../../../data/GHE/deployment/deployments/2021-10-18/vignettes/yspec.Rmd
yspec.Rmd
The yspec
package will read your data specification file when it is written in a specific yaml
format. Using the object created from this file, you can validate data assembly outputs, label the data frame to be written in sas xport format, create a define.pdf
document, and more.
For each data set in your project that needs documentation, create a yaml file that lists the columns in the data set along with details about the data in that column. This yaml file can be loaded into your R session into an object that you can work with. This is referred to as a spec
object. The term spec
refers to a single documented data object / data file.
Once all of your data sets have been documented with their own yaml file, you can create another object called a yproj
objects. This is used to template the rendering of a single integrated data definitions file for your entire project.
Keep reading the vignette to see how it works.
An example specification file looks like:
SETUP__:
description: Example PopPK analysis data set
sponsor: example-project
projectnumber: EXAMPK1011F
use_internal_db: true
glue:
bmiunits: "kg/m$^2$"
flags:
covariate: [AGE:SCR, HT, AST:ALT]
lookup_file: "look.yml"
C:
NUM:
ID:
SUBJ: !look
TIME: !look
label: time after first dose
unit: hour
SEQ:
label: data type
values: {observation: 0, dose: 1}
CMT:
EVID:
make_factor: true
lookup: true
AMT: !look
unit: mg
DV: !look
unit: "micrograms/L"
unit.tex: "$\\mu$g/L"
Once the data specification yaml
file is written, it can be loaded in R
spec <- ys_load(specfile)
spec
name c d unit short source
C + + . comment character ysdb_internal.yml
NUM - - . record number ysdb_internal.yml
ID - - . subject identifier ysdb_internal.yml
SUBJ + - . subject identifier ysdb_internal.yml
TIME - - hour TIME look.yml
SEQ - + . SEQ .
CMT - - . compartment number ysdb_internal.yml
EVID - - . event ID ysdb_internal.yml
AMT - - mg dose amount ysdb_internal.yml
DV - - micrograms/L dependent variable ysdb_internal.yml
AGE - - years age ysdb_internal.yml
WT - - kg weight ysdb_internal.yml
CRCL - - ml/min CRCL .
ALB - - g/dL albumin ysdb_internal.yml
BMI - - m2/kg BMI ysdb_internal.yml
AAG - - mg/dL alpha-1-acid glycoprotein .
SCR - - mg/dL serum creatinine .
AST - - . aspartate aminotransferase .
ALT - - . alanine aminotransferase .
HT - - cm height ysdb_internal.yml
CP - + . Child-Pugh score look.yml
TAFD - - hours time after first dose .
TAD - - hours time after dose .
LDOS - - mg last dose amount .
MDV - - . MDV ysdb_internal.yml
BLQ - + . below limit of quantification .
PHASE - - . study phase indicator .
STUDY - + . study number .
RF + + . renal function stage .
Data from specific columns can be printed
spec$WT
name value
col WT
type numeric
short weight
unit kg
range 40 to 100
or summarized
summary(spec, WT, DV, EGFR)
