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

The `lubridate` package

The lubridate package is a very useful package for dealing with all the little annoying aspects of dates/times
Largely replaces the default date/time functions in base R
Methods for date/time arithmetic
Handles time zones, leap year, leap seconds, etc.

install.packages("lubridate")
## Not part of `tidyverse` package

Dates in R

Dates are represented by the Date class and can be coerced from a character string using the ymd() function.

library(lubridate)
x <- ymd("1970-01-01")
x

[1] "1970-01-01"

class(x)

[1] "Date"

unclass(x)

[1] 0

x <- ymd("2019-10-03")
unclass(x)

[1] 18172

Date objects have their own special print methods that will always format as “YYYY-MM-DD”.

Alternate Formulations

Different locales have different ways 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.

x <- c("2016-04-05", 
       "2016/05/06",
       "2016,10,4")
ymd(x)

[1] "2016-04-05" "2016-05-06" "2016-10-04"

Times in R

Times are represented using the POSIXct or the POSIXlt class

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
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

Times are represented as the number of seconds since 1970-01-01 00:00:00.

x <- ymd_hms("2019-10-03 13:30:00")
class(x)

[1] "POSIXct" "POSIXt"

unclass(x)

[1] 1570109400
attr(,"tzone")
[1] "UTC"

If you want to know more about the international date/time standard, you can read about ISO Standard 8601.

Inputing Time Data

Times can be coerced from a character string with ymd_hms()

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"

Time Zones!

Time zones were created to make your data analyses more difficult.

ymd_hms() function will by default use UTC as the time zone
Specifying tz = "" will use the local time zone
Better to specify time zone when possible to avoid ambiguity

You can go to Wikipedia to find the list of time zones

Daylight savings time
Some states are in two time zones
Southern hemisphere is opposite

Specifying Times in R

Finally, there is the strptime() function in case your dates are written in a different format

datestring <- c("January 10, 2012 10:40", 
                "December 9, 2011 9:10")
x <- strptime(datestring, "%B %d, %Y %H:%M", 
              tz = "America/Los_Angeles")
x

[1] "2012-01-10 10:40:00 PST" "2011-12-09 09:10:00 PST"

Check ?strptime for details of formatting strings
When reading in data with read_csv(), you may need to read in as character first and then convert to date/time

Operations on Dates and Times

Arithmetic

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

Time difference of 356.5178 days

x + y  ## Nope!

Error in `+.POSIXt`(x, y): binary '+' is not defined for "POSIXt" objects

Add a second to a time

y + 1

[1] "2011-01-09 11:34:22 EST"

Just keep the date portion

y <- date(y)
y

[1] "2011-01-09"

Add a number to the date (in this case 1 day)

y + 1

[1] "2011-01-10"

Leaps and Bounds

Even keeps track of leap years, leap seconds, daylight savings, and time zones.

Leap years

x <- ymd("2012-03-01")
y <- ymd("2012-02-28")
x - y

Time difference of 2 days

Beware of time zones!

x <- ymd_hms("2012-10-25 01:00:00", tz = "")
y <- ymd_hms("2012-10-25 06:00:00", tz = "GMT")
y - x

Time difference of 1 hours

There are also leap seconds.

.leap.seconds

 [1] "1972-06-30 20:00:00 EDT" "1972-12-31 19:00:00 EST"
 [3] "1973-12-31 19:00:00 EST" "1974-12-31 19:00:00 EST"
 [5] "1975-12-31 19:00:00 EST" "1976-12-31 19:00:00 EST"
 [7] "1977-12-31 19:00:00 EST" "1978-12-31 19:00:00 EST"
 [9] "1979-12-31 19:00:00 EST" "1981-06-30 20:00:00 EDT"
[11] "1982-06-30 20:00:00 EDT" "1983-06-30 20:00:00 EDT"
[13] "1985-06-30 20:00:00 EDT" "1987-12-31 19:00:00 EST"
[15] "1989-12-31 19:00:00 EST" "1990-12-31 19:00:00 EST"
[17] "1992-06-30 20:00:00 EDT" "1993-06-30 20:00:00 EDT"
[19] "1994-06-30 20:00:00 EDT" "1995-12-31 19:00:00 EST"
[21] "1997-06-30 20:00:00 EDT" "1998-12-31 19:00:00 EST"
[23] "2005-12-31 19:00:00 EST" "2008-12-31 19:00:00 EST"
[25] "2012-06-30 20:00:00 EDT" "2015-06-30 20:00:00 EDT"
[27] "2016-12-31 19:00:00 EST"

