Datetimes werken ook prettig

Net als met Date-objecten kun je ook met POSIXct-objecten plotten en rekenen.

Als voorbeeld zie je in deze oefening hoe snel mensen nieuwe versies van R downloaden, door de downloadlogs van de RStudio CRAN-mirror te bekijken.

R 3.2.0 is uitgebracht op "2015-04-16 07:13:33", dus cran-logs_2015-04-17.csv bevat een willekeurige steekproef van downloads op de 16e, 17e en 18e.

Deze oefening maakt deel uit van de cursus

Werken met datums en tijden in R

Cursus bekijken

Oefeninstructies

Gebruik read_csv() om cran-logs_2015-04-17.csv te importeren.
Print logs om te zien welke informatie we over elke download hebben.
Sla het uitgiftemoment van R 3.2.0 op als een POSIXct-object.
Zoek uit wanneer het eerste verzoek voor 3.2.0 is gedaan door te filteren op waarden in de kolom datetime die groter zijn dan release_time.
Bekijk ten slotte hoe downloads toenemen door histogrammen te maken van de downloadtijd voor 3.2.0 en de vorige versie 3.1.3. We hebben het meeste van de code al gegeven; je hoeft alleen de x-esthetiek in te stellen op de kolom datetime.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import "cran-logs_2015-04-17.csv" with read_csv()
logs <- read_csv(___)

# Print logs
___

# Store the release time as a POSIXct object
release_time <- ___("2015-04-16 07:13:33", tz = "UTC")

# When is the first download of 3.2.0?
logs %>% 
  filter(___,
    r_version == "3.2.0")

# Examine histograms of downloads by version
ggplot(logs, aes(x = ___)) +
  geom_histogram() +
  geom_vline(aes(xintercept = as.numeric(release_time)))+
  facet_wrap(~ r_version, ncol = 1)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Werken met datums en tijden in R

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Introductie tot datums Exercise 2: ISO 8601-datums herkennen Exercise 3: Datums opgeven Exercise 4: Automatische import Exercise 5: Waarom data gebruiken?Exercise 6: Plotten Exercise 7: Rekenkundige en logische operatoren Exercise 8: En hoe zit het met tijden?Exercise 9: Datetimes in R krijgen Exercise 10: Datetimes werken ook prettig

Huidige oefening

Exercise 11: Waarom 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