Date-time data can be frustrating to work with in R. R commands for date-times are generally unintuitive and change depending on the type of date-time object being used. Moreover, the methods we use with date-times must be robust to time zones, leap days, daylight savings times, and other time related quirks, and R lacks these capabilities in some situations. Lubridate makes it easier to do the things R does with date-times and possible to do the things R does not.
If you are new to lubridate, the best place to start is the date and times chapter in R for data science.
# The easiest way to get lubridate is to install the whole tidyverse: install.packages("tidyverse") # Alternatively, install just lubridate: install.packages("lubridate") # Or the the development version from GitHub: # install.packages("devtools") devtools::install_github("tidyverse/lubridate")
library(lubridate, warn.conflicts = FALSE)
Easy and fast parsing of date-times:
``` r ymd(20101215)
Simple functions to get and set components of a date-time, such as
``` r bday <- dmy("14/10/1979") month(bday)
wday(bday, label = TRUE)
year(bday) <- 2016 wday(bday, label = TRUE)
Helper functions for handling time zones:
``` r time <- ymd_hms("2010-12-13 15:30:30") time
Lubridate also expands the type of mathematical operations that can be performed with date-time objects. It introduces three new time span classes borrowed from https://www.joda.org.
durations, which measure the exact amount of time between two
periods, which accurately track clock times despite leap years,
leap seconds, and day light savings time
intervals, a protean summary of the time information between two
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