Dates and Times
Arwan
11/8/2020
lubridate
Disini saya akan mencoba belajar tentang “Data Wrangling”. “Data Wrangling” adalah suatu usaha agar data yang saya miliki menjadi bentuk yang dapat digunakan/berguna untuk melakukan “vizualitation” dan “modelling”. Pada bagian ini saya akan belajar tentang dates and times variabel menggunakan lubridate.
note : sedang malas nyusun kata-kata dan ngetik jadi copas
library(tidyverse)
library(lubridate)
library(nycflights13)Creating Date/Times
today()## [1] "2021-02-04"now()## [1] "2021-02-04 01:43:31 +07"From string
ymd("2017-01-31")## [1] "2017-01-31"mdy("January 31st, 2017")## [1] "2017-01-31"dmy("31-Jan-2017")## [1] "2017-01-31"ymd(20170131)## [1] "2017-01-31"ymd_hms("2017-01-31 20:11:59")## [1] "2017-01-31 20:11:59 UTC"mdy_hm("01/31/2017 08:01")## [1] "2017-01-31 08:01:00 UTC"ymd(20170131, tz = "UTC")## [1] "2017-01-31 UTC"From Individual Components
Instead of a single string, sometimes you’ll have the individual com‐ponents of the date-time spread across multiple columns. This is what we have in the flights data:
flights %>%
select(year, month, day, hour, minute)## # A tibble: 336,776 x 5
## year month day hour minute
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 5 15
## 2 2013 1 1 5 29
## 3 2013 1 1 5 40
## 4 2013 1 1 5 45
## 5 2013 1 1 6 0
## 6 2013 1 1 5 58
## 7 2013 1 1 6 0
## 8 2013 1 1 6 0
## 9 2013 1 1 6 0
## 10 2013 1 1 6 0
## # ... with 336,766 more rowsflights## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # ... with 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>To create a date/time from this sort of input, use make_date() for dates, or make_datetime() for date-times:
flights %>%
select(year, month, day, hour, minute) %>%
mutate(
departure = make_datetime(year, month, day, hour, minute)
)## # A tibble: 336,776 x 6
## year month day hour minute departure
## <int> <int> <int> <dbl> <dbl> <dttm>
## 1 2013 1 1 5 15 2013-01-01 05:15:00
## 2 2013 1 1 5 29 2013-01-01 05:29:00
## 3 2013 1 1 5 40 2013-01-01 05:40:00
## 4 2013 1 1 5 45 2013-01-01 05:45:00
## 5 2013 1 1 6 0 2013-01-01 06:00:00
## 6 2013 1 1 5 58 2013-01-01 05:58:00
## 7 2013 1 1 6 0 2013-01-01 06:00:00
## 8 2013 1 1 6 0 2013-01-01 06:00:00
## 9 2013 1 1 6 0 2013-01-01 06:00:00
## 10 2013 1 1 6 0 2013-01-01 06:00:00
## # ... with 336,766 more rowsThe times in flight dataset is represented in a slightly odd format, so we use modulus arithmetic to pull out the hour and minute components. Once I’ve created the date-time variables,
make_datetime_100 <- function(year, month, day, time) {
make_datetime(year, month, day, time %/% 100, time %% 100)
}
flights_dt <- flights %>%
filter(!is.na(dep_time), !is.na(arr_time)) %>%
mutate(
dep_time = make_datetime_100(year, month, day, dep_time),
arr_time = make_datetime_100(year, month, day, arr_time),
sched_dep_time = make_datetime_100(
year, month, day, sched_dep_time
),
sched_arr_time = make_datetime_100(
year, month, day, sched_arr_time
)
) %>%
select(origin, dest, ends_with("delay"), ends_with("time"))
flights_dt## # A tibble: 328,063 x 9
