Anche i datetime si comportano bene

Proprio come gli oggetti Date, puoi creare grafici e fare calcoli con gli oggetti POSIXct.

Come esempio, in questo esercizio vedrai con quale rapidità le persone scaricano le nuove versioni di R, esaminando i log di download dallo specchio CRAN di RStudio.

R 3.2.0 è stato rilasciato il "2015-04-16 07:13:33", quindi cran-logs_2015-04-17.csv contiene un campione casuale di download del 16, 17 e 18.

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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Istruzioni dell'esercizio

Usa read_csv() per importare cran-logs_2015-04-17.csv.
Stampa logs per vedere le informazioni che abbiamo su ogni download.
Memorizza l'orario di rilascio di R 3.2.0 come oggetto POSIXct.
Scopri quando è stata effettuata la prima richiesta per la 3.2.0 filtrando per i valori nella colonna datetime che sono maggiori di release_time.
Infine, osserva come aumentano i download creando istogrammi dell'orario di download per la 3.2.0 e per la versione precedente 3.1.3. Ti abbiamo fornito gran parte del codice: devi solo impostare l'estetica x sulla colonna datetime.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Lavorare con date e orari in R

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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: Introduzione alle date Exercise 2: Riconoscere le date ISO 8601 Exercise 3: Specificare le date Exercise 4: Importazione automatica Exercise 5: Perché usare le date?Exercise 6: Creare grafici Exercise 7: Operatori aritmetici e logici Exercise 8: E per gli orari?Exercise 9: Importare datetime in R Exercise 10: Anche i datetime si comportano bene

Esercizio in corso

Exercise 11: Perché 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