69 Working with dates
Written by Mariam Walaa and last updated on 7 October 2021.
69.1 Introduction
In this lesson, you will learn how to:
- Work with dates using the
lubridatelibrary
Prerequisite skills include:
- Installing and loading packages
- Using
mutate()andsummarise()
Highlights:
69.2 Overview
Let’s start by loading the tidyverse.
Notice that lubridate is not listed as one of the libraries loaded into your R session
when you load tidyverse (which you can view in your output on your own RStudio), and
this is because it is not part of the core tidyverse so it will need to be loaded
separately.
library(lubridate)
#>
#> Attaching package: 'lubridate'
#> The following objects are masked from 'package:base':
#>
#> date, intersect, setdiff, unionTwo of the functions the lubridate library will give us is today() and now(), which
we can immediately start to use, without data or parameters.
today()
#> [1] "2022-02-08"
now()
#> [1] "2022-02-08 12:33:47 EST"You may want to use these functions today() and now() for various reasons, such as including the date and time in a file name. You can do that as follows.
69.2.1 Example
We will look at Caribou data from Alex Cooksons dataset repository for this tutorial.
glimpse(caribou)
#> Rows: 249,450
#> Columns: 7
#> $ event_id <dbl> 2259197332, 2259197333, 2259197334, 225…
#> $ animal_id <chr> "GR_C01", "GR_C01", "GR_C01", "GR_C01",…
#> $ study_site <chr> "Graham", "Graham", "Graham", "Graham",…
#> $ season <chr> "Winter", "Winter", "Winter", "Winter",…
#> $ timestamp <dttm> 2001-02-21 05:00:00, 2001-02-21 09:00:…
#> $ longitude <dbl> -122.5200, -122.5224, -122.5232, -122.5…
#> $ latitude <dbl> 56.23950, 56.23985, 56.24000, 56.23187,…We can see that the timestamp column has an associated data type ‘dttm.’ This stands for
datetime. Given that this is already the correct data type, we can extract a lot of
information from it using lubridate functions.
# extracting year
caribou %>% mutate(year = year(timestamp))
#> # A tibble: 249,450 × 8
#> event_id animal_id study_site season timestamp
#> <dbl> <chr> <chr> <chr> <dttm>
#> 1 2.26e9 GR_C01 Graham Winter 2001-02-21 05:00:00
#> 2 2.26e9 GR_C01 Graham Winter 2001-02-21 09:00:00
#> 3 2.26e9 GR_C01 Graham Winter 2001-02-21 13:00:00
#> 4 2.26e9 GR_C01 Graham Winter 2001-02-21 17:01:00
#> 5 2.26e9 GR_C01 Graham Winter 2001-02-21 21:00:00
#> 6 2.26e9 GR_C01 Graham Winter 2001-02-22 01:00:00
#> 7 2.26e9 GR_C01 Graham Winter 2001-02-22 05:00:00
#> 8 2.26e9 GR_C01 Graham Winter 2001-02-22 09:00:00
#> 9 2.26e9 GR_C01 Graham Winter 2001-02-22 13:00:00
#> 10 2.26e9 GR_C01 Graham Winter 2001-02-22 17:00:00
#> # … with 249,440 more rows, and 3 more variables:
#> # longitude <dbl>, latitude <dbl>, year <dbl>
# extracting week day
caribou %>% mutate(week_day = wday(timestamp))
#> # A tibble: 249,450 × 8
#> event_id animal_id study_site season timestamp
#> <dbl> <chr> <chr> <chr> <dttm>
#> 1 2.26e9 GR_C01 Graham Winter 2001-02-21 05:00:00
#> 2 2.26e9 GR_C01 Graham Winter 2001-02-21 09:00:00
#> 3 2.26e9 GR_C01 Graham Winter 2001-02-21 13:00:00
#> 4 2.26e9 GR_C01 Graham Winter 2001-02-21 17:01:00
#> 5 2.26e9 GR_C01 Graham Winter 2001-02-21 21:00:00
#> 6 2.26e9 GR_C01 Graham Winter 2001-02-22 01:00:00
#> 7 2.26e9 GR_C01 Graham Winter 2001-02-22 05:00:00
#> 8 2.26e9 GR_C01 Graham Winter 2001-02-22 09:00:00
#> 9 2.26e9 GR_C01 Graham Winter 2001-02-22 13:00:00
#> 10 2.26e9 GR_C01 Graham Winter 2001-02-22 17:00:00
#> # … with 249,440 more rows, and 3 more variables:
#> # longitude <dbl>, latitude <dbl>, week_day <dbl>
# extracting whether it's a leap year!
caribou %>% mutate(leap_year = leap_year(timestamp))
#> # A tibble: 249,450 × 8
#> event_id animal_id study_site season timestamp
#> <dbl> <chr> <chr> <chr> <dttm>
#> 1 2.26e9 GR_C01 Graham Winter 2001-02-21 05:00:00
#> 2 2.26e9 GR_C01 Graham Winter 2001-02-21 09:00:00
#> 3 2.26e9 GR_C01 Graham Winter 2001-02-21 13:00:00
#> 4 2.26e9 GR_C01 Graham Winter 2001-02-21 17:01:00
#> 5 2.26e9 GR_C01 Graham Winter 2001-02-21 21:00:00
#> 6 2.26e9 GR_C01 Graham Winter 2001-02-22 01:00:00
#> 7 2.26e9 GR_C01 Graham Winter 2001-02-22 05:00:00
#> 8 2.26e9 GR_C01 Graham Winter 2001-02-22 09:00:00
#> 9 2.26e9 GR_C01 Graham Winter 2001-02-22 13:00:00
#> 10 2.26e9 GR_C01 Graham Winter 2001-02-22 17:00:00
#> # … with 249,440 more rows, and 3 more variables:
#> # longitude <dbl>, latitude <dbl>, leap_year <lgl>You can see there are many additional variables we can extract with the lubridate
package. You can check out the lubridate
cheatsheet to
learn more.
69.3 Exercises
69.3.2 Exercise 2
Find the earliest and latest dates in the timestamp column.
Hint: Remember the summary functions you learned in summarize() tutorial!
69.4 Common Mistakes & Errors
- You might try to apply
lubridatefunctions on to a column that looks like a timestamp column, but it may still not be of the appropriate data type, which is either ‘dt,’ ‘tm,’ or ‘dttm.’ Be sure to convert to the proper data type first!