R-to-Python Translation Skill

R-to-Python translation reference for quantitative social science data analysis. Maps R ecosystem packages (tidyverse/dplyr, ggplot2, fixest, survey, sf, plm, lme4, marginaleffects, rdrobust) to DAAF Python equivalents (polars, plotnine, pyfixest, statsmodels, linearmodels, svy, geopandas). Use when user mentions R/RStudio background, requests R-equivalent code comments, needs to understand Python analysis code from an R perspective, or wants to translate R data analysis concepts to Python. Covers paradigm differences, verb-by-verb operation translations, regression modeling, causal inference, visualization, and workflow adaptation.

Cross-language translation reference for researchers moving between the R and Python data analysis ecosystems. This skill maps R packages, idioms, and workflows to their DAAF Python equivalents so that R-background users can audit, understand, and learn from DAAF-produced code, and so that code-producing agents can annotate their output with R equivalents when directed.

This skill is a routing hub — it provides overview tables, decision trees, and directs readers to the detailed reference files listed below. The reference files contain the exhaustive verb-by-verb mappings, code examples, and edge-case documentation.

What This Skill Does

• Maps the R data analysis ecosystem to DAAF's Python stack across data wrangling, modeling, visualization, causal inference, surveys, spatial analysis, and workflow tooling
• Provides a structured annotation protocol for agents to add inline R-equivalent comments to Python code
• Identifies paradigm gaps where R and Python diverge fundamentally, so users know where to expect friction

Use cases:

1. R user auditing DAAF Python code and needing to understand what operations are being performed
2. Agent annotating code with R-equivalent comments for an R-background researcher
3. R user learning Python for data analysis and needing a conceptual bridge
4. Translating a specific R operation or idiom to its Python equivalent
5. Understanding where R tools have no direct Python equivalent (and what the workaround is)

How to Use This Skill

Reference File Structure

Each topic in

./references/

paradigm-differences.md

polars-dplyr.md

polars-strings-dates-factors.md

regression-modeling.md

visualization.md

causal-inference.md

survey-spatial-ml.md

workflow-environment.md

external-resources.md

gotchas.md

Reading Order

1. R user auditing DAAF code:

   paradigm-differences.md

   then the relevant domain file (e.g.,

   polars-dplyr.md

   for data wrangling,

   regression-modeling.md

   for models) then

   gotchas.md

2. Agent annotating code with R equivalents: Agent Code Annotation Protocol section below, then the relevant domain file for the code being annotated
3. Learning Python from R background:

   paradigm-differences.md

   then

   polars-dplyr.md

   then

   workflow-environment.md

   then

   external-resources.md

4. Looking up a specific translation: Quick Decision Trees below, then the relevant reference file

Quick Decision Trees

"How do I do X from R in Python?"

What kind of R operation?
├─ Data wrangling (filter, mutate, join, pivot, summarise)
│   └─ ./references/polars-dplyr.md
├─ Regression / statistical modeling
│   └─ ./references/regression-modeling.md
├─ Plotting / visualization
│   └─ ./references/visualization.md
├─ Causal inference (DiD, RDD, IV, event studies)
│   └─ ./references/causal-inference.md
├─ Surveys / spatial / machine learning
│   └─ ./references/survey-spatial-ml.md
└─ Fundamental language differences (types, syntax, environment)
    └─ ./references/paradigm-differences.md

"Why does this Python code look different from R?"

What looks unfamiliar?
├─ Expression syntax (pl.col().method().alias())
│   └─ ./references/paradigm-differences.md
├─ Missing values (None vs NaN vs null vs NA)
│   └─ ./references/paradigm-differences.md
├─ Formula interface (~) behaves differently
│   └─ ./references/regression-modeling.md
├─ Import patterns and namespacing
│   └─ ./references/gotchas.md
└─ No interactive REPL / console workflow
    └─ ./references/workflow-environment.md

"I want to translate an R script to Python"

What does the R script do?
├─ Loads and wrangles data (read_csv, dplyr verbs)
│   └─ ./references/polars-dplyr.md
├─ Runs regressions (lm, feols, plm)
│   └─ ./references/regression-modeling.md
├─ Creates plots (ggplot, plotly)
│   └─ ./references/visualization.md
├─ Uses survey weights (svydesign, svymean)
│   └─ ./references/survey-spatial-ml.md
├─ Spatial operations (sf, st_join)
│   └─ ./references/survey-spatial-ml.md
├─ Multiple of the above
│   └─ Start with ./references/paradigm-differences.md, then each relevant file
└─ Uses a package not listed above
    └─ ./references/external-resources.md for broader ecosystem guidance

"Something isn't working and I think it's an R habit"

What went wrong?
├─ 1-indexed access gave wrong element
│   └─ ./references/gotchas.md
├─ Factor/categorical behaves differently
│   └─ ./references/gotchas.md
├─ NA handling surprised me
│   └─ ./references/paradigm-differences.md
├─ Pipe operator (|> or %>%) not available
│   └─ ./references/paradigm-differences.md
├─ library() vs import confusion
│   └─ ./references/gotchas.md
└─ Model output structure is different
    └─ ./references/regression-modeling.md

"Which Python package replaces my R package?"