## origin dest dep_delay arr_delay dep_time sched_dep_time
## <chr> <chr> <dbl> <dbl> <dttm> <dttm>
## 1 EWR IAH 2 11 2013-01-01 05:17:00 2013-01-01 05:15:00
## 2 LGA IAH 4 20 2013-01-01 05:33:00 2013-01-01 05:29:00
## 3 JFK MIA 2 33 2013-01-01 05:42:00 2013-01-01 05:40:00
## 4 JFK BQN -1 -18 2013-01-01 05:44:00 2013-01-01 05:45:00
## 5 LGA ATL -6 -25 2013-01-01 05:54:00 2013-01-01 06:00:00
## 6 EWR ORD -4 12 2013-01-01 05:54:00 2013-01-01 05:58:00
## 7 EWR FLL -5 19 2013-01-01 05:55:00 2013-01-01 06:00:00
## 8 LGA IAD -3 -14 2013-01-01 05:57:00 2013-01-01 06:00:00
## 9 JFK MCO -3 -8 2013-01-01 05:57:00 2013-01-01 06:00:00
## 10 LGA ORD -2 8 2013-01-01 05:58:00 2013-01-01 06:00:00
## # ... with 328,053 more rows, and 3 more variables: arr_time <dttm>,
## # sched_arr_time <dttm>, air_time <dbl>Menurut saya code diatas kurang tepat, karena terdapat data yang jika waktu “sched”nya ditambah waktu delay maka hasilnya lebih dari 24:00 atau dengan kata lain ganti hari. contoh data pertama pada dataset dibawah, terlihat “sched” jam 09:00 dan delay 1301 menit atau 21 jam 41 menit bila dijumlahkan akan lebih dari 24:00 yang berarti beda hari.
flights_dt%>%
arrange(desc(dep_delay))## # A tibble: 328,063 x 9
## origin dest dep_delay arr_delay dep_time sched_dep_time
## <chr> <chr> <dbl> <dbl> <dttm> <dttm>
## 1 JFK HNL 1301 1272 2013-01-09 06:41:00 2013-01-09 09:00:00
## 2 JFK CMH 1137 1127 2013-06-15 14:32:00 2013-06-15 19:35:00
## 3 EWR ORD 1126 1109 2013-01-10 11:21:00 2013-01-10 16:35:00
## 4 JFK SFO 1014 1007 2013-09-20 11:39:00 2013-09-20 18:45:00
## 5 JFK CVG 1005 989 2013-07-22 08:45:00 2013-07-22 16:00:00
## 6 JFK TPA 960 931 2013-04-10 11:00:00 2013-04-10 19:00:00
## 7 LGA MSP 911 915 2013-03-17 23:21:00 2013-03-17 08:10:00
## 8 JFK PDX 899 850 2013-06-27 09:59:00 2013-06-27 19:00:00
## 9 LGA ATL 898 895 2013-07-22 22:57:00 2013-07-22 07:59:00
## 10 EWR MIA 896 878 2013-12-05 07:56:00 2013-12-05 17:00:00
## # ... with 328,053 more rows, and 3 more variables: arr_time <dttm>,
## # sched_arr_time <dttm>, air_time <dbl>With that data, I can visualize the distribution of departure times across the year:
flights_dt %>%
ggplot(aes(dep_time)) +
geom_freqpoly(binwidth = 86400) # 86400 seconds = 1 day
Or within a single day (banyaknya penerbangan dalam 1 hari setiap 10 menit):
flights_dt %>%
filter(dep_time < ymd(20130102)) %>%
ggplot(aes(dep_time)) +
geom_freqpoly(binwidth = 600) # 600 s = 10 minutes
From Other Types
as_datetime(today())## [1] "2021-02-04 UTC"as_date(now())## [1] "2021-02-04"Sometimes you’ll get date/times as numeric offsets from the “Unix Epoch,” 1970-01-01. If the offset is in seconds, use as_datetime(); if it’s in days, use as_date():
as_datetime(60 * 60 * 10)## [1] "1970-01-01 10:00:00 UTC"as_date(365 * 10 + 2)## [1] "1980-01-01"Date-Time Components
Getting Components
You can pull out individual parts of the date with the accessor functions year(), month(), mday() (day of the month), yday() (day of the year), wday() (day of the week), hour(), minute(), and second():
datetime <- ymd_hms("2016-07-08 12:34:56")
datetime## [1] "2016-07-08 12:34:56 UTC"year(datetime)## [1] 2016month(datetime)## [1] 7mday(datetime)## [1] 8yday(datetime)## [1] 190wday(datetime)## [1] 6For month() and wday() you can set label = TRUE to return the abbreviated name of the month or day of the week. Set abbr = FALSE to return the full name:
month(datetime, label = TRUE)## [1] Jul
## 12 Levels: Jan < Feb < Mar < Apr < May < Jun < Jul < Aug < Sep < ... < Decwday(datetime, label = TRUE, abbr = FALSE)## [1] Friday
## 7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < SaturdayWe can use wday() to see that more flights depart (berdasarkan hari).
