R vs. SAS: A Comparison for Clinical Programmers

Introduction

For decades, SAS has been one of the dominant programming languages and platforms used for clinical-trial data processing, statistical analysis, and regulatory submissions.

At the same time, R has become an increasingly important language for clinical statistics, data science, visualization, reporting, simulation, and statistical programming.

This creates an important practical question for clinical programmers:

The answer is generally yes.

But learning R effectively requires understanding that R is not simply "SAS with different syntax."

The two ecosystems have different programming models, data structures, development workflows, statistical libraries, and approaches to reproducibility.

R and SAS at a Glance

The Most Important Conceptual Difference

A traditional SAS program often reads like a sequence of instructions:

DATA adsl;
    SET dm;
    WHERE SAFFL = "Y";
    AGEGR1 = "65 years and older";
RUN;

PROC SORT DATA=adsl;
    BY TRT01P USUBJID;
RUN;

An R workflow is more naturally expressed as a series of transformations applied to objects.

adsl <- dm |>
  dplyr::filter(SAFFL == "Y") |>
  dplyr::mutate(
    AGEGR1 = "65 years and older"
  ) |>
  dplyr::arrange(TRT01P, USUBJID)

The difference is more than cosmetic.

In SAS, the programmer frequently thinks in terms of:

• DATA steps
• Procedures
• Input and output datasets
• BY-group processing
• Macro variables
• Formats and informats

In R, the programmer frequently thinks in terms of:

• Objects
• Functions
• Data frames
• Vectors
• Pipelines
• Packages
• Environments

A Visual Comparison of the Two Programming Models

The SAS DATA Step

The SAS DATA step is one of the most important concepts for clinical programmers.

A typical DATA step reads observations, performs calculations or conditional logic, and writes a new SAS dataset.

data adsl;
    set dm;

    if saffl = "Y";

    agegr1 = "65 years and older";

    if age < 65 then
        agegr1 = "<65 years";

run;

The equivalent R implementation might use dplyr.

adsl <- dm |>
  dplyr::filter(SAFFL == "Y") |>
  dplyr::mutate(
    AGEGR1 = dplyr::if_else(
      AGE < 65,
      "<65 years",
      "65 years and older"
    )
  )

The conceptual mapping is:

Conditional Logic

Conditional logic is one of the easiest areas for a SAS programmer to recognize in R.

SAS IF/THEN/ELSE

data adsl;
    set dm;

    if age < 18 then
        agegrp = "Pediatric";
    else if age < 65 then
        agegrp = "Adult";
    else
        agegrp = "Older Adult";
run;

R case_when()

adsl <- dm |>
  dplyr::mutate(
    agegrp = dplyr::case_when(
      AGE < 18 ~ "Pediatric",
      AGE < 65 ~ "Adult",
      TRUE ~ "Older Adult"
    )
  )

For multiple conditions, case_when() is often the most readable equivalent to a long sequence of SAS conditional statements.

WHERE Versus IF

SAS programmers should pay close attention to the distinction between WHERE and IF.

data adsl;
    set dm;
    where saffl = "Y";
run;

In R, a filtering operation is usually explicit.

adsl <- dm |>
  dplyr::filter(SAFFL == "Y")

This is one of the first places where a direct syntax translation is easy.

SAS PROC SORT vs. R arrange()

Sorting is a fundamental SAS operation.

proc sort data=adsl;
    by TRT01P USUBJID;
run;

The corresponding dplyr code is:

adsl <- adsl |>
  dplyr::arrange(TRT01P, USUBJID)

BY-Group Processing

BY-group processing is central to many SAS clinical-programming workflows.

proc sort data=adae;
    by usubjid aestdt;
run;

data adae2;
    set adae;
    by usubjid aestdt;

    if first.usubjid then
        n_event = 0;

    n_event + 1;
run;

In R, grouped operations are usually expressed explicitly.

adae2 <- adae |>
  dplyr::arrange(USUBJID, AESTDT) |>
  dplyr::group_by(USUBJID) |>
  dplyr::mutate(
    n_event = dplyr::row_number()
  ) |>
  dplyr::ungroup()

This is a major conceptual transition for SAS programmers.

Instead of thinking:

think:

FIRST. and LAST. Variables

The SAS automatic variables FIRST.variable and LAST.variable are particularly common in clinical programming.

data firstae;
    set adae;
    by usubjid aestdt;

    if first.usubjid;
run;

A common R equivalent is to use ranking or slicing operations.

firstae <- adae |>
  dplyr::arrange(USUBJID, AESTDT) |>
  dplyr::group_by(USUBJID) |>
  dplyr::slice_head(n = 1) |>
  dplyr::ungroup()

The important lesson is that there is usually not a one-to-one R replacement for every SAS automatic variable. R provides several higher-level ways to express the same analytical intent.

PROC SQL vs. R Joins

SAS programmers frequently use PROC SQL to merge clinical datasets.

proc sql;
    create table adsl_adae as
    select a.*,
           b.aeterm,
           b.aestdt
    from adsl as a
    left join adae as b
        on a.usubjid = b.usubjid;
quit;

In R:

adsl_adae <- adsl |>
  dplyr::left_join(
    adae |>
      dplyr::select(
        USUBJID,
        AETERM,
        AESTDT
      ),
    by = "USUBJID"
  )

The most common join mappings are:

Many-to-Many Joins Require Special Attention

One of the most important clinical-programming risks when moving between SAS and R is accidental row multiplication.

Suppose:

• ADSL contains one record per subject.
• ADAE contains multiple adverse events per subject.

A subject-level-to-event-level join naturally creates multiple rows per subject.

That may be correct.

