Author: Bhanoji Duppada
Edition: First Edition, 2026
Website: learnhub101.com
This work was created with AI assistance under the direction and editorial supervision of Bhanoji Duppada. All technical content has been reviewed, verified, and edited by the author. The author retains full copyright and creative responsibility.
Copyright © 2026 Bhanoji Duppada. All rights reserved.
No part of this publication may be reproduced or transmitted without prior written permission of the author.
By Bhanoji Duppada
The Definitive Guide to Moving from SAS to R in Pharmaceutical Programming
The pharmaceutical industry is undergoing its most significant technology shift in decades. After 40 years of SAS dominance, R is now accepted by the FDA for regulatory submissions, and companies like Roche, GSK, and Novartis are actively migrating their clinical programming workflows to R. This isn't a distant future scenario---it's happening now, and the programmers who master both languages will find themselves at the forefront of this transformation.
This book is written specifically for clinical SAS programmers making this transition. It doesn't assume you know R---it assumes you know SAS deeply and want to leverage that knowledge to learn R faster. Every chapter shows SAS code alongside the R equivalent, so you can see exactly how concepts translate. You'll discover that many concepts you've mastered in SAS have direct parallels in R, while other areas require fundamentally different thinking.
The book covers the complete pharmaverse ecosystem: admiral for ADaM derivations, sdtmchecks for SDTM validation, xportr for XPT transport files, metacore for metadata management, gtsummary and rtables for TLF production, and renv for reproducible environments. By the time you finish this book, you'll be capable of building a complete, submission-ready clinical programming pipeline in R.
The FDA's acceptance of R for regulatory submissions marked a watershed moment for pharmaceutical programming. The agency's Statistical Software Clarifying Statement explicitly states that it does not require use of any specific software for statistical analyses, and R submissions have been successfully accepted. Japan's PMDA and the European Medicines Agency have similarly embraced R-based submissions.
This regulatory acceptance has triggered a cascade effect across the industry. Major pharmaceutical companies are no longer asking whether to adopt R, but how quickly they can implement it. Cost savings from eliminating SAS licensing fees---which can exceed $100,000 annually per seat for enterprise licenses---provide compelling business justification, while R's superior visualization capabilities and cutting-edge statistical methods offer technical advantages.
Universities increasingly teach R as the primary statistical programming language. New biostatisticians and programmers entering the workforce often have stronger R skills than SAS skills. Companies that cling exclusively to SAS risk limiting their talent pool and falling behind competitors who can leverage the latest statistical innovations.
Beyond cost savings, R's open-source nature provides transparency that regulators increasingly value. When you submit R code, reviewers can install the exact same packages and reproduce your results without licensing barriers. The entire computational environment can be documented and shared, enhancing reproducibility and trust in submitted analyses.
Many R books teach the language from scratch, assuming no programming background. Others target academic statisticians working with experimental data. This book does neither.
Instead, this book treats your SAS expertise as an asset to accelerate your R learning. When you already understand concepts like data step processing, BY-group operations, merge logic, and macro programming, you don't need lengthy explanations of why these concepts matter---you need to see how they translate to R syntax.
Every major concept in this book appears in a consistent format: SAS code on one side, R code on the other, with annotations explaining key differences. Consider this example of creating a derived variable:
/* SAS: Creating AVISIT from VISIT in ADAE */
data adae;
set sdtm.ae;
length AVISIT $50 AVISITN 8;
if VISIT = "SCREENING" then do;
AVISIT = "Screening";
AVISITN = -1;
end;
else if VISIT = "BASELINE" then do;
AVISIT = "Baseline";
AVISITN = 0;
end;
else if VISIT =: "WEEK" then do;
AVISIT = propcase(VISIT);
AVISITN = input(compress(VISIT, , "kd"), best.);
end;
else do;
AVISIT = VISIT;
AVISITN = .;
end;
run;
# R: Creating AVISIT from VISIT in ADAE
library(dplyr)
library(stringr)
adae <- sdtm_ae %>%
mutate(
AVISIT = case_when(
VISIT == "SCREENING" ~ "Screening",
VISIT == "BASELINE" ~ "Baseline",
str_starts(VISIT, "WEEK") ~ str_to_title(VISIT),
TRUE ~ VISIT
),
AVISITN = case_when(
VISIT == "SCREENING" ~ -1,
VISIT == "BASELINE" ~ 0,
str_starts(VISIT, "WEEK") ~ as.numeric(str_extract(VISIT, "\\d+")),
TRUE ~ NA_real_
)
)
Notice how the logic is nearly identical, but the syntax differs significantly. The SAS version uses DO-END blocks and explicit length statements; the R version uses case_when() for vectorized conditional logic and handles variable types implicitly. Both achieve the same result, but understanding these syntactic differences is crucial for efficient translation.
