30-Day Course
Clinical SAS → R

Course Overview

Thirty daily modules across six phases. Work through them in order, or jump to any topic. Track your progress below — it is saved in your browser.

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Phase 1: R Basics & SAS Equivalents

Days 1–5

Get oriented in R: data types, character and numeric functions, and how to import and export the data you already move every day in SAS.

Day 1

Your First Steps in R: Data Types & the SAS Mindset Shift

How R thinks about data compared to SAS, and the vocabulary you need before writing a single transformation.

Day 2

Character Functions: From SCAN, SUBSTR & TRANWRD to stringr

Every string operation you rely on in the DATA step has a clean, readable equivalent in base R or the stringr package.

Day 3

Numeric Functions: ROUND, INT, SUM and the Missing-Value Trap

Numeric functions translate cleanly — but R handles missing values differently, and that difference matters for every derivation.

Day 4

Data Types & Structures: Vectors, data.frames and the tibble

Before manipulating clinical data you need a feel for R's containers — vectors, lists, data.frames and tibbles — and how they relate to a SAS dataset.

Day 5

Import & Export: Reading SAS, CSV and Excel with haven & readr

Bring your existing .sas7bdat and .xpt files into R — and write them back out — using haven, readr and readxl.

Phase 2: Data Manipulation

Days 6–10

The DATA step, reimagined. Learn dplyr verbs — filter, select, mutate, arrange, summarise — and tidy joins.

Day 6

The DATA Step Reimagined: filter() & select()

Row and column selection move from WHERE/IF and KEEP/DROP to two readable dplyr verbs.

Day 7

Deriving Variables: mutate() vs. Assignment

Creating and transforming columns with mutate(), if_else() and case_when().

Day 8

Sorting & De-duplication: arrange() & distinct()

PROC SORT — including NODUPKEY and BY-group firsts — the dplyr way.

Day 9

Summarizing: group_by() + summarise() vs. PROC MEANS

Aggregate statistics by group without leaving your data pipeline.

Day 10

Joins & Stacking: from MERGE/SET to dplyr joins & bind_rows()

Combine datasets horizontally and vertically with explicit, key-safe joins.

Phase 3: Functions & Macro-like Behavior

Days 11–15

Replace macros with user-defined functions, iterate cleanly with purrr, and control program flow the R way.

Day 11

User-Defined Functions: Replacing the SAS Macro

Why an R function is clearer, safer and easier to test than a %MACRO.

Day 12

Control Structures: if, for, while and the Vectorized Alternative

When to loop, and when R's vectorization makes loops unnecessary.

Day 13

Functional Iteration with purrr::map()

Apply a function across many elements or datasets without writing loops.

Day 14

Conditional & Tidy Evaluation Patterns

Writing flexible functions that take column names — the tidyverse way.

Day 15

Reusable Code: Scripts, Packages & Defensive Programming

Organizing functions, adding checks, and structuring a study codebase.

Phase 4: Clinical Data Handling & CDISC Basics

Days 16–20

Read clinical formats, manage metadata and controlled terminology, and meet the CDISC model in an R workflow.

Day 16

Clinical Data in R: Labels, Formats & Controlled Terminology

Carrying metadata through an R workflow with labelled and haven.

Day 17

Dates & Times: from SAS Date Values to lubridate

ISO 8601, partial dates and date arithmetic for clinical derivations.

Day 18

Reshaping: pivot_longer() & pivot_wider() vs. PROC TRANSPOSE

Move between wide and long layouts — the backbone of BDS datasets.

Day 19

Data Quality Checks & Validation in R

Programmatic checks, duplicate detection and reconciliation.

Day 20

The CDISC Model in R: SDTM & ADaM Overview

How the open-source clinical R ecosystem (pharmaverse) maps to CDISC.

Phase 5: SDTM Implementation

Days 21–25

Build SDTM domains with admiral, haven and xportr — and map familiar PROCs to their open-source counterparts.

Day 21

Building an SDTM DM Domain with dplyr

Mapping raw demographics to the SDTM DM structure.

Day 22

Findings Domains (LB/VS) & the SDTM --DTC Date Standard

Long-structured findings domains and ISO date handling.

Day 23

Intro to admiral: Derivations the Standardized Way

Using admiral helper functions for common SDTM/ADaM derivations.

Day 24

haven & xportr: Producing Compliant Transport Files

Writing submission-ready .xpt with correct types, lengths and labels.

Day 25

SDTM Case Study: Raw to Submission-Ready Domain

An end-to-end build tying together the SDTM module.

Phase 6: ADaM, TLFs & Advanced Topics

Days 26–30

Derive ADaM datasets, produce tables, listings and figures, and finish with case studies and validation best practices.

Day 26

Building ADSL with admiral

The subject-level analysis dataset, derived end to end.

Day 27

BDS ADaM Datasets (ADLB / ADVS)

Basic Data Structure: parameters, analysis values and baselines.

Day 28

Tables, Listings & Figures with rtables, tern & gtsummary

Producing regulated TLF outputs from analysis datasets.

Day 29

Reproducibility, Validation & Environment Management

renv, double programming, unit tests and a qualified R environment.

Day 30

Capstone: An End-to-End Open-Source Submission Pipeline

Putting raw-to-SDTM-to-ADaM-to-TLF together — and your transition roadmap.

30 modules total. Days 1–5 are fully expanded reference lessons; every module follows the same structure of objectives, comparison table, code examples, explanation and exercise.