But if both input datasets contain multiple records per subject, an unintended many-to-many join can multiply records dramatically.

PROC TRANSPOSE vs. pivot_longer() and pivot_wider()

SAS programmers frequently use PROC TRANSPOSE to convert between wide and long structures.

SAS

proc transpose data=lab
    out=lab_long;
    by usubjid;
    var ALT AST BILI;
run;

In R, pivot_longer() performs a similar wide-to-long transformation.

lab_long <- lab |>
  tidyr::pivot_longer(
    cols = c(ALT, AST, BILI),
    names_to = "PARAM",
    values_to = "AVAL"
  )

The opposite operation is:

lab_wide <- lab_long |>
  tidyr::pivot_wider(
    names_from = PARAM,
    values_from = AVAL
  )

PROC FREQ vs. count() and summarise()

A basic SAS frequency table might be:

proc freq data=adsl;
    tables sex*trt01p;
run;

In R:

adsl |>
  dplyr::count(
    SEX,
    TRT01P
  )

For percentages:

adsl |>
  dplyr::count(
    TRT01P,
    SEX
  ) |>
  dplyr::group_by(TRT01P) |>
  dplyr::mutate(
    PCT = n / sum(n) * 100
  ) |>
  dplyr::ungroup()

Clinical programmers should remember that producing a simple count is much easier than producing a fully formatted regulatory table. The latter requires additional decisions about denominators, missing categories, zero-count categories, ordering, labels, rounding, and presentation.

PROC MEANS vs. summarise()

SAS:

proc means data=adsl
    n mean std median min max;
    var age;
run;

R:

adsl |>
  dplyr::summarise(
    N = sum(!is.na(AGE)),
    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)
  )

The important difference is that R's missing-value behavior often has to be specified explicitly.

Missing Values: A Critical Difference

SAS and R both support missing values, but they do not behave identically.

In SAS, a numeric missing value is represented by:

.

In R, the standard missing value is:

NA

SAS programmers should be particularly careful with logical comparisons.

SAS

if age ne . then
    include = "Y";

R

include <- !is.na(AGE)

Do not write:

AGE != NA

to test for nonmissing values.

The correct R pattern is:

!is.na(AGE)

Missing Values in Statistical Functions

Another important difference is how R functions treat missing values.

mean(x)

returns NA if the vector contains missing values.

The usual clinical-programming form is:

mean(x, na.rm = TRUE)

Similarly:

median(x, na.rm = TRUE)
sd(x, na.rm = TRUE)
sum(x, na.rm = TRUE)

This is analogous to the way many SAS summary procedures exclude missing numeric values from calculations by default.

Character and Numeric Variables

SAS distinguishes numeric and character variables at the dataset level.

R also has different vector types, but R's data structures are more flexible.

SAS Dates vs. R Dates

SAS dates are stored internally as the number of days relative to a reference date.

R's Date class similarly supports arithmetic on dates, but the programming syntax differs.

SAS

AESTDY = AESTDT - RFSTDTC;

R

AESTDY <- as.Date(AESTDT) -
          as.Date(RFSTDTC)

In practice, clinical R programming often uses packages such as lubridate to simplify date and datetime operations.

Character Dates in Clinical Data

CDISC-oriented datasets frequently contain ISO 8601 character dates such as:

"2026-04-15"
"2026-04-15T14:30"
"2026-04-15T14:30:00"

R programmers need to explicitly parse these values into date or datetime objects when performing arithmetic.

date_value <- as.Date(
  "2026-04-15"
)

For more complex ISO 8601 values:

lubridate::ymd_hms(
  "2026-04-15T14:30:00"
)

Formats and Informats

SAS formats and informats are an important concept that does not have a perfect one-to-one equivalent in base R.

For example:

format AEDTM datetime20.;

changes how SAS displays the underlying value.

In R, the underlying object and its printed representation are more commonly controlled through classes and formatting functions.

format(
  date_value,
  "%d%b%Y"
)

For clinical reporting, it is often useful to separate:

• The underlying analysis variable
• The human-readable display value
• The table-formatting logic

User-Defined Formats

SAS formats are heavily used in clinical TLF programming.

proc format;
    value trtfmt
        1 = "Placebo"
        2 = "Treatment A"
        3 = "Treatment B";
run;

In R, equivalent labeling can be implemented using named vectors or factor levels.

trt_labels <- c(
  "1" = "Placebo",
  "2" = "Treatment A",
  "3" = "Treatment B"
)

adsl <- adsl |>
  dplyr::mutate(
    TRT_LABEL = unname(
      trt_labels[as.character(TRT01N)]
    )
  )

Factors can also encode categorical levels and ordering.

adsl$TRT_LABEL <- factor(
  adsl$TRT_LABEL,
  levels = c(
    "Placebo",
    "Treatment A",
    "Treatment B"
  )
)

Macros vs. Functions

SAS macros are one of the most powerful tools available to experienced SAS programmers.

%macro make_summary(
    data=,
    variable=,
    output=
);

proc means data=&data;
    var &variable;
    output out=&output
        mean=mean
        std=sd;
run;

%mend;

R's primary equivalent is usually a function.

make_summary <- function(
    data,
    variable
){

  data |>
    dplyr::summarise(
      mean = mean(
        {{ variable }},
        na.rm = TRUE
      ),
      sd = sd(
        {{ variable }},
        na.rm = TRUE
      )
    )
}

The programming philosophy is different.

A SAS macro generally manipulates SAS source code or generates SAS code.

An R function generally receives objects, transforms them, and returns objects.

