paleolimbot commented on code in PR #37843:
URL: https://github.com/apache/arrow/pull/37843#discussion_r1338666415
##########
r/tests/testthat/test-schema.R:
##########
@@ -295,7 +295,9 @@ test_that("schema name assignment", {
test_that("schema extraction", {
skip_if_not_available("dataset")
+
tbl <- arrow_table(example_data)
+ expect_equal(schema(example_data), tbl$schema)
Review Comment:
I think it is probably worth testing some edge cases with `data.frame()`
like (1) at df with duplicate names and (2) a data frame with zero columns!
##########
r/R/schema.R:
##########
@@ -285,6 +285,9 @@ infer_schema.Dataset <- function(x) x$schema
#' @export
infer_schema.arrow_dplyr_query <- function(x) implicit_schema(x)
+#' @export
+infer_schema.data.frame <- function(x) infer_schema(arrow_table(x))
Review Comment:
`arrow_table(some_data_frame)` is potentially a very expensive operation
(possibly requiring a full copy of the data if, for example, they are all
string columns).
Another option is `infer_schema(arrow_table(x[integer(), , drop = FALSE]))`,
although this won't quite work for things like `list()` columns where we
actually need some values to properly infer the type.
Unless I'm missing some prior art in the package, I think `schema(!!!
lapply(x, infer_type))` might be the way to go.
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