---
title: Dates and times
author: David Kane and Tanay Janmanchi
tutorial:
  id: dates-and-times
output:
  learnr::tutorial:
    progressive: yes
    allow_skip: yes
runtime: shiny_prerendered
description: 'Tutorial for Chapter 17: Dates and times'
---

```{r setup, include = FALSE}
library(learnr)
library(tutorial.helpers)
library(tidyverse)
library(nycflights13)
library(gt)

knitr::opts_chunk$set(echo = FALSE)
options(tutorial.exercise.timelimit = 60, 
        tutorial.storage = "local") 

make_datetime_100 <- function(year, month, day, time) {
  make_datetime(year, month, day, time %/% 100, time %% 100)
}

flights_dt <- flights |> 
  filter(!is.na(dep_time), !is.na(arr_time)) |> 
  mutate(
    dep_time = make_datetime_100(year, month, day, dep_time),
    arr_time = make_datetime_100(year, month, day, arr_time),
    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
  ) |> 
  select(origin, dest, ends_with("delay"), ends_with("time"))

date2015 <- "
  date
  01/02/15
"

x1 <- ymd_hms("2024-06-01 12:00:00", tz = "America/New_York")
x2 <- ymd_hms("2024-06-01 18:00:00", tz = "Europe/Copenhagen")
x3 <- ymd_hms("2024-06-02 04:00:00", tz = "Pacific/Auckland")
x4 <- c(x1, x2, x3)


y2023 <- ymd("2023-01-01") %--% ymd("2024-01-01")
y2024 <- ymd("2024-01-01") %--% ymd("2025-01-01")
flights_dt2 <- flights_dt |> 
  mutate(
    overnight = arr_time < dep_time,
    arr_time = arr_time + days(overnight),
    sched_arr_time = sched_arr_time + days(overnight)
  )


datetime <- ymd_hms("2026-07-08 12:34:56")
h_age <- today() - ymd("1979-10-14")
one_am <- ymd_hms("2026-03-08 01:00:00", tz = "America/New_York")
flights_dt <- flights_dt |> 
  mutate(
    overnight = arr_time < dep_time,
    arr_time = arr_time + days(overnight),
    sched_arr_time = sched_arr_time + days(overnight)
  )
```

```{r copy-code-chunk, child = system.file("child_documents/copy_button.Rmd", package = "tutorial.helpers")}
```

```{r info-section, child = system.file("child_documents/info_section.Rmd", package = "tutorial.helpers")}
```

## Introduction
### 

This tutorial covers [Chapter 17: Dates and times](https://r4ds.hadley.nz/datetimes.html) from [*R for Data Science (2e)*](https://r4ds.hadley.nz/) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. You will learn how to use the [**lubridate**](https://lubridate.tidyverse.org/) on the `flights` data from the [**nyc13flights**](https://nycflights13.tidyverse.org/) package.


## Creating date/times through import
### 

In this chapter we are going to focus on dates and date-times as R doesn’t have a native class for storing times. If you need one, you can use the **hms** package. `hms()` stands for **h**our, **m**inute, **s**econd.   

### Exercise 1

Let's load the **tidyverse** library. 

```{r creating-date-times-through-im-1, exercise = TRUE}

```

```{r creating-date-times-through-im-1-hint-1, eval = FALSE}
library(...)
```

```{r creating-date-times-through-im-1-test, include = FALSE}
library(tidyverse)
```

### Exercise 2

Run `today()` in the code chunk below.

```{r creating-date-times-through-im-2, exercise = TRUE}

```

```{r creating-date-times-through-im-2-hint-1, eval = FALSE}
...()
```

```{r creating-date-times-through-im-2-test, include = FALSE}
today()
```

### 

To get the current date you can use `today()`. If you want the current date-time, you can use `now()`

### Exercise 3

Run `now()` in the code chunk below. With this we can get the current date-time.

```{r creating-date-times-through-im-3, exercise = TRUE}

```

```{r creating-date-times-through-im-3-hint-1, eval = FALSE}
...()
```

```{r creating-date-times-through-im-3-test, include = FALSE}
now()
```

### 

A date-time is a date plus a time: it uniquely identifies an instant in time (typically to the nearest second). Tibbles print this as `<dttm>`. Base R calls these POSIXct, but doesn’t exactly trip off the tongue.



