Specifying an order with `parse_date_time()`
What about if you have something in a really weird order like dym_msh? There's no named function just for that order, but that is where parse_date_time() comes in. parse_date_time() takes an additional argument, orders, where you can specify the order of the components in the date.
For example, to parse "2010 September 20th" you could say parse_date_time("2010 September 20th", orders = "ymd") and that would be equivalent to using the ymd() function from the previous exercise.
One advantage of parse_date_time() is that you can use more format characters. For example, you can specify weekday names with A, I for 12 hour time, am/pm indicators with p and many others. You can see a whole list on the help page ?parse_date_time.
Another big advantage is that you can specify a vector of orders, and that allows parsing of dates where multiple formats might be used.
You'll try it out in this exercise.
This is a part of the course
“Working with Dates and Times in R”
Exercise instructions
xis a trickier datetime. Use the clues in the instructions to parsex.two_ordershas two different orders, parse both by specifying the order to bec("mdy", "dmy").- Parse
short_dateswithorders = c("dOmY", "OmY", "Y"). What happens to the dates that don't have months or days specified?
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Specify an order string to parse x
x <- "Monday June 1st 2010 at 4pm"
___(x, orders = "___")
# Specify order to include both "mdy" and "dmy"
two_orders <- c("October 7, 2001", "October 13, 2002", "April 13, 2003",
"17 April 2005", "23 April 2017")
parse_date_time(two_orders, orders = ___)
# Specify order to include "dOmY", "OmY" and "Y"
short_dates <- c("11 December 1282", "May 1372", "1253")
parse_date_time(short_dates, orders = ___)
This exercise is part of the course
Working with Dates and Times in R
Learn the essentials of parsing, manipulating and computing with dates and times in R.
Dates and times come in a huge assortment of formats, so your first hurdle is often to parse the format you have into an R datetime. This chapter teaches you to import dates and times with the lubridate package. You'll also learn how to extract parts of a datetime. You'll practice by exploring the weather in R's birthplace, Auckland NZ.
Exercise 1: Parsing dates with lubridateExercise 2: Selecting the right parsing functionExercise 3: Specifying an order with `parse_date_time()`Exercise 4: Weather in AucklandExercise 5: Import daily weather dataExercise 6: Import hourly weather dataExercise 7: Extracting parts of a datetimeExercise 8: What can you extract?Exercise 9: Adding useful labelsExercise 10: Extracting for plottingExercise 11: Extracting for filtering and summarizingExercise 12: Rounding datetimesExercise 13: Practice roundingExercise 14: Rounding with the weather dataWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.