Extracting Elements of Dates/Times

There are a set of helper functions in lubridate that can extract sub-elements of dates/times

Date Elements

x <- ymd_hms(c("2012-10-25 01:13:46",
               "2015-04-23 15:11:23"), tz = "")
year(x)

[1] 2012 2015

month(x)

[1] 10  4

day(x)

[1] 25 23

weekdays(x)

[1] "Thursday" "Thursday"

Time Elements

x <- ymd_hms(c("2012-10-25 01:13:46",
               "2015-04-23 15:11:23"), tz = "")
minute(x)

[1] 13 11

second(x)

[1] 46 23

hour(x)

[1]  1 15

week(x)

[1] 43 17

Visualizing Dates

Reading in the Data

library(readr)
storm <- read_csv("../data/storm_events_2002.csv.gz", progress = FALSE)
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 and DEATHS_DIRECT variables

library(dplyr)
select(storm, BEGIN_DATE_TIME, EVENT_TYPE, DEATHS_DIRECT)

# A tibble: 52,956 x 3
  BEGIN_DATE_TIME    EVENT_TYPE               DEATHS_DIRECT
  <chr>              <chr>                            <int>
1 03-JUL-03 21:30:00 Thunderstorm Wind                    0
2 04-JUL-03 08:35:00 Marine Thunderstorm Wind             0
3 04-JUL-03 08:35:00 Marine Thunderstorm Wind             0
4 11-AUG-03 16:33:00 Thunderstorm Wind                    0
5 11-AUG-03 18:00:00 Hail                                 0
# ... with 5.295e+04 more rows

We can first convert the date/time to a date/time R object.

storm_sub <- select(storm, BEGIN_DATE_TIME, EVENT_TYPE, DEATHS_DIRECT) %>%
  mutate(begin = dmy_hms(BEGIN_DATE_TIME)) %>%
  rename(type = EVENT_TYPE,
         deaths = DEATHS_DIRECT) %>%
  select(begin, type, deaths)
storm_sub

# A tibble: 52,956 x 3
  begin               type                     deaths
  <dttm>              <chr>                     <int>
1 2003-07-03 21:30:00 Thunderstorm Wind             0
2 2003-07-04 08:35:00 Marine Thunderstorm Wind      0
3 2003-07-04 08:35:00 Marine Thunderstorm Wind      0
4 2003-08-11 16:33:00 Thunderstorm Wind             0
5 2003-08-11 18:00:00 Hail                          0
# ... with 5.295e+04 more rows

Histograms of Dates/Times

We can make a histogram of the dates/times to get a sense of when storm events occur.

library(ggplot2)
storm_sub %>%
  ggplot(aes(x = begin)) + 
  geom_histogram(bins = 20) + 
  theme_bw()

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))

Scatterplots of Dates/Times

storm_sub %>%
  ggplot(aes(begin, 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.

storm_sub %>%
  filter(month(begin) == 6, day(begin) == 16) %>%
  ggplot(aes(begin, deaths)) + 
  geom_point()

Summary

Dates and times have special classes in R that allow for numerical and statistical calculations
Dates use the Date class
Times use the POSIXct and POSIXlt class
Character strings can be coerced to Date/Time classes using the ymd() and ymd_hms() functions. In strange cases, you can use the strptime()or the as.Date() functions.
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

Dates and Times in R

October 3, 2018

The `lubridate` package

Dates in R

Alternate Formulations

Times in R

Inputing Time Data

Time Zones!

Specifying Times in R

Operations on Dates and Times

Arithmetic

Leaps and Bounds

Extracting Elements of Dates/Times

Date Elements

Time Elements

Visualizing Dates

Reading in the Data

Histograms of Dates/Times

Scatterplots of Dates/Times

Summary

Dates and Times in R

October 3, 2018

The lubridate package

Dates in R

Alternate Formulations

Times in R

Inputing Time Data

Time Zones!

Specifying Times in R

Operations on Dates and Times

Arithmetic

Leaps and Bounds

Extracting Elements of Dates/Times

Date Elements

Time Elements

Visualizing Dates

Reading in the Data

Histograms of Dates/Times

Scatterplots of Dates/Times

Summary

The `lubridate` package