### Exercise 4

Enter `date2015` and hit "Run Code".

```{r creating-date-times-through-im-4, exercise = TRUE}

```

```{r creating-date-times-through-im-4-hint-1, eval = FALSE}
date2015
```

```{r creating-date-times-through-im-4-test, include = FALSE}
date2015
```

`date2015` is a character vector with a single element which includes both the variable name, `date` and, after a newline character, a value: `01/02/15`.

### Exercise 5

Run `read_csv()` on `date2015`.

```{r creating-date-times-through-im-5, exercise = TRUE}

```

```{r creating-date-times-through-im-5-hint-1, eval = FALSE}
read_csv(...)
```

```{r creating-date-times-through-im-5-test, include = FALSE}
read_csv(date2015)
```

### 

With `read_csv()` we are able to analyze the aspects of date and date-time with `col_types` such as `col_date()`, and `col_datetime()`. We can view the year features, month features, day features, time features, and other features.

### 

If your CSV contains an ISO8601 date or date-time, you don’t need to do anything; readr will automatically recognize it.  

If you haven’t heard of ISO8601 before, it’s an international standard for writing dates where the components of a date are organized from biggest to smallest separated by `-`. To learn more about ISO8601, visit [here](https://www.iso.org/iso-8601-date-and-time-format.html)

Since `date2015` does not contain this, we must manually format the date.

### 

For other date-time formats, you’ll need to use `col_types` plus `col_date()` or `col_datetime()` along with a date-time format. The date-time format used by readr is a standard used across many programming languages, describing a date component with a `%` followed by a single character. For example, `%Y-%m-%d` specifies a date that’s a year, `-`, month (as number) `-`, day. The table below lists all the options.

```{r}
data <- data.frame(
  Type = c("Year", "", "Month", "", "", "Day", "", "Time", rep("", 7), "Other", ""),
  Code = c("%Y", "%y", "%m", "%b", "%B", "%d", "%e", "%H", "%I", "%p", "%M", "%S", "%0S", "%Z", "%z", "%.", "%*"),
  Meaning = c("4 digit year", "2 digit year", "Number", "Abbreviated name", "Full name",
              "One or two digits", "Two digits", "24-hour hour", "12-hour hour", "AM/PM",
              "Minutes", "Seconds", "Seconds with decimal component", "Time zone name",
              "Offset from UTC", "Skip one non-digit", "Skip any number of non-digits"),
  Example = c("2021", "21", "2", "Feb", "February", "2", "02", "13", "1", "pm", "35", "45",
              "45.35", "America/Chicago", "+0800", ":", "")
)

gt(data) |>
  tab_header(title = "") |>
  cols_align(align = "left") |>
  tab_options(column_labels.font.weight = "bold") |>
  tab_style(
    style = cell_borders(
      sides = "bottom",
      color = "light gray",
      weight = px(1)
    ),
    locations = cells_body(
      rows = c(2, 5, 7, 15, 17)
    )
  )
```

### Exercise 6

We will use `col_types` to format the date in the `date2015`. Set `col_types` to `cols(date = col_date("%m/%d/%y"))` in `read_csv()`.

```{r creating-date-times-through-im-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r creating-date-times-through-im-6-hint-1, eval = FALSE}
read_csv(date2015, ... = cols(date = col_date("%m/%d/%y")))
```

```{r creating-date-times-through-im-6-test, include = FALSE}
read_csv(date2015, col_types = cols(date = col_date("%m/%d/%y")))
```

### 

With this we can see a tibble that formats our date in year/month/day format. It thinks that the "01" is the month, "02" is the day and "15" is the year.

### Exercise 7

Let's try a different format. Set `col_types` to `cols(date = col_date("%d/%m/%y"))` in `read_csv()`.

```{r creating-date-times-through-im-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r creating-date-times-through-im-7-hint-1, eval = FALSE}
read_csv(date2015, col_types = ...)
```

```{r creating-date-times-through-im-7-test, include = FALSE}
read_csv(date2015, col_types = cols(date = col_date("%d/%m/%y")))
```

### 

Now, we can see that the date is different than the one outputted previously. It is still in the same format, but, now we can see that the day and month have been switched.