Macro Variables vs. R Objects

SAS

%let analysis_date = 15APR2026;

%put &analysis_date;

R

analysis_date <- as.Date(
  "2026-04-15"
)

analysis_date

In R, most values should simply be represented as objects rather than as pieces of source-code text.

Macro Loops vs. R Iteration

A SAS programmer might write:

%do i = 1 %to 5;

    /* generated SAS code */

%end;

In R, explicit loops are available:

for(i in 1:5){

  print(i)

}

But many R workflows prefer vectorized functions or functional programming tools.

purrr::map(
  1:5,
  ~ print(.x)
)

For clinical programming, however, readability should generally take priority over forcing every task into a functional-programming pattern.

PROC REPORT vs. R Table Packages

PROC REPORT is widely used for producing clinical tables in SAS.

proc report data=summary nowd;

    column TRT01P N MEAN SD;

    define TRT01P / group;
    define N / "N";
    define MEAN / "Mean";
    define SD / "SD";

run;

R has several ecosystems for table production, including:

• gt
• gtsummary
• flextable
• huxtable
• officer

For clinical-trial reporting, gtsummary is particularly useful for standard descriptive tables, while gt provides extensive table-formatting control.

Statistical Programming

One of the biggest strengths of both SAS and R is the breadth of statistical methods available.

The syntax differs substantially.

Two-Sample t Test

SAS:

proc ttest data=adtte;
    class TRT01P;
    var AVAL;
run;

R:

t.test(
  AVAL ~ TRT01P,
  data = adtte
)

Chi-Square Test

SAS:

proc freq data=adsl;
    tables TRT01P*SEX / chisq;
run;

R:

tab <- table(
  adsl$TRT01P,
  adsl$SEX
)

chisq.test(tab)

Linear Models

SAS:

proc glm data=adlb;
    class TRT01P;
    model AVAL = TRT01P BASE;
run;

R:

fit <- lm(
  AVAL ~ TRT01P + BASE,
  data = adlb
)

summary(fit)

The R formula interface is one of the most important concepts for SAS programmers learning R.

The R Formula Interface

An R formula such as:

AVAL ~ TRT01P + BASE

For many R statistical models, the formula describes the relationship between the response and explanatory variables.

Mixed Models: SAS PROC MIXED vs. R

Mixed models are extremely important in longitudinal clinical trials.

A SAS model might be:

proc mixed data=adlb;

    class USUBJID TRT01P AVISIT;

    model AVAL =
        TRT01P
        AVISIT
        TRT01P*AVISIT
        BASE;

    repeated AVISIT /
        subject=USUBJID
        type=cs;

run;

R packages such as nlme and lme4 provide mixed-model capabilities.

library(nlme)

fit <- lme(
  AVAL ~ TRT01P * AVISIT + BASE,
  random = ~ 1 | USUBJID,
  correlation =
    corCompSymm(
      form = ~ AVISIT | USUBJID
    ),
  data = adlb
)

summary(fit)

Survival Analysis

SAS programmers often use PROC PHREG.

proc phreg data=adtte;

    class TRT01P;

    model AVAL* CNSR(1) =
        TRT01P AGE SEX;

run;

In R:

library(survival)

fit <- coxph(
  Surv(AVAL, 1 - CNSR) ~
    TRT01P + AGE + SEX,
  data = adtte
)

summary(fit)

Again, the conceptual mapping is more useful than attempting to memorize syntax character by character.

Kaplan-Meier Analysis

SAS:

proc lifetest data=adtte
    plots=survival;

    time AVAL*CNSR(1);
    strata TRT01P;

run;

R:

library(survival)

km_fit <- survfit(
  Surv(AVAL, 1 - CNSR) ~ TRT01P,
  data = adtte
)

plot(km_fit)

For publication-quality clinical graphics, R users frequently combine survival with survminer or a custom ggplot2-based workflow.

ODS vs. R Markdown / Quarto

SAS programmers are familiar with the Output Delivery System, or ODS.

ODS provides mechanisms for directing SAS output into formats such as:

• HTML
• PDF
• RTF
• Excel-compatible outputs
• Other reporting destinations

R has a different reporting ecosystem.

Common tools include:

• R Markdown
• Quarto
• knitr
• rmarkdown
• gt
• officer

The major conceptual advantage is that the report can contain the analysis code, output, narrative, tables, and figures in a single reproducible workflow.

Reproducible Reporting

Consider a clinical report containing:

• Demographics
• Disposition
• Adverse events
• Laboratory summaries
• Efficacy analyses
• Figures

A traditional workflow may involve generating individual outputs and then assembling them into a report.

A reproducible R workflow can instead follow:

ADSL in SAS and R

A simplified ADSL derivation might include:

• Subject identifier
• Treatment assignment
• Age
• Sex
• Race
• Safety flag
• Intent-to-treat flag
• Randomization date
• First-dose date

SAS:

data adsl;

    merge dm ex;
    by usubjid;

    if saffl = "Y";

    trt01p = TRT01P;

    if age < 65 then
        agegr1 = "<65";
    else
        agegr1 = "≥65";

run;

R:

adsl <- dm |>
  dplyr::left_join(
    ex |>
      dplyr::select(
        USUBJID,
        TRT01P,
        RFSTDTC
      ),
    by = "USUBJID"
  ) |>
  dplyr::filter(SAFFL == "Y") |>
  dplyr::mutate(
    AGEGR1 = dplyr::case_when(
      AGE < 65 ~ "<65",
      AGE >= 65 ~ "≥65",
      TRUE ~ NA_character_
    )
  )

ADAE Derivations

Adverse-event analysis often requires subject-level and event-level derivations.

For example, suppose the analysis requires treatment-emergent adverse events defined as events beginning on or after first dose.

