duckplyr 1.0.0 #724
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duckplyr 1.0.0 #724
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Furthermore, this blog post might need a benchmark. Maybe it could be structured around "why bother" (despite already having code that works without duckplyr, despite the fallbacks and some "annoying" incompatibilities like factors and timezones): duckplyr already works fairly well, and is under active development. And the choice is IMHO probably not duckplyr vs dplyr but rather duckplyr vs other dplyr backends. So large data support is crucial. |
krlmlr
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Feb 11, 2025
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| ```{r} | ||
| out <- babynames |> | ||
| duckdb_tibble(prudence = "lavish") |> # default value of prudence :-) |
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as_duckdb_tibble() or .prudence :
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| duckdb_tibble(prudence = "lavish") |> # default value of prudence :-) | |
| as_duckdb_tibble(prudence = "lavish") |> # default value of prudence :-) |
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maelle
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Feb 11, 2025
| - [computation to files](https://duckplyr.tidyverse.org/reference/compute_file.html) using `compute_parquet()` or `compute_csv()`. | ||
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| A drawback of analyzing large data with duckplyr is that the limitations of duckplyr won't be compensated by fallbacks, since fallbacks to dplyr necessitate putting data into memory. | ||
| Therefore, if your pipeline encounters fallbacks, you might want to work around them by converting the duck frame into a table through `compute()` then running SQL code through the experimental `read_sql_duckdb()` function. Again, over time, we expect more native support for dplyr functionality. |
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@krlmlr could we tweak the example to use ceiling() that isn't supported I think? So it'd look more realistic. (I do not know SQL 🙈 )
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@krlmlr the "stingy" example does not work, it should generate an error but does not. 🤔
I'm a bit undecided regarding structure. I tried starting with basic usage, but even simply discussing
library()vs individual activation viaduck_tibble()is better done with some understanding of prudence I think.