R for SAS Users: MAP Every SAS Procedure to Its R Equivalent

If you've spent years writing DATA steps and PROC calls, switching to R can feel like learning to write left-handed. This guide gives you a direct, runnable translation for every common SAS construct, DATA steps, PROCs, macros, merges, and formats, so you can read R code as fluently as you read SAS today.

What's the fastest way to reproduce PROC MEANS in R?

Most SAS-to-R guides start with philosophy. We'll start with the procedure you run a hundred times a week. Here's PROC MEANS DATA=mtcars; CLASS cyl; VAR mpg; RUN; rewritten in R using base R's aggregate(), same grouping, same statistics, same output shape. Run it. If the result looks like the PROC MEANS output you'd see in SAS, you already know more R than you think.

Three lines of R reproduced an entire PROC MEANS call. aggregate() takes a formula (outcome ~ grouping), the data frame, and a function applied per group. The function returns a named numeric vector and aggregate() stitches the results into a tidy table. Read this as: for each level of cyl, compute N, Mean, and SD of mpg, exactly what CLASS cyl; VAR mpg; says in SAS.

Same numbers, two dialects. Pick whichever reads better to you, most teams settle on dplyr for new code and keep aggregate() for one-liners in scripts.

How does the SAS DATA step map to R?

The DATA step is the heart of SAS, it reads a row, modifies it, writes a row, repeats. R doesn't think in rows; it thinks in columns. Once you internalise that one swap, every DATA step starts looking like a sequence of column assignments.

Let's build the same table in code. We'll start with a small students data frame, derive adult with ifelse, then add a multi-branch grade with case_when (the dplyr equivalent of nested IF/ELSE IF).

Two assignments built two new columns across the entire data frame at once. ifelse() is the binary case (IF/ELSE); case_when() is the SAS IF/ELSE IF/ELSE IF chain, written from most to least specific. The trailing TRUE ~ "D" is the catch-all branch, the ELSE that runs when nothing above matched.

Now the housekeeping verbs: drop, keep, rename. In SAS these are statements inside the DATA step; in R they're plain assignments to the data frame.

Three statements, three column edits. Setting a column to NULL deletes it; subsetting with a vector of names keeps only those columns; reassigning into names() renames in place. None of this requires looping over rows.

Which R functions replace PROC FREQ, PROC SORT, and PROC TRANSPOSE?

Three of the most common PROCs after PROC MEANS, and all three have one-line replacements in R.

Let's run all three. Start with PROC FREQ, a one-way frequency table, then a cross-tab with a chi-square test.

table() builds the contingency table; chisq.test() runs the same chi-square test SAS would have produced. The named arguments to table() become the row and column labels. Two functions cover what PROC FREQ does in a dozen lines.

PROC SORT next. Base R uses order(), which returns the row indices in sorted order; dplyr's arrange() reads more naturally for multi-key sorts.

order() accepts multiple columns; prefix a column with - to reverse its direction. Three keys deep, this still works, and the result is a logical translation of BY cyl DESCENDING mpg.

Now PROC TRANSPOSE. Long-to-wide reshaping is the one place where SAS's syntax is famously confusing, and R's tidyr makes the same operation almost trivial.

pivot_wider() reads top-to-bottom: take long_df, use the values in measure as new column names, fill them with values from value. Anyone reading this six months from now will understand it. The same can rarely be said of a PROC TRANSPOSE call.

How do you translate PROC REG, PROC LOGISTIC, and PROC GLM?

Every linear model in R uses the same formula interface: outcome ~ predictor1 + predictor2 + .... Once you've seen one, you've seen them all. PROC REG, PROC LOGISTIC, PROC GLM, and PROC MIXED collapse into lm(), glm(), aov(), and lme4::lmer() respectively.

PROC REG first, ordinary least squares regression.

The output is the same set of numbers PROC REG would print: estimates, standard errors, t-values, p-values, and R-squared. The formula mpg ~ wt + hp + qsec is character-for-character what MODEL mpg = wt hp qsec; says in SAS, just with ~ swapped for = and + between predictors.

PROC LOGISTIC next. Same idea, different family.

glm() with family = binomial is logistic regression. exp(coef(...)) converts the log-odds coefficients to odds ratios, which is what the ODDSRATIO option in PROC LOGISTIC prints. One line covers what SAS spreads across an option list.

PROC GLM gives you ANOVA. R's aov() does the same job.

Wrapping cyl in factor() is the equivalent of declaring it on a CLASS statement, it tells R to treat it as categorical instead of numeric. The F-statistic and p-value match the PROC GLM output exactly.

How does R handle SAS MERGE and BY-group processing?

MERGE a b; BY id; is the SAS join. R has two equivalent paths: base R's merge() (closest to the SAS syntax) and dplyr's inner_join() / left_join() family (closer to SQL).

Let's run them on a small customers/orders pair so you can see the row counts change with each join type.

Five rows, because Bob has two orders and Carol has none. all.x = TRUE is the flag that says "keep every row from the left side even if there's no match on the right", that's the IF a; line you'd write in a SAS MERGE.

