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redcaptargets

redcaptargets integrates REDCap databases with the targets pipeline toolkit to streamline reproducible clinical research workflows. It leverages the redcapAPI package to connect to REDCap and automatically generates dynamic targets for both metadata and instrument-level data. By leveraging REDCap logging, redcaptargets detects meaningful changes and re-pulls data only when needed, minimizing compute time and ensuring local copies remain up to date. Modular factory functions minimize boilerplate and adapt seamlessly to evolving study designs.

Installation

You can install the development version of redcaptargets from GitHub using:

# install.packages("remotes") # if not already installed
remotes::install_github("overdodactyl/redcaptargets")

If you’re using renv, you can install the package with:

renv::install("overdodactyl/redcaptargets")

Motivation

REDCap doesn’t support SQL-like queries, so users often resort to pulling all data across all instruments into their workspace for analysis. This leads to slow, repetitive code, especially in longitudinal studies where:

  • New participants enroll continuously

  • Forms and surveys evolve

  • Analyses need frequent refreshes

redcaptargets simplifies this by integrating REDCap with the targets pipeline toolkit:

  • Automatically detects meaningful changes via REDCap logs

  • Dynamically creates targets for each instrument and metadata

  • Skips unnecessary pulls to save time and compute

  • Enables modular, reproducible workflows with built-in caching via gittargets

Example

This example shows how to use tar_redcap()—the main function in redcaptargets—to pull all REDCap instruments and metadata into a targets pipeline.

_targets.R: Minimal example 

library(targets)
library(redcaptargets)

# Establish REDCap Connection
redcap_con <- redcapAPI::redcapConnection(
  url = Sys.getenv("REDCAP_URL"),
  token = Sys.getenv("REDCAP_INTERNAL_82203")
)

# Source functions in R/ directory
tar_source()

# Targets pipeline
list(
  tar_redcap(redcap, redcap_con),
  tar_target(
    n_enrolled,
    command = nrow(redcap_demographics),
    description = "Enrollment Count"
  )
)

Trigger the pipeline 

targets::tar_make()
#> + redcap_visits dispatched
#> ✔ redcap_visits completed [503ms, 432 B]
#> + redcap_labs dispatched
#> ✔ redcap_labs completed [112ms, 477 B]
#> + redcap_meta_db dispatched
#> ✔ redcap_meta_db completed [89ms, 568 B]
#> + redcap_demographics dispatched
#> ✔ redcap_demographics completed [131ms, 623 B]
#> + redcap_record_id dispatched
#> ✔ redcap_record_id completed [0ms, 62 B]
#> + n_enrolled dispatched
#> ✔ n_enrolled completed [0ms, 48 B]
#> ✔ ended pipeline [1.1s, 6 completed, 0 skipped]

View generated targets

Each instrument in the REDCap project will have its own target, along with one for metadata:

manifest <- targets::tar_manifest()
manifest[c("name", "description")]
#> # A tibble: 6 × 2
#>   name                description              
#>   <chr>               <chr>                    
#> 1 redcap_visits       REDCap Data: visits      
#> 2 redcap_labs         REDCap Data: labs        
#> 3 redcap_meta_db      REDCap Metadata          
#> 4 redcap_demographics REDCap Data: demographics
#> 5 redcap_record_id    REDCap Record ID         
#> 6 n_enrolled          Enrollment Count