Create and format times
Similar to working with dates, you can use as.POSIXct() to convert from a character string to a POSIXct object, and format() to convert from a POSIXct object to a character string. Again, you have a wide variety of symbols:
%H: hours as a decimal number (00-23)%I: hours as a decimal number (01-12)%M: minutes as a decimal number%S: seconds as a decimal number%T: shorthand notation for the typical format%H:%M:%S%p: AM/PM indicator
For a full list of conversion symbols, consult the strptime documentation in the console:
?strptime
Again,as.POSIXct() uses a default format to match character strings. In this case, it's %Y-%m-%d %H:%M:%S. In this exercise, abstraction is made of different time zones.
This is a part of the course
“Intermediate R”
Exercise instructions
- Convert two strings that represent timestamps,
str1andstr2, toPOSIXctobjects calledtime1andtime2. - Using
format(), create a string fromtime1containing only the minutes. - From
time2, extract the hours and minutes as "hours:minutes AM/PM". Refer to the assignment text above to find the correct conversion symbols!
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Definition of character strings representing times
str1 <- "May 23, '96 hours:23 minutes:01 seconds:45"
str2 <- "2012-3-12 14:23:08"
# Convert the strings to POSIXct objects: time1, time2
time1 <- as.POSIXct(str1, format = "%B %d, '%y hours:%H minutes:%M seconds:%S")
# Convert times to formatted stringsThis exercise is part of the course
Intermediate R
Continue your journey to becoming an R ninja by learning about conditional statements, loops, and vector functions.
Mastering R programming is not only about understanding its programming concepts. Having a solid understanding of a wide range of R functions is also important. This chapter introduces you to many useful functions for data structure manipulation, regular expressions, and working with times and dates.
Exercise 1: Useful FunctionsExercise 2: Mathematical utilitiesExercise 3: Find the errorExercise 4: Data UtilitiesExercise 5: Find the error (2)Exercise 6: Beat Gauss using RExercise 7: Regular ExpressionsExercise 8: grepl & grepExercise 9: grepl & grep (2)Exercise 10: sub & gsubExercise 11: sub & gsub (2)Exercise 12: Times & DatesExercise 13: Right here, right nowExercise 14: Create and format datesExercise 15: Create and format timesExercise 16: Calculations with DatesExercise 17: Calculations with TimesExercise 18: Time is of the essenceWhat is DataCamp?
Learn the data skills you need online at your own pace—from non-coding essentials to data science and machine learning.