name c d unit short source
1 C + + . comment character ysdb_internal.yml
2 NUM - - . record number ysdb_internal.yml
3 ID - - . subject identifier ysdb_internal.yml
4 SUBJ + - . subject identifier ysdb_internal.yml
5 TIME - - hour TIME look.yml
6 SEQ - + . SEQ .
7 CMT - - . compartment number ysdb_internal.yml
8 EVID - - . event ID ysdb_internal.yml
9 AMT - - mg dose amount ysdb_internal.yml
10 DV - - micrograms/L dependent variable ysdb_internal.yml
11 AGE - - years age ysdb_internal.yml
12 WT - - kg weight ysdb_internal.yml
13 CRCL - - ml/min CRCL .
14 ALB - - g/dL albumin ysdb_internal.yml
15 BMI - - m2/kg BMI ysdb_internal.yml
16 AAG - - mg/dL alpha-1-acid glycoprotein .
17 SCR - - mg/dL serum creatinine .
18 AST - - . aspartate aminotransferase .
19 ALT - - . alanine aminotransferase .
20 HT - - cm height ysdb_internal.yml
21 CP - + . Child-Pugh score look.yml
22 TAFD - - hours time after first dose .
23 TAD - - hours time after dose .
24 LDOS - - mg last dose amount .
25 MDV - - . MDV ysdb_internal.yml
26 BLQ - + . below limit of quantification .
27 PHASE - - . study phase indicator .
28 STUDY - + . study number .
29 RF + + . renal function stage .
short
: a short name for the column (e.g weight
); this will default to the column name (col
) … sometimes that makes senselabel
: to be used to label the data set and to populate the define.pdf
document (e.g. patient weight at baseline
)unit
: when it’s appropriate (e.g. kg
)decode
: for discrete data items (e.g. if SEX is values: [0,1]
then then use decode: [male, female]
; or include the make_factor: true
fieldUse the ys_check()
function, with the data frame as the first argument and the spec object as the second argument
data <- ys_help$data()
ys_check(data, spec)
## The data set passed all checks.
The specification object can be rendered to a specification file with the ys_document
function
ys_document(spec, stem = "working_document")
With output here.
ys_document
will pass along arguments to rmarkdown::render
so that you can control those aspects of how the document is rendered. You can also create custom output formats to get the data table to render in the way that you like.
To create an project-wide listing of documented data sets, we create a yproj
or project object. We create this from the spec objects that we read about in the previous section. Let’s load another object to use along with the object loaded in the previous section.
pdspec <- load_spec_ex("DEM104101F_PKPD.yml")
Now, we have two objects to work with:
head(spec)
## name c d unit short source
## 1 C + + . comment character ysdb_internal.yml
## 2 NUM - - . record number ysdb_internal.yml
## 3 ID - - . subject identifier ysdb_internal.yml
## 4 SUBJ + - . subject identifier ysdb_internal.yml
## 5 TIME - - hour TIME look.yml
## 6 SEQ - + . SEQ .
## 7 CMT - - . compartment number ysdb_internal.yml
## 8 EVID - - . event ID ysdb_internal.yml
## 9 AMT - - mg dose amount ysdb_internal.yml
## 10 DV - - micrograms/L dependent variable ysdb_internal.yml
head(pdspec)
## name c d unit short source
## 1 C + - . C .
## 2 MDV - - . MDV .
## 3 SEQ - + . SEQ .
## 4 AMT - - mg AMT .
## 5 II - - hours II .
## 6 CMT - - . Compartment .
## 7 TAFD - - hours TAFD .
## 8 WT - - kg Weight .
## 9 EGFR - - ml/min/1.73 m2 eGFR .
## 10 SEX - + . SEX .
We can create a project object from both objects
proj <- ys_project(spec,pdspec)
proj
## projectnumber: EXAMPK1011F
## sponsor: example-project
## --------------------------------------------
## datafiles:
## name description data_stem
## analysis1 Example PopPK analysis data set analysis1
## DEM104101F_PKPD Population PKPD analysis data set DEM104101F_PKPD
To render the project file we’ll use the same ys_document()
function.
This time, we’ll add some extra (optional) arguments that will help us get the document to look the way we want:
ys_document(
proj,
stem = "project_document",
build_dir = definetemplate(),
author = "Michelle Johnson",
title = "Analysis data specification"
)
Using the build_dir
argument gets us the document rendered with Metrum Research Group branding. Also, author
and title
are passed into the configuration fields for this document.
To get a document that is formatted according to FDA requirements, use:
ys_document(
proj,
type = "regulatory",
stem = "fda_document",
build_dir = definetemplate(),
author = "Michelle Johnson",
title = "Analysis data specification"
)