#duckplyr — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #duckplyr, aggregated by home.social.
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Rducks: Register arbitrary #Rstats User-Defined Functions in duckdb-r (1.5.2) including (sacrilege) distributed chunk execution mode via #mirai+libnng. Arrow C Data marshaling dance that that may be removed later. Trivial #duckplyr integration. Stream DuckDB queries repo: github.com/sounkou-bioi...
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Rducks: Register arbitrary #Rstats User-Defined Functions in duckdb-r (1.5.2) including (sacrilege) distributed chunk execution mode via #mirai+libnng. Arrow C Data marshaling dance that that may be removed later. Trivial #duckplyr integration. Stream DuckDB queries repo: github.com/sounkou-bioi...
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Rducks: Register arbitrary #Rstats User-Defined Functions in duckdb-r (1.5.2) including (sacrilege) distributed chunk execution mode via #mirai+libnng. Arrow C Data marshaling dance that that may be removed later. Trivial #duckplyr integration. Stream DuckDB queries repo: github.com/sounkou-bioi...
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Rducks: Register arbitrary #Rstats User-Defined Functions in duckdb-r (1.5.2) including (sacrilege) distributed chunk execution mode via #mirai+libnng. Arrow C Data marshaling dance that that may be removed later. Trivial #duckplyr integration. Stream DuckDB queries repo: github.com/sounkou-bioi...
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Rducks: Register arbitrary #Rstats User-Defined Functions in duckdb-r (1.5.2) including (sacrilege) distributed chunk execution mode via #mirai+libnng. Arrow C Data marshaling dance that that may be removed later. Trivial #duckplyr integration. Stream DuckDB queries repo: github.com/sounkou-bioi...
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#duckplyr with no fallbacks, because why not Learned that prefixing a function call with `dd$` is a way to call arbitrary duckdb functions (in our case a registered #RStats UDF) duckplyr.tidyverse.org/dev/articles...
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#duckplyr with no fallbacks, because why not Learned that prefixing a function call with `dd$` is a way to call arbitrary duckdb functions (in our case a registered #RStats UDF) duckplyr.tidyverse.org/dev/articles...
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#duckplyr with no fallbacks, because why not Learned that prefixing a function call with `dd$` is a way to call arbitrary duckdb functions (in our case a registered #RStats UDF) duckplyr.tidyverse.org/dev/articles...
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#duckplyr with no fallbacks, because why not Learned that prefixing a function call with `dd$` is a way to call arbitrary duckdb functions (in our case a registered #RStats UDF) duckplyr.tidyverse.org/dev/articles...
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#duckplyr with no fallbacks, because why not Learned that prefixing a function call with `dd$` is a way to call arbitrary duckdb functions (in our case a registered #RStats UDF) duckplyr.tidyverse.org/dev/articles...
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Shouting to the void: How to properly namespace #duckdb / #duckplyr in my #rstats packages?
One of @hadleywickham core style recommendations for package development is that every external function needs to be explicitly namespaced:
function_in_my_package <- function(df, x, ...) {
df |> dplyr::mutate(xx = stringr::str_do_something(x))
# implict return
}1/maybe 4
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Shouting to the void: How to properly namespace #duckdb / #duckplyr in my #rstats packages?
One of @hadleywickham core style recommendations for package development is that every external function needs to be explicitly namespaced:
function_in_my_package <- function(df, x, ...) {
df |> dplyr::mutate(xx = stringr::str_do_something(x))
# implict return
}1/maybe 4
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Shouting to the void: How to properly namespace #duckdb / #duckplyr in my #rstats packages?
One of @hadleywickham core style recommendations for package development is that every external function needs to be explicitly namespaced:
function_in_my_package <- function(df, x, ...) {
df |> dplyr::mutate(xx = stringr::str_do_something(x))
# implict return
}1/maybe 4
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Shouting to the void: How to properly namespace #duckdb / #duckplyr in my #rstats packages?
One of @hadleywickham core style recommendations for package development is that every external function needs to be explicitly namespaced:
function_in_my_package <- function(df, x, ...) {
df |> dplyr::mutate(xx = stringr::str_do_something(x))
# implict return
}1/maybe 4
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Shouting to the void: How to properly namespace #duckdb / #duckplyr in my #rstats packages?
One of @hadleywickham core style recommendations for package development is that every external function needs to be explicitly namespaced:
function_in_my_package <- function(df, x, ...) {
df |> dplyr::mutate(xx = stringr::str_do_something(x))
# implict return
}1/maybe 4
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"duckplyr fully joins the tidyverse!"
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"duckplyr fully joins the tidyverse!"
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"duckplyr fully joins the tidyverse!"
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"duckplyr fully joins the tidyverse!"
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"duckplyr fully joins the tidyverse!"
