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Data table backend for dplyr
https://dtplyr.tidyverse.org
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dtplyr no longer working with across #436

Closed eipi10 closed 1 year ago

eipi10 commented 1 year ago

It looks like dtplyr 1.3.1 no longer works with across. Reprexes below show error with version 1.3.1 and same code working as expected with version 1.2.2.

dtplyr 1.3.1

library(dtplyr)
library(tidyverse)

iris %>% 
  lazy_dt() %>% 
  mutate(across(where(is.numeric), ~ . * 2))
#> Error in `across()`:
#> ! This tidyselect interface doesn't support predicates.
#> Backtrace:
#>      ▆
#>   1. ├─iris %>% lazy_dt() %>% ...
#>   2. ├─dplyr::mutate(., across(where(is.numeric), ~. * 2))
#>   3. └─dtplyr:::mutate.dtplyr_step(...)
#>   4.   └─dtplyr:::capture_new_vars(.data, ..., .by = by)
#>   5.     └─dtplyr:::dt_squash(dot, data = .data, is_top = TRUE)
#>   6.       └─dtplyr:::dt_squash(get_expr(x), get_env(x), data, j = j, is_top)
#>   7.         └─dtplyr:::dt_squash_across(x, env, data, j = j, is_top)
#>   8.           └─dtplyr:::across_setup(...)
#>   9.             └─tidyselect::eval_select(...)
#>  10.               └─tidyselect:::eval_select_impl(...)
#>  11.                 ├─tidyselect:::with_subscript_errors(...)
#>  12.                 │ └─rlang::try_fetch(...)
#>  13.                 │   └─base::withCallingHandlers(...)
#>  14.                 └─tidyselect:::vars_select_eval(...)
#>  15.                   └─tidyselect:::walk_data_tree(expr, data_mask, context_mask)
#>  16.                     └─tidyselect:::as_indices_sel_impl(...)
#>  17.                       └─cli::cli_abort(...)
#>  18.                         └─rlang::abort(...)

Created on 2023-05-04 with reprex v2.0.2

Session info ``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.3 (2023-03-15) #> os macOS Ventura 13.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Los_Angeles #> date 2023-05-04 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0) #> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0) #> data.table 1.14.8 2023-02-17 [1] CRAN (R 4.2.0) #> digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.0) #> dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0) #> dtplyr * 1.3.1 2023-03-22 [1] CRAN (R 4.2.0) #> evaluate 0.20 2023-01-17 [1] CRAN (R 4.2.0) #> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0) #> forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0) #> fs 1.6.2 2023-04-25 [1] CRAN (R 4.2.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0) #> ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0) #> hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0) #> htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0) #> knitr 1.42 2023-01-25 [1] CRAN (R 4.2.0) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0) #> lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0) #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> purrr * 1.0.1 2023-01-10 [1] CRAN (R 4.2.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0) #> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.0) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.0) #> rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0) #> rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.2) #> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0) #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0) #> stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.0) #> styler 1.9.1 2023-03-04 [1] CRAN (R 4.2.0) #> tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0) #> tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0) #> tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.0) #> timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.2) #> tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0) #> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0) #> vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0) #> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0) #> #> [1] /Users/jschwartz/Library/R/arm64/4.2/library #> [2] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```

dtplyr 1.2.2

library(dtplyr)
library(tidyverse)

iris %>% 
  lazy_dt() %>% 
  mutate(across(where(is.numeric), ~ . * 2))
#> Source: local data table [150 x 5]
#> Call:   copy(`_DT1`)[, .SD]
#> 
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> 5          5           3.6          1.4         0.2 setosa 
#> 6          5.4         3.9          1.7         0.4 setosa 
#> # ℹ 144 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