flights_dt %>%
mutate(wday = wday(dep_time, label = TRUE)) %>%
ggplot(aes(x = wday)) +
geom_bar()+
ylab("banyaknya keberangkatan")+
labs(title = "Banyaknya keberangkatan Berdasarkan Hari")+
theme(plot.title = element_text(hjust = 0.5))+
xlab("hari")
There’s an interesting pattern if we look at the average departure delay by minute within the hour. (grafik rata-rata delay keberangkatan berdasarkan menit keberangkatan dalam satu jam).
flights_dt %>%
mutate(minute = minute(dep_time)) %>%
group_by(minute) %>%
summarize(
avg_delay = mean(arr_delay, na.rm = TRUE),
n = n()) %>%
ggplot(aes(minute, avg_delay)) +
geom_line()+
geom_point()+
ylab("average delay (minute)")+
labs(title = "rata-rata delay keberangkatan berdasarkan satu jam")+
theme(plot.title = element_text(hjust = 0.5))+
xlab("menit keberangkatan")## `summarise()` ungrouping output (override with `.groups` argument)
Interestingly, if we look at the scheduled departure time we don’t see such a strong pattern:
sched_dep <- flights_dt %>%
mutate(minute = minute(sched_dep_time)) %>%
group_by(minute) %>%
summarize(
avg_delay = mean(arr_delay, na.rm = TRUE),
n = n())## `summarise()` ungrouping output (override with `.groups` argument)ggplot(sched_dep, aes(minute, avg_delay)) +
geom_line()+
geom_point()+
ylab("average delay (minute)")+
xlab("menit keberangkatan")
So why do we see that pattern with the actual departure times? Well, like much data collected by humans, there’s a strong bias toward flights leaving at “nice” departure times. Always be alert for this sort of pattern whenever you work with data that involves human judgment!
ggplot(sched_dep, aes(minute, n)) +
geom_line()+
geom_point()
Waktu keberangkatan yang terjadi mah gini.
flights_dt %>%
mutate(minute = minute(dep_time)) %>%
group_by(minute) %>%
summarize(
avg_delay = mean(arr_delay, na.rm = TRUE),
n = n()) %>%
ggplot( aes(minute, n)) +
geom_line()+
geom_point()## `summarise()` ungrouping output (override with `.groups` argument)
Rounding
An alternative approach to plotting individual components is to round the date to a nearby unit of time, with floor_date(), round_date(), and ceiling_date().
