r2sas Converter
Translate statistical code between R, SAS, SPSS, and Excel VBA — in the browser, instantly.
R Input
8 lines · 179 chars
SAS Output
/* ============================================================ *//* Converted from R to SAS *//* Generated: 2026-06-04 *//* NOTE: Auto-translation is approximate. Review before use. *//* ============================================================ */ /* R: df <- read.csv("data.csv") */proc import datafile="data.csv" out=DF dbms=csv replace; getnames=yes;run; /* R: model <- lm(y ~ x1 + x2 + x3, data = df) */proc reg data=df outest=model_est; model y = x1 x2 x3;run; /* R: summary(model) */proc means data=model n mean std min max;run; /* R: print(coef(model)) */proc print data=coef(model);run; /* R: cat("R-squared:", summary(model)$r.squared, "") */%PUT R-squared: &summary(model)$r.squared ;
29 lines · 794 chars·yellow = comments/warnings·green = translated code
Quick examples
R → SAS
- lm() → PROC REG
- glm(binomial) → PROC LOGISTIC
- glm(poisson) → PROC GENMOD
- t.test() → PROC TTEST
- aov() → PROC ANOVA
- chisq.test() → PROC FREQ/CHISQ
- summary() → PROC MEANS
- filter() → DATA WHERE
- ggplot + geom_* → PROC SGPLOT
- read.csv() → PROC IMPORT
R → SPSS
- lm() → REGRESSION
- glm(binomial) → LOGISTIC REGRESSION
- t.test() → T-TEST
- aov() → ONEWAY
- chisq.test() → CROSSTABS
- summary() → DESCRIPTIVES
- table() → FREQUENCIES
- filter() → SELECT IF
- mutate() → COMPUTE
- arrange() → SORT CASES
R → Excel VBA
- mean() → =AVERAGE()
- sum() → =SUM()
- sd() → =STDEV()
- max() / min() → =MAX() / =MIN()
- round() → =ROUND()
- nchar() → =LEN()
- toupper() → =UPPER()
- paste0() → =CONCATENATE()
- lm() → Data Analysis Toolpak
- read.csv() → Power Query
SAS → R
- PROC REG → lm()
- PROC LOGISTIC → glm(binomial)
- PROC GENMOD → glm(family)
- PROC TTEST → t.test()
- PROC FREQ → table()
- PROC MEANS → summary()
- PROC SORT → dplyr::arrange()
- PROC IMPORT → read.csv()
- DATA WHERE → dplyr::filter()
- PROC SGPLOT → ggplot2
Notes on auto-translation
Auto-translation is approximate. Complex pipelines, custom functions, and domain-specific libraries will require manual review. Generated code includes comments for ambiguous constructs. Color coding: green = translated code, yellow = comments/warnings/TODOs, gray = metadata comments. For the full R package with file-level conversion, macro transforms, and more: install r2sas from GitHub.