英文:
`tidyr::crossing()` with a list of tibbles unexpectedly reduces rows
问题
以下是我困惑的示例。当我将一个包含1行的表格与一个包含2行的表格相交叉时，我期望得到一个包含2行的表格。这对于原子列类型有效。但是，如果2行表格是数据框的列表，我得到的不是期望的2行表格，而是一个包含1行的表格。这对我来说是没有道理的。有人能解释为什么我应该期望这样，或者是否有什么我遗漏的东西。
作为错误的交叉发布在 GitHub 上：https://github.com/tidyverse/tidyr/issues/1487
library(tibble)#> 警告：package ‘tibble’ was built under R version 4.2.2library(tidyr)#> 警告：package ‘tidyr’ was built under R version 4.2.2#;; 这是有道理的，是符合期望的：#;; 将一个包含1行的表格与一个包含2行的表格交叉，我得到一个包含两行的表格。(df1 <- tibble(x=1))#> # A tibble: 1 × 1#> x#> <dbl>#> 1 1(df2 <- tibble(y=1:2))#> # A tibble: 2 × 1#> y#> <int>#> 1 1#> 2 2crossing(df1, df2)#> # A tibble: 2 × 2#> x y#> <dbl> <int>#> 1 1 1#> 2 1 2#;; 这是没有道理的。#;; 如果第二个包含2行的表格是数据框的列表，我仍然期望得到一个包含2行的表格，但我得到的是一个包含1行的表格。(df3 <- tibble(y=list(tibble(y=2), tibble(y=2))))#> # A tibble: 2 × 1#> y#> <list>#> 1 <tibble [1 × 1]>#> 2 <tibble [1 × 1]>crossing(df1, df3)#> # A tibble: 1 × 2#> x y#> <dbl> <list>#> 1 1 <tibble [1 × 1]>sessioninfo::session_info()#> ─ 会话信息 ─────────────────────────────────────────────────────────────#> 设置 值#> 版本 R version 4.2.1 Patched (2022-07-06 r82554 ucrt)#> 操作系统 Windows 10 x64 (build 19044)#> 系统 x86_64, mingw32#> 用户界面 RTerm#> 语言 (EN)#> 区域 English_United States.utf8#> 语言环境 English_United States.utf8#> 时区 America/Chicago#> 日期 2023-02-26#> pandoc 2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)#>#> ─ 包 ───────────────────────────────────────────────────────────────────#> 包 * 版本 日期 (UTC) 基础R来源#> cli 3.6.0 2023-01-09 [1] CRAN (R 4.2.2)#> digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.2)#> dplyr 1.1.0 2023-01-29 [1] CRAN (R 4.2.2)#> evaluate 0.20 2023-01-17 [1] CRAN (R 4.2.2)#> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.2)#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.1)#> fs 1.6.1 2023-02-06 [1] CRAN (R 4.2.2)#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1)#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1)#> htmltools 0.5.4 2022-12-07 [1] CRAN (R 4.2.2)#> knitr 1.42 2023-01-25 [1] CRAN (R 4.2.2)#> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1)#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1)#> pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.1)#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.1<details><summary>英文:</summary>Below is a reprex of my confusion. I expect when I cross a 1-row tibble with a 2-row tibble, I get a 2-row tibble. This does work with atomic column types. However, if the 2-row tibble is a list of dataframes, I don't get a 2-row table, like expected, but a 1-row table. This does not make sense to me. Can someone explain why it is what I should expect, or if there is something I'm missing.Cross-posted as a bug on github: https://github.com/tidyverse/tidyr/issues/1487``` rlibrary(tibble)#> Warning: package 'tibble' was built under R version 4.2.2library(tidyr)#> Warning: package 'tidyr' was built under R version 4.2.2#;; This makes sense, is expected:#;; Crossing a 1-row table with a 2-row table, I get a two row table.(df1 <- tibble(x=1))#> # A tibble: 1 × 1#> x#> <dbl>#> 1 1(df2 <- tibble(y=1:2))#> # A tibble: 2 × 1#> y#> <int>#> 1 1#> 2 2crossing(df1, df2)#> # A tibble: 2 × 2#> x y#> <dbl> <int>#> 1 1 1#> 2 1 2#;; This does not make sense.