Introduction to lubridate for dates and times in R.
Before class, you can prepare by reading the following materials:
Material for this lecture was borrowed and adopted from
At the end of this lesson you will:
Date
, POSIXct
and POSIXlt
class types in R to represent dates and timeslubridate
packageIn this lesson, we will learn how to work with dates and times in R. These may seem simple as you use them all of the time in your day-to-day life, but the more you work with them, the more complicated they seem to get.
Dates and times are hard because they have to reconcile two physical phenomena (the rotation of the Earth and its orbit around the sun) with a whole raft of geopolitical phenomena including months, time zones, and daylight savings time (DST).
This lesson will not teach you every last detail about dates and times, but it will give you a solid grounding of practical skills that will help you with common data analysis challenges.
R has developed a special representation of dates and times
Dates are represented by the Date
class
Times are represented by the POSIXct
or the POSIXlt
class
Dates are stored internally as the number of days since 1970-01-01
Times are stored internally as the number of seconds since 1970-01-01
lubridate
packageHere, we will focus on the lubridate
R package, which makes it easier to work with dates and times in R.
check out the cheat sheet at https://lubridate.tidyverse.org
A few things to note about it:
It largely replaces the default date/time functions in base R
It contains methods for date/time arithmetic
It handles time zones, leap year, leap seconds, etc.
lubridate
is installed when you install tidyverse
, but it is not loaded when you load tidyverse
. Alternatively, you can install it separately.
install.packages("lubridate")
There are three types of date/time data that refer to an instant in time:
A date. Tibbles print this as <date>
.
A time within a day. Tibbles print this as <time>
.
A date-time is a date plus a time: it uniquely identifies an instant in time (typically to the nearest second). Tibbles print this as <dttm>
. Elsewhere in R these are called POSIXct
.
In this lesson, we will focus on dates and date-times as R does not have a native class for storing times. If you need one, you can use the hms
package.
You should always use the simplest possible data type that works for your needs. That means if you can use a date instead of a date-time, you should. Date-times are substantially more complicated because of the need to handle time zones, which we’ll come back to at the end of the chapter.
To get the current date or date-time you can use today()
or now()
from lubridate
:
Otherwise, there are three ways you are likely to create a date/time:
They work as follows.
Dates are of the Date
class.
Dates can be coerced from a character strings using some helper functions from lubridate
. They automatically work out the format once you specify the order of the component.
To use the helper functions, identify the order in which year, month, and day appear in your dates, then arrange “y”, “m”, and “d” in the same order.
That gives you the name of the lubridate
function that will parse your date. For example:
ymd("1970-01-01")
[1] "1970-01-01"
ymd("2017-01-31")
[1] "2017-01-31"
mdy("January 31st, 2017")
[1] "2017-01-31"
dmy("31-Jan-2017")
[1] "2017-01-31"
Pro tips:
When reading in data with read_csv()
, you may need to read in as character first and then convert to date/time
Date
objects have their own special print()
methods that will always format as “YYYY-MM-DD”
These functions also take unquoted numbers.
ymd(20170131)
[1] "2017-01-31"
Different locales have different ways of formatting dates
ymd("2016-09-13") ## International standard
[1] "2016-09-13"
ymd("2016/09/13") ## Just figure it out
[1] "2016-09-13"
mdy("09-13-2016") ## Mostly U.S.
[1] "2016-09-13"
dmy("13-09-2016") ## Europe
[1] "2016-09-13"
All of the above are valid and lead to the exact same object.
Even if the individual dates are formatted differently, ymd()
can usually figure it out.
Sometimes the date components will come across multiple columns in a dataset.
