Dates and Times in R Without Losing Your Sanity

Working with dates and times in R can be frustrating! This isn’t R’s fault – dates and times are naturally complicated. One must consider time zones, leap years, leap seconds, Daylight Savings, hundreds of potential date and time formats, and other quirky complexities. The goal of this article is to give you the tools and knowledge to deal with dates and times in R so you can avoid common mistakes, saving your hair and extending your lifespan by a month or two.


The easiest way to make a date in R is to use as.Date(x) where x is some date in yyyy-mm-dd format. For example,

mydate <- as.Date("2018-01-01")
[1] "2018-01-01"
[1] "Date"

Similarly, we can make a vector of dates by passing a vector of characters to as.Date()

mydates <- as.Date(c("2018-01-01", "2018-05-31"))
[1] "2018-01-01" "2018-05-31"

Perhaps the hardest, most common issue that arises when dealing with dates is that they often come in weird or cryptic formats. In these cases, you’ll need to tell as.Date() the format of the raw date values so that R can convert them into the canonical yyyy-mm-dd format. For example

# year month day without spaces
ugh <- c("20180205", "20170303")
as.Date(ugh, format = "%Y%m%d")
[1] "2018-02-05" "2017-03-03"

# month day year with slashes
really <- c("2/5/18", "3/3/18")
as.Date(really, format = "%m/%d/%y")
[1] "2018-02-05" "2018-03-03"

# day month year with underscores
whywouldsomeonedothis <- c("5_2_18", "3_3_18")
as.Date(whywouldsomeonedothis, format = "%d_%m_%y")
[1] "2018-02-05" "2018-03-03"

# month day year with full month names
annoying <- c("February 5, 2018", "March 3, 2018")
as.Date(annoying, format = "%B %d, %Y")
[1] "2018-02-05" "2018-03-03"

You might be wondering how I figured out those format strings. The key here is to read R’s documentation for strptime() (see ?strptime). In it, you’ll find all the special conversion specifications that tell R what to look for. Here’s a snippet from the docs

Let’s pause and consider what a date in R actually is. Consider

[1] 17532

That’s right – you can cast a date to numeric. That’s because internally, R stores a date as “number of days since January 1st, 1970”.

Now that we know how to make dates in R, let’s learn how to do stuff with them.

Add or subtract days from a date

mydate <- as.Date("2018-01-01")
mydate + 1
[1] "2018-01-02"
mydate - 7
[1] "2017-12-25"
mydate + c(0,1,2,3,4,5,6)
[1] "2018-01-01" "2018-01-02" "2018-01-03" "2018-01-04" "2018-01-05" "2018-01-06" "2018-01-07"

Get the time between two dates

d1 <- as.Date("2017-01-01")
d2 <- as.Date("2018-01-01")
d2 - d1
Time difference of 365 days
difftime(d2, d1, units = "day")
Time difference of 365 days
difftime(d2, d1, units = "hour")
Time difference of 8760 hours

Determine the weekday of a date

mydate <- as.Date("2018-01-01")
[1] "Monday"

Change the format of a date

mydate <- Sys.Date()  # Get today's date
myprettydate <- format(mydate, format = "%B %d, %Y")

[1] "2018-04-27"
[1] "April 27, 2018"

Note however that myprettydate is type character, not Date.

[1] "Date"
[1] "character"


The lubridate package in R let’s us do a lot of other cool things with dates. Let’s give it a go.

# install.packages(lubridate)

Extract the day of month and month of year

[1] 27
[1] 4

Adding and Subtracting Months

Conceptually this is a tricky topic. What does it mean to “add one month” to a date? For example, I think we’d all agree that one month after 2018-01-01 is 2018-02-01. But what’s one month after 2018-01-31?

Base R provides a very handy seq.Date() method which can be used to create a sequence of dates that differ by days, months, years, etc. For example if we create a sequence of dates that differ by one month starting from 2018-01-31, we get

seq.Date(as.Date("2018-01-31"), length.out = 3, by = "month")
[1] "2018-01-31" "2018-03-03" "2018-03-31"

In this case R is literally adding 1 to the month component of each date so that the resulting (attempted) dates are 2018-01-31, 2018-02-31, 2018-03-31, and 2018-04-31. Obviously dates like 2018-02-31 and 2018-04-31 don’t exist, so R counts the difference between the day component and the last valid date in each month, and then adds that amount to the last valid month to get a valid date. For example, 2018-02-31 is (in a sense) 3 days after 2018-02-28, so R resolves the date to 2018-03-03.