#;; If the second 2-row table is a list of dataframes, I still expect a 2-row#;; table, but I get a 1-row table.(df3 <- tibble(y=list(tibble(y=2), tibble(y=2))))#> # A tibble: 2 × 1#> y#> <list>#> 1 <tibble [1 × 1]>#> 2 <tibble [1 × 1]>crossing(df1, df3)#> # A tibble: 1 × 2#> x y#> <dbl> <list>#> 1 1 <tibble [1 × 1]>sessioninfo::session_info()#> ─ Session info ───────────────────────────────────────────────────────────────#> setting value#> version R version 4.2.1 Patched (2022-07-06 r82554 ucrt)#> os Windows 10 x64 (build 19044)#> system x86_64, mingw32#> ui RTerm#> language (EN)#> collate English_United States.utf8#> ctype English_United States.utf8#> tz America/Chicago#> date 2023-02-26#> pandoc 2.19.2 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)#>#> ─ Packages ───────────────────────────────────────────────────────────────────#> package * version date (UTC) lib source#> cli 3.6.0 2023-01-09 [1] CRAN (R 4.2.2)#> digest 0.6.31 2022-12-11 [1] CRAN (R 4.2.2)#> dplyr 1.1.0 2023-01-29 [1] CRAN (R 4.2.2)#> evaluate 0.20 2023-01-17 [1] CRAN (R 4.2.2)#> fansi 1.0.4 2023-01-22 [1] CRAN (R 4.2.2)#> fastmap 1.1.0 2021-01-25 [1] CRAN (R 4.2.1)#> fs 1.6.1 2023-02-06 [1] CRAN (R 4.2.2)#> generics 0.1.3 2022-07-05 [1] CRAN (R 4.2.1)#> glue 1.6.2 2022-02-24 [1] CRAN (R 4.2.1)#> htmltools 0.5.4 2022-12-07 [1] CRAN (R 4.2.2)#> knitr 1.42 2023-01-25 [1] CRAN (R 4.2.2)#> lifecycle 1.0.3 2022-10-07 [1] CRAN (R 4.2.1)#> magrittr 2.0.3 2022-03-30 [1] CRAN (R 4.2.1)#> pillar 1.8.1 2022-08-19 [1] CRAN (R 4.2.1)#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 4.2.1)#> purrr 1.0.1 2023-01-10 [1] CRAN (R 4.2.2)#> R.cache 0.16.0 2022-07-21 [1] CRAN (R 4.2.2)#> R.methodsS3 1.8.2 2022-06-13 [1] CRAN (R 4.2.2)#> R.oo 1.25.0 2022-06-12 [1] CRAN (R 4.2.2)#> R.utils 2.12.2 2022-11-11 [1] CRAN (R 4.2.2)#> R6 2.5.1 2021-08-19 [1] CRAN (R 4.2.1)#> reprex 2.0.2 2022-08-17 [1] CRAN (R 4.2.2)#> rlang 1.0.6 2022-09-24 [1] CRAN (R 4.2.1)#> rmarkdown 2.20 2023-01-19 [1] CRAN (R 4.2.2)#> rstudioapi 0.14 2022-08-22 [1] CRAN (R 4.2.2)#> sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.1)#> styler 1.9.0 2023-01-15 [1] CRAN (R 4.2.2)#> tibble * 3.1.8 2022-07-22 [1] CRAN (R 4.2.2)#> tidyr * 1.3.0 2023-01-24 [1] CRAN (R 4.2.2)#> tidyselect 1.2.0 2022-10-10 [1] CRAN (R 4.2.1)#> utf8 1.2.3 2023-01-31 [1] CRAN (R 4.2.2)#> vctrs 0.5.2 2023-01-23 [1] CRAN (R 4.2.2)#> withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.1)#> xfun 0.37 2023-01-31 [1] CRAN (R 4.2.2)#> yaml 2.3.7 2023-01-23 [1] CRAN (R 4.2.2)#>#> [1] C:/Users/irinzn/R/R-4.2.1patched/library#>#> ──────────────────────────────────────────────────────────────────────────────
<sup>Created on 2023-02-26 with reprex v2.0.2</sup>
答案1
得分: 1
查看 ?crossing
> ‘crossing()是对expand_grid()` 的封装，用于去重和排序其输入
请使用 expand_grid 代替
expand_grid(df1, df3)# 一个数据表：2 行 × 2 列x y<dbl> <list>1 1 <tibble [1 × 1]>2 1 <tibble [1 × 1]>
英文:
See ?crossing
>‘crossing()’ is a wrapper around ‘expand_grid()’ that
de-duplicates and sorts its inputs
Use expand_grid instead
expand_grid(df1, df3)# A tibble: 2 × 2x y<dbl> <list>1 1 <tibble [1 × 1]>2 1 <tibble [1 × 1]>
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