| name | port-python-to-r |
| description | Use when porting Python logic from the SportsDataverse Python package (sdv-py) into a SportsDataverse R package — cfbfastR, hoopR, wehoop, baseballr, fastRhockey, softballR, etc. Enforces a parity-test-first workflow (golden fixture from the Python output → failing testthat test → port → green) plus the tidyverse/data.table idiom map and R package conventions (roxygen2 docs, snake_case, tibble returns, pkgdown reference coverage). Invoke for "port this to cfbfastR", "translate this polars logic to dplyr", "re-implement the sdv-py parser in R", or "bring this Python fix back to the R package". |
Port Python (polars) → R, parity-test-first
The reverse of port-r-to-python. Same failure mode, same fix: make the divergence a
failing testthat test before the port, so a wrong/empty column surfaces immediately
rather than after a downstream join or a pkgdown example breaks.
Create one todo per numbered step below.
1. Pin the canonical Python source + target R package
Identify the sdv-py function you are porting (read it directly — sportsdataverse/<sport>/...)
and the target R package + repo in the workspace:
cfbfastR-dev/cfbfastR, hoopR-dev/hoopR, wehoop-dev/wehoop,
baseball-dev/baseballr, hockey-dev/fastRhockey, softballR-dev/softballR.
Note the cross-package naming: R uses snake_case function names (cfbfastR::cfbd_*,
espn_cfb_*) and returns tibbles. Quote the Python function + line range in the
test/roxygen provenance.
2. Capture a golden fixture (the Python output)
Run the sdv-py function on a small representative input and persist the output
(df.write_csv(...) / write_parquet) as the parity oracle. Capture the asserted columns
plus join keys, and record the sdv-py version + inputs alongside it. Place it under the R
package's tests/testthat/ fixtures area (or a scratch dir for a one-off reconciliation).
3. Write the failing testthat test FIRST
Before porting, add a test_that(...) that reads the golden fixture and asserts the
(not-yet-written) R output matches — expect_equal() for categorical/id columns,
expect_equal(..., tolerance = ...) for float model columns. State the tolerance and why.
Run devtools::test(); confirm it fails for the right reason.
4. Translate idioms — polars/pandas → tidyverse (or data.table)
| polars / pandas idiom | tidyverse R |
|---|
df.with_columns((...).alias("x")) | dplyr::mutate(x = ...) |
pl.when(c1).then(v1)...otherwise(d) | dplyr::case_when(c1 ~ v1, ..., TRUE ~ d) |
df.group_by("g").agg(...) | dplyr::group_by(g) %>% dplyr::summarise(...) |
pl.col("x").shift(1).over("game_id") | dplyr::group_by(game_id) %>% dplyr::mutate(dplyr::lag(x)) |
pl.col("x").cum_sum().over("g") | dplyr::group_by(g) %>% dplyr::mutate(cumsum(x)) |
df.filter(pl.col("c") == True) | dplyr::filter(c) |
df.join(o, on="k", how="left") | dplyr::left_join(o, by = "k") |
df.join(o, how="full", coalesce=True) | dplyr::full_join(o, by="k") then coalesce |
pl.col("s").str.extract(re, g) | stringr::str_match(s, re)[, g+1] |
df.pivot(...) | tidyr::pivot_wider(...) |
df.melt(...) / unpivot | tidyr::pivot_longer(...) |
df.with_row_index("i") | dplyr::mutate(i = dplyr::row_number() - 1) |
Hard rules (SDV R conventions — the high-frequency port bugs in this direction):
- Return a tibble, snake_case columns, stable column set even on empty input
(
tibble::tibble(col = character())), matching the sibling functions in the package.
- Vectorize, don't loop.
case_when/if_else over rowwise loops; mirror the
polars expression structure rather than transcribing an imperative pass.
- Join-key dtype discipline carries over. Ids must match type on both sides; an
integer id stringified as
"123.0" is the same foot-gun in R — coerce the raw integer.
Match player names case-insensitively (stringr::regex(..., ignore_case = TRUE)).
- Regex flavor differs. R's ICU/PCRE regex does support lookaround (unlike polars),
but don't assume a polars pattern is portable verbatim — re-test extraction on the fixture.
NA vs polars null. polars null → R NA of the right type; watch NA_real_ vs
NA_character_ in case_when branches (all branches must share a type).
5. Document + wire pkgdown
Every exported R function needs a complete roxygen block: @param for each arg, an
@return describing the tibble (ideally a column table), and runnable @examples. Then:
devtools::document() to regenerate man/*.Rd + NAMESPACE.- Add the function to
_pkgdown.yml reference so it appears on the site.
- Run the
roxygen-doc-reviewer / returns-table-auditor agents to check completeness.
6. Go green, then run the R gate
devtools::document()
devtools::test()
devtools::check()
Match the package's existing lint/style (most SDV R packages follow tidyverse style;
styler::style_pkg() if configured). Commit with the package's Conventional-Commit scope
(feat(cfb): ...) and no AI co-author trailer (SDV ecosystem rule).
See also
port-r-to-python — the reverse direction (R → sdv-py polars).r-skills:tdd / r-skills:r-package-development — testthat-first R workflow + package structure.- The target package's
CLAUDE.md / CONTRIBUTING.md is authoritative for its conventions.