This book is organized into four parts, each building on the previous to take you from R basics to production-ready clinical programming.
Chapter 1: The R Revolution in Pharma explores the business and regulatory drivers behind R adoption, examines case studies from companies that have successfully transitioned, and helps you understand where R fits in your organization's technology roadmap.
Chapter 2: Setting Up Your R Clinical Environment guides you through installing R, RStudio, and essential packages. You'll learn about renv for package management (the R equivalent of maintaining a validated SAS environment), and how to structure projects for reproducibility and regulatory compliance.
Chapter 3: The Rosetta Stone: SAS ↔ R Syntax provides a comprehensive mapping between SAS and R concepts. This chapter serves as a reference you'll return to throughout your transition, covering data types, operators, functions, and programming constructs.
Chapter 4: Reading and Writing Clinical Data covers importing SAS datasets (using haven), reading XPT files, handling Excel specifications, and writing submission-ready transport files with xportr. Special attention is given to preserving variable attributes like labels and formats during import/export.
Chapter 5: dplyr Essentials (vs DATA Step) teaches the tidyverse approach to data manipulation. You'll master filter(), select(), mutate(), arrange(), and summarize()---the R equivalents of WHERE, KEEP/DROP, assignment statements, PROC SORT, and PROC MEANS. The chapter also covers group_by() for BY-group processing and various join functions that replace MERGE.
Chapter 6: PROC SQL vs R compares SQL approaches in both languages. You'll learn when to use dplyr versus raw SQL in R, how to connect to databases with dbplyr, and when sqldf provides a comfortable transition path for complex SQL operations.
Chapter 7: Macros vs Functions addresses one of the biggest conceptual shifts for SAS programmers. R functions are more powerful and flexible than SAS macros, but they work differently. This chapter teaches you to think in terms of functions, build reusable code libraries, and leverage functional programming patterns.
Chapter 8: The Pharmaverse Introduction orients you to the ecosystem of R packages designed specifically for pharmaceutical programming. You'll understand how packages like admiral, xportr, and metacore work together to form a complete clinical programming toolkit.
Chapter 9: SDTM Mapping in R demonstrates techniques for converting raw clinical data into SDTM domains. While much SDTM work still occurs in SAS at many companies, understanding R approaches prepares you for fully R-based pipelines.
Chapter 10: ADSL in R with admiral walks through building a complete Subject-Level Analysis Dataset using the admiral package. You'll learn admiral's derivation functions, how they parallel your SAS macro libraries, and how to customize them for study-specific requirements.
Chapter 11: ADAE in R with admiral extends your admiral knowledge to Adverse Event analysis datasets, covering duration calculations, severity grading, treatment-emergent flags, and other ADAE-specific derivations.
Chapter 12: Publication Tables (gtsummary, gt, rtables) teaches the three major table-generation frameworks in clinical R programming. You'll learn when to use each, how to create demographic tables, adverse event summaries, and efficacy tables that meet publication and submission standards.
Chapter 13: The Complete Submission Pipeline brings everything together, walking through a realistic submission workflow from raw data to final deliverables. You'll learn about validation strategies, creating define.xml files, packaging submissions, and maintaining audit trails.
If you're holding this book, you're probably a clinical SAS programmer who has heard the whispers: "R is coming." Maybe your manager mentioned it in a team meeting. Maybe you saw a job posting that listed "R proficiency preferred." Maybe you attended a PharmaSUG conference and half the talks were about Pharmaverse.