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New duckplyr version 1.1.0. Some good bugfixes for materialisation issues and new as_tbl() among other things.
https://cran.rstudio.com/web/packages/duckplyr/news/news.html
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New duckplyr version 1.1.0. Some good bugfixes for materialisation issues and new as_tbl() among other things.
https://cran.rstudio.com/web/packages/duckplyr/news/news.html
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New duckplyr version 1.1.0. Some good bugfixes for materialisation issues and new as_tbl() among other things.
https://cran.rstudio.com/web/packages/duckplyr/news/news.html
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New duckplyr version 1.1.0. Some good bugfixes for materialisation issues and new as_tbl() among other things.
https://cran.rstudio.com/web/packages/duckplyr/news/news.html
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New duckplyr version 1.1.0. Some good bugfixes for materialisation issues and new as_tbl() among other things.
https://cran.rstudio.com/web/packages/duckplyr/news/news.html
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Dear #Rstats experts, I read the #duckplyr docs at https://github.com/tidyverse/duckplyr?tab=readme-ov-file#analyzing-larger-than-memory-data (and waiting for my slow machine to instal duckplyr). I wonder if anyone can point me to a tutorial like https://francoismichonneau.net/2022/10/import-big-csv/#the-dataset-api-in-r which converted me to arrow. My starting point at work can often be a large flat csv file which needs to 1) be converted into a parquet file without being read into RAM and 2) be analyzed later on. 2) is addressed by duckplyr as well, but 1) is not clear to me. Thanks and a wonderful 2025!
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Dear #Rstats experts, I read the #duckplyr docs at https://github.com/tidyverse/duckplyr?tab=readme-ov-file#analyzing-larger-than-memory-data (and waiting for my slow machine to instal duckplyr). I wonder if anyone can point me to a tutorial like https://francoismichonneau.net/2022/10/import-big-csv/#the-dataset-api-in-r which converted me to arrow. My starting point at work can often be a large flat csv file which needs to 1) be converted into a parquet file without being read into RAM and 2) be analyzed later on. 2) is addressed by duckplyr as well, but 1) is not clear to me. Thanks and a wonderful 2025!
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Dear #Rstats experts, I read the #duckplyr docs at https://github.com/tidyverse/duckplyr?tab=readme-ov-file#analyzing-larger-than-memory-data (and waiting for my slow machine to instal duckplyr). I wonder if anyone can point me to a tutorial like https://francoismichonneau.net/2022/10/import-big-csv/#the-dataset-api-in-r which converted me to arrow. My starting point at work can often be a large flat csv file which needs to 1) be converted into a parquet file without being read into RAM and 2) be analyzed later on. 2) is addressed by duckplyr as well, but 1) is not clear to me. Thanks and a wonderful 2025!
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Dear #Rstats experts, I read the #duckplyr docs at https://github.com/tidyverse/duckplyr?tab=readme-ov-file#analyzing-larger-than-memory-data (and waiting for my slow machine to instal duckplyr). I wonder if anyone can point me to a tutorial like https://francoismichonneau.net/2022/10/import-big-csv/#the-dataset-api-in-r which converted me to arrow. My starting point at work can often be a large flat csv file which needs to 1) be converted into a parquet file without being read into RAM and 2) be analyzed later on. 2) is addressed by duckplyr as well, but 1) is not clear to me. Thanks and a wonderful 2025!
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Dear #Rstats experts, I read the #duckplyr docs at https://github.com/tidyverse/duckplyr?tab=readme-ov-file#analyzing-larger-than-memory-data (and waiting for my slow machine to instal duckplyr). I wonder if anyone can point me to a tutorial like https://francoismichonneau.net/2022/10/import-big-csv/#the-dataset-api-in-r which converted me to arrow. My starting point at work can often be a large flat csv file which needs to 1) be converted into a parquet file without being read into RAM and 2) be analyzed later on. 2) is addressed by duckplyr as well, but 1) is not clear to me. Thanks and a wonderful 2025!
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Just saw in the unknown site (ex-birdie site), a post mentioning that #duckplyr (https://duckplyr.tidyverse.org/) that uses #duckdb as backend automagically and overrides several methods from #dplyr, is now part of the #tidyverse 🎉
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Just saw in the unknown site (ex-birdie site), a post mentioning that #duckplyr (https://duckplyr.tidyverse.org/) that uses #duckdb as backend automagically and overrides several methods from #dplyr, is now part of the #tidyverse 🎉
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Just saw in the unknown site (ex-birdie site), a post mentioning that #duckplyr (https://duckplyr.tidyverse.org/) that uses #duckdb as backend automagically and overrides several methods from #dplyr, is now part of the #tidyverse 🎉
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Just saw in the unknown site (ex-birdie site), a post mentioning that #duckplyr (https://duckplyr.tidyverse.org/) that uses #duckdb as backend automagically and overrides several methods from #dplyr, is now part of the #tidyverse 🎉
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Just saw in the unknown site (ex-birdie site), a post mentioning that #duckplyr (https://duckplyr.tidyverse.org/) that uses #duckdb as backend automagically and overrides several methods from #dplyr, is now part of the #tidyverse 🎉