Import daily weather data

In practice you won't be parsing isolated dates and times, they'll be part of a larger dataset. Throughout the chapter after you've mastered a skill with a simpler example (the release times of R for example), you'll practice your lubridate skills in context by working with weather data from Auckland NZ.

There are two data sets: akl_weather_daily.csv a set of once daily summaries for 10 years, and akl_weather_hourly_2016.csv observations every half hour for 2016. You'll import the daily data in this exercise and the hourly weather in the next exercise.

You'll be using functions from dplyr, so if you are feeling rusty, you might want to review filter(), select() and mutate().

This exercise is part of the course

Working with Dates and Times in R

Exercise instructions

Import the daily data, "akl_weather_daily.csv" with read_csv().
Print akl_daily_raw to confirm the date column hasn't been interpreted as a date. Can you see why?
Using mutate() overwrite the column date with a parsed version of date. You need to specify the parsing function. Hint: the first date should be September 1.
Print akl_daily to verify the date column is now a Date.
Take a look at the data by plotting date on the x-axis and max_temp of the y-axis.

Hands-on interactive exercise

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

library(lubridate)
library(readr)
library(dplyr)
library(ggplot2)

# Import CSV with read_csv()
akl_daily_raw <- read_csv(___)

# Print akl_daily_raw
___

# Parse date 
akl_daily <- akl_daily_raw %>%
  mutate(date = ___(date))

# Print akl_daily
___

# Plot to check work
ggplot(akl_daily, aes(x = ___, y = ___)) +
  geom_line()

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 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

Current Exercise

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