Alternatively, lubridate provides functionality for adding months to a date that behaves slightly differently.

as.Date("2018-01-31") %m+% months(c(0,1,2))
[1] "2018-01-31" "2018-02-28" "2018-03-31"

In this case, 2018-02-31 resolves to 2018-02-28 so that the month component of the result is truly one month ahead of 2018-01-31.


Now that we’ve discussed dates, let’s move on to the hard stuff – datetimes. Let’s start by making a datetime from scratch.

mydatetime <- as.POSIXct("2018-01-01 05:30:00")
[1] "2018-01-01 05:30:00 CST"

Notice that R prints mydatetime with the timezone CST. That’s because my operating system was set up with the America/Chicago timezone (I’m based in New Orleans) and R is assuming it’s the timezone I want. (Use Sys.timezone() to see what timezone your OS is using.) If we want to make a datetime with a specific timezone, we can use the tz argument of as.POSIXct(). For example

dtTokyo <- as.POSIXct("2018-01-01 5:00:00", tz = "Asia/Tokyo")
dtCalifornia <- as.POSIXct("2018-01-01 03:30:00", tz = "America/Los_Angeles")
dtSydney <- as.POSIXct("2018-01-01 22:30:00", tz = "Australia/Sydney")

For a list of timezones you can plug into the tz argument, see this Wikipedia article.

Note that dtCalifornia and dtSydney are actually “the same” meaning, if you were in LA at 3:30 on 2018-01-01 and you called your friend in Sydney, your friend’s clock would show 22:30 (10:30 PM).

dtCalifornia == dtSydney
[1] TRUE

In R, a vector of datetimes must all have the same timezone attribute. So, if you want to store the above datetimes together and retain the timezone information, the best approach is to store them in a dataframe with a DateTime column and a Timezone column like so:

df <- data.frame(
+   DateTime = c(dtTokyo, dtCalifornia, dtSydney),
+   Timezone = c(attr(dtTokyo, "tzone"), attr(dtCalifornia, "tzone"), attr(dtSydney, "tzone")),
+   stringsAsFactors = F
+ )
             DateTime            Timezone
1 2017-12-31 14:00:00          Asia/Tokyo
2 2018-01-01 05:30:00 America/Los_Angeles
3 2018-01-01 05:30:00    Australia/Sydney

Notice that R actually converts the DateTime column to “America/Chicago” time (my local timezone). While this might be nice for me, my colleagues in California and New York might not appreciate it. Instead, a better approach is to store the datetimes in Univeral Coordinated Time (UTC). UTC is like a reference point for which all other timezone representations are based upon.

[1] "2017-12-31 14:00:00 CST" "2018-01-01 05:30:00 CST" "2018-01-01 05:30:00 CST"
attr(df$DateTime, "tzone") <- "UTC"
[1] "2017-12-31 20:00:00 UTC" "2018-01-01 11:30:00 UTC" "2018-01-01 11:30:00 UTC"

The important takeaway here is that, we are NOT altering the datetime values. We are simply altering the way those datetimes are displayed. With that said, the timezone information is still important. For example, suppose we want to add a column to our dataframe called Date indicating the date of each instance.

df$Date <- as.Date(df$DateTime)
             DateTime            Timezone       Date
1 2017-12-31 20:00:00          Asia/Tokyo 2017-12-31
2 2018-01-01 11:30:00 America/Los_Angeles 2018-01-01
3 2018-01-01 11:30:00    Australia/Sydney 2018-01-01

The above is probably NOT what we want. While those dates are correct in reference to UTC time, the 1st row for Tokyo shows Date 2017-12-31. However, in Tokyo, that date would actually be recognized as 2018-01-01. Fortunately as.Date() has a tz parameter where we can specify a timezone, although it’s not vectorized so we have to apply it row-by-row.

dats <- mapply(FUN = function(datetime, timezone) as.Date(datetime, tz = timezone), df$DateTime, df$Timezone)
dats <- as.Date(dats, origin = "1970-01-01")
df$Date <- dats
             DateTime            Timezone       Date
1 2017-12-31 20:00:00          Asia/Tokyo 2018-01-01
2 2018-01-01 11:30:00 America/Los_Angeles 2018-01-01
3 2018-01-01 11:30:00    Australia/Sydney 2018-01-01
# dats <- as.Date(dats, origin = "1970-01-01") is necessary here because mapply() resolves the dates to their numeric values.