Let me be direct with you: R is not replacing SAS tomorrow. But it is changing the game permanently. And the programmers who add R to their toolkit now will have a career advantage that compounds every year.
I wrote this book because I've been where you are. I spent 15 years writing clinical SAS --- SDTM mapping, ADaM derivations, TLF generation, FDA submissions. SAS was my entire professional identity. When R started gaining traction in pharma, my first reaction was skepticism: "Why fix what isn't broken?"
Then I watched Roche submit an entire clinical trial package to the FDA using R. Then Novo Nordisk did it. Then GSK. And I realized this wasn't a trend --- it was a transformation.
The global pharmaceutical industry spends approximately $80 billion annually on clinical R&D. For decades, SAS has been the undisputed standard. But starting around 2018, a coalition of pharma companies --- Roche, GSK, Novo Nordisk, J&J --- began building open-source R packages for clinical trial programming. Not exploratory analysis, but production-grade regulatory submission tools.
This was formalized as Pharmaverse in 2021 --- a community-driven ecosystem of validated R packages covering SDTM mapping, ADaM creation, TLF generation, metadata management, and XPT export. By 2025, complete end-to-end clinical trials had been submitted to the FDA entirely in R --- multiple times.
First major pharma to submit an FDA package generated entirely in R. Their 3-year journey: pilots (2019) -> parallel runs (2020-21) -> full R submission (2022). They still use both languages.
Completed their own R-based FDA filing with heavy focus on the validation framework --- proving R outputs matched SAS to the decimal point. Their documentation framework is now an industry reference.
With hundreds of programmers across CROs, their R adoption required organizational transformation. Key finding: R programs for ADaM creation were 30-40% shorter than SAS equivalents due to admiral's high-level functions.
Invested heavily in community building and Pharmaverse package development. Their message: open-source clinical programming is technically feasible, economically smart, and scientifically sound.
The FDA has NEVER mandated SAS. What FDA requires: (1) CDISC-compliant data in XPT format, (2) reproducible analysis, (3) adequate documentation. In 2021, CDER explicitly acknowledged R as acceptable. The R Consortium worked directly with FDA to demonstrate R packages could pass review.
Bottom line: FDA does not care what language generated the results --- only that the data is valid and reproducible.
SAS license: $5K-$50K+ per seat/year. A 200-programmer CRO spends $2-5M annually on licenses alone. R: Free. Always.
Transition cost for a 200-person org: ~$500K-$1M over 2-3 years (training + parallel ops). vs. $2-5M/year in perpetuity for SAS.
Every pharma CFO has seen these numbers. The question isn't IF companies will reduce SAS spending --- it's WHEN.
SAS-only programmers are becoming less competitive. In 2020, "R" appeared in ~15% of pharma job postings. By 2025, over 60%. Salary premium for SAS+R skills: 15-25%.
Three career tiers are emerging:
- Tier 1 (Highest demand): SAS + R/Pharmaverse --- lead migrations, hybrid workflows, train teams
- Tier 2 (Stable): Pure SAS --- legacy studies, shrinking new opportunities
- Tier 3 (Growing): R-primary --- modern skills but lack clinical domain knowledge
This book moves you from Tier 2 to Tier 1.
Your existing knowledge is 80% of the battle. You already know CDISC, the data lifecycle, regulatory requirements, table shells, SAPs, validation. A CS graduate can write an R function in 5 minutes but needs years to understand why TRTEMFL matters or what a CDER reviewer looks for.
Every R concept in this book starts with the SAS equivalent, then shows the R translation. You'll never feel lost.
Part I (Ch 1-3): Industry context, R setup, SAS-to-R basics Part II (Ch 4-7): Data wrangling --- dplyr vs DATA step, SQL, functions Part III (Ch 8-12): CDISC in R --- Pharmaverse, SDTM, ADaM/admiral, metadata, XPT Part IV (Ch 13-14): TLF generation --- tables and figures in R Part V (Ch 15-17): Validation, dual submission, migration playbook
100+ side-by-side code examples. Production-ready code. Real clinical scenarios.
You don't need to abandon SAS or become an R expert overnight. You just need to start. By the end of this book you'll read, write, and debug clinical R programs, understand Pharmaverse, and have a plan for integrating R into your SAS workflow.