### Exercise 8

Set `col_types` to `cols(date = col_date("%y/%m/%d"))` in `read_csv()`.

```{r creating-date-times-through-im-8, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r creating-date-times-through-im-8-hint-1, eval = FALSE}
read_csv(date2015, col_types = ...)
```

```{r creating-date-times-through-im-8-test, include = FALSE}
read_csv(date2015, col_types = cols(date = col_date("%y/%m/%d")))
```

### 

Note that no matter how you specify the date format, it’s always displayed the same way once you get it into R. However, we see that the date is once again different. The code reads the "01" as the year and so on.

### 

Oftentimes when working with non-English dates and `%b` or `%B` you will need to use `locale()`. Check out [**date_names_langs()**](https://readr.tidyverse.org/reference/date_names.html) for more information.

## Creating date/times with strings
### 

The date-time specification language is powerful, but requires careful analysis of the date format. An alternative approach is to use lubridate’s helpers which attempt to automatically determine the format once you specify the order of the component.

### Exercise 1

Run `mdy()` on `"January 31st, 2017"`.

```{r creating-date-times-with-strin-1, exercise = TRUE}

```

```{r creating-date-times-with-strin-1-hint-1, eval = FALSE}
...("January 31st, 2017")
```

```{r creating-date-times-with-strin-1-test, include = FALSE}
mdy("January 31st, 2017")
```

### Exercise 2

Run `dmy()` on `"31-Jan-2017"`

```{r creating-date-times-with-strin-2, exercise = TRUE}

```

```{r creating-date-times-with-strin-2-hint-1, eval = FALSE}

```

```{r creating-date-times-with-strin-2-test, include = FALSE}
dmy("31-Jan-2017")
```

### Exercise 3

Run `ymd()` on `"2017-01-31"`.

```{r creating-date-times-with-strin-3, exercise = TRUE}

```

```{r creating-date-times-with-strin-3-hint-1, eval = FALSE}
ymd(...)
```

```{r creating-date-times-with-strin-3-test, include = FALSE}
ymd("2017-01-31")
```

### 

When using these three formats we need to identify the order in which year, month, and day appear in your dates, then arrange “y”, “m”, and “d” in the same order. That gives you the name of the lubridate function that will parse your date.

### Exercise 4

We can also use the "hms" function to create an datetime. Set `ymd_hms()` to `"2017-01-31 20:11:59`".

```{r creating-date-times-with-strin-4, exercise = TRUE}

```

```{r creating-date-times-with-strin-4-hint-1, eval = FALSE}
ymd_hms(...)
```

```{r creating-date-times-with-strin-4-test, include = FALSE}
ymd_hms("2017-01-31 20:11:59")
```

### 

`ymd()` and friends create dates. To create a date-time, add an underscore and one or more of “h”, “m”, and “s” to the name of the parsing function

### Exercise 5

You can also force the creation of a date-time from a date by supplying a timezone. Copy and paste your code from Exercise 1 and set `tz` to "UTC".

```{r creating-date-times-with-strin-5, exercise = TRUE}

```

```{r creating-date-times-with-strin-5-hint-1, eval = FALSE}
ymd("2017-01-31", tz = ...)
```

```{r creating-date-times-with-strin-5-test, include = FALSE}

```

### 

With this we are able to create a datetime that is automatically set to UTC. The UTC timezone known as GMT, or Greenwich Mean Time, doesn't use daylight saving time, making it a bit easier to compute with.


## Creating dates and times with individual components
### 

We can use the datetime features on pieces of data. We will use the datetime component on the `flights` data.

### 

Instead of a single string, sometimes you’ll have the individual components of the date-time spread across multiple columns. For example, look at what we have in the `flights` data

```{r}
flights |> 
  select(year, month, day, hour, minute)
```

### 

We have all the components needed to make a datetime, but, we still need to make it manually.

