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siera

CRAN R-CMD-check

Are you looking for a way to automate TFLs?

With siera, users ingest Analysis Results Standard - ARS (a CDISC Foundational standard) metadata and auto-generate R scripts that, when run in with provided ADaM datasets, provide Analysis Results Datasets (ARDs).

In order to use the readARS() function, users will need to provide the following:

  1. A Functional JSON file, representing ARS Metadata for a Reporting Event (to get started, see TFL Designer)
  2. An output directory where the R scripts will be placed
  3. A folder containing the related ADaM datasets for the ARDs to be generated

Installation

The current version (0.1.0) of siera can be installed from CRAN with:

install.packages("siera")
#> package 'siera' successfully unpacked and MD5 sums checked
#> 
#> The downloaded binary packages are in
#>  C:\Users\mbosm\AppData\Local\Temp\RtmpsTBaHn\downloaded_packages

Example

library(siera)

siera includes several example files, which we use throughout the documentation. These include a JSON ARS file, as well as some csv ADaMs (ADSL and ADAE) which can be run with the R scripts produced by readARS function. Use the helper ARS_example() with no arguments to list them or call it with an example filename to get the path.

# To see a list of example files:
ARS_example()
#> [1] "ADAE.csv"                           "ADSL.csv"                          
#> [3] "ARS_V1_Common_Safety_Displays.json"

# A temporary path to a specific file:
ARS_example("ARS_V1_Common_Safety_Displays.json")
#> [1] "C:/Users/mbosm/AppData/Local/Temp/RtmpmME8pc/temp_libpath3f08e0d63c1/siera/extdata/ARS_V1_Common_Safety_Displays.json"

Next, we will ingest the example json ARS file to meta-programme ready-to-run R scripts, which will produce the ARDs.

# Path to the the ARS JSON File. 
json_path <- ARS_example("ARS_V1_Common_Safety_Displays.json")

# Path to a folder which will contain the meta-programmed R scripts (feel free to update 
# to a more suitable path)
output_folder <- tempdir()

# this folder contains ADaM datasets to produce ARD (we will use temporary 
# directory tempdir(), but feel free to download the ADaMs required and use the location they are stored in.
# This can be done with e.g. dirname(ARS_example("ADSL.csv"))
ADaM_folder <- tempdir()

# run the readARS function with these 3 parameters.  This creates R scripts (1 for each output in output_folder)
readARS(json_path, output_folder, ADaM_folder)

Once the R programs are created, they can be individually run, provided that the ADaM datasets are in the location as provided to the readARS function.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.