Created on 2023-05-04 with reprex v2.0.2

Session info ``` r sessioninfo::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 4.2.3 (2023-03-15) #> os macOS Ventura 13.3.1 #> system aarch64, darwin20 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz America/Los_Angeles #> date 2023-05-04 #> pandoc 2.19.2 @ /Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/ (via rmarkdown) #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date (UTC) lib source #> cli 3.6.1 2023-03-23 [1] CRAN (R 4.2.0) #> colorspace 2.1-0 2023-01-23 [1] CRAN (R 4.2.0) #> crayon 1.5.2 2022-09-29 [1] CRAN (R 4.2.1) #> data.table 1.14.8 2023-02-17 [1] CRAN (R 4.2.0) #> digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.0) #> dplyr * 1.1.2 2023-04-20 [1] CRAN (R 4.2.0) #> dtplyr * 1.2.2 2022-08-20 [1] CRAN (R 4.2.3) #> evaluate 0.20 2023-01-17 [1] CRAN (R 4.2.0) #> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.0) #> fastmap 1.1.1 2023-02-24 [1] CRAN (R 4.2.0) #> forcats * 1.0.0 2023-01-29 [1] CRAN (R 4.2.0) #> fs 1.6.2 2023-04-25 [1] CRAN (R 4.2.0) #> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.0) #> ggplot2 * 3.4.2 2023-04-03 [1] CRAN (R 4.2.0) #> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.0) #> gtable 0.3.3 2023-03-21 [1] CRAN (R 4.2.0) #> hms 1.1.3 2023-03-21 [1] CRAN (R 4.2.0) #> htmltools 0.5.5 2023-03-23 [1] CRAN (R 4.2.0) #> knitr 1.42 2023-01-25 [1] CRAN (R 4.2.0) #> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.0) #> lubridate * 1.9.2 2023-02-10 [1] CRAN (R 4.2.0) #> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.0) #> munsell 0.5.0 2018-06-12 [1] CRAN (R 4.2.0) #> pillar 1.9.0 2023-03-22 [1] CRAN (R 4.2.0) #> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.0) #> purrr * 1.0.1 2023-01-10 [1] CRAN (R 4.2.0) #> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.0) #> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.0) #> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.0) #> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.0) #> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.0) #> readr * 2.1.4 2023-02-10 [1] CRAN (R 4.2.0) #> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.0) #> rlang 1.1.1 2023-04-28 [1] CRAN (R 4.2.0) #> rmarkdown 2.21 2023-03-26 [1] CRAN (R 4.2.2) #> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.0) #> scales 1.2.1 2022-08-20 [1] CRAN (R 4.2.0) #> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0) #> stringi 1.7.12 2023-01-11 [1] CRAN (R 4.2.0) #> stringr * 1.5.0 2022-12-02 [1] CRAN (R 4.2.0) #> styler 1.9.1 2023-03-04 [1] CRAN (R 4.2.0) #> tibble * 3.2.1 2023-03-20 [1] CRAN (R 4.2.0) #> tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.0) #> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.0) #> tidyverse * 2.0.0 2023-02-22 [1] CRAN (R 4.2.0) #> timechange 0.2.0 2023-01-11 [1] CRAN (R 4.2.2) #> tzdb 0.3.0 2022-03-28 [1] CRAN (R 4.2.0) #> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.0) #> vctrs 0.6.2 2023-04-19 [1] CRAN (R 4.2.0) #> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0) #> xfun 0.39 2023-04-20 [1] CRAN (R 4.2.0) #> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.0) #> #> [1] /Users/jschwartz/Library/R/arm64/4.2/library #> [2] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library #> #> ────────────────────────────────────────────────────────────────────────────── ```
markfairbanks commented 1 year ago

The where() selection helper doesn't work with dtplyr. It used to fail silently in v1.2.2. Now it throws an error.

If you look at the result your mutate(across()) call wasn't actually doing anything:

library(dplyr, w = FALSE)
library(dtplyr)

packageVersion("dtplyr")
#> [1] '1.2.2'

iris %>% 
  lazy_dt()
#> Source: local data table [150 x 5]
#> Call:   `_DT1`
#> 
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> 5          5           3.6          1.4         0.2 setosa 
#> 6          5.4         3.9          1.7         0.4 setosa 
#> # ℹ 144 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results

iris %>%
  lazy_dt() %>%
  mutate(across(where(is.numeric), ~ .x + 1))
#> Source: local data table [150 x 5]
#> Call:   copy(`_DT2`)[, .SD]
#> 
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> 5          5           3.6          1.4         0.2 setosa 
#> 6          5.4         3.9          1.7         0.4 setosa 
#> # ℹ 144 more rows
#> 
#> # Use as.data.table()/as.data.frame()/as_tibble() to access results
eipi10 commented 1 year ago

Heh, heh. I should have looked more closely!