feat: implement as_polars_series() for some clock package types

[eitsupi]

Part of #591

It looks like it could be implemented on the R side only.
There is room for improvement in performance because of the use of when-then-otherwise to handle overflow when converting UInt64 to Int64, but I think it is a good starting point.

char_times = c(
  "1900-01-01 01:01:01.123456789",
  "2212-01-01 12:34:57.123456789",
  "3712-01-01 12:34:56.123456789"
)
fmt = "%Y-%m-%d %H:%M:%OS"
clock_times = list(
  ns = clock::naive_time_parse(char_times , format = fmt, precision = "nanosecond"),
  us =  clock::naive_time_parse(char_times , format = fmt, precision = "microsecond"),
  ms =  clock::naive_time_parse(char_times , format = fmt, precision = "millisecond"),
  s = clock::naive_time_parse(char_times , format = fmt, precision = "second"),
  d = clock::naive_time_parse(char_times , format = fmt, precision = "day")
)

clock_times
#> $ns
#> <naive_time<nanosecond>[3]>
#> [1] "1900-01-01T01:01:01.123456789" "2212-01-01T12:34:57.123456789"
#> [3] "1958-05-04T13:51:14.994801941"
#>
#> $us
#> <naive_time<microsecond>[3]>
#> [1] "1900-01-01T01:01:01.123456" "2212-01-01T12:34:57.123456"
#> [3] "3712-01-01T12:34:56.123456"
#>
#> $ms
#> <naive_time<millisecond>[3]>
#> [1] "1900-01-01T01:01:01.123" "2212-01-01T12:34:57.123"
#> [3] "3712-01-01T12:34:56.123"
#>
#> $s
#> <naive_time<second>[3]>
#> [1] "1900-01-01T01:01:01" "2212-01-01T12:34:57" "3712-01-01T12:34:56"
#>
#> $d
#> <naive_time<day>[3]>
#> [1] "1900-01-01" "2212-01-02" "3712-01-02"

lapply(clock_times, polars::as_polars_series)
#> $ns
#> polars Series: shape: (3,)
#> Series: '' [datetime[ns]]
#> [
#>  1900-01-01 01:01:01.123456789
#>  2212-01-01 12:34:57.123456789
#>  1958-05-04 13:51:14.994801941
#> ]
#>
#> $us
#> polars Series: shape: (3,)
#> Series: '' [datetime[μs]]
#> [
#>  1900-01-01 01:01:01.123456
#>  2212-01-01 12:34:57.123456
#>  3712-01-01 12:34:56.123456
#> ]
#>
#> $ms
#> polars Series: shape: (3,)
#> Series: '' [datetime[ms]]
#> [
#>  1900-01-01 01:01:01.123
#>  2212-01-01 12:34:57.123
#>  3712-01-01 12:34:56.123
#> ]
#>
#> $s
#> polars Series: shape: (3,)
#> Series: '' [datetime[ms]]
#> [
#>  1900-01-01 01:01:01
#>  2212-01-01 12:34:57
#>  3712-01-01 12:34:56
#> ]
#>
#> $d
#> polars Series: shape: (3,)
#> Series: '' [datetime[ms]]
#> [
#>  1900-01-01 00:00:00
#>  2212-01-02 00:00:00
#>  3712-01-02 00:00:00
#> ]

^{Created on 2024-02-28 with reprex v2.0.2}

[eitsupi]

If this is less performant than string parsing, it is not worth the complexity of the implementation so I will do some benchmarking later.

[eitsupi]

In my environment, this implementation seems to be faster for more than 100,000 rows.

library(clock)
library(polars)

time_clock <- seq_len(10^5) |>
  as.POSIXct(tz = "UTC") |>
  as_zoned_time()

bench::mark(
  via_str = {
    time_clock |>
      as.character() |>
      (\(x) as_polars_series(x)$str$
        replace(r"(\[.*\])", "")$str$
        strptime(
        pl$Datetime("ms"), "%Y-%m-%dT%H:%M:%S%:z"
      ))()
  },
  via_double = {
    as_polars_series(time_clock)
  },
  check = FALSE
)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
#> # A tibble: 2 × 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 via_str       712ms    712ms      1.40     6.9MB     1.40
#> 2 via_double    127ms    145ms      7.01    5.98MB     1.75

^{Created on 2024-02-29 with reprex v2.0.2}

[etiennebacher]

I'm really no expert on datetime handling so I don't have much to say about this, but LGTM

[etiennebacher]

Just saw the conversation in the linked issue to the clock repo, do you want to use strings instead?

[eitsupi]

Just saw the conversation in the linked issue to the clock repo, do you want to use strings instead?

I think we can leave it as is for now and replace it with an implementation via string when the clock is updated because of the performance benefits as commented above.