Contoh penggunaan. round_date(), floor_date(), and ceiling_date()
x <- ymd_hms("2020-08-11 12:01:59.23")
round_date(x, "second")## [1] "2020-08-11 12:01:59 UTC"round_date(x, "minute")## [1] "2020-08-11 12:02:00 UTC"round_date(x, "5 mins")## [1] "2020-08-11 12:00:00 UTC"round_date(x, "hour")## [1] "2020-08-11 12:00:00 UTC"round_date(x, "2 hours")## [1] "2020-08-11 12:00:00 UTC"round_date(x, "day")## [1] "2020-08-12 UTC"round_date(x, "week")## [1] "2020-08-09 UTC"round_date(x, "month")## [1] "2020-08-01 UTC"round_date(x, "bimonth")## [1] "2020-09-01 UTC"round_date(x, "quarter") #== round_date(x, "3 months")## [1] "2020-07-01 UTC"round_date(x, "halfyear")## [1] "2020-07-01 UTC"round_date(x, "year")## [1] "2021-01-01 UTC"floor_date(x, "second")## [1] "2020-08-11 12:01:59 UTC"floor_date(x, "minute")## [1] "2020-08-11 12:01:00 UTC"floor_date(x, "hour")## [1] "2020-08-11 12:00:00 UTC"floor_date(x, "day")## [1] "2020-08-11 UTC"floor_date(x, "week")## [1] "2020-08-09 UTC"floor_date(x, "month")## [1] "2020-08-01 UTC"floor_date(x, "bimonth")## [1] "2020-07-01 UTC"floor_date(x, "quarter")## [1] "2020-07-01 UTC"floor_date(x, "season")## [1] "2020-06-01 UTC"floor_date(x, "halfyear")## [1] "2020-07-01 UTC"floor_date(x, "year")## [1] "2020-01-01 UTC"ceiling_date(x, "second")## [1] "2020-08-11 12:02:00 UTC"ceiling_date(x, "minute")## [1] "2020-08-11 12:02:00 UTC"ceiling_date(x, "5 mins")## [1] "2020-08-11 12:05:00 UTC"ceiling_date(x, "hour")## [1] "2020-08-11 13:00:00 UTC"ceiling_date(x, "day")## [1] "2020-08-12 UTC"ceiling_date(x, "week")## [1] "2020-08-16 UTC"ceiling_date(x, "month")## [1] "2020-09-01 UTC"ceiling_date(x, "bimonth") == ceiling_date(x, "2 months")## [1] TRUEceiling_date(x, "quarter")## [1] "2020-10-01 UTC"ceiling_date(x, "season")## [1] "2020-09-01 UTC"ceiling_date(x, "halfyear")## [1] "2021-01-01 UTC"ceiling_date(x, "year")## [1] "2021-01-01 UTC"This, for example, allows us to plot the number of flights per week:
flights_dt %>%
count(week = floor_date(dep_time, "week")) %>%
ggplot(aes(week, n)) +
geom_line()+
geom_point()
Setting Components
You can also use each accessor function to set the components of a date/time:
(datetime <- ymd_hms("2016-07-08 12:34:56"))## [1] "2016-07-08 12:34:56 UTC"# mengubah tahun
year(datetime) <- 2020
datetime## [1] "2020-07-08 12:34:56 UTC"#mengubah bulan
month(datetime) <- 01
datetime## [1] "2020-01-08 12:34:56 UTC"#mengubah jam
hour(datetime) <- hour(datetime) + 1
datetime## [1] "2020-01-08 13:34:56 UTC"Alternatively, rather than modifying in place, you can create a new date-time with update(). This also allows you to set multiple values at once:
update(datetime, year = 2025, month = 3, mday = 2, hour = 13)## [1] "2025-03-02 13:34:56 UTC"Bisa juga begini.
ymd("2020-02-01") %>%
update(mday = 30)## [1] "2020-03-01"ymd("2020-02-01") %>%
update(hour = 400)## [1] "2020-02-17 16:00:00 UTC"ymd("2020-02-10") %>%
update(yday = 1)## [1] "2020-01-01"Time Spans
Durations
In R, when you subtract two dates, you get a difftime object:
h_age <- today() - ymd(19971028)
h_age## Time difference of 8500 daysas.duration(h_age)## [1] "734400000s (~23.27 years)"dseconds(15)## [1] "15s"dminutes(10)## [1] "600s (~10 minutes)"dhours(c(12, 24))## [1] "43200s (~12 hours)" "86400s (~1 days)"ddays(0:7)## [1] "0s" "86400s (~1 days)" "172800s (~2 days)"
## [4] "259200s (~3 days)" "345600s (~4 days)" "432000s (~5 days)"
## [7] "518400s (~6 days)" "604800s (~1 weeks)"dweeks(3)## [1] "1814400s (~3 weeks)"dyears(1)## [1] "31557600s (~1 years)"2 * dyears(1)## [1] "63115200s (~2 years)"dyears(1) + dweeks(12) + dhours(15)## [1] "38869200s (~1.23 years)"tomorrow <- today() + ddays(1)
tomorrow## [1] "2021-02-05"last_year <- today() - dyears(1)
last_year## [1] "2020-02-04 18:00:00 UTC"However, because durations represent an exact number of seconds, sometimes you might get an unexpected result:
one_pm <- ymd_hms(
"2016-03-12 13:00:00",
tz = "America/New_York"
)
one_pm## [1] "2016-03-12 13:00:00 EST"one_pm + ddays(1)## [1] "2016-03-13 14:00:00 EDT"Why is one day after 1 p.m. on March 12, 2 p.m. on March 13?! If you look carefully at the date you might also notice that the time zones have changed. Because of DST, March 12 only has 23 hours, so if we add a full day’s worth of seconds we end up with a different time.
