REDCap's API. A tibble is
returned, with one row per potential problem (and a suggestion how to
avoid it). Ideally an 0-row tibble is returned.

``` r
REDCapR::validate_for_write(ds_patient, convert_logical_to_integer = TRUE)
```

If you encounter problems that can be checked with automation, please
tell us in [an issue](https://github.com/OuhscBbmc/REDCapR/issues).
We'll work with you to incorporate the new check into
`REDCapR::validate_for_write()`.

When a dataset's problems are caught before reaching the server, the
solutions are easier to identify and implement.

## Prep: Write Small Subset First

If this is your first time with a complicated project, consider loading
a small subset of rows and columns. In this case, we start with only
three columns and two rows.

``` r
# patient-subset
ds_patient |>
  dplyr::select(              # First three columns
    id_code,
    date,
    is_mobile,
  ) |>
  dplyr::slice(1:2) |>        # First two rows
  REDCapR::redcap_write(
    ds_to_write = _,
    redcap_uri  = credential$redcap_uri,
    token       = credential$token,
    convert_logical_to_integer = TRUE
  )
```

## Prep: Recode Variables where Necessary

Some variables in the data.frame might be represented differently than
in REDCap.

A common transformation is changing strings into the integers that
underlie radio buttons. Common approaches are
[`dplyr::case_match()`](https://dplyr.tidyverse.org/reference/case_match.html)
and using joining to lookup tables (if the mappings are expressed in a
csv). Here's an in-line example of `dplyr::case_match()`.

``` r
ds_patient <-
  ds_patient |>
  dplyr::mutate(
    race =
      dplyr::case_match(
        race,
        "White"                       ~  1L,
        "Black or African American"   ~  2L,
        "Asian"                       ~  3L,
        "Native American"             ~  4L,
        "Pacific Islander"            ~  5L,
        "Multiracial"                 ~  6L,
        "Refused or don't know"       ~  7L
      )
  )
```

```{r codebook-race}
#| echo = FALSE,
#| out.extra = 'style = "fig.width=1200px"'
knitr::include_graphics("images/codebook-race.png")
```

## Write Entire Patient-level Table

If the small subset works, we usually jump ahead and try all columns and
rows.

If this larger table fails, split the difference between (a) the smaller
working example and (b) the larger failing example. See if this middle
point (that has fewer rows and/or columns than the failing point)
succeeds or fails. Then repeat. This "bisection" or "binary search"
[debugging
technique](https://medium.com/codecastpublication/debugging-tools-and-techniques-binary-search-2da5bb4282c7)
is helpful in many areas of programming and statistical modeling.

``` r
# patient-entire
ds_patient |>
  REDCapR::redcap_write(
    ds_to_write = _,
    redcap_uri  = credential$redcap_uri,
    token       = credential$token,
    convert_logical_to_integer = TRUE
  )
```

# Part 3 - Write Data: Repeating Instrument

## Add Plumbing Variables

As stated in the vignette's intro, the structure of the dataset uploaded
to the server must be precise. When uploading repeating instruments,
there are several important columns:

1.  `record_id`: typically indicates the patient's id. (This field can
    be renamed for the project.)
2.  `redcap_event_name`: If the project is longitudinal or has arms,
    this indicates the event. Otherwise, you don't need to add this
    variable.
3.  `redcap_repeat_instrument`: Indicates the instrument/form that is
    repeating for these columns.
4.  `redcap_repeat_instance`: Typically a sequential positive integer
    (*e.g.*, 1, 2, 3, ...) indicating the order.

The combination of these variables needs to be unique. Please read the
[Retrieving Longitudinal and Repeating
Structures](https://ouhscbbmc.github.io/REDCapR/articles/longitudinal-and-repeating.html)
vignette for details of these variables and their meanings.

