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Dates and times with lubridate
R
beginner
Slides
Session content
Word of warning
- dates and times are hard everywhere
- R is no exception
- this session is a beginner’s guide to lubridate
- not the only way of dealing with dates
- not always the best
- on balance the most consistent, and least quirky tools for dates
This session
- beginner-friendly
- focus on core parsing, get/set, and rounding functions
- lots on dates, a bit of date-times, no times
Resources
R dates
- days since 1970-01-01
as_date(0)[1] “1970-01-01”
R dates
as_date(19860)[1] “2024-05-17”
as_date(1:5)[1] “1970-01-02” “1970-01-03” “1970-01-04” “1970-01-05” “1970-01-06”
[1] “Date”
R date-times
- seconds since 1970-01-01 00:00:00 UTC
as_datetime(0)[1] “1970-01-01 UTC”
as_datetime(1715935369)[1] “2024-05-17 08:42:49 UTC”
as_datetime(0:5)[1] “1970-01-01 00:00:00 UTC” “1970-01-01 00:00:01 UTC” [3] “1970-01-01 00:00:02 UTC” “1970-01-01 00:00:03 UTC” [5] “1970-01-01 00:00:04 UTC” “1970-01-01 00:00:05 UTC”
class(as_datetime(0))[1] “POSIXct” “POSIXt”
Famously…
as_datetime(2 ^ 31-1) # 32 bit signed int[1] “2038-01-19 03:14:07 UTC”
Parsing dates is important
- most functions that accept dates (like ggplot) will mis-behave if you feed them date-shaped-words
- e.g. alphabetically-ordered dates
- we also want to be able to calculate with dates
Couple of fun intro functions
today()[1] “2025-10-07”
date_decimal(2024.37534)[1] “2024-05-17 08:59:11 UTC”
now()[1] “2025-10-07 11:27:42 BST”
now("Japan")[1] “2025-10-07 19:27:42 JST”
random_zone <- sample(OlsonNames(), 1)
cat(paste("The date-time in", random_zone, "is", now(sample(OlsonNames(), 1))))The date-time in Etc/GMT+10 is 2025-10-07 06:27:42.669919
Parsing
-
as_date()is fine assuming you have your date as a number of days - but usually, we’ll need to parse our dates
as_date(45429) # excel-format 1900 date[1] “2094-05-19”
as_date(45429 - 25569) # dirty but effective[1] “2024-05-17”
as_date(45429, origin = "1899-12-30") # better[1] “2024-05-17”
Parsing
- more often, we’ll be taking human-readable dates and parsing them
- that’s a pain, because there are loads of inconsistent ways of representing dates
- worse, lots of dates are ambiguous (5/6/24 and 6/5/24 might refer to the same day)
date_input <- c("17/5/24", "2024-05-17", "Friday 17th May 2024", "17*May*24", "5/17/2024" )
as_date(date_input) # as_date expects ISO-8601ish dates[1] “2017-05-24” “2024-05-17” NA “2017-05-24” NA
# one correct, two silently incorrent, two NAsparse_date_time
parse_date_time(date_input, orders = "ymd")[1] “2017-05-24 UTC” “2024-05-17 UTC” NA “2017-05-24 UTC” [5] NA
parse_date_time(date_input, orders = c("dmy", "ymd", "dmy", "dmy", "mdy"))[1] “2024-05-17 UTC” “2024-05-17 UTC” “2024-05-17 UTC” “2024-05-17 UTC” [5] “2024-05-17 UTC”
dmy and co
- you can also use the orders (like dmy) as standalone parsing functions:
[1] “2024-05-17” “2024-05-17” “2024-05-17”
ymd_hms("2024-05/17 9-05-01")[1] “2024-05-17 09:05:01 UTC”
So you can make dates/date-times. So what?
[1] “2025-10-07”
[1] 2025
[1] FALSE
[1] 4
[1] 2
[1] 2025.2
Months and weeks
[1] 10
[1] Oct 12 Levels: Jan < Feb < Mar < Apr < May < Jun < Jul < Aug < Sep < … < Dec
[1] 40
epiweek(today()) # special ways of counting weeks. See https://en.wikipedia.org/wiki/ISO_week_date and https://www.cmmcp.org/mosquito-surveillance-data/pages/epi-week-calendars-2008-2024[1] 41
Days
[1] 7
[1] 3
[1] 7
Hour and minute
[1] 11
[1] 27
[1] TRUE
[1] TRUE
Set
[1] “2025-10-07 11:00:00 BST”
- or, more generally:
[1] 5
day(test_date) <- 11
test_date[1] “2023-06-11”
Round
floor_date(today(), unit = "week")[1] “2025-10-05”
round_date(today(), unit = "week")[1] “2025-10-05”
ceiling_date(today(), unit = "month")[1] “2025-11-01”
[1] “2025-09-30”