SAS

data adae;

    set ae;

    if AESTDT >= TRTSDT then
        TRTEMFL = "Y";
    else
        TRTEMFL = "N";

run;

R

adae <- ae |>
  dplyr::mutate(
    TRTEMFL = dplyr::if_else(
      AESTDT >= TRTSDT,
      "Y",
      "N"
    )
  )

The derivation itself is straightforward.

The difficult part in a real clinical study is usually the definition of the dates, not the programming syntax.

Programming Language vs. Analysis Specification

This distinction is critical.

Suppose the SAP states:

Neither SAS nor R determines the correct answer automatically.

The programmer must translate the specification into deterministic logic.

Therefore:

The programming language is only one part of this chain.

Partial Dates

Clinical datasets frequently contain incomplete dates.

For example:

2026-04
2026
2026-04-15

A programmer should never silently invent a full date unless the protocol, SAP, derivation specification, or data-standard convention defines how the partial date should be handled.

Both SAS and R can process partial dates, but the clinical rule must come first.

Generating TLFs

A common misconception is that R is mainly useful for visualization and exploratory analysis.

Modern R workflows can also generate:

• Tables
• Listings
• Figures
• Statistical analyses
• Clinical reports
• Supporting datasets

For example, a demographic summary can be generated with gtsummary.

library(gtsummary)

tbl_summary(
  adsl,
  by = TRT01P,
  include = c(
    AGE,
    SEX,
    RACE
  )
)

A more customized gt workflow can provide fine-grained control over formatting.

library(gt)

summary_data |>
  gt() |>
  tab_header(
    title = "Demographic Characteristics"
  )

Clinical Listings

Listings are often easier to create in R than highly structured tables because a listing is essentially a carefully formatted dataset.

listing <- adae |>
  dplyr::select(
    USUBJID,
    TRT01P,
    AETERM,
    AESEV,
    AESER,
    AESTDT
  ) |>
  dplyr::arrange(
    TRT01P,
    USUBJID,
    AESTDT
  )

The remaining work is presentation: page breaks, titles, footnotes, headers, column widths, and output format.

Visualization: SAS vs. ggplot2

R has a particularly strong advantage in custom statistical visualization.

A basic SAS graph may be generated through ODS Graphics.

An R graph can be constructed using a layered grammar.

library(ggplot2)

ggplot(
  adlb,
  aes(
    x = AVISIT,
    y = AVAL,
    group = USUBJID
  )
) +
  geom_line(alpha = 0.4) +
  geom_point() +
  labs(
    x = "Visit",
    y = "Laboratory Value"
  ) +
  theme_classic()

The ggplot2 philosophy is based on combining layers such as:

• Data
• Aesthetic mappings
• Geometries
• Scales
• Statistics
• Facets
• Coordinate systems
• Themes

Why ggplot2 Is So Important for Clinical Programmers

Clinical programming increasingly requires more than traditional tables.

Examples include:

• Waterfall plots
• Spider plots
• Swimmer plots
• Kaplan-Meier plots
• Forest plots
• Boxplots
• Spaghetti plots
• Volcano plots
• Laboratory shift plots
• Exposure-response plots

The layered structure of ggplot2 makes it possible to construct these figures from relatively small reusable components.

Packages: A Major Difference Between the Ecosystems

SAS provides a large integrated environment of procedures and supporting functionality.

R uses packages extensively.

A clinical R project might include:

library(dplyr)
library(tidyr)
library(ggplot2)
library(gtsummary)
library(gt)
library(survival)
library(nlme)

The package ecosystem is one of R's greatest strengths, but it also creates responsibilities.

A clinical programmer needs to know:

• Which package performs the analysis
• Which version was used
• Which dependencies are required
• Whether the implementation is appropriate
• How the method has been validated
• How the computational environment can be reproduced

Package Versioning

A production clinical R workflow should not rely on an undocumented collection of packages installed on a programmer's computer.

Tools such as renv can help capture project dependencies and package versions.

renv::init()

renv::snapshot()

renv::restore()

The underlying principle is:

SAS Libraries vs. R Projects

A SAS programmer may organize data into libraries:

libname adam "/project/adam";

data adam.adsl;
    set adam.source_adsl;
run;

An R project usually organizes files through directories and project-relative paths.

library(here)

adsl <- readr::read_csv(
  here(
    "data",
    "adam",
    "adsl.csv"
  )
)

Project-relative paths are generally preferable to hard-coded personal paths.

read.csv("C:/Users/Aaron/Desktop/project/data/adsl.csv")

SAS Logs vs. R Logs and Diagnostics

SAS programmers are accustomed to examining the SAS log after every major program.

Important information includes:

• Errors
• Warnings
• NOTE messages
• Observation counts
• Variable attributes
• Merge diagnostics

R provides diagnostics through:

• Console output
• Warnings
• Errors
• Messages
• Object inspection
• Assertions
• Testing frameworks

For example:

nrow(adsl)

names(adsl)

str(adsl)

summary(adsl)

Assertions can make assumptions explicit.

stopifnot(
  all(!is.na(adsl$USUBJID))
)

Validation: SAS Has an Important Historical Advantage

One reason SAS remains deeply embedded in clinical development is its long history of use in regulated pharmaceutical environments.

Organizations often have established:

• SAS programming standards
• Validation procedures
• Macro libraries
• QC processes
• Statistical programming specifications
• Submission experience
• Inspection experience

Moving an organization from SAS to R therefore involves much more than teaching programmers a new syntax.

It involves evaluating the entire software lifecycle.

Does R Need Validation?

Yes.