### Exercise 1

Continue this pipe with `mutate()`. Create a new variable, `departure`, set it equal to `make_data()` and set the arguments inside to `year, month, day, hour, minute`.

```{r creating-dates-and-times-with-1, exercise = TRUE}
flights |> 
  select(year, month, day, hour, minute)
```

```{r creating-dates-and-times-with-1-hint-1, eval = FALSE}
... |>
  mutate(departure = ...)
```

```{r creating-dates-and-times-with-1-hint-2, eval = FALSE}
... |>
  mutate(departure = make_datetime(..., ..., ..., ..., ...))
```

```{r creating-dates-and-times-with-1-test, include = FALSE}
flights |> 
  select(year, month, day, hour, minute) |> 
  mutate(departure = make_datetime(year, month, day, hour, minute))
```

### 

To create a date/time from this sort of input, we use `make_date()` for dates, or `make_datetime()` for date-times

### Exercise 2

Now we will create datetimes for arrival time, departure time, scheduled arrival time and scheduled departure time. 

Good news, we did this for you! Run the code.

```{r creating-dates-and-times-with-2, exercise = TRUE}
make_datetime_100 <- function(year, month, day, time) {
  make_datetime(year, month, day, time %/% 100, time %% 100)
}

flights_dt <- flights |> 
  filter(!is.na(dep_time), !is.na(arr_time)) |> 
  mutate(
    dep_time = make_datetime_100(year, month, day, dep_time),
    arr_time = make_datetime_100(year, month, day, arr_time),
    sched_dep_time = make_datetime_100(year, month, day, sched_dep_time),
    sched_arr_time = make_datetime_100(year, month, day, sched_arr_time)
  ) |> 
  select(origin, dest, ends_with("delay"), ends_with("time"))

```

### 

`make_datetime_100` is a function that takes in the `year`, `month`, `day` and `time` variables to create a datetime. It is not always necessary to make a function like this, but, it does make life easier.

### 

We are going to focus on four columns `dep_time`, `sched_dep_time`, `arr_time`, `sched_arr_time` because they are all <dttm> (datetime).

### Exercise 3

Pipe `flights_dt` to `ggplot`. Inside the `aes` argument, set `x` to `dep_time`. 

```{r creating-dates-and-times-with-3, exercise = TRUE}

```

```{r creating-dates-and-times-with-3-hint-1, eval = FALSE}
flights_dt |>
  ggplot(aes(... = ...)) + ...
```

```{r creating-dates-and-times-with-3-hint-2, eval = FALSE}
flights_dt |>
  ggplot(aes(... = ...)) 
```

```{r creating-dates-and-times-with-3-test, include = FALSE}
flights_dt |> 
  ggplot(aes(x = dep_time))
```

### 

The plot should be blank since we have not added any layers.

### Exercise 4

Add `geom_freqpoly` and set the `binwidth` to `86400`.

```{r creating-dates-and-times-with-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r creating-dates-and-times-with-4-hint-1, eval = FALSE}
... +
  geom_freqpoly(binwidth = ...)
```

```{r creating-dates-and-times-with-4-test, include = FALSE}
flights_dt |> 
  ggplot(aes(x = dep_time)) + 
  geom_freqpoly(binwidth = 86400)
```

### 

With this data, we can visualize the distribution of departure times across the year. Note, we use 86400 for the binwidth since there are 86400 seconds in a year.

### Exercise 5

Pipe `flights_dt` to `filter`. Set `dep_time` to be less than `ymd(20130102)`. 

```{r creating-dates-and-times-with-5, exercise = TRUE}

```

```{r creating-dates-and-times-with-5-hint-1, eval = FALSE}
flights_dt |>
  filter(dep_time < ymd(...))
```

```{r creating-dates-and-times-with-5-test, include = FALSE}
flights_dt |> 
  filter(dep_time < ymd(20130102))
```

### 

We filter the `dep_time` in this way so that we only get data for the first of January. If we wanted a random date in the middle of the year, we would have to use `&&` and have two different arguments, one limiting it with `>` and another with `<.`


### Exercise 6

Continue the pipe to `ggplot`, map `dep_time` to the x-axis. 

```{r creating-dates-and-times-with-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r creating-dates-and-times-with-6-hint-1, eval = FALSE}
... |> 
  ggplot(aes(x = ...))
```

```{r creating-dates-and-times-with-6-test, include = FALSE}
flights_dt |> 
  filter(dep_time < ymd(20130102)) |> 
  ggplot(aes(x = dep_time))
```

### Exercise 7

Add `geom_freqpoly`, set the `binwidth` argument to `600`

```{r creating-dates-and-times-with-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r creating-dates-and-times-with-7-hint-1, eval = FALSE}
... +
  geom_freqpoly(binwidth = ...)
```

```{r creating-dates-and-times-with-7-test, include = FALSE}
flights_dt |> 
  filter(dep_time < ymd(20130102)) |> 
  ggplot(aes(x = dep_time)) + 
  geom_freqpoly(binwidth = 600)
```

### 

Now we can visualize the distribution of departure times within a single day

### 

Note that when you use date-times in a numeric context (like in a histogram), 1 means 1 second, so a binwidth of 86400 means one day. For dates, 1 means 1 day.