(Kurang ngerti juga ini tentang DST kok tanggal 12 Maret cuman 23 jam)
Periods
To solve this problem, lubridate provides periods. Periods are time spans but don’t have a fixed length in seconds; instead they work with “human” times, like days and months. That allows them to work in a more intuitive way:
one_pm## [1] "2016-03-12 13:00:00 EST"one_pm + days(1)## [1] "2016-03-13 13:00:00 EDT"seconds(15)## [1] "15S"minutes(10)## [1] "10M 0S"hours(c(12, 24))## [1] "12H 0M 0S" "24H 0M 0S"days(7)## [1] "7d 0H 0M 0S"months(1:6)## [1] "1m 0d 0H 0M 0S" "2m 0d 0H 0M 0S" "3m 0d 0H 0M 0S" "4m 0d 0H 0M 0S"
## [5] "5m 0d 0H 0M 0S" "6m 0d 0H 0M 0S"weeks(3)## [1] "21d 0H 0M 0S"years(1)## [1] "1y 0m 0d 0H 0M 0S"10 * (months(6) + days(1))## [1] "60m 10d 0H 0M 0S"days(50) + hours(25) + minutes(2)## [1] "50d 25H 2M 0S"ymd("2020-01-01") + dyears(1)## [1] "2020-12-31 06:00:00 UTC"ymd("2020-01-01") + years(1)## [1] "2021-01-01"one_pm + ddays(1)## [1] "2016-03-13 14:00:00 EDT"one_pm + days(1)## [1] "2016-03-13 13:00:00 EDT"Let’s use periods to fix an oddity related to our flight dates. Some planes appear to have arrived at their destination before they departed from New York City:
flights_dt %>%
filter(arr_time < dep_time)## # A tibble: 10,633 x 9
## origin dest dep_delay arr_delay dep_time sched_dep_time
## <chr> <chr> <dbl> <dbl> <dttm> <dttm>
## 1 EWR BQN 9 -4 2013-01-01 19:29:00 2013-01-01 19:20:00
## 2 JFK DFW 59 NA 2013-01-01 19:39:00 2013-01-01 18:40:00
## 3 EWR TPA -2 9 2013-01-01 20:58:00 2013-01-01 21:00:00
## 4 EWR SJU -6 -12 2013-01-01 21:02:00 2013-01-01 21:08:00
## 5 EWR SFO 11 -14 2013-01-01 21:08:00 2013-01-01 20:57:00
## 6 LGA FLL -10 -2 2013-01-01 21:20:00 2013-01-01 21:30:00
## 7 EWR MCO 41 43 2013-01-01 21:21:00 2013-01-01 20:40:00
## 8 JFK LAX -7 -24 2013-01-01 21:28:00 2013-01-01 21:35:00
## 9 EWR FLL 49 28 2013-01-01 21:34:00 2013-01-01 20:45:00
## 10 EWR FLL -9 -14 2013-01-01 21:36:00 2013-01-01 21:45:00
## # ... with 10,623 more rows, and 3 more variables: arr_time <dttm>,
## # sched_arr_time <dttm>, air_time <dbl>These are overnight flights. We used the same date information for both the departure and the arrival times, but these flights arrived on the following day. We can fix this by adding days(1) to the arrival time of each overnight flight:
flights_dt1 <- flights_dt %>%
mutate(
overnight = arr_time < dep_time,
arr_time = arr_time + days(overnight * 1),
sched_arr_time = sched_arr_time + days(overnight * 1)
)
flights_dt1## # A tibble: 328,063 x 10