You need to pass specific variables so that the REDCap server
understands the hierarchical structure of the data points.

``` r
# repeat-plumbing
ds_daily <-
  ds_daily |>
  dplyr::group_by(id_code) |>
  dplyr::mutate(
    redcap_repeat_instrument  = "daily",
    redcap_repeat_instance    = dplyr::row_number(da_date),
    daily_complete            = REDCapR::constant("form_complete"),
  ) |>
  dplyr::ungroup() |>
  dplyr::select(
    id_code,                        # Or `record_id`, if you didn't rename it
    # redcap_event_name,            # If the project is longitudinal or has arms
    redcap_repeat_instrument,       # The name of the repeating instrument/form
    redcap_repeat_instance,         # The sequence of the repeating instrument
    tidyselect::everything(),       # All columns not explicitly passed to `dplyr::select()`
    daily_complete,                 # Indicates incomplete, unverified, or complete
  )

# Check for potential problems.  (Remember zero rows are good.)
REDCapR::validate_for_write(ds_daily, convert_logical_to_integer = TRUE)

ds_daily
```

## Writing Repeating Instrument Variables

``` r
# daily-entire
ds_daily |>
  REDCapR::redcap_write(
    ds_to_write = _,
    redcap_uri  = credential$redcap_uri,
    token       = credential$token,
    convert_logical_to_integer = TRUE
  )
```

# Part 4 - Next Steps

## More Complexity

This vignette required only two data.frames, but more complex projects
sometimes need more. For example, each repeating instrument should be
its own data.frame and writing step. Arms and longitudinal events need
to be considered too.

## Batching

By default, `REDCapR::redcap_write()` requests datasets of 100 patients
as a time, and stacks the resulting subsets together before returning a
data.frame. This can be adjusted to improve performance; the 'Details'
section of `REDCapR::redcap_write()` discusses the trade offs.

I usually shoot for \~10 seconds per batch.

## Manual vs API

Manual downloading/uploading might make sense if you're do the operation
only once. But when does it ever stop after the first time?

If you have trouble uploading, consider adding a few fake patients &
measurements and then download the csv. It might reveal something you
didn't anticipate. But be aware that it will be in the block matrix
format (*i.e.*, everything jammed into one rectangle.)

## REDCap's CDIS

The [Clinical Data Interoperability Services](https://projectredcap.org/software/cdis/) (CDIS) 
use [FHIR](https://www.hl7.org/fhir/overview.html) to move data from
your institution's [EMR/EHR](https://www.healthit.gov/faq/what-are-differences-between-electronic-medical-records-electronic-health-records-and-personal)
(eg, Epic, Cerner) to REDCap. 
Research staff have control over which patient records are selected or eligible.
Conceptually it's similar to writing to REDCap's with the API, 
but at much bigger scale.
Realistically, it takes months to get through your institution's human layers.
Once established, a project would be populated with EMR data 
in much less development time  
--assuming the desired data models corresponds with
FHIR [endpoints](https://hl7.org/fhir/endpoint.html).

# Notes

This vignette was originally designed for the [2023
R/Medicine](https://events.linuxfoundation.org/r-medicine/) workshop,
*Using REDCap and R to Rapidly Produce Biomedical Publications Cleaning
Medical Data* with [Raymond R.
Balise](https://github.com/RaymondBalise), Belén Hervera, Daniel Maya,
Anna Calderon, Tyler Bartholomew, Stephan Kadauke, and João Pedro
Carmezim Correia and the [2024
R/Medicine](https://rconsortium.github.io/RMedicine_website/Program.html)
workshop, *REDCap + R: Teaming Up in the Tidyverse*, with Stephan
Kadauke. The workshop slides are for
[2023](https://github.com/RaymondBalise/r_med_redcap_2023_public) and
[2024](https://github.com/skadauke/rmedicine_2024_redcap_r_workshop).

This work was made possible in part by the NIH grant
[U54GM104938](https://taggs.hhs.gov/Detail/AwardDetail?arg_AwardNum=U54GM104938&arg_ProgOfficeCode=127)
to the [Oklahoma Shared Clinical and Translational
Resource)](http://osctr.ouhsc.edu).

# Session Information

For the sake of documentation and reproducibility, the current report
was rendered in the following environment. Click the line below to
expand.

<details>

<summary>Environment </summary>

```{r session-info, echo=FALSE}
if (requireNamespace("sessioninfo", quietly = TRUE)) {
  sessioninfo::session_info()
} else {
  sessionInfo()
}
```

</details>
