jonkeane commented on a change in pull request #12319:
URL: https://github.com/apache/arrow/pull/12319#discussion_r798743253
##########
File path: r/R/dplyr-funcs-type.R
##########
@@ -248,3 +249,33 @@ register_bindings_type_elementwise <- function() {
is_inf & !call_binding("is.na", is_inf)
})
}
+
+register_bindings_type_format <- function() {
+ register_binding("format", function(x, ...) {
+ if (inherits(x, "Expression") &&
+ x$type_id() %in% Type[c("TIMESTAMP", "DATE32", "DATE64")]) {
+ binding_format_datetime(x, ...)
+ } else {
+ abort(paste0("`format()` not yet supported for `", class(x$type())[[1]],
"`"))
+ }
+ })
+}
+
+binding_format_datetime <- function(x, format = "", tz = "", usetz = FALSE) {
+
+ if (usetz) {
+ format <- paste(format, "%Z")
+ }
+
+ if (call_binding("is.POSIXct", x)) {
+ if (tz == "" && x$type()$timezone() != "") {
+ tz <- x$type()$timezone()
+ } else if (tz == "") {
+ tz <- Sys.timezone()
+ }
+ ts <- Expression$create("cast", x, options = list(to_type =
timestamp(x$type()$unit(), tz)))
Review comment:
Especially with #12240 I don't think we'll need this cast part here.
Even without that PR, I'm not sure there's a huge amount of benefit of assuming
(and then converting) the timezones like R does it (and that's actually counter
to what Arrow is supposed to do with timestamps that don't have timezones)
##########
File path: r/tests/testthat/test-dplyr-funcs-type.R
##########
@@ -768,3 +768,84 @@ test_that("nested structs can be created from scalars and
existing data frames",
tibble(a = 1:2)
)
})
+
+test_that("format date/time", {
+ skip_on_os("windows") # https://issues.apache.org/jira/browse/ARROW-13168
+
+ times <- tibble(
+ datetime = c(lubridate::ymd_hms("2018-10-07 19:04:05", tz = "Etc/GMT+6"),
NA),
+ date = c(as.Date("2021-01-01"), NA)
+ )
+ formats <- "%a %A %w %d %b %B %m %y %Y %H %I %p %M %z %Z %j %U %W %x %X %%
%G %V %u"
+ formats_date <- "%a %A %w %d %b %B %m %y %Y %H %I %p %M %j %U %W %x %X %% %G
%V %u"
+
+ compare_dplyr_binding(
+ .input %>%
+ mutate(x = format(datetime, format = formats)) %>%
+ collect(),
+ times
+ )
+
+ compare_dplyr_binding(
+ .input %>%
+ mutate(x = format(date, format = formats_date)) %>%
+ collect(),
+ times
+ )
+
+ compare_dplyr_binding(
+ .input %>%
+ mutate(x = format(datetime, format = formats, tz = "Pacific/Marquesas"))
%>%
+ collect(),
+ times
+ )
+
+ compare_dplyr_binding(
+ .input %>%
+ mutate(x = format(datetime, format = formats, tz = "EST", usetz = TRUE))
%>%
Review comment:
Like here, if we wanted to assert that formating with a different
timezone gives us the right thing, that would be a good place to do something
other than `"Pacific/Marquesas"`
##########
File path: r/tests/testthat/test-dplyr-funcs-type.R
##########
@@ -768,3 +768,84 @@ test_that("nested structs can be created from scalars and
existing data frames",
tibble(a = 1:2)
)
})
+
+test_that("format date/time", {
+ skip_on_os("windows") # https://issues.apache.org/jira/browse/ARROW-13168
+
+ times <- tibble(
+ datetime = c(lubridate::ymd_hms("2018-10-07 19:04:05", tz = "Etc/GMT+6"),
NA),
Review comment:
Is there something particular about `"Etc/GMT+6"`? We use
`"Pacific/Marquesas"` elsewhere to be 1. a timezone most people don't already
have configured and 2. a timezone that is different from UTC and 3. a timezone
that isn't even on the hour all of which to attempt to catch places where we
assume any of those three. _Generally_ if we're picking a random timezone we
should favor `"Pacific/Marquesas"` unless there are specific reasons to do
something else.
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