The dplyr version reads the same way, just with the join verb in front of the data frames.

Carol is gone, inner_join() keeps only the rows where id exists in both tables. Same result you'd get from merge a (in=x) b (in=y); by id; if x and y; in SAS, in eight characters.

How do SAS macros become R functions?

SAS macros are text generators, they paste code together at compile time, with &var references swapped for actual symbols before the SAS compiler ever sees the program. R doesn't need any of that. Functions in R take inputs, return outputs, and live in the same namespace as everything else. Most SAS macros become shorter once translated.

Here's a real macro you've probably written some version of: a reusable summary procedure that takes a dataset, a variable, and a class column.

The R version is shorter, easier to debug, and gives you a real return value you can pipe into the next step. as.formula(paste(...)) is how you build a formula from string inputs, that's the only piece that looks unusual at first, and you'll use the same pattern every time you turn a macro into a function.

What's the complete PROC → R cheat sheet?

Bookmark this section. It covers the 20 procedures that account for most of the SAS code you'll ever need to translate.

Practice Exercises

These combine multiple concepts from the tutorial. Use distinct variable names so your work doesn't overwrite the tutorial state.

Exercise 1: PROC MEANS + filter in one pipeline

Take airquality, keep only rows where Month == 5, then compute the mean and SD of Ozone (ignoring NA values). Save the result to my_summary.

Exercise 2: DATA step + PROC MEANS combined

You have an employees data frame. Compute a bonus column equal to 10% of salary for employees earning more than 50000, then aggregate the average bonus by dept. Save the result to my_bonus.

Exercise 3: SAS macro → R function

Translate the macro below to an R function called lm_fit(data, dv, iv) that fits a simple regression and returns the model object. Call it on mtcars with dv = "mpg", iv = "wt". Save the model to my_model.

%macro lm_fit(data=, dv=, iv=);
  proc reg data=&data;
    model &dv = &iv;
  run;
%mend;

Complete Example: A Full SAS → R Rewrite

Here's a realistic SAS program you might inherit from a colleague. It reads sales data, derives a few columns, computes per-region summaries, and fits a regression model.

/* The original SAS program */
DATA sales_clean;
  SET sales;
  IF revenue > 0;                    /* drop refunds */
  margin = revenue - cost;
  margin_pct = margin / revenue;
RUN;

PROC MEANS DATA=sales_clean MEAN N;
  CLASS region;
  VAR margin margin_pct;
RUN;

PROC REG DATA=sales_clean;
  MODEL margin = units price;
RUN;

Three SAS steps, three R steps. Watch how the DATA step, PROC MEANS, and PROC REG fold into a single readable pipeline.

Two things worth noticing. First, the entire DATA step shrank to a filter() + mutate() chain, no IF statement, no RUN;, no semicolons. Second, the three SAS steps you'd run sequentially in a SAS session became three R objects (sales_clean, sales_summary, sales_model) you can pass around, save, or feed into a report. Each step's output is a real value, not just printed text.

Summary

The translation reduces to a small set of one-line mappings you'll use over and over:

• PROC MEANS → aggregate() or dplyr::summarise()
• PROC FREQ → table() and prop.table()
• PROC SORT → order() or dplyr::arrange()
• PROC TRANSPOSE → tidyr::pivot_wider() (or pivot_longer() for the other direction)
• PROC REG → lm(y ~ x1 + x2, data = df)
• PROC LOGISTIC → glm(y ~ x, family = binomial)
• PROC GLM / ANOVA → aov(y ~ factor(group))
• MERGE BY id → merge(a, b, by = "id", all.x = TRUE) or dplyr::left_join()
• DATA step assignment → df$x <- df$a + df$b or mutate(x = a + b)
• SAS macro → ordinary R function

The biggest mental shift is row-at-a-time → column-at-a-time. Once that clicks, the rest is vocabulary. Read the parent guide on whether R is worth learning in 2026 for the bigger-picture case, and use this page as your translation cheat sheet whenever you hit a PROC you haven't migrated yet.

References

1. Muenchen, R.A., R for SAS and SPSS Users, 2nd ed., Springer (2011). The definitive book-length translation reference. Link
2. r4stats.com, Comparison of SAS, SPSS, and R, with add-on package mappings. Link
3. Appsilon, Transitioning from SAS to R: How to Import, Process, and Export. Link
4. R Core Team, An Introduction to R. The official R manual. Link
5. dplyr documentation, summarise(), group_by(), mutate(), joins. Link
6. tidyr documentation, pivot_wider() and pivot_longer(). Link
7. haven package, read and write SAS, SPSS, and Stata files. Link
8. R Validation Hub (pharmaR), validating R for FDA-regulated work. Link
9. Clinical Standards Hub, PROC R: SAS Viya 2026.03 announcement. Link

Continue Learning

• Is R Worth Learning in 2026?, The full case for picking up R if you already know SAS.
• R for Stata Users, Sister guide for Stata migrants, with the same PROC-equivalent treatment for Stata commands.
• R for SPSS Users, Sister guide for SPSS users covering syntax and data manipulation.