library(nycflights13)
flights %>%
select(year, month, day)
# A tibble: 336,776 × 3
year month day
<int> <int> <int>
1 2013 1 1
2 2013 1 1
3 2013 1 1
4 2013 1 1
5 2013 1 1
6 2013 1 1
7 2013 1 1
8 2013 1 1
9 2013 1 1
10 2013 1 1
# … with 336,766 more rows
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) %>%
mutate(departure = make_date(year, month, day))
# A tibble: 336,776 × 4
year month day departure
<int> <int> <int> <date>
1 2013 1 1 2013-01-01
2 2013 1 1 2013-01-01
3 2013 1 1 2013-01-01
4 2013 1 1 2013-01-01
5 2013 1 1 2013-01-01
6 2013 1 1 2013-01-01
7 2013 1 1 2013-01-01
8 2013 1 1 2013-01-01
9 2013 1 1 2013-01-01
10 2013 1 1 2013-01-01
# … with 336,766 more rows
The flights
also contains a hour
and minute
column. Use make_datetime()
to create a date-time column called departure
:
# try it yourself
You may want to switch between a date-time and a date. That is the job of as_datetime()
and as_date()
:
today()
[1] "2021-10-05"
as_datetime(today())
[1] "2021-10-05 UTC"
now()
[1] "2021-10-05 15:01:23 EDT"
[1] "2021-10-05"
ymd()
and friends create dates. To create a date-time
from a character string, add an underscore and one or more of “h”, “m”, and “s” to the name of the parsing function:
Times can be coerced from a character string with ymd_hms()
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"
You can also force the creation of a date-time from a date by supplying a timezone:
ymd_hms("2016-09-13 14:00:00")
[1] "2016-09-13 14:00:00 UTC"
ymd_hms("2016-09-13 14:00:00", tz = "America/New_York")
[1] "2016-09-13 14:00:00 EDT"
ymd_hms("2016-09-13 14:00:00", tz = "")
[1] "2016-09-13 14:00:00 EDT"
POSIXct
or the POSIXlt
classLet’s get into some hairy details about date-times. Date-times are represented using the POSIXct
or the POSIXlt
class in R. What are these things?
POSIXct
POSIXct
is just a very large integer under the hood. It is a useful class when you want to store times in something like a data frame.
Technically, the POSIXct
class represents the number of seconds since 1 January 1970. (In case you were wondering, “POSIXct” stands for “Portable Operating System Interface”, calendar time.)
[1] "POSIXct" "POSIXt"
unclass(x)
[1] 3600
attr(,"tzone")
[1] "UTC"
typeof(x)
[1] "double"
attributes(x)
$class
[1] "POSIXct" "POSIXt"
$tzone
[1] "UTC"
POSIXlt
POSIXlt
is a list
underneath and it stores a bunch of other useful information like the day of the week, day of the year, month, day of the month
y <- as.POSIXlt(x)
typeof(y)
[1] "list"
attributes(y)
$names
[1] "sec" "min" "hour" "mday" "mon" "year" "wday" "yday"
[9] "isdst"
$class
[1] "POSIXlt" "POSIXt"
$tzone
[1] "UTC"
Pro tips: POSIXlt
s are rare inside the tidyverse. They do crop up in base R, because they are needed to extract specific components of a date, like the year or month.
Since lubridate
provides helpers for you to do this instead, you do not really need them imho.
POSIXct
’s are always easier to work with, so if you find you have a POSIXlt
, you should always convert it to a regular data time lubridate::as_datetime()
.
Time zones were created to make your data analyses more difficult as a data analyst. Here are a few fun things to think about:
ymd_hms()
function will by default use Coordinated Universal Time (UTC) as the time zone. UTC is the primary time standard by which the world regulates clocks and time.You can go to Wikipedia to find the list of time zones
tz = ""
in one of the ymd()
and friends functions will use the local time zonex <- ymd_hm("1970-01-01 01:00", tz = "")
x
[1] "1970-01-01 01:00:00 EST"
attributes(x)
$class
[1] "POSIXct" "POSIXt"
$tzone
[1] ""
Pro tip: The tzone
attribute is optional. It controls how the time is printed, not what absolute time it refers to.
A few other fun things to think about related to time zones:
Almost always better to specify time zone when possible to avoid ambiguity
Daylight savings time (DST)
Some states are in two time zones
Southern hemisphere is opposite
You can add and subtract dates and times. You can do comparisons too (i.e. ==
, <=
)
x <- ymd("2012-01-01", tz = "") ## Midnight
y <- dmy_hms("9 Jan 2011 11:34:21", tz = "")
x - y ## this works
Time difference of 356.5178 days
x < y ## this works
[1] FALSE
x > y ## this works
[1] TRUE
x == y ## this works
[1] FALSE
x + y ## what??? why does this not work?
Error in `+.POSIXt`(x, y): binary '+' is not defined for "POSIXt" objects
Remember: POSIXct
objects are a measure of seconds from an origin, usually the UNIX epoch (1st Jan 1970).
Just add the requisite number of seconds to the object:
x + 3*60*60 # add 3 hours
[1] "2012-01-01 03:00:00 EST"
x
[1] "2012-01-01 EST"
Same goes for days. For example, you can just keep the date portion using date()
:
y <- date(y)
y
[1] "2011-01-09"
And then add a number to the date (in this case 1 day)
y + 1
[1] "2011-01-10"
Cool eh?
