GH-39603: [R] Error: Cannot convert Dictionary Array of type dictionary<values=large_string, indices=uint32, ordered=0> to R#49710
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Pull request overview
Fixes #39603 by extending the non-ALTREP Converter_Dictionary path in the R bindings to support UINT32, INT64, and UINT64 dictionary index types, in addition to adding a safety check for dictionaries larger than R's 32-bit factor code range.
Changes:
- Allow
UINT32/INT64/UINT64index types inConverter_Dictionary's constructor andIngest_some_nullsdispatch. - Add a runtime check that errors when the dictionary length exceeds
std::numeric_limits<int>::max()(R factor code limit). - Add a new test that round-trips a factor through
Table$create()withuint32/int64/uint64dictionary index types.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
| r/src/array_to_vector.cpp | Adds UINT32/INT64/UINT64 cases to dictionary index handling and adds a dictionary-length overflow guard. |
| r/tests/testthat/test-Table.R | Adds a test round-tripping a factor with wider dictionary index types back to R. |
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| tab_uint32 <- Table$create(fact, schema = schema(fct = dictionary(uint32(), utf8()))) | ||
| expect_equal(tab_uint32$schema, schema(fct = dictionary(uint32(), utf8()))) | ||
| expect_equal_data_frame(tab_uint32, fact) | ||
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| tab_int64 <- Table$create(fact, schema = schema(fct = dictionary(int64(), utf8()))) | ||
| expect_equal(tab_int64$schema, schema(fct = dictionary(int64(), utf8()))) | ||
| expect_equal_data_frame(tab_int64, fact) | ||
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| tab_uint64 <- Table$create(fact, schema = schema(fct = dictionary(uint64(), utf8()))) | ||
| expect_equal(tab_uint64$schema, schema(fct = dictionary(uint64(), utf8()))) | ||
| expect_equal_data_frame(tab_uint64, fact) |
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Rationale for this change
Dictionary Arrays with specific type indices caused error
What changes are included in this PR?
Allows those type indices and add check to ensure no overflow
Are these changes tested?
Yes
Are there any user-facing changes?
No
AI Usage
I used AI to create the changes but manually reviewed myself afterwards
dictionary<values=large_string, indices=uint32, ordered=0>to R #39603