## origin dest dep_delay arr_delay dep_time sched_dep_time
## <chr> <chr> <dbl> <dbl> <dttm> <dttm>
## 1 EWR IAH 2 11 2013-01-01 05:17:00 2013-01-01 05:15:00
## 2 LGA IAH 4 20 2013-01-01 05:33:00 2013-01-01 05:29:00
## 3 JFK MIA 2 33 2013-01-01 05:42:00 2013-01-01 05:40:00
## 4 JFK BQN -1 -18 2013-01-01 05:44:00 2013-01-01 05:45:00
## 5 LGA ATL -6 -25 2013-01-01 05:54:00 2013-01-01 06:00:00
## 6 EWR ORD -4 12 2013-01-01 05:54:00 2013-01-01 05:58:00
## 7 EWR FLL -5 19 2013-01-01 05:55:00 2013-01-01 06:00:00
## 8 LGA IAD -3 -14 2013-01-01 05:57:00 2013-01-01 06:00:00
## 9 JFK MCO -3 -8 2013-01-01 05:57:00 2013-01-01 06:00:00
## 10 LGA ORD -2 8 2013-01-01 05:58:00 2013-01-01 06:00:00
## # ... with 328,053 more rows, and 4 more variables: arr_time <dttm>,
## # sched_arr_time <dttm>, air_time <dbl>, overnight <lgl>flights_dt1 %>%
filter(overnight, arr_time < dep_time)## # A tibble: 0 x 10
## # ... with 10 variables: origin <chr>, dest <chr>, dep_delay <dbl>,
## # arr_delay <dbl>, dep_time <dttm>, sched_dep_time <dttm>, arr_time <dttm>,
## # sched_arr_time <dttm>, air_time <dbl>, overnight <lgl>Intervals
saya kurang mengerti dengan ini, apalagi ini pipe apaan “%–%”.
years(1) / days(1)## [1] 365.25next_year <- today() + years(1)
(today() %--% next_year) / ddays(1)## [1] 365(today() %--% next_year) %/% days(1)## [1] 365Time Zones
You can find out what R thinks your current time zone.
Sys.timezone()## [1] "Asia/Bangkok"And see the complete list of all time zone names.
length(OlsonNames())## [1] 594head(OlsonNames())## [1] "Africa/Abidjan" "Africa/Accra" "Africa/Addis_Ababa"
## [4] "Africa/Algiers" "Africa/Asmara" "Africa/Asmera"In R, the time zone is an attribute of the date-time that only controls printing. For example, these three objects represent the same instant in time:
(x1 <- ymd_hms("2015-06-01 12:00:00", tz = "America/New_York"))## [1] "2015-06-01 12:00:00 EDT"(x2 <- ymd_hms("2015-06-01 18:00:00", tz = "Europe/Copenhagen"))## [1] "2015-06-01 18:00:00 CEST"(x3 <- ymd_hms("2015-06-02 04:00:00", tz = "Pacific/Auckland"))## [1] "2015-06-02 04:00:00 NZST"You can verify that they’re the same time using subtraction:
x1 - x2## Time difference of 0 secsx1 - x3## Time difference of 0 secsOperations that combine date-times, like c(), will often drop the time zone. In that case, the date-times will display in your local time zone:
x4 <- c(x1, x2, x3)
x4## [1] "2015-06-01 12:00:00 EDT" "2015-06-01 12:00:00 EDT"
## [3] "2015-06-01 12:00:00 EDT"You can change the time zone in two ways:
x4a <- with_tz(x4, tzone = "Australia/Lord_Howe")
x4a## [1] "2015-06-02 02:30:00 +1030" "2015-06-02 02:30:00 +1030"
## [3] "2015-06-02 02:30:00 +1030"x4a-x4## Time differences in secs
## [1] 0 0 0x4b <- force_tz(x4, tzone = "Australia/Lord_Howe")
x4b## [1] "2015-06-01 12:00:00 +1030" "2015-06-01 12:00:00 +1030"
## [3] "2015-06-01 12:00:00 +1030"x4b-x4## Time differences in hours
## [1] -14.5 -14.5 -14.5