The R revolution in pharma isn't coming. It's here. Let's set up your environment in Chapter 2.
If you're coming from SAS, the R setup feels different. There's no license server, no IT ticket for installation, no waiting 3 weeks for access. You download R, download RStudio, and start coding. Today.
Here's the minimum setup for clinical programming:
Go to https://cran.r-project.org/ and download the latest version of R for your operating system.
Windows: Click "Download R for Windows" → "base" → download the .exe installer. Run it with default settings.
Mac: Click "Download R for macOS" → download the .pkg file and install.
Linux (Ubuntu/Debian):
sudo apt update
sudo apt install r-base r-base-dev
After installation, open a terminal and type R --version. You should see version 4.3 or higher.
Go to https://posit.co/downloads/ and download RStudio Desktop (free edition).
Install it like any application. When you open RStudio, it automatically finds your R installation.
Here's what you already know, translated:
| SAS Concept | RStudio Equivalent |
|---|---|
| Enhanced Editor | Source pane (top-left) --- where you write code |
| SAS Log | Console pane (bottom-left) --- shows output and messages |
| WORK library browser | Environment pane (top-right) --- shows all loaded datasets |
| Results Viewer | Plots/Viewer pane (bottom-right) --- tables and figures |
| PROC CONTENTS | glimpse(dataset) or str(dataset) |
| LIBNAME statement | dataset <- read_sas("path/file.sas7bdat") |
| Submit code (F3 in EG) | Ctrl+Enter (runs current line/selection) |
| Run entire program | Ctrl+Shift+Enter (runs entire file) |
The keyboard shortcut you'll use most: Ctrl+Enter --- runs the current line and moves to the next. This is your new F3.
Open RStudio and run this in the console:
# Core data manipulation (your new DATA step + PROC SQL)
install.packages("tidyverse")
# Read/write SAS datasets and XPT files
install.packages("haven")
# ADaM dataset creation (your new SDTM/ADaM macro library)
install.packages("admiral")
# Metadata-driven XPT export for FDA submissions
install.packages("xportr")
install.packages("metacore")
install.packages("metatools")
# Table generation (your new PROC REPORT)
install.packages("gt")
install.packages("gtsummary")
install.packages("Tplyr")
# Figures (your new PROC SGPLOT)
install.packages("survminer") # Kaplan-Meier plots
# Reproducibility (your new installation qualification)
install.packages("renv")
This takes 5-10 minutes. You only do it once.
In RStudio: File → New Project → New Directory → New Project
Name it something like study_XYZ_001. This creates a .Rproj file that acts like your SAS AUTOEXEC --- it sets the working directory and project context.
Recommended folder structure:
study_XYZ_001/
├── study_XYZ_001.Rproj # Project file (double-click to open)
├── renv.lock # Package version lockfile
├── R/ # Your R programs
│ ├── sdtm/ # SDTM mapping programs
│ ├── adam/ # ADaM derivation programs
│ └── tlf/ # Table/listing/figure programs
├── data/
│ ├── raw/ # SAS7BDAT or XPT source data
│ ├── sdtm/ # Output SDTM datasets
│ └── adam/ # Output ADaM datasets
├── output/ # RTF, PDF, HTML outputs
└── docs/ # Protocol, SAP, specs
This is critical for regulatory work. In SAS, your IT team qualifies the SAS installation once. In R, renv does the same thing --- it records exactly which package versions you used so anyone can reproduce your results years later.
# Initialize renv in your project
renv::init()
# After installing all packages, take a snapshot
renv::snapshot()
# This creates renv.lock --- a file listing every package and version
# Share this file with your team for identical environments
Why this matters: If your ADSL program uses admiral version 1.1.0, and a reviewer runs it with version 1.2.0, the results might differ. renv prevents this.
