Getting datetimes into R

Just like dates without times, if you want R to recognize a string as a datetime you need to convert it, although now you use as.POSIXct(). as.POSIXct() expects strings to be in the format YYYY-MM-DD HH:MM:SS.

The only tricky thing is that times will be interpreted in local time based on your machine's set up. You can check your timezone with Sys.timezone(). If you want the time to be interpreted in a different timezone, you just set the tz argument of as.POSIXct(). You'll learn more about time zones in Chapter 4.

In this exercise you'll input a couple of datetimes by hand and then see that read_csv() also handles datetimes automatically in a lot of cases.

This exercise is part of the course

Working with Dates and Times in R

Exercise instructions

Use as.POSIXct() and an appropriate string to input the datetime corresponding to Oct 1st 2010 at 12:12:00.
Enter the same datetime again, but now specify the timezone as "America/Los_Angeles".
Use read_csv() to read in rversions.csv again.
Examine the structure of the datetime column to verify read_csv() has correctly interpreted it as a datetime.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Use as.POSIXct to enter the datetime 
as.POSIXct(___)

# Use as.POSIXct again but set the timezone to `"America/Los_Angeles"`
as.POSIXct(___, tz = ___)

# Use read_csv to import rversions.csv
releases <- read_csv(___)

# Examine structure of datetime column
str(___)

Edit and Run Code

This exercise is part of the course

Working with Dates and Times in R

IntermediateSkill Level

4.8+

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R doesn't know something is a date or time unless you tell it. In this chapter you'll learn about some of the ways R stores dates and times by exploring how often R versions are released, and how quickly people download them. You'll also get a sneak peek at what you'll learn in the following chapters.

Exercise 1: Introduction to dates Exercise 2: Recognizing ISO 8601 dates Exercise 3: Specifying dates Exercise 4: Automatic import Exercise 5: Why use dates?Exercise 6: Plotting Exercise 7: Arithmetic and logical operators Exercise 8: What about times?Exercise 9: Getting datetimes into R

Current Exercise

Exercise 10: Datetimes behave nicely too Exercise 11: Why lubridate?

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 lubridate Exercise 2: Selecting the right parsing function Exercise 3: Specifying an order with `parse_date_time()`Exercise 4: Weather in Auckland Exercise 5: Import daily weather data Exercise 6: Import hourly weather data Exercise 7: Extracting parts of a datetime Exercise 8: What can you extract?Exercise 9: Adding useful labels Exercise 10: Extracting for plotting Exercise 11: Extracting for filtering and summarizing Exercise 12: Rounding datetimes Exercise 13: Practice rounding Exercise 14: Rounding with the weather data

Getting datetimes into R is just the first step. Now that you know how to parse datetimes, you need to learn how to do calculations with them. In this chapter, you'll learn the different ways of representing spans of time with lubridate and how to leverage them to do arithmetic on datetimes. By the end of the chapter, you'll have calculated how long it's been since the first man stepped on the moon, generated sequences of dates to help schedule reminders, calculated when an eclipse occurs, and explored the reigns of monarch's of England (and which ones might have seen Halley's comet!).

Exercise 1: Taking differences of datetimes Exercise 2: How long has it been?Exercise 3: How many seconds are in a day?Exercise 4: Time spans.Exercise 5: Adding or subtracting a time span to a datetime Exercise 6: Duration or Period?Exercise 7: Arithmetic with timespans Exercise 8: Generating sequences of datetimes Exercise 9: The tricky thing about months Exercise 10: Intervals Exercise 11: Examining intervals. Reigns of kings and queens Exercise 12: Comparing intervals and datetimes Exercise 13: Converting to durations and periods

You now know most of what you need to tackle data that includes dates and times, but there are a few other problems you might encounter in practice. In this final chapter you'll learn a little more about these problems by returning to some of the earlier data examples and learning how to handle time zones, deal with times when you don't care about dates, parse dates quickly, and output dates and times.

Exercise 1: Time zones Exercise 2: Setting the timezone Exercise 3: Viewing in a timezone Exercise 4: Timezones in the weather data Exercise 5: Times without dates Exercise 6: More on importing and exporting datetimes Exercise 7: Fast parsing with fasttime Exercise 8: Fast parsing with lubridate::fast_strptime Exercise 9: Outputting pretty dates and times Exercise 10: Wrap-up