## Creating date/times from other types
### 

You may want to switch between a date-time and a date. That’s the job of `as_datetime()` and `as_date()`

### Exercise 1

Run `as_datetime()` on `today()`

```{r creating-date-times-from-other-1, exercise = TRUE}

```

```{r creating-date-times-from-other-1-hint-1, eval = FALSE}
as_datetime(...)
```

```{r creating-date-times-from-other-1-test, include = FALSE}
as_datetime(today())
```

### 

If we were to just run `today()`, we would get the date. But, if we use `as_datetime()`, we get a datetime.

### Exercise 2

Run `as_date()` on `now()`

```{r creating-date-times-from-other-2, exercise = TRUE}

```

```{r creating-date-times-from-other-2-hint-1, eval = FALSE}
as_date(...)
```

```{r creating-date-times-from-other-2-test, include = FALSE}
as_date(now())
```

### 

When we normally run `now()`, we get the time and date, but once we run `as_date()`, we just get the date.

### Exercise 3

Run `as_datetime(60 * 60 * 10)`

```{r creating-date-times-from-other-3, exercise = TRUE}

```

```{r creating-date-times-from-other-3-hint-1, eval = FALSE}
as_datetime(...)
```

```{r creating-date-times-from-other-3-test, include = FALSE}
as_datetime(60 * 60 * 10)
```

### Exercise 4

Run `as_date(365 * 10 + 2)`

```{r creating-date-times-from-other-4, exercise = TRUE}

```

```{r creating-date-times-from-other-4-hint-1, eval = FALSE}
as_date(...)
```

```{r creating-date-times-from-other-4-test, include = FALSE}
as_date(365 * 10 + 2)
```

### 

Sometimes you’ll get date/times as numeric offsets from the “Unix Epoch”, `1970-01-01`. If the offset is in seconds, use `as_datetime()`; if it’s in days, use `as_date()`.

In other words, we can use total days/seconds to create a date/datetime


## Date-time components with getting components
### 

Now that you know how to get date-time data into R’s date-time data structures, let’s explore what you can do with them. These next sections will focus on the accessor functions that let you get and set individual components. The next section will look at how arithmetic works with date-times.


### Exercise 1

Create a new datetime using `ymd_hms()` with the argument `"2026-07-08 12:34:56"`. Set is equal to a new object named `datetime`

```{r datetime-components-with-getti-1, exercise = TRUE}

```

```{r datetime-components-with-getti-1-hint-1, eval = FALSE}
datetime <- ymd_hms("...")
```

```{r datetime-components-with-getti-1-test, include = FALSE}
datetime <- ymd_hms("2026-07-08 12:34:56")
```

### 

We will perform a variety of functions on this.

### Exercise 2

Run `year()` on `datetime`

```{r datetime-components-with-getti-2, exercise = TRUE}

```

```{r datetime-components-with-getti-2-hint-1, eval = FALSE}
year(...)
```

```{r datetime-components-with-getti-2-test, include = FALSE}
year(datetime)
```

### 

```{r}
data <- data.frame(
  Type = c("Year", ""),
  Code = c("%Y", "%y"),
  Meaning = c("4 digit year", "2 digit year"),
  Example = c("2021", "21")
)

gt(data) |>
  tab_header(title = "") |>
  cols_align(align = "center") |>
  tab_options(column_labels.font.weight = "bold")
```

`year()` returns the `%Y`, looking at the table above, we can see that this is the 4 digit year. Even if we were to replace "2026" with "26", `year(datetime)` would still return 2026. Don't believe us? Try it for yourself in the console!


