R Basics for Pharmacometrics

Build the R foundations you actually need for pharmacometrics: reproducible workflows, core data structures, and readable code.
Tip

How to use this section:
Build habits here that will carry through wrangling, visualization, modeling, and case studies. This is your technical foundation.

Note

You can follow this section using RStudio or Positron.
The workflows are the same—only the interface differs slightly.

Learning Objectives

By the end of this section, you will be able to:

  • Navigate RStudio or Positron confidently.
  • Organize work using scripts and projects.
  • Understand core R object types used in PMx workflows.
  • Work comfortably with data frames and tibbles.
  • Apply vectorized logic for QC-style tasks.
  • Write small reusable functions.
  • Build clean, readable pipelines using the pipe operator.
  • Create basic plots for quick data checks and PMx-style diagnostics.

The goal is fluency and judgment, not memorization.

Why R Basics Matter in PMx

Pharmacometrics workflows are not built on isolated commands — they are built on:

  • structured data
  • vectorized logic
  • clear transformations
  • reproducible scripts

If your foundations are shaky, everything downstream becomes fragile.

This section ensures that when you reach:

  • Data Wrangling
  • Visualization
  • Modeling
  • Case Studies

—you are not fighting the language itself.

Warning

Most PMx “analysis problems” are not modeling problems — they are data structure or logic problems. Strong R fundamentals prevent those issues early.

What This Section Emphasizes

This section focuses on what you actually use in PMx work:

  • Vectors and basic types (numeric, character, logical)
  • Data frames/tibbles as the backbone of PK datasets
  • Vectorized logic for flags and QC rules
  • Indexing and subsetting for controlled data access
  • Functions for reusable logic
  • Pipes for readable, step-by-step workflows
  • Basic plotting for quick diagnostics and concentration-time visualization
  • The tidyverse ecosystem as your primary toolkit

These are the patterns you will see repeatedly throughout the course.

What This Section Does Not Try to Do

This is not a full R programming curriculum.

We intentionally delay or omit:

  • advanced looping constructs
  • performance tuning
  • metaprogramming
  • complex object-oriented systems
  • obscure base R edge cases

Early PMx work relies far more on clarity and structure than on advanced programming techniques.

Note

Modern PMx data workflows rely heavily on vectorized operations and tidy pipelines — not complex control flow.

How This Fits Into the Course

This section is the foundation for the full Computational Foundations for Pharmacometrics (R Track) curriculum:

  1. R Basics ← you are here
  2. Data Wrangling
  3. Data Visualization
  4. Modeling
  5. Case Studies

If you already know some R, you may move quickly — but pay attention to the PMx framing and conventions introduced here.

Lessons in This Section

  1. Getting Oriented in R
    R, RStudio, and Positron — scripts, projects, and reproducible workflows

  2. Objects, Vectors, and Basic Types
    How R stores data and why vectorization matters in PMx

  3. Packages and the Tidyverse
    Installing/loading packages, namespaces, and dependency management

  4. Dataframes and Tibbles
    The core structure behind PK datasets and PMx analysis tables

  5. Logical Operations and Conditionals
    Vectorized logic for QC, flags, and rule-based checks

  6. Indexing and Subsetting
    Extracting rows and columns safely and intentionally

  7. Functions
    Writing small, reusable helpers for PMx workflows

  8. Pipes and Readable Code
    Building clear pipelines your future self (and teammates) can review

  9. Basic Plotting with Base R
    Creating simple plots for quick diagnostics and PK concentration-time data

How to Use This Section

  • Work through lessons in order if you are new to R.
  • Skim selectively if experienced — but do not skip the PMx framing.
  • Run code locally whenever possible.
  • Focus on recognizing patterns rather than memorizing syntax.

You should finish this section feeling comfortable reading and writing small, clean R scripts.

A Note on Mindset

Learning R for PMx is not about being clever.
It is about being:

  • clear
  • reproducible
  • explicit
  • defensible

Those values will show up in:

  • your datasets
  • your QC logic
  • your plots
  • your models
  • your reports

Everything that follows in this course builds on that foundation.