In practice, I would typically do this with the data.table package which is much faster for large datasets.

dt <-
dt[, Date := as.Date(DateTime, tz = Timezone[1L]), by = Timezone]
              DateTime            Timezone       Date
1: 2017-12-31 20:00:00          Asia/Tokyo 2018-01-01
2: 2018-01-01 11:30:00 America/Los_Angeles 2018-01-01
3: 2018-01-01 11:30:00    Australia/Sydney 2018-01-01

Daylight savings can pain when working with datetimes. Consider this weird behavior… In case you weren’t aware, the beginning of Daylight Savings in US Central skips 2AM.

seq.POSIXt(from = as.POSIXct("2018-03-11 01:00:00", tz = "America/Chicago"), length.out = 6, by = "15 mins")
[1] "2018-03-11 01:00:00 CST" "2018-03-11 01:15:00 CST" "2018-03-11 01:30:00 CST" "2018-03-11 01:45:00 CST"
[5] "2018-03-11 03:00:00 CDT" "2018-03-11 03:15:00 CDT"

Similarly the end of Daylight Savings repeats 1AM.

seq.POSIXt(from = as.POSIXct("2018-11-04 01:00:00", tz = "America/Chicago"), length.out = 6, by = "15 mins")
[1] "2018-11-04 01:00:00 CDT" "2018-11-04 01:15:00 CDT" "2018-11-04 01:30:00 CDT" "2018-11-04 01:45:00 CDT"
[5] "2018-11-04 01:00:00 CST" "2018-11-04 01:15:00 CST"

With these things in mind, take note of how R handles the following:

as.POSIXct("2018-03-11 02:15:00", tz = "America/Chicago")  # Turns into 12:00AM
[1] "2018-03-11 CST"
as.POSIXct("2018-11-04 01:15:00", tz = "America/Chicago")
[1] "2018-11-04 01:15:00 CDT"
as.POSIXct("2018-11-04 01:15:00", tz = "America/Chicago") + 60*60  # Add 1 hour (60*60 seconds)
[1] "2018-11-04 01:15:00 CST"

To make things more confusing, not all regions observe Daylight Savings. Some regions do a half-hour rollback and others do a 45-minute rollback. Due to the whacky and inconsistent behavior of datetimes, it’s often easiest to work with UTC if possible. (UTC does not observe daylight savings.)

Lastly, let’s take a look at some common and useful tasks involving datetimes.

Formatting (see ?strptime)

mydatetime <- Sys.time()
format(mydatetime, "%Y-%m-%d, %I:%M %p", usetz = T)
[1] "2018-04-27, 12:42 PM CDT"
format(mydatetime, "%I:%M %p", usetz = T)
[1] "12:42 PM CDT"
format(mydatetime, "%I:%M:%S %p", usetz = T)
[1] "12:42:04 PM CDT"

Adding time

mydatetime <- as.POSIXct("2018-11-04 00:00:00", tz = "America/Chicago")
mydatetime + 60  # add 60 seconds
[1] "2018-11-04 00:01:00 CDT"
mydatetime + 24*60*60  # add 24 hours (NOTE this is the end of Daylight Savings)
[1] "2018-11-04 23:00:00 CST"
mydatetime + lubridate::days(1)  # add 1 day
[1] "2018-11-05 CST"

Note that leap seconds exist, but R’s POSIXct class ignores them.

Create a sequence of datetimes

mydatetime <- as.POSIXct("2018-01-01 00:00:00", tz = "UTC")
seq.POSIXt(from = mydatetime, length.out = 3, by = "5 mins")
[1] "2018-01-01 00:00:00 UTC" "2018-01-01 00:05:00 UTC" "2018-01-01 00:10:00 UTC"

Rounding datetimes

mydatetime <- as.POSIXct("2018-01-01 12:35:00", tz = "UTC")
round_date(mydatetime, unit = "hour")  # nearest hour
[1] "2018-01-01 13:00:00 UTC"
floor_date(mydatetime, unit = "hour")  # nearest hour not ahead
[1] "2018-01-01 12:00:00 UTC"
ceiling_date(mydatetime, unit = "hour")  # nearest hour not behind
[1] "2018-01-01 13:00:00 UTC"

In closing, R has the tools you need to do just about anything involving dates and times. However, it’s your responsibility to think about things like leap years, Daylight Savings, timezones etc. in order to avoid common traps. Use UTC and the lubridate package often. Cheers!