The fact that R is open source does not mean that analyses performed using R are automatically validated.

The relevant question is not:

but rather:

A regulated clinical analysis should have appropriate controls over:

• Source code
• Analysis specifications
• Data inputs
• Software versions
• Dependencies
• Execution environment
• Testing
• Review
• Change control
• Traceability
• Archived outputs

Independent Programming and QC

One common clinical-programming model is independent QC.

For example:

A cross-language approach is also possible:

Numerical Differences

SAS and R can sometimes produce slightly different numerical results even when the analytical method is conceptually the same.

Potential causes include:

• Different algorithms
• Different optimization routines
• Different convergence criteria
• Different default contrasts
• Different degrees-of-freedom methods
• Different handling of missing values
• Different numerical tolerances
• Different covariance estimators

Therefore, validation should compare the correct quantities at appropriate precision rather than assuming every decimal digit must be identical.

Floating-Point Arithmetic

Both environments use finite-precision numerical computation.

For example, mathematically:

but finite-precision computers may not represent these decimal numbers exactly.

In R:

0.1 + 0.2 == 0.3

may not behave as a mathematically naive comparison would suggest.

Clinical validation therefore should use appropriate tolerances where numerical equality is not expected to be exact.

Assertions in Clinical R Programming

Assertions are useful for converting programming assumptions into explicit checks.

stopifnot(
  all(!duplicated(adsl$USUBJID))
)

This can test the expectation that ADSL contains one record per subject.

A more explicit approach might be:

duplicate_subjects <- adsl |>
  dplyr::count(USUBJID) |>
  dplyr::filter(n > 1)

if(nrow(duplicate_subjects) > 0){

  stop(
    "ADSL contains duplicate USUBJID values."
  )

}

Unit Testing

R has mature testing frameworks such as testthat.

A derivation function can be tested directly.

test_that(
  "percent change is calculated correctly",
  {

    expect_equal(
      derive_pchg(
        baseline = 100,
        value = 75
      ),
      -25
    )

  }
)

This style of testing is particularly useful for reusable clinical functions.

Reusable R Functions for Clinical Programming

A major opportunity when transitioning from SAS to R is to replace repeated macro logic with reusable functions.

For example:

derive_pchg <- function(
  value,
  baseline
){

  ifelse(
    is.na(value) |
      is.na(baseline) |
      baseline == 0,
    NA_real_,
    (value - baseline) /
      baseline * 100
  )

}

The function can then be reused:

adlb <- adlb |>
  dplyr::mutate(
    PCHG = derive_pchg(
      AVAL,
      BASE
    )
  )

This promotes consistency across tables and figures.

Vectorization

SAS programmers often write row-oriented DATA step logic.

R often performs operations over complete vectors.

For example:

PCHG <-
  (AVAL - BASE) /
  BASE * 100

This can operate on an entire vector without explicitly writing a loop.

The conceptual difference is:

versus:

This is not an absolute rule, but it is a useful mental model.

Data.table vs. dplyr

R itself has multiple approaches to data manipulation.

Two important ecosystems are:

• dplyr
• data.table

A clinical programmer does not necessarily need to choose one forever.

For many users, dplyr provides readable code that is relatively approachable for SAS programmers.

For very large datasets, data.table can provide high-performance data manipulation.

A data.table Example

library(data.table)

adsl <- as.data.table(adsl)

adsl[
  SAFFL == "Y",
  AGEGR1 := fifelse(
    AGE < 65,
    "<65",
    "≥65"
  )
]

The syntax is compact and powerful, but it represents another programming style.

SAS Macro Libraries vs. R Packages

A mature SAS environment may have hundreds of reusable macros.

An R organization can package reusable functions into an internal package.

For example:

clinicalTools::derive_pchg()

clinicalTools::derive_age_group()

clinicalTools::make_demog_table()

clinicalTools::validate_adsl()

This can create an internal clinical-programming ecosystem analogous to a shared SAS macro library, while providing additional package-management features.

Git and Version Control

One of the biggest practical advantages of an R-centered development workflow is the natural integration with modern software-engineering practices such as Git.

Clinical SAS programs can also be managed with Git, of course.

The difference is cultural and tooling-related rather than a fundamental language limitation.

A Git-based workflow allows programmers to:

• Track changes
• Review differences
• Create branches
• Merge work
• Tag releases
• Collaborate asynchronously
• Maintain an audit trail of source-code changes

Why Version Control Matters in Clinical Programming

Consider a derivation that changes between interim and final analyses.

Without version control, it can become difficult to determine:

• Which program generated a particular output
• When the derivation changed
• Why it changed
• Who made the change
• Which datasets were used

A disciplined version-control strategy can make these questions much easier to answer.

Environment Management

SAS environments are often centrally administered.

R environments may vary more across individual machines unless deliberately controlled.

A production R workflow should therefore define:

• R version
• Package versions
• Operating-system dependencies
• System libraries where relevant
• Configuration settings
• Execution procedures

Containers can also be used where appropriate to standardize computational environments.

Reproducibility Comparison

R vs. SAS for CDISC Work

Clinical programmers frequently need to work with:

• SDTM
• ADaM
• Define-XML
• Controlled terminology
• Analysis specifications
• Submission datasets

The data standards themselves do not require a particular programming language.

A variable such as:

USUBJID

It is a variable defined within a clinical-data standard.

Reading XPT Files in R

R can work with SAS transport files, which are commonly used in clinical-data exchange.

library(haven)

adsl <- read_xpt(
  "adsl.xpt"
)

The reverse workflow is also possible for suitable output requirements.

write_xpt(
  adsl,
  "adsl.xpt"
)

This allows R-based workflows to participate in environments where SAS transport-format datasets are required.