Which R package?
├─ dplyr / tidyr / readr / tibble → polars
│   └─ ./references/polars-dplyr.md
├─ ggplot2 → plotnine
│   └─ ./references/visualization.md
├─ plotly (R) → plotly (Python)
│   └─ ./references/visualization.md
├─ fixest → pyfixest
│   └─ ./references/regression-modeling.md
├─ stats (lm, glm) → statsmodels
│   └─ ./references/regression-modeling.md
├─ plm / lme4 / estimatr → linearmodels
│   └─ ./references/regression-modeling.md
├─ survey → svy
│   └─ ./references/survey-spatial-ml.md
├─ sf / terra → geopandas
│   └─ ./references/survey-spatial-ml.md
├─ tidymodels / caret → scikit-learn
│   └─ ./references/survey-spatial-ml.md
├─ marginaleffects → marginaleffects (Python)
│   └─ ./references/regression-modeling.md
├─ rdrobust / did / synthdid → rdrobust / pyfixest DiD
│   └─ ./references/causal-inference.md
└─ Quarto / RMarkdown → marimo
    └─ ./references/workflow-environment.md

Package Mapping Overview

px.scatter()

plot_ly()

Fidelity key: High = near-direct translation, same mental model. Medium = same capability, different API patterns. Low = fundamentally different paradigm requiring conceptual remapping.

Library Versions

Translations in this skill reference specific library versions. Python versions are pinned in DAAF's Docker environment (Python 3.12). R versions reference CRAN releases as of March 2026. When syntax or behavior has changed between versions, the reference files note the change.

Unpinned packages: linearmodels, marginaleffects, and rdrobust install the latest version at Docker build time. Translations for these packages reference their documented API as of March 2026.

R version note: R package versions are from CRAN as of March 2026 (R 4.5.3). Check

packageVersion("pkg")

Top 10 Paradigm Differences

These are the friction points R users encounter most frequently when reading or writing DAAF Python code. Each is covered in depth in the referenced file.

df %>% mutate(x = a + b)

df.with_columns((pl.col("a") + pl.col("b")).alias("x"))

paradigm-differences.md

~

regression-modeling.md

NA

None

NaN

null

paradigm-differences.md

mutate(new = expr)

with_columns(expr.alias("new"))

polars-dplyr.md

with_row_index()

paradigm-differences.md

.to_pandas()

paradigm-differences.md

factor()

pl.Categorical

pd.Categorical

gotchas.md

paradigm-differences.md

x[1]

x[0]

gotchas.md

library()

import

gotchas.md

Agent Code Annotation Protocol

This section defines when and how code-producing agents add inline R-equivalent comments to DAAF Python scripts.

When to Annotate

Annotations are added only when the orchestrator explicitly passes an R-background directive to the agent. This is not a default behavior.

Trigger conditions (orchestrator activates this when any apply):

• User states they have an R / RStudio background
• User requests R-equivalent comments in code
• User asks to understand Python code from an R perspective

How the orchestrator passes the directive: The orchestrator adds the following to the agent prompt:

"User has R background. Load r-python-translation skill. Add inline R-equivalent comments for non-trivial data operations."

Comment Format

# R: df %>% filter(year == 2020)
filtered = df.filter(pl.col("year") == 2020)

# R: df %>% mutate(pct = count / sum(count))
result = df.with_columns(
    (pl.col("count") / pl.col("count").sum()).alias("pct")
)

# R: feols(y ~ x1 + x2 | state + year, data = df, cluster = ~state)
fit = pf.feols("y ~ x1 + x2 | state + year", data=pdf, vcov={"CRV1": "state"})

What to Annotate

• Annotate: Data wrangling (polars operations), modeling calls (pyfixest, statsmodels, linearmodels), visualization layer construction (plotnine, plotly), causal inference method calls
• Do NOT annotate: Import statements,

  print()

  /

  assert

  validation lines, file I/O boilerplate (

  pl.read_parquet

  ,

  df.write_parquet

  ), config sections, section separator comments

Rules

• One

  # R:

  comment per logical operation, placed on the line immediately above the Python code
• Keep annotations to a single line; abbreviate complex R pipelines if needed
• R annotations are in addition to standard IAT comments (

  # INTENT:

  ,

  # REASONING:

  ,

  # ASSUMES:

  ), not a replacement
• Consumer agents: research-executor, code-reviewer, debugger, data-ingest

Related Skills

polars

pyfixest

plotnine

plotly

statsmodels

linearmodels

scikit-learn

geopandas

svy

marimo

stata-python-translation

Note: Individual tool skills contain library-specific usage guidance (syntax, gotchas, performance). This skill provides the R-to-Python conceptual bridge — use both together when an R-background user is working with a specific library.

Topic Index

%>%

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/paradigm-differences.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/polars-strings-dates-factors.md

./references/polars-strings-dates-factors.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/regression-modeling.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/causal-inference.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/external-resources.md

./references/external-resources.md

./references/external-resources.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/gotchas.md

./references/polars-strings-dates-factors.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/polars-dplyr.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/visualization.md

./references/causal-inference.md

./references/causal-inference.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/survey-spatial-ml.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/workflow-environment.md

./references/external-resources.md

./references/external-resources.md