Run this verification script:
# Check R version
cat("R version:", R.version$version.string, "\n")
# Check key packages
for (pkg in c("tidyverse", "haven", "admiral", "xportr", "gt")) {
v <- tryCatch(packageVersion(pkg), error = function(e) "NOT INSTALLED")
cat(pkg, ":", as.character(v), "\n")
}
# Test reading a SAS dataset (if you have one)
# library(haven)
# dm <- read_sas("data/raw/dm.sas7bdat")
# glimpse(dm)
cat("\nSetup complete! Ready for clinical R programming.\n")
No library references. In SAS you say libname adam "/path/" then reference adam.adsl. In R you load a dataset into memory: adsl <- read_sas("path/adsl.sas7bdat"). The dataset is now a variable called adsl.
No submitting to a server. R runs on your machine. The code and data are local. For large studies, your company may provide an R server (Posit Workbench), but the syntax is identical.
No SAS log. R output and errors appear in the Console. Warnings are yellow, errors are red. There's no separate log file by default (though you can create one with sink()).
Packages replace PROCs. Instead of PROC MEANS, you use summarise(). Instead of PROC SORT, you use arrange(). Instead of PROC REPORT, you use gt() or tbl_summary(). The function names are different but the concepts are identical.
Your R environment is ready. Chapter 3 is the Rosetta Stone --- a side-by-side comparison of 50 SAS and R operations that you'll reference constantly as you learn. Bookmark that chapter.
This chapter is your daily reference. Bookmark it. Print it. Tape it to your monitor. Every operation you do in SAS has an R equivalent --- this chapter shows you all 50.
| SAS | R | Notes |
|---|---|---|
| Numeric variable | numeric (double) |
R uses 64-bit doubles by default |
| Character variable | character |
No fixed length needed |
| Date (SAS numeric) | Date class |
R dates print as "2025-03-15" |
| Datetime | POSIXct |
Seconds since 1970-01-01 |
| Missing numeric (.) | NA |
Universal missing in R |
| Missing character ("") | NA |
Same NA for all types |
| SAS dataset | tibble or data.frame |
tibble is the modern version |
| SAS library | R environment / project folder | No direct equivalent |
| Format/Informat | factor levels or formatting functions |
Different philosophy |
Critical difference: NA vs .
In SAS, missing numeric is . and it sorts LOWEST (before any number). In R, NA is excluded from calculations by default and has special handling in sorting.
# SAS: x + . = . R: x + NA = NA (same behavior)
# SAS: . < 0 is TRUE R: NA < 0 is NA (DIFFERENT!)
# To replicate SAS behavior of treating NA as smallest:
library(dplyr)
arrange(df, !is.na(AGE), AGE) # NAs first, then ascending
/* SAS */
libname mylib "/data/study";
data work.dm;
set mylib.dm;
run;
# R
library(haven)
dm <- read_sas("/data/study/dm.sas7bdat")
/* SAS */
libname xpt xport "/data/dm.xpt" access=readonly;
data dm; set xpt.dm; run;
# R
dm <- read_xpt("/data/dm.xpt")
/* SAS */
data severe;
set ae;
where AESEV = "SEVERE";
run;
# R
severe <- ae %>% filter(AESEV == "SEVERE")
/* SAS */
data small;
set dm(keep=USUBJID AGE SEX RACE);
run;
# R
small <- dm %>% select(USUBJID, AGE, SEX, RACE)
/* SAS */
data adsl;
set dm;
AGEGR1 = ifc(AGE >= 65, ">=65", "<65");
BMI = WEIGHT / (HEIGHT/100)**2;
run;
# R
adsl <- dm %>% mutate(
AGEGR1 = if_else(AGE >= 65, ">=65", "<65"),
BMI = WEIGHT / (HEIGHT/100)^2
)
/* SAS */
proc sort data=ae; by USUBJID AESTDTC; run;
# R
ae <- ae %>% arrange(USUBJID, AESTDTC)
/* SAS */
proc sort data=dm out=dm_unique nodupkey; by USUBJID; run;