Variable Labels

SAS datasets often carry variable labels.

R packages such as haven can preserve SAS-style metadata such as labels when reading supported datasets.

library(haven)

adsl <- read_xpt(
  "adsl.xpt"
)

attr(
  adsl$AGE,
  "label"
)

Metadata preservation should always be checked rather than assumed.

SAS vs. R for Statistical Tables

A useful comparison is not "Which language makes tables faster?"

Instead, consider the entire workflow:

Both SAS and R can implement this workflow.

The difference is in the available tooling and programming conventions.

Example: Adverse Event Summary

Suppose the analysis requires the number and percentage of subjects with at least one treatment-emergent adverse event.

The key denominator is typically the number of subjects in the relevant analysis population and treatment group.

This is more complicated than simply counting AE records.

A subject with five adverse events should generally contribute:

to the number of subjects with at least one event, not:

event records.

R Example

ae_subjects <- adae |>
  dplyr::filter(
    TRTEMFL == "Y"
  ) |>
  dplyr::distinct(
    USUBJID,
    TRT01P
  ) |>
  dplyr::count(
    TRT01P,
    name = "N"
  )

The important clinical-programming principle is the unit of analysis.

R vs. SAS for Listings

Listings can be generated using combinations of:

• gt
• flextable
• officer
• Quarto
• R Markdown

A listing workflow can therefore become highly automated.

adae |>
  dplyr::select(
    USUBJID,
    TRT01P,
    AETERM,
    AESEV,
    AESER
  ) |>
  dplyr::arrange(
    TRT01P,
    USUBJID
  ) |>
  gt::gt()

R vs. SAS for Figures

R is particularly attractive when the clinical-programming organization needs many customized figures.

For example, a waterfall plot can be built from:

ggplot(
  response,
  aes(
    x = reorder(
      USUBJID,
      PCHG
    ),
    y = PCHG
  )
) +
  geom_col() +
  geom_hline(
    yintercept = -30,
    linetype = "dashed"
  ) +
  coord_flip()

The same underlying analysis dataset can then be reused to generate a spider plot, swimmer plot, or other efficacy visualization.

Interactive Graphics

R also has strong support for interactive graphics through tools such as:

• plotly
• Shiny
• htmlwidgets

This can be useful for exploratory analysis.

For example, a clinical statistician might inspect a patient-level dataset interactively before creating the final static CSR figure.

Shiny for Clinical Review

Shiny allows R code to power interactive web applications.

A clinical application could allow a reviewer to:

• Select a treatment group
• Select a patient
• Inspect laboratory values
• Display adverse events
• Review tumor trajectories
• Filter analysis populations

This can be extremely useful during exploratory clinical review, although interactive applications should be subject to appropriate controls when they are used in regulated workflows.

Performance

Performance comparisons between R and SAS are highly dependent on:

• Dataset size
• Algorithm
• Package
• Hardware
• Memory
• Database architecture
• Programming implementation

It is therefore misleading to state that one language is universally faster.

A well-designed R program can be extremely fast.

A poorly designed R program can also be slow.

The same is true for SAS.

Memory Management

R traditionally emphasizes in-memory data analysis, although modern R workflows can interact with databases and other storage systems.

Clinical programmers working with large datasets should understand:

• Object sizes
• Copies of data frames
• Efficient joins
• Column selection
• Database-backed workflows
• data.table

Do not load every dataset into memory simply because it is possible.

SQL Is Still Useful in R

SAS programmers sometimes assume that moving to R means abandoning SQL.

That is not true.

R can work with SQL databases directly and can also provide SQL-like functionality through several tools.

For example, database-backed workflows can allow large clinical datasets to remain in the database until only the necessary result is collected into R.

Hybrid SAS + R Workflows

A pharmaceutical organization does not necessarily need to choose a single language.

A realistic workflow might use:

Another organization might use R for most of the workflow and retain SAS for specific legacy procedures or established submission processes.

Where SAS Often Has an Advantage

Where R Often Has an Advantage

Where the Two Are Surprisingly Similar

Despite their differences, SAS and R can perform many of the same analytical tasks.

The SAS Programmer's R Translation Dictionary

A Complete Miniature Clinical Example

Suppose we want to create an analysis dataset containing subjects who are safety evaluable, derive an age group, and calculate treatment-group summaries.

SAS Implementation

data adsl;

    set dm;

    where SAFFL = "Y";

    if age < 65 then
        AGEGR1 = "<65";
    else if age >= 65 then
        AGEGR1 = "≥65";
    else
        AGEGR1 = "Missing";

run;

proc sort data=adsl;
    by TRT01P;
run;

proc means data=adsl n mean std median;
    class TRT01P;
    var AGE;
run;

R Implementation

adsl <- dm |>
  dplyr::filter(
    SAFFL == "Y"
  ) |>
  dplyr::mutate(
    AGEGR1 = dplyr::case_when(
      AGE < 65 ~ "<65",
      AGE >= 65 ~ "≥65",
      TRUE ~ "Missing"
    )
  ) |>
  dplyr::arrange(
    TRT01P
  )

summary <- adsl |>
  dplyr::group_by(
    TRT01P
  ) |>
  dplyr::summarise(
    N = sum(!is.na(AGE)),
    MEAN = mean(
      AGE,
      na.rm = TRUE
    ),
    SD = sd(
      AGE,
      na.rm = TRUE
    ),
    MEDIAN = median(
      AGE,
      na.rm = TRUE
    ),
    .groups = "drop"
  )

The analytical intent is essentially the same.

The R code, however, explicitly creates a summary object rather than relying on a procedure to print the results.

