SDTM Test Data for the 'Pharmaverse' Family of Packages.
pharmaversesdtm
Test data (SDTM) for the pharmaverse family of packages
Purpose
To provide a one-stop-shop for SDTM test data in the pharmaverse family of packages. This includes datasets that are therapeutic area (TA)-agnostic (DM
, VS
, EG
, etc.) as well TA-specific ones (RS
, TR
, OE
, etc.).
Installation
The package is available from CRAN and can be installed by running install.packages("pharmaversesdtm")
. To install the latest development version of the package directly from GitHub use the following code:
if (!requireNamespace("remotes", quietly = TRUE)) {
install.packages("remotes")
}
remotes::install_github("pharmaverse/pharmaversesdtm", ref = "main")
Data Sources
Some of the test datasets has been sourced from the CDISC pilot project, while other datasets have been constructed ad-hoc by the admiral team. Please check the Reference page for detailed information regarding the source of specific datasets.
Naming Conventions {#naming}
- Datasets that are TA-agnostic: same as SDTM domain name (e.g.,
dm
,rs
). - Datasets that are TA-specific: domain_TA_others, others go from broader categories to more specific ones (e.g.,
oe_ophtha
,rs_onco
,rs_onco_irecist
).
Note: If an SDTM domain is used by multiple TAs, {pharmaversesdtm}
may provide multiple versions of the corresponding test dataset. For instance, the package contains ex
and ex_ophtha
as the latter contains ophthalmology-specific variables such as EXLAT
and EXLOC
, and EXROUTE
is exchanged for a plausible ophthalmology value.
How To Update
Firstly, make a GitHub issue in {pharmaversesdtm}
with the planned updates and tag @pharmaverse/admiral
so that one of the development core team can sanity check the request. Then there are two main ways to extend the test data: either by adding new datasets or extending existing datasets with new records/variables. Whichever method you choose, it is worth noting the following:
- Programs that generate test data are stored in the
data-raw/
folder. - Each of these programs is written as a standalone R script: if any packages need to be loaded for a given program, then call
library()
at the start of the program (but please do not calllibrary(pharmaversesdtm)
). - Most of the packages that you are likely to need will already be specified in the
renv.lock
file, so they will already be installed if you have been keeping in sync--you can check this by enteringrenv::status()
in the Console. However, you may also wish to install{metatools}
, which is currently not specified in therenv.lock
file. If you feel that you need to install any other packages in addition to those just mentioned, then please tag@pharmaverse/admiral
to discuss with the development core team. - When you have created a program in the
data-raw/
folder, you need to run it as a standalone R script, in order to generate a test dataset that will become part of the{pharmaversesdtm}
package, but you do not need to build the package. - Following best practice, each dataset is stored as a
.rda
file whose name is consistent with the name of the dataset, e.g., datasetxx
is stored asxx.rda
. The easiest way to achieve this is to useusethis::use_data(xx)
- The programs in
data-raw/
are stored within the{pharmaversesdtm}
GitHub repository, but they are not part of the{pharmaversesdtm}
package--thedata-raw/
folder is specified in.Rbuildignore
. - When you run a program that is in the
data-raw/
folder, you generate a dataset that is written to thedata/
folder, which will become part of the{pharmaversesdtm}
package. - The names and sources of test datasets are specified in
R/data.R
, for the purpose of generating documentation in theman/
folder.
Adding New SDTM Datasets
- Create a program in the
data-raw/
folder, named<name>.R
, where<name>
should follow the naming convention, to generate the test data and output<name>.rda
to thedata/
folder.- Use CDISC pilot data such as
dm
as input in this program in order to create realistic synthetic data that remains consistent with other domains (not mandatory). - Note that no personal data should be used as part of this package, even if anonymized.
- Use CDISC pilot data such as
- Run the program.
- Reflect this update, by specifying
<name>
inR/data.R
. - Run
devtools::document()
in order to updateNAMESPACE
and update the.Rd
files inman/
. - Add your GitHub handle to
.github/CODEOWNERS
. - Update
NEWS.md
.
Updating Existing SDTM Datasets
- Locate the existing program
<name>.R
in thedata-raw/
folder, update it accordingly. - Run the program, and output updated
<name>.rda
to thedata/
folder. - Run
devtools::document()
in order to updateNAMESPACE
and update the.Rd
files inman/
. - Add your GitHub handle to
.github/CODEOWNERS
. - Update
NEWS.md
.