In metrumresearchgroup/yspec: Data Specification for Pharmacometrics

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = ".",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

yspec

Use yspec to document analysis data sets and utilize data attributes in a modeling and simulation workflow.

Installation

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

# install.packages("devtools")
devtools::install_github("metrumresearchgroup/yspec")

Create a yspec object

Before or while programming an analysis data set, write out data definitions in yaml format:

library(yspec)
library(dplyr)
library(tidyr)

readLines("inst/internal/analysis1.yml")[1:20] %>% writeLines()

Now use yspec to read that into a object in R

spec <- ys_load("inst/internal/analysis1.yml")

Query this object to get a sense of what is in the data overall

head(spec)

or on a column by column basis for continuous data

spec$WT

as well as categorical data

spec$BLQ

And we can render a define.pdf file as well

knitr:::include_graphics("man/figures/define.png")

Using yspec

This section illustrates a few examples for how yspec might be used (other than creating define.pdf).

To make it easier to get started with yspec, we've included example data and corresponding yspec object in the package

data <- ys_help$data()
spec <- ys_help$spec()

Add factors

When you have discrete data, "decodes" can be provided and used to create factors in the data. We have that for the RF column

spec$RF

Now we'll have a column called RF_f which is a factor version of RF

ys_add_factors(data, spec, RF) %>% count(RF, RF_f)

Recode

Every column can have a "short" name; for WT it is

spec$WT$short

Every continuous data can also have a unit; again for WT

spec$WT$unit

We use the spec to "recode" using this information. First create a data summary in long format

summ <- 
  data %>% 
  select(WT, ALB, AGE) %>% 
  pivot_longer(everything()) %>%
  group_by(name) %>% 
  summarise(Mean = mean(value), Sd = sd(value))

summ  

To recode, pull the information from spec

summ %>% 
  mutate(name = ys_recode(name, spec,  unit = TRUE, title_case = TRUE))

Manipulating yspec

There are several functions for working on your yspec object

Select some columns

body_size <- ys_select(spec, WT, BMI, HT)

body_size

Filter based on some flags that were set

ys_filter(spec, covariate)

Rename

spec %>% ys_select(BWT = WT, AGE, SCR) 

Projects

An analysis project typically has several data sets that can be documented together. We make a project like this

pk   <- ys_load("inst/spec/DEM104101F_PK.yml")
pkpd <- ys_load("inst/spec/DEM104101F_PKPD.yml")
ae   <- ys_load("inst/spec/DEM104101F_AE.yml")

proj <- ys_project(pk, pkpd, ae)
proj

This object can be rendered into a single define.pdf document.

metrumresearchgroup/yspec documentation built on May 24, 2024, 12:48 a.m.