### Exercise 3

Run `month()` on `datetime`

```{r datetime-components-with-getti-3, exercise = TRUE}

```

```{r datetime-components-with-getti-3-hint-1, eval = FALSE}
month(...)
```

```{r datetime-components-with-getti-3-test, include = FALSE}
month(datetime)
```

### 

```{r}
data <- data.frame(
  Type = c("Month", "", ""),
  Code = c("%m", "%b", "%B"),
  Meaning = c("Number", "Abbreviated name", "Full Name"),
  Example = c("2", "Feb", "February")
)

gt(data) |>
  tab_header(title = "") |>
  cols_align(align = "center") |>
  tab_options(column_labels.font.weight = "bold")
```

`month()` returns the `%m`, looking at the table, we can see that this is the number of the month. If we were to replace "07" with the `%b` (Jul) or the `%B` (July), we would still get `7` as our output.

### Exercise 4

Run `mday()`, `yday()` and `wday()` on `datetime`

```{r datetime-components-with-getti-4, exercise = TRUE}



```

```{r datetime-components-with-getti-4-hint-1, eval = FALSE}
mday(...)
yday(...)
wday(...)
```

```{r datetime-components-with-getti-4-test, include = FALSE}
mday(datetime)
yday(datetime)
wday(datetime)
```

### 

```{r}
data <- data.frame(
  Type = c("Day", ""),
  Code = c("%d", "%e"),
  Meaning = c("One or two digits", "Two digits"),
  Example = c("2", "02")
)

gt(data) |>
  tab_header(title = "") |>
  cols_align(align = "center") |>
  tab_options(column_labels.font.weight = "bold")
```

`mday()`, `yday()` and `wday()` all return the `d%`. They do not return a second digit unless necessary.

### Exercise 5

Lastly, run `hour()`, `minute()`, `second()` and `tz()` on `datetime`.

```{r datetime-components-with-getti-5, exercise = TRUE}



```

```{r datetime-components-with-getti-5-hint-1, eval = FALSE}
hour(...)
minute(...)
second(...)
tz(...)
```

```{r datetime-components-with-getti-5-test, include = FALSE}
hour(datetime)
minute(datetime)
second(datetime)
tz(datetime)
```

### 

```{r}
data <- data.frame(
  Type = c("Time", rep("", 7)),
  Code = c("%H", "%I", "%p", "%M", "%S", "%0S", "%Z", "%z"),
  Meaning = c("24-hour hour", "12-hour hour", "AM/PM", "Minutes", "Seconds", 
              "Seconds with decimal component", "Time zone name", "Offset from UTC"),
  Example = c("13", "1", "pm", "35", "45", "45.35", "America/Chicago", "+0800")
)

gt(data) |>
  tab_header(title = "") |>
  cols_align(align = "center") |>
  tab_options(column_labels.font.weight = "bold")
```

`hour()` returns the `%H`, or the 24-hour hour. `minute()` returns the standard minute, or `%M`. However, `second()` returns the `%OS`, not the `%S`. If you add a decimal to the end of the timestamp and run `second()`, the decimal is returned as well. `tz()` returns the timezone. By fault, it will return "UTC". To specify, add the timezone with after a space following the time.

### 

You can pull out individual parts of the date with the accessor functions `year()`, `month()`, `mday()` (day of the month), `yday()` (day of the year), `wday()` (day of the week), `hour()`, `minute()`, `second()` and `tz()`. These are effectively the opposites of `make_datetime()`.


### Exercise 6

Now, run `month()` with the argument `datetime`, set `label = TRUE`

```{r datetime-components-with-getti-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-6-hint-1, eval = FALSE}
month(..., label = ...)
```

```{r datetime-components-with-getti-6-test, include = FALSE}
month(datetime, label = TRUE)
```

### Exercise 7

Run `wday()` with the argument `datetime`, set `label = TRUE` and `abbr` to `FALSE`

```{r datetime-components-with-getti-7, exercise = TRUE}

```

```{r datetime-components-with-getti-7-hint-1, eval = FALSE}
wday(..., label = ..., abbr = ...)
```

```{r datetime-components-with-getti-7-test, include = FALSE}
wday(datetime, label = TRUE, abbr = FALSE)
```

### 

For `month()` and `wday()` you can set `label = TRUE` to return the abbreviated name of the month (`%b`) or day of the week. You can set `abbr = FALSE` to return the full name (`%B`).

