paleolimbot edited a comment on pull request #11690:
URL: https://github.com/apache/arrow/pull/11690#issuecomment-977888123
Ok...summary of the changes:
- This now uses `arrow_not_supported()` for `check.rows` and `row.names` in
the `data.frame()` translation
- Added tests for using literals and existing data frames
<details>
``` r
library(arrow, warn.conflicts = FALSE)
library(dplyr, warn.conflicts = FALSE)
df <- record_batch(a = 1:2)
# "normal"
df %>% mutate(df_col = tibble(a2 = a)) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a2
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>% mutate(df_col = data.frame(a2 = a)) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a2
#> <int> <int>
#> 1 1 1
#> 2 2 2
# scalars and existing data frames
df %>% mutate(df_col = tibble(a2 = "nested value")) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a2
#> <int> <chr>
#> 1 1 nested value
#> 2 2 nested value
one_row_df <- tibble(a2 = "nested value")
df %>% mutate(df_col = one_row_df) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a2
#> <int> <chr>
#> 1 1 nested value
#> 2 2 nested value
# this is surprising behaviour (to me) of Scalar$create(c("nested value",
"nested value2"))
df %>% mutate(df_col = tibble(a2 = c("nested value", "nested value2"))) %>%
collect()
#> # A tibble: 2 × 2
#> a df_col$a2
#> <int> <list<character>>
#> 1 1 [2]
#> 2 2 [2]
# opened https://issues.apache.org/jira/browse/ARROW-14828 to fix this
two_row_df <- tibble(a2 = c("nested value", "nested value2"))
df %>% mutate(df_col = two_row_df) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a2
#> <int> <chr>
#> 1 1 nested value
#> 2 2 nested value
# duplicated cols
df %>% mutate(df_col = tibble(a, a)) %>% collect()
#> Warning: Expression tibble(a, a) not supported in Arrow; pulling data
into R
#> Error: Problem with `mutate()` column `df_col`.
#> ℹ `df_col = tibble(a, a)`.
#> x Column name `a` must not be duplicated.
#> Use .name_repair to specify repair.
df %>% mutate(df_col = data.frame(a, a)) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a $a.1
#> <int> <int> <int>
#> 1 1 1 1
#> 2 2 2 2
df %>% mutate(df_col = data.frame(a, a, check.names = FALSE)) %>% collect()
#> # A tibble: 2 × 2
#> a df_col$a $a
#> <int> <int> <int>
#> 1 1 1 1
#> 2 2 2 2
# empty names
df %>%
mutate(df_col = data.frame(a, check.names = TRUE, fix.empty.names = TRUE))
%>%
collect()
#> # A tibble: 2 × 2
#> a df_col$a
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>%
mutate(df_col = data.frame(a, check.names = TRUE, fix.empty.names =
FALSE)) %>%
collect()
#> # A tibble: 2 × 2
#> a df_col$``
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>%
mutate(df_col = data.frame(a, check.names = FALSE, fix.empty.names =
TRUE)) %>%
collect()
#> # A tibble: 2 × 2
#> a df_col$a
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>%
mutate(df_col = data.frame(a, check.names = FALSE, fix.empty.names =
FALSE)) %>%
collect()
#> # A tibble: 2 × 2
#> a df_col$``
#> <int> <int>
#> 1 1 1
#> 2 2 2
# arrow_not_supported
df %>% mutate(df_col = tibble(a, .rows = 1L)) %>% collect()
#> Warning: In tibble(a, .rows = 1L), .rows not supported in Arrow; pulling
data
#> into R
#> # A tibble: 2 × 2
#> a df_col$a
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>% mutate(df_col = tibble(a, .name_repair = "universal")) %>% collect()
#> Warning: In tibble(a, .name_repair = "universal"), .name_repair not
supported in
#> Arrow; pulling data into R
#> # A tibble: 2 × 2
#> a df_col$a
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>% mutate(df_col = data.frame(a, check.rows = TRUE)) %>% collect()
#> Warning: In data.frame(a, check.rows = TRUE), check.rows not supported in
Arrow;
#> pulling data into R
#> # A tibble: 2 × 2
#> a df_col$a
#> <int> <int>
#> 1 1 1
#> 2 2 2
df %>% mutate(df_col = data.frame(a, row.names = TRUE)) %>% collect()
#> Warning: In data.frame(a, row.names = TRUE), row.names not supported in
Arrow;
#> pulling data into R
#> # A tibble: 2 × 2
#> a df_col
#> <int> <named list>
#> 1 1 <NULL>
#> 2 2 <NULL>
```
<sup>Created on 2021-11-24 by the [reprex
package](https://reprex.tidyverse.org) (v2.0.1)</sup>
</details>
I *didn't* add a test for `mutate(df_col = tibble(a2 = c("nested value",
"nested value2")))` and `mutate(df_col = two_row_df)` because these both give
surprising values to me that don't align with what dplyr would give you. I
think they should be fixed and tested at the `Scalar$create()` level, not here,
but I'm happy to add in more here with some guidance on the desired behaviour.
I opened ARROW-14828 for `Scalar$create()` on a two-row data.frame.
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