Weather in Auckland

1. Weather in Auckland

In practice you'll often come across dates and times when data is being recorded on a timed schedule. It may be a website logging traffic, a sales team reporting weekly results, or scientific instruments measuring the environment. To help you see how your lubridate skills integrate into a data analysis pipeline, you'll be exploring the weather in Auckland. Why Auckland? It's R's birthplace, although this weather is from the airport which is about 20km from the University where R was developed. The data comes from Weather Underground, a weather website, but their data for Auckland comes from parsing METARS data, an automated weather service for pilots. There are two data sets you'll explore: daily observations and sub-hourly observations.

2. akl_weather_daily.csv

Let's take a look at part of the data file for the daily data. It's a CSV file, so the variables are separated by commas. For each day over 10 years we have the max, min, and mean temperature, mean relative humidity, a record on any events and the cloud cover. Can you see the date info? You know how to handle that, and you'll parse it in shortly.

3. akl_weather_hourly_2016.csv

The subhourly data is a little different. Can you see where the date is recorded? It is split over year month and mday variables. To convert this to a date you need to know about one more lubridate function make_date.

4. make_date(year, month, day)

make_date has arguments year, month and day to allow you to specify a date from its individual components. For example make_date(year equals 2013, month equals 2, day equals 27) produces the date object corresponding to Feb 27 2013. The components can be vectors which makes it convenient for constructing dates from individual columns, which you'll do to import the hourly data. There's also a make_datetime function that adds hour, min and sec arguments to build a datetime from it's components.

5. dplyr Review

The tasks you'll tackle with the Auckland weather are easily completed by combining your new lubridate skills with dplyr. As a quick review the useful dplyr verbs are: mutate which adds columns, filter to subset rows, select to subset columns and arrange to order rows. summarise summarizes rows down to one row, and is most useful in conjunction with group_by which allows you to calculate grouped summaries. All the dplyr verbs take a data frame as the first argument and return a data frame. This makes them

6. Pipe %>%

very amenable to piping. Remember the pipe operator takes the result of the left hand side and passes it as the first argument to the right hand side. So, instead of writing manipulations in a nested manner we can write them in a more readable linear fashion.

7. Let's practice!

Ok, you're set to get that weather data into R.

This exercise is part of the course

Working with Dates and Times in R

IntermediateSkill Level

5.0+

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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

Current Exercise

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