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R-to-Python translation for data analysis. Maps R packages (tidyverse, ggplot2, fixest, survey, sf, plm) to Python equivalents (polars, plotnine, pyfixest, svy, geopandas).
Passed 20 of 22 test cases with the skill, and 18 of 22 without it.
2 fixed18 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
I have an R script that computes survey-weighted summary stats using `svydesign()` and `svymean()` from the `survey` package. I need to convert this workflow into Python. Could you provide the equivalent Python script and explain the key conceptual differences in how survey design objects and missing values are handled between the two ecosystems?
| Checked | Without skill | With skill |
|---|---|---|
| The Python code uses the svy library for survey-weighted calculations. | Missed | Passed |
| The explanation highlights that R uses a unified NA while Python uses None, NaN, or null depending on context. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
Use cases:
Each topic in ./references/ contains focused documentation:
| File | Purpose | When to Read |
|---|---|---|
paradigm-differences.md | Core language and paradigm differences | Encountering fundamental R-vs-Python confusion |
polars-dplyr.md | Core dplyr/tidyr to polars verb mapping (select, filter, mutate, joins, reshaping, window functions, lazy eval) | Reading or writing data manipulation code |
polars-strings-dates-factors.md | String, date/time, and factor operations (stringr, lubridate, forcats to polars) | Working with string/date/categorical columns |
regression-modeling.md | fixest/stats/plm to pyfixest/statsmodels/linearmodels | Reading or writing regression code |
visualization.md | ggplot2/plotly R to plotnine/plotly Python | Reading or writing visualization code |
causal-inference.md | R causal inference ecosystem to Python equivalents | Working with DiD, RDD, IV, event studies |
survey-spatial-ml.md | survey/sf/tidymodels to svy/geopandas/scikit-learn | Working with surveys, spatial data, or ML |
workflow-environment.md | RStudio/Quarto workflow to DAAF/marimo workflow | Adapting to DAAF's execution model |
external-resources.md | Curated guides and tutorials with provenance | Seeking additional learning materials |
gotchas.md | Common R-user mistakes in Python | Debugging or reviewing code from R perspective |
test cases passed, vs 18 / 22 without
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