Even keeps track of leap years, leap seconds, daylight savings, and time zones.
Leap years
Not a leap year
BUT beware of time zones!
Time difference of 0 secs
There are also things called leap seconds.
.leap.seconds
[1] "1972-07-01 GMT" "1973-01-01 GMT" "1974-01-01 GMT"
[4] "1975-01-01 GMT" "1976-01-01 GMT" "1977-01-01 GMT"
[7] "1978-01-01 GMT" "1979-01-01 GMT" "1980-01-01 GMT"
[10] "1981-07-01 GMT" "1982-07-01 GMT" "1983-07-01 GMT"
[13] "1985-07-01 GMT" "1988-01-01 GMT" "1990-01-01 GMT"
[16] "1991-01-01 GMT" "1992-07-01 GMT" "1993-07-01 GMT"
[19] "1994-07-01 GMT" "1996-01-01 GMT" "1997-07-01 GMT"
[22] "1999-01-01 GMT" "2006-01-01 GMT" "2009-01-01 GMT"
[25] "2012-07-01 GMT" "2015-07-01 GMT" "2017-01-01 GMT"
There are a set of helper functions in lubridate
that can extract sub-elements of dates/times
library(here)
library(readr)
storm <- read_csv(here("data", "storms_2004.csv.gz"), progress = FALSE)
storm
# A tibble: 52,409 × 51
BEGIN_YEARMONTH BEGIN_DAY BEGIN_TIME END_YEARMONTH END_DAY END_TIME
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 200412 29 1800 200412 30 1200
2 200412 29 1800 200412 30 1200
3 200412 8 1800 200412 8 1800
4 200412 19 1500 200412 19 1700
5 200412 14 600 200412 14 800
6 200412 21 400 200412 21 800
7 200412 21 400 200412 21 800
8 200412 26 1500 200412 27 800
9 200412 26 1500 200412 27 800
10 200412 11 800 200412 11 1300
# … with 52,399 more rows, and 45 more variables: EPISODE_ID <dbl>,
# EVENT_ID <dbl>, STATE <chr>, STATE_FIPS <dbl>, YEAR <dbl>,
# MONTH_NAME <chr>, EVENT_TYPE <chr>, CZ_TYPE <chr>, CZ_FIPS <dbl>,
# CZ_NAME <chr>, WFO <chr>, BEGIN_DATE_TIME <chr>,
# CZ_TIMEZONE <chr>, END_DATE_TIME <chr>, INJURIES_DIRECT <dbl>,
# INJURIES_INDIRECT <dbl>, DEATHS_DIRECT <dbl>,
# DEATHS_INDIRECT <dbl>, DAMAGE_PROPERTY <chr>, …
names(storm)
[1] "BEGIN_YEARMONTH" "BEGIN_DAY" "BEGIN_TIME"
[4] "END_YEARMONTH" "END_DAY" "END_TIME"
[7] "EPISODE_ID" "EVENT_ID" "STATE"
[10] "STATE_FIPS" "YEAR" "MONTH_NAME"
[13] "EVENT_TYPE" "CZ_TYPE" "CZ_FIPS"
[16] "CZ_NAME" "WFO" "BEGIN_DATE_TIME"
[19] "CZ_TIMEZONE" "END_DATE_TIME" "INJURIES_DIRECT"
[22] "INJURIES_INDIRECT" "DEATHS_DIRECT" "DEATHS_INDIRECT"
[25] "DAMAGE_PROPERTY" "DAMAGE_CROPS" "SOURCE"
[28] "MAGNITUDE" "MAGNITUDE_TYPE" "FLOOD_CAUSE"
[31] "CATEGORY" "TOR_F_SCALE" "TOR_LENGTH"
[34] "TOR_WIDTH" "TOR_OTHER_WFO" "TOR_OTHER_CZ_STATE"
[37] "TOR_OTHER_CZ_FIPS" "TOR_OTHER_CZ_NAME" "BEGIN_RANGE"
[40] "BEGIN_AZIMUTH" "BEGIN_LOCATION" "END_RANGE"
[43] "END_AZIMUTH" "END_LOCATION" "BEGIN_LAT"
[46] "BEGIN_LON" "END_LAT" "END_LON"
[49] "EPISODE_NARRATIVE" "EVENT_NARRATIVE" "DATA_SOURCE"
Let’s take a look at the BEGIN_DATE_TIME
, EVENT_TYPE
, and DEATHS_DIRECT
variables. Try to convert the BEGIN_DATE_TIME
date/time column to a date/time R object.
