Valid Time Zones in Lubridate

Valid time zones in lubridate

Take a search through the OlsonNames() in the standard base R package, which provides a list of all the valid timezones on the host system. e.g.:

grep("Brazil", OlsonNames(), value=TRUE)

...provides four possible results for Brazil.

Digging into R package: Time Zones in lubridate

You can find a list of the timezones available by lubridate with:

lubridate::olson_time_zones()

This information is easily found by typing

??timezones

in the console.
The link describing this function is the second entry in the list of the help page in my case:

Another possibility is to search the manual of lubridate for "available time zones".

Understanding timezone strings in R

CEST probably stands for Central European Summer Time.
So during daylight saving time, CET becomes CEST, and in winter it does not:

as.POSIXct(c("2016-1-1 13:00", "2016-3-1 13:00",
             "2016-5-1 13:00", "2016-6-1 13:00",
             "2016-9-1 13:00","2016-11-1 13:00"), tz="CET") 

returns:

 "2016-01-01 13:00:00 CET" "2016-03-01 13:00:00 CET"  "2016-05-01 13:00:00 CEST"  
 "2016-06-01 13:00:00 CEST" "2016-09-01 13:00:00 CEST" "2016-11-01 13:00:00 CET"

However, as @Matt_Johnson explained, CEST is not on official timezone,

so as.POSIXct("2016-1-1 13:00, tz="CEST") fails.

What does remain strange, that CEST is acceptable in a string, even if the time is outside daylight saving time:

as.POSIXct("2016-1-1 13:00 CEST")
[1] "2016-01-01 13:00:00 CET"

The help files from as.POSIXct and strptime don't offer any explaination here.

Why does lubridate appear to change time zones for two dates combined into a vector?

Using c() will remove the timezone attribute. Hence you have to reassign it:

xy <- c(x,y)
attr(xy, "tzone") <- "UTC"
> xy
[1] "2016-02-08 UTC" "2016-03-29 UTC"

Source and more information: Peter Ehlers on R Help

Transform string in date through Lubridate with variation in month, day, year hour min am/pm and time zone

First thing I did was to setup a tibble with your 2 date vectors

tibble(
  date1 = c("February 11th 2017, 6:05am PST", "April 24th 2018, 4:09pm PDT"),
  date2 = c("2013-12-14 00:58:00 CET", "2013-06-19 18:00:00 CEST"),
) %>%
  {. ->> my_dates}

my_dates

# # A tibble: 2 x 2
# date1                          date2                   
# <chr>                          <chr>                   
# February 11th 2017, 6:05am PST 2013-12-14 00:58:00 CET 
# April 24th 2018, 4:09pm PDT    2013-06-19 18:00:00 CEST

Then, make a tibble of the timezone abbreviations and their offset from UTC

# setup timezones and UTC offsets
tribble(
  ~tz, ~offset,
  'PST', -8,
  'PDT', -7,
  'CET', +1,
  'CEST', +2
) %>%
  {. ->> my_tz}

my_tz

# # A tibble: 4 x 2
# tz    offset
# <chr>  <dbl>
# PST       -8
# PDT       -7
# CET        1
# CEST       2

Then, we tidy the datetimes up by removing the character suffix after the day number in date1 (the 'th' bit after '11th'). We also pull out the timezone code and put that in a separate column; the timezone column allows us to left_join() my_tz in, giving us the UTC offset.

We use string-handling functions from the stringr package, and regex expressions to find, extract and replace the components. A very handy tool for testing regex patterns can be found here https://regex101.com/r/5pr3LL/1/

my_dates %>%
  mutate(
    # remove the character suffix after the day number (eg 11th)
    day_suffix = str_extract(date1, '[0-9]+[a-z]+') %>% str_extract('[a-z]+'),
    date1 = str_replace(date1, day_suffix, ''),
    day_suffix = NULL,

    # extract timezone info
    date1_tz = str_extract(date1, '[a-zA-Z]+$'),
    date2_tz = str_extract(date2, '[a-zA-Z]+$'),
  ) %>%

  # join in timezones for date1
  left_join(my_tz, by = c('date1_tz' = 'tz')) %>%
  rename(
    offset_date1 = offset
  ) %>%

  # join in timezones for date2
  left_join(my_tz, by = c('date2_tz' = 'tz')) %>%
  rename(
    offset_date2 = offset
  ) %>% 
  
  {. ->> my_dates_info}

my_dates_info

# # A tibble: 2 x 6
# date1                        date2                    date1_tz date2_tz offset_date1 offset_date2
# <chr>                        <chr>                    <chr>    <chr>           <dbl>        <dbl>
# February 11 2017, 6:05am PST 2013-12-14 00:58:00 CET  PST      CET                -8            1
# April 24 2018, 4:09pm PDT    2013-06-19 18:00:00 CEST PDT      CEST               -7            2

So now, we can use lubridate::as_datetime() to convert date1 and date2 to dttm (datetime) format. as_datetime() takes a character-format datetime and converts it to datetime format. You must specify the format of the character string using symbols and abbreviations explained here. For example, here we use %B to refer to the full name of the month, %d is the day number and %Y is the (4-digit) year number etc.

Note: because we don't specify the timezone inside as_datetime(), the underlying timezone stored with these datetimes defaults to UTC (as seen by using tz()). This is why we call these columns date*_orig, to remind us the timezone is the original datetime's timezone. Then we add the offset to the datetime object, so we now have these times in UTC (and the underlying timezone signature of these values is UTC, so that's ideal).

# now define datetimes in local and UTC timezones (note: technically the tz is UTC for both)
my_dates_info %>% 
  mutate(
    date1_orig = as_datetime(date1, format = '%B %d %Y, %I:%M%p '),
    date1_utc = date1_orig + hours(offset_date1),
    date2_orig = as_datetime(date2, format = '%Y-%m-%d %H:%M:%S'),
    date2_utc = date2_orig + hours(offset_date2),
  ) %>% 
  {. ->> my_dates_utc}

my_dates_utc

# # A tibble: 2 x 10
# date1                        date2                    date1_tz date2_tz offset_date1 offset_date2 date1_orig          date1_utc           date2_orig          date2_utc          
# <chr>                        <chr>                    <chr>    <chr>           <dbl>        <dbl> <dttm>              <dttm>              <dttm>              <dttm>             
# February 11 2017, 6:05am PST 2013-12-14 00:58:00 CET  PST      CET                -8            1 2017-02-11 06:05:00 2017-02-10 22:05:00 2013-12-14 00:58:00 2013-12-14 01:58:00
# April 24 2018, 4:09pm PDT    2013-06-19 18:00:00 CEST PDT      CEST               -7            2 2018-04-24 16:09:00 2018-04-24 09:09:00 2013-06-19 18:00:00 2013-06-19 20:00:00

Now that we have both sets of dates in datetime format, and in the same timezone, we can calculate time differences between them.

# now calculate difference between them
my_dates_utc %>% 
  select(date1_utc, date2_utc) %>% 
  mutate(
    difference_days = interval(start = date1_utc, end = date2_utc) %>% time_length(unit = 'days')
  )

# # A tibble: 2 x 3
# date1_utc           date2_utc           difference_days
# <dttm>              <dttm>                        <dbl>
# 2017-02-10 22:05:00 2013-12-14 01:58:00          -1155.
# 2018-04-24 09:09:00 2013-06-19 20:00:00          -1770.

This should be fine for small-scale operations. If you had more than 2 different datetime format vectors, it would be worth considering a more complex operation where you transform the data from wide to long format. This would save repeating the same/similar code for each column, like we have done for date1 and date2 in this example.

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