The tidyverse for pharmacometrics is an intermediate/advanced level workshop designed for existing R users ready to supercharge their productivity. This workshop will cover how to maximize the ulitily of core language features in R, as well as introduce newer packages such as ggplot2, dplyr, and tidyr, purrr, and broom to maximize productivity from data acquisition, management, analysis, and visualization stages of a pharmacometricians day-to-day workflow.

workshop modules:

Content

Participant Learning

Core R

participants learn about key topics in how R operates and peak under the hood to better understand how to faster, more robust code.

Thinking in R

a dive into how R behaves and the common R idioms to help develop your skills in knowing how to approach and problem and where to look and what to ask for help.

Data manipulations in base R

understand the key data manipulation techniques and functions offered in base R. Discuss some commonly used 'dangerous practices' and the better way of performing these tasks.

Advanced data manipulations with dplyr

Introduction to the successor of the data manipulation package plyr. dplyr offers a robust, easy-to-read, and fast (up to 1000x faster) functions to perform common statistical summaries and data manipulations.

Data visualization to ggplot2

an introduction to ggplot2. For participants that have already been exposed to ggplot2, a more advanced side challenge will be presented.

Introduction to Function Writing

function writing is a key component of writing clear, maintainable code. Participants will learn to stop copying-and-pasting scripts and learn to encapsulate their work in powerful functions.

tidying data with stringr and tidyr

participants will be introduced to the libraries stringr and tidyr to dramatically reduce the time required to turn messy clinical datasets into clean modelling-ready data.

advanced function writing

building on the previous introduction to function writing, advanced topics such as function scope, debugging techniques, and common pitfalls will prepare participants in how to write more powerful and generalizable functions.

functional programming principles

participants will be introduced two recently developed libraries specifically for functional programming, and apply this to understanding clinical trial simulation best practices

Project Organization Tips and Reproducible Research

In this module, some fundamental techniques to better design analyses will be presented, including a short case study. Finally, rmarkdown notebooks will be introduced to demonstrate how participants can create reproducible analyses that are easily transferable into reports and presentations.