library(dplyr)
storm %>%
select(BEGIN_DATE_TIME, EVENT_TYPE, DEATHS_DIRECT) %>%
mutate(date = dmy_hms(BEGIN_DATE_TIME))
# A tibble: 52,409 × 4
BEGIN_DATE_TIME EVENT_TYPE DEATHS_DIRECT date
<chr> <chr> <dbl> <dttm>
1 29-DEC-04 18:00:00 Heavy Snow 0 2004-12-29 18:00:00
2 29-DEC-04 18:00:00 Heavy Snow 0 2004-12-29 18:00:00
3 08-DEC-04 18:00:00 Winter Storm 0 2004-12-08 18:00:00
4 19-DEC-04 15:00:00 High Wind 0 2004-12-19 15:00:00
5 14-DEC-04 06:00:00 Winter Weather 0 2004-12-14 06:00:00
6 21-DEC-04 04:00:00 Winter Storm 0 2004-12-21 04:00:00
7 21-DEC-04 04:00:00 Winter Storm 0 2004-12-21 04:00:00
8 26-DEC-04 15:00:00 Winter Storm 0 2004-12-26 15:00:00
9 26-DEC-04 15:00:00 Winter Storm 0 2004-12-26 15:00:00
10 11-DEC-04 08:00:00 Storm Surge/Tide 0 2004-12-11 08:00:00
# … with 52,399 more rows
# try it yourself
Next, we do some wrangling to create a storm_sub
data frame (code chunk set to echo=FALSE
for the purposes of the lecture, but code is in the R Markdown).
storm_sub
# A tibble: 52,409 × 3
begin type deaths
<dttm> <chr> <dbl>
1 2004-12-29 18:00:00 Heavy Snow 0
2 2004-12-29 18:00:00 Heavy Snow 0
3 2004-12-08 18:00:00 Winter Storm 0
4 2004-12-19 15:00:00 High Wind 0
5 2004-12-14 06:00:00 Winter Weather 0
6 2004-12-21 04:00:00 Winter Storm 0
7 2004-12-21 04:00:00 Winter Storm 0
8 2004-12-26 15:00:00 Winter Storm 0
9 2004-12-26 15:00:00 Winter Storm 0
10 2004-12-11 08:00:00 Storm Surge/Tide 0
# … with 52,399 more rows
We can make a histogram of the dates/times to get a sense of when storm events occur.
We can group by event type too.
library(ggplot2)
storm_sub %>%
ggplot(aes(x = begin)) +
facet_wrap(~ type) +
geom_histogram(bins = 20) +
theme_bw() +
theme(axis.text.x.bottom = element_text(angle = 90))
storm_sub %>%
ggplot(aes(x = begin, y = deaths)) +
geom_point()
If we focus on a single month, the x-axis adapts.
storm_sub %>%
filter(month(begin) == 6) %>%
ggplot(aes(begin, deaths)) +
geom_point()
Similarly, we can focus on a single day.
Dates and times have special classes in R that allow for numerical and statistical calculations
Dates use the Date
class
Date-Times (and Times) use the POSIXct
and POSIXlt
class
Character strings can be coerced to Date/Time classes using the ymd()
and ymd_hms()
functions and friends.
The lubridate
package is essential for manipulating date/time data
Both plot
and ggplot
“know” about dates and times and will handle axis labels appropriately.
Here are some post-lecture questions to help you think about the material discussed.
Questions:
tzone
argument to today()
do? Why is it important?lubridate
function to parse each of the following dates:d1 <- "January 1, 2010"
d2 <- "2015-Mar-07"
d3 <- "06-Jun-2017"
d4 <- c("August 19 (2015)", "July 1 (2015)")
d5 <- "12/30/14" # Dec 30, 20
Using the flights
dataset, how does the distribution of flight times within a day change over the course of the year?
Compare dep_time
, sched_dep_time
and dep_delay
. Are they consistent? Explain your findings.
Text and figures are licensed under Creative Commons Attribution CC BY-NC-SA 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Hicks (2021, Oct. 5). Statistical Computing: Working with dates and times. Retrieved from https://stephaniehicks.com/jhustatcomputing2021/posts/2021-10-05-working-with-dates-and-times/
BibTeX citation
@misc{hicks2021working, author = {Hicks, Stephanie}, title = {Statistical Computing: Working with dates and times}, url = {https://stephaniehicks.com/jhustatcomputing2021/posts/2021-10-05-working-with-dates-and-times/}, year = {2021} }