### Exercise 8

Start a pipe with `flights_dt` to `mutate()`. Make a new variable `weekday`, set it equal to `wday()` with the arguments `dep_time` and `label = TRUE`.

```{r datetime-components-with-getti-8, exercise = TRUE}

```

```{r datetime-components-with-getti-8-hint-1, eval = FALSE}
flights_dt |>
  mutate(weekday = wday(..., ....))
```

```{r datetime-components-with-getti-8-test, include = FALSE}
flights_dt |> 
  mutate(wday = wday(dep_time, label = TRUE))
```

### 

By setting the `label = TRUE` we can return the abbreviated name of the day of the week. If we wanted the full name, we would've added the argument `abbr = FALSE`

### Exercise 9

Continue the pipe to `ggplot()`. Map `wday` to the x-axis. 

```{r datetime-components-with-getti-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-9-hint-1, eval = FALSE}
... |>
  ggplot(aes(x = ...))
```

```{r datetime-components-with-getti-9-test, include = FALSE}
flights_dt |> 
  mutate(wday = wday(dep_time, label = TRUE)) |> 
  ggplot(aes(x = wday))
```

### Exercise 10

Finish off your pipe with `geom_bar()`

```{r datetime-components-with-getti-10, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-10-hint-1, eval = FALSE}
...+
  geom_bar()
```

```{r datetime-components-with-getti-10-test, include = FALSE}
flights_dt |> 
  mutate(wday = wday(dep_time, label = TRUE)) |> 
  ggplot(aes(x = wday)) +
  geom_bar()
```

### 

We can use `wday()` to see that more flights depart during the week than on the weekend.

### Exercise 11

Start a new pipe with `flights_dt` to `mutate()`, create a new variable `minute`, and set it equal to `minute(dep_time)`

```{r datetime-components-with-getti-11, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-11-hint-1, eval = FALSE}
flights_dt |> 
  mutate(... = minute(...))
```

```{r datetime-components-with-getti-11-test, include = FALSE}
flights_dt |> 
  mutate(minute = minute(dep_time))
```

### 

We create a new variable so that it is easier to group, which we will do next.

### Exercise 12

Continue the pipe to `group_by()`, with the argument `minute()`. Next, pipe the function to `summarize()`, set the argument `avg_delay` equal to `mean(dep_delay, na.rm = TRUE)` and `n = n()`

```{r datetime-components-with-getti-12, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-12-hint-1, eval = FALSE}
... |> 
  group_by(...) |> 
  summarize(
    avg_delay = ...,
    n = ...
  )
```

```{r datetime-components-with-getti-12-test, include = FALSE}
sched_dep <- flights_dt |> 
  mutate(minute = minute(sched_dep_time)) |> 
  group_by(minute) |> 
  summarize(
    avg_delay = mean(arr_delay, na.rm = TRUE),
    n = n()
  )
```

### 

We get the average delay by using `mean()`. We group each `avg_delay` by the `minute`, this is the same thing as using `.by` in summarize.

### Exercise 13

Continue the pipe to `ggplot()`. Map `minute` to the x-axis and `avg_delay` to the y-axis. Add the layer `geom_line()`

```{r datetime-components-with-getti-13, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-13-hint-1, eval = FALSE}
... |>
  ggplot(aes(..., ...)) +
  geom_line()
```

```{r datetime-components-with-getti-13-test, include = FALSE}
flights_dt |> 
  mutate(minute = minute(dep_time)) |> 
  group_by(minute) |> 
  summarize(
    avg_delay = mean(dep_delay, na.rm = TRUE),
    n = n()
  ) |> 
  ggplot(aes(x = minute, y = avg_delay)) +
  geom_line()
```

### 

We can also look at the average departure delay by minute within the hour. There’s an interesting pattern: flights leaving in minutes 20-30 and 50-60 have much lower delays than the rest of the hour!

