The goal of REDCapExporter is to provide a simple and relatively secure way to downloading and formatting data from a REDCap project.

While REDCap (Research Electronic Data Capture) is a fantastic tool for data capture, the dissemination of the collected data in a format that multiple data analysis can easily access and share can be improved. The REDCapExporter package provides a set of tools to provide easy access to REDCap data via the REDCap API with two aims:

1. Scripting functions to allow for easy access to, and formatting of, a data set within a REDCap project.
2. Produce a skeleton R data package to house, document, archive, and disseminate a data set.

Why is REDCapExporter needed?

If you are familiar with REDCap then you know that exporting the data from the web interface is straight forward, easy to use, and powerful. However, there is a fair amount of (meta) data that could be valuable to data analysis which is not included in the export. The REDCapExporter aims to report and cross link the meta data, collected data, and (expanded) documentation in one location. The resulting .tar.gz data package is easy to disseminate.

Version control of data is difficult. Small data files can easily be added to git repositories, and larger files using git-lfs. Subversion can handle larger data sets with its own pros and cons. However, adding the data to multiple repositories will consume a lot of disk space unnecessarily and there are issues arising from sensitive data being versioned and while using public repository hosts. A R data package will provide an implicit versioning of data while keeping only one copy of the data on the local disk.

Install the released version from CRAN:

install.packages("REDCapExporter", repos = "https://cran.rstudio.com")

You can install the development version of REDCapExporter from GitHub with:

# install.packages("pak")
pak::pak("dewittpe/REDCapExporter")

You have multiple REDCap projects and you have an API token for each project allowing for data download.

There are several ways to do this. A simple way is based on the keyring package.

library(REDCapExporter)

# Check if a file based keyring called "REDCapExporter" exists and create one if
# not.
REDCapExporter_keyring_check()

# Add, or verify existance of, an API token for PROJECT1
REDCapExporter_add_api_token("PROJECT1")

# Add, or verify existance of, an API token for PROJECT2
REDCapExporter_add_api_token("PROJECT2")

To set the token to use for the current session, set an environmental variable and use REDCapExporter_get_api_token to retrieve the token from the keyring.

Sys.setenv(REDCap_API_TOKEN = REDCapExporter_get_api_token("PROJECT1"))

You may set one more environmental variable, or specify it explicitly in the call to export_core. It is recommended to set the environmental variable for easy of use.

Sys.setenv(REDCap_API_URI = "https://<your-redcap-domain>/api/")

Export the data from REDCap and format it, based on the metadata of the REDCap project via:

project1_redcap_core <- export_core()
project1 <- format_record(project1_redcap_core)

The example data provided in this package are statistics from the 2000-2001 National Hockey League Stanley Cup Champion Colorado Avalanche. The data was transcribed from Hockey Reference into a REDCap Project hosed at the University of Colorado Denver.

The data set avs_raw_core was generated via the following, after setting up my keyring and token as described above.

library(REDCapExporter)
Sys.setenv(REDCap_API_URI = "https://redcap.ucdenver.edu/api/")
Sys.setenv(REDCap_API_TOKEN = REDCapExporter_get_api_token("2000_2001_Avalanche"))
avs_raw_core <- export_core()
#> Getting Project Info
#> Getting project metadata
#> Getting user data
#> Getting project record
avsDF <- format_record(avs_raw_core)

A simple summary statistic from the data set: the number of goals scored by position:

aggregate(goals ~ position, data = avsDF, FUN = sum)
#>     position goals
#> 1       Goal     0
#> 2  Left Wing    71
#> 3 Right Wing    72
#> 4     Center    93
#> 5    Defence    34