That Difference Is Important

In SAS:

proc means data=adsl;
    class TRT01P;
    var AGE;
run;

the procedure produces output.

In R:

summary <- adsl |>
  group_by(TRT01P) |>
  summarise(...)

the programmer usually creates an object containing the result.

That object can then be:

• Displayed
• Validated
• Joined to another object
• Formatted
• Plotted
• Written to a file
• Inserted into a report

Programming Pipelines

The R pipe:

|>

allows transformations to be chained.

result <- data |>
  filter(...) |>
  mutate(...) |>
  group_by(...) |>
  summarise(...) |>
  arrange(...)

A useful interpretation is:

where each step transforms one object into another.

Why SAS Programmers Sometimes Find Pipes Strange

SAS programmers are accustomed to explicit output datasets:

data step1;
    set source;
run;

data step2;
    set step1;
run;

data step3;
    set step2;
run;

An R pipeline compresses these transformations:

result <- source |>
  step1() |>
  step2() |>
  step3()

The resulting code can be more concise, but excessive chaining can also make debugging difficult.

R and SAS Are Not Identical Statistical Engines

Even when two procedures have similar names, the implementation may differ.

For a clinical analysis, programmers should verify:

• Model specification
• Parameterization
• Reference groups
• Contrasts
• Estimation method
• Covariance structure
• Degrees of freedom
• Missing-data handling
• Convergence criteria
• Confidence interval construction
• Output interpretation

A successful translation is therefore not:

It is:

Reference Categories

Categorical-variable coding can be a source of unexpected differences.

For example, an R factor has an ordered set of levels.

adsl$TRT01P <- factor(
  adsl$TRT01P,
  levels = c(
    "Placebo",
    "Treatment A"
  )
)

The reference level can influence model coefficients.

Clinical programmers should therefore explicitly control reference levels rather than relying on accidental alphabetical ordering.

Explicit Modeling Specifications

A good R analysis program should make important choices visible.

adsl$TRT01P <- relevel(
  factor(adsl$TRT01P),
  ref = "Placebo"
)

This is preferable to assuming that the desired reference category will always happen to be the first factor level.

Programming Standards

A clinical R team should establish standards covering at least:

• File naming
• Object naming
• Function naming
• Package usage
• Pipe style
• Date handling
• Missing-value handling
• Factor handling
• Error handling
• Logging
• Testing
• Documentation
• Version control
• Output directories
• Environment management

The exact standards can vary, but consistency is essential.

Naming Conventions

Clinical datasets often use uppercase CDISC variable names.

That convention can be retained in R.

ADSL
ADAE
ADLB
USUBJID
TRT01P
SAFFL
PCHG

Derived internal variables can follow the same organization's naming standards.

There is no requirement that R programmers rename every clinical variable to lowercase.

Debugging: SAS vs. R

SAS Debugging

Common approaches include:

• Read the SAS log
• Use PUT statements
• Inspect intermediate datasets
• Use PROC CONTENTS
• Use PROC PRINT
• Review observation counts

R Debugging

Common approaches include:

• Inspect objects directly
• Use print()
• Use str()
• Use glimpse()
• Use View()
• Use browser()
• Use traceback()
• Use unit tests

For example:

dplyr::glimpse(adsl)

dplyr::count(
  adsl,
  TRT01P
)

Intermediate Dataset Philosophy

SAS programmers often create multiple intermediate datasets:

data step1;
    ...
run;

data step2;
    ...
run;

data step3;
    ...
run;

R programmers sometimes compress all transformations into one pipeline.

That is not always preferable.

For complex clinical derivations, named intermediate objects can improve traceability:

adsl_population <- ...
adsl_dates <- ...
adsl_flags <- ...
adsl_final <- ...

The best choice depends on complexity and organizational standards.

Traceability Is More Important Than Brevity

A 10-line program is not necessarily better than a 50-line program.

In clinical programming, code should be:

• Understandable
• Traceable
• Testable
• Maintainable
• Reproducible
• Reviewable

Short code that obscures the derivation can be worse than longer code that clearly documents each step.

Common Mistakes When SAS Programmers Learn R

1. Trying to translate syntax literally. Learn the underlying operation rather than replacing each SAS keyword with an R keyword.
2. Ignoring R's missing-value rules. Remember NA and is.na().
3. Forgetting that joins can multiply records. Always understand dataset keys.
4. Relying on implicit factor ordering. Explicitly control reference categories and display order.
5. Writing everything as one enormous pipeline. Intermediate objects can improve traceability.
6. Using too many packages without governance. A clinical environment should define approved or evaluated packages and versions as appropriate.
7. Assuming open source means unvalidated. R workflows require appropriate controls and validation just as other regulated computational workflows do.
8. Assuming identical procedures produce identical results. Check statistical specifications and computational details.
9. Ignoring the computational environment. Package and R versions can matter.
10. Replacing every SAS macro with generated text. Prefer ordinary R functions when possible.
11. Failing to define the unit of analysis. A subject-level count is not the same as an event-level count.
12. Using exploratory code directly in production. Exploratory scripts should be cleaned, tested, documented, and controlled before being used for production analyses.

When SAS Is Still the Better Choice

There are situations where continuing to use SAS is entirely reasonable.

Examples include:

• An organization has mature SAS infrastructure.
• The entire submission workflow is SAS-based.
• The programming team has deep SAS expertise.
• Existing validated macro libraries provide substantial value.
• Switching languages would create unnecessary operational risk.
• A specific procedure or workflow is already well established.

Using SAS is not evidence of being technologically behind.

It may simply be the appropriate engineering decision for the environment.