# R
dm_unique <- dm %>% distinct(USUBJID, .keep_all = TRUE)
/* SAS */
proc sort data=dm; by USUBJID; run;
proc sort data=ae; by USUBJID; run;
data dm_ae;
merge dm(in=a) ae(in=b);
by USUBJID;
if a;
run;
# R
dm_ae <- dm %>% left_join(ae, by = "USUBJID")
/* SAS */
proc means data=adsl n mean std median min max;
class TRT01A;
var AGE;
run;
# R
adsl %>%
group_by(TRT01A) %>%
summarise(
n = n(),
mean = mean(AGE, na.rm = TRUE),
sd = sd(AGE, na.rm = TRUE),
median = median(AGE, na.rm = TRUE),
min = min(AGE, na.rm = TRUE),
max = max(AGE, na.rm = TRUE)
)
/* SAS */
proc freq data=adsl;
tables SEX * TRT01A / nocol norow;
run;
# R
adsl %>% count(TRT01A, SEX)
# Or for a publication table:
library(gtsummary)
adsl %>% tbl_summary(by = TRT01A, include = SEX)
data all_ae; set ae_study1 ae_study2; run;
all_ae <- bind_rows(ae_study1, ae_study2)
siteid = substr(USUBJID, 9, 3);
siteid <- str_sub(USUBJID, 9, 11)
numdt = input(AESTDTC, yymmdd10.);
format numdt date9.;
numdt <- ymd(AESTDTC) # lubridate
prev_val = lag(AVAL);
prev_val <- lag(AVAL) # dplyr::lag
proc transpose data=wide out=long;
by USUBJID;
var SYSBP DIABP HR;
run;
long <- wide %>% pivot_longer(
cols = c(SYSBP, DIABP, HR),
names_to = "PARAMCD",
values_to = "AVAL"
)
select(AESEV);
when("MILD") AESEVN = 1;
when("MODERATE") AESEVN = 2;
when("SEVERE") AESEVN = 3;
otherwise AESEVN = .;
end;
AESEVN <- case_when(
AESEV == "MILD" ~ 1,
AESEV == "MODERATE" ~ 2,
AESEV == "SEVERE" ~ 3,
TRUE ~ NA_real_
)
data first_ae;
set ae; by USUBJID;
if first.USUBJID;
run;
first_ae <- ae %>%
group_by(USUBJID) %>%
slice(1) %>%
ungroup()
data cum;
set ex; by USUBJID;
retain cum_dose 0;
if first.USUBJID then cum_dose = 0;
cum_dose + EXDOSE;
run;
cum <- ex %>%
group_by(USUBJID) %>%
mutate(cum_dose = cumsum(EXDOSE)) %>%
ungroup()
data males females;
set dm;
if SEX = "M" then output males;
else if SEX = "F" then output females;
run;
males <- dm %>% filter(SEX == "M")
females <- dm %>% filter(SEX == "F")
libname xptout xport "/output/dm.xpt";
data xptout.dm; set sdtm.dm; run;
library(xportr)
dm %>%
xportr_type(metacore) %>%
xportr_length(metacore) %>%
xportr_label(metacore) %>%
xportr_write("/output/dm.xpt")
The single most important R concept for SAS programmers is the pipe: %>% (or |> in base R 4.1+).
It replaces the need for temporary datasets. Instead of this SAS pattern:
data step1; set raw; where AGE >= 18; run;
data step2; set step1; keep USUBJID AGE SEX; run;
proc sort data=step2; by USUBJID; run;
You write one R pipeline:
result <- raw %>%
filter(AGE >= 18) %>%
select(USUBJID, AGE, SEX) %>%
arrange(USUBJID)
Read %>% as "then." Take raw, THEN filter, THEN select, THEN arrange.
Using = instead of == in comparisons. filter(SEX = "M") is wrong. Use filter(SEX == "M").
Forgetting na.rm = TRUE. mean(AGE) returns NA if any value is missing. Always use mean(AGE, na.rm = TRUE).
Not ungroup-ing after group_by. Forgetting ungroup() after grouped operations causes mysterious bugs downstream.
Case sensitivity. R is case-sensitive. USUBJID and usubjid are different variables.
Using $ for column access in pipes. Inside dplyr pipes, use bare column names: filter(AGE > 65), not filter(df$AGE > 65).
You now have the Rosetta Stone. Chapter 4 dives into reading and writing clinical data --- XPT files, SAS datasets, Excel, CSV --- with full examples using the haven and readr packages.
This is a free sample of the first 3 chapters. The complete guide continues with dozens more chapters of production-ready code, CDISC standards, and FDA submission practices.