siera

Overview

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

Installation

The current version 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\Rtmpaw19cU\downloaded_packages

Requirements

siera has two main functions used to ingest ARS metadata - one for JSON input - readARS, and another for Excel input - readARS_xl. Depending on the metadata input type, the appropriate function should be used.

When run, these functions ingest the provided metadata, and produce R scripts that, when run as-is (with the applicable ADaM dataset), will generate an ARD for each output specified in the metadata.

In order to make use of these functions, the following are required as arguments:

1. A functional ARS file, representing ARS Metadata for a Reporting Event
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

Example to get started

library(siera)

The following example focuses on a JSON ARS metadata input file, thus making use of the readARS function. For an example of ingesting the ARS metadata as an Excel file, see the article “Read ARS from Excel”.

Note the following regarding these examples: For readARS (JSON metadata):

• operations are performed within the readARS function, thus example-driven

For readARS_xl (Excel metadata):

• operations are defined in the metadata, using AnalysisMethodCodeTemplate and AnalysisMethodCodeParameters classes, basing the operations on functions in the cards and cardx packages.

In order to facilitate the examples, 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/R/win-library/4.4/siera/extdata/ARS_V1_Common_Safety_Displays.json"

To get started with an example of ingesting the ARS JSON metadata,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.