When R Is Particularly Attractive

R can be particularly valuable when an organization needs:

• Advanced visualization
• Modern statistical methods
• Simulation
• Interactive applications
• Reproducible reports
• Integrated statistical workflows
• Open-source tooling
• Strong software-development practices

When a Hybrid Strategy Is Best

For many organizations, a hybrid approach can be highly effective.

Learning R If You Already Know SAS

SAS experience provides a major advantage when learning R.

You already understand:

• Clinical data structures
• Analysis populations
• Derivations
• ADaM concepts
• TLFs
• Statistical procedures
• Missing data
• Validation
• QC
• Clinical reporting

The main learning challenge is therefore not clinical statistics.

It is learning a different computational language.

A Recommended Learning Sequence

The 80/20 R Skill Set for SAS Programmers

A SAS programmer does not need to learn every feature of R before becoming productive.

The most valuable initial skills are:

What Not to Learn First

A common mistake is starting with advanced R programming concepts such as:

• Metaprogramming
• Environments
• Closures
• S3/S4 internals
• Advanced functional programming
• Package internals

These concepts can become valuable later.

But a clinical programmer can become highly productive without mastering them initially.

A Practical SAS-to-R Exercise

Take a SAS program you already understand.

For example:

data adsl;
    set dm;
    where saffl = "Y";

    if age < 65 then
        agegrp = "<65";
    else
        agegrp = "≥65";
run;

proc sort data=adsl;
    by TRT01P USUBJID;
run;

Do not immediately rewrite it.

First identify the operations:

1. Read DM.
2. Filter safety population.
3. Derive age group.
4. Sort by treatment and subject.

Then implement those operations in R:

adsl <- dm |>
  dplyr::filter(
    SAFFL == "Y"
  ) |>
  dplyr::mutate(
    AGEGRP = dplyr::if_else(
      AGE < 65,
      "<65",
      "≥65"
    )
  ) |>
  dplyr::arrange(
    TRT01P,
    USUBJID
  )

This is a much better learning exercise than trying to memorize a SAS-to-R syntax dictionary.

R vs. SAS: A Deeper Comparison

The Regulatory Question

The important regulatory question is not simply:

It is:

The answer depends on the complete process.

That process includes:

• Validated or appropriately evaluated software components
• Controlled source code
• Defined specifications
• Testing
• Independent review
• Change control
• Environment management
• Documentation
• Traceability
• Archiving

R Does Not Eliminate the Need for Statistical Programmers

One misconception is that modern R packages make clinical programming automatic.

They do not.

A package can calculate a statistic.

It cannot determine whether the statistic is the correct one for the study.

A package can generate a beautiful table.

It cannot determine whether the denominator is correct.

A package can create a Kaplan-Meier curve.

It cannot determine whether the censoring rules match the SAP.

Where the Programmer's Expertise Matters Most

The highest-value skills remain:

• Understanding the analysis specification
• Understanding the data model
• Identifying the correct analysis population
• Understanding derivation rules
• Recognizing edge cases
• Validating results
• Communicating with statisticians and clinicians
• Building maintainable production code

A Clinical Programmer's Decision Framework

When deciding between SAS and R, ask:

R vs. SAS: Practical Recommendation

For a clinical programmer who already knows SAS, learning R is usually best viewed as an expansion of the programming toolkit rather than a replacement for everything already known.

SAS expertise remains valuable because it provides:

• Deep clinical-trial programming experience
• Knowledge of regulatory workflows
• Understanding of TLF production
• Experience with CDISC datasets
• Knowledge of statistical programming conventions
• Experience with independent QC

R adds:

• Modern data manipulation
• Advanced visualization
• Flexible statistical methods
• Reproducible reporting
• Interactive analysis
• Open-source package development
• Modern software engineering practices

The Best Long-Term Skill Set

For the modern clinical programmer, the strongest combination is not:

but:

This combination provides both traditional clinical-programming depth and modern analytical flexibility.

Summary Table

The Most Important Lessons

1. R is not simply SAS with different syntax. It uses a different programming model and encourages different workflows.
2. Learn concepts before syntax. Think "filter," "join," "group," "summarize," and "reshape" rather than memorizing keyword substitutions.
3. Clinical knowledge transfers extremely well. Your knowledge of ADaM, TLFs, analysis populations, derivations, and QC remains valuable when moving from SAS to R.
4. Missing values require special attention. Use NA and is.na() correctly.
5. Joins require key awareness. Never assume that a successful join is a correct join.
6. R functions are generally a better replacement for SAS macros than code-generating text.
7. Statistical equivalence must be demonstrated. Similar-looking SAS and R models can have different defaults and implementations.
8. R is particularly strong for visualization and reproducible reporting.
9. SAS remains highly valuable in regulated clinical development. Its established infrastructure should not be dismissed.
10. A hybrid SAS/R environment can be highly effective. Use the tool that best fits the scientific and operational requirement.

Quick Reference: SAS to R

Final Clinical Programming Checklist

References and Further Reading

R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
Wickham, H., Averick, M., Bryan, J., et al. (2019). Welcome to the Tidyverse. Journal of Open Source Software, 4(43), 1686.
Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer.
Wickham, H., François, R., Henry, L., Müller, K., & Vaughan, D. dplyr: A Grammar of Data Manipulation.
Wickham, H., & Grolemund, G. (2017). R for Data Science. O'Reilly Media.
RStudio / Posit. Quarto Documentation.
R Core Team. Writing R Extensions.
U.S. Food and Drug Administration. Data Standards Catalog and Study Data Technical Conformance guidance.
CDISC. SDTM and ADaM standards and implementation guidance.