### Exercise 14

Copy your code from the last plot and change the argument inside of `minute` to be `sched_dep_time` instead of `dep_time`

```{r datetime-components-with-getti-14, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r datetime-components-with-getti-14-hint-1, eval = FALSE}
flights_dt |> 
  mutate(minute = minute(...)) |> 
  ...
```

```{r datetime-components-with-getti-14-test, include = FALSE}
flights_dt |> 
  mutate(minute = minute(sched_dep_time)) |> 
  group_by(minute) |> 
  summarize(
    avg_delay = mean(dep_delay, na.rm = TRUE),
    n = n()
  ) |> 
  ggplot(aes(x = minute, y = avg_delay)) +
  geom_line()
```

### 

Interestingly, if we look at the *scheduled* departure time we don’t see such a strong pattern:

## Date and time components with rounding
### 

An alternative approach to plotting individual components is to round the date to a nearby unit of time, with `floor_date()`, `round_date()`, and `ceiling_date()`. Each function takes a vector of dates to adjust and then the name of the unit to round down (floor), round up (ceiling), or round to. 

### Exercise 1

Pipe `flights_dt` to `count()`. Set `week` to `floor_date(dep_time, "week")`.

```{r date-and-time-components-with-1, exercise = TRUE}

```

```{r date-and-time-components-with-1-hint-1, eval = FALSE}
... |> 
  count(... = floor_date(..., "..."))
```

```{r date-and-time-components-with-1-test, include = FALSE}
flights_dt |> 
  count(week = floor_date(dep_time, "week"))
```

### 

`count()` lets you quickly count the unique values of one or more variables

The first argument of `floor_date()` is the source we are pulling the data from. The second argument is which variable we want to be rounded.

### Exercise 2

Continue the pipe with `ggplot()`. Map `week` to the x-axis and `n` to the y-axis. Add `geom_line()` and `geom_point()`

```{r date-and-time-components-with-2, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r date-and-time-components-with-2-hint-1, eval = FALSE}
... |> 
  ggplot(aes(x = ..., y = ...) +
    ...() +
    ...()
```

```{r date-and-time-components-with-2-test, include = FALSE}
flights_dt |> 
  count(week = floor_date(dep_time, "week")) |> 
  ggplot(aes(x = week, y = n)) +
  geom_line() + 
  geom_point()
```

### 

Every flight that happens in a certain week is added up and plotted as one point using `floor_date()`. Now, we can see the distribution of flights each week in the year.

### Exercise 3

Start a new pipe with `flights_dt` to `mutate()`. Set `dep_hour` to `hms::as_hms(dep_time - floor_date(dep_time, "day")))`

```{r date-and-time-components-with-3, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r date-and-time-components-with-3-hint-1, eval = FALSE}
flights_dt |> 
  mutate(...)
```

```{r date-and-time-components-with-3-test, include = FALSE}
flights_dt |> 
  mutate(dep_hour = hms::as_hms(dep_time - floor_date(dep_time, "day")))
```

### 

Computing the difference between a pair of date-times yields a difftime. A difftime class object records a time span of seconds, minutes, hours, days, or weeks. This ambiguity can make difftimes a little painful to work with, so we add `hms::as_hms`, to have it return a timestamp instead.

### Exercise 4

Continue the pipe to `ggplot()`. Map `dep_hour` to the x-axis. Add the `geom_freqpoly()` layer and set the `binwidth` to `1800`

```{r date-and-time-components-with-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r date-and-time-components-with-4-hint-1, eval = FALSE}
... |> 
  ggplot(aes(x = dep_hour)) +
  geom_freqpoly(binwidth = 60 * 30)
```

```{r date-and-time-components-with-4-test, include = FALSE}
flights_dt |> 
  mutate(dep_hour = hms::as_hms(dep_time - floor_date(dep_time, "day"))) |> 
  ggplot(aes(x = dep_hour)) +
  geom_freqpoly(binwidth = 60 * 30)
```

### 

Here we use rounding to show the distribution of flights across the course of a day by computing the difference between `dep_time` and the earliest instant of that day, which we find by using `floor_date()`. If we wanted the latest instant of that day, we would use `ceiling_date()`

<!-- Add some questions with round_date() and ceiling_date() -->



## Modifying date/time components
### 

You can also use each accessor function to modify the components of a date/time. This doesn’t come up much in data analysis, but can be useful when cleaning data that has clearly incorrect dates.

### Exercise 1

Let's modify this date: "2026-07-08 12:34:56" with the `ymd_hms()` function. Create a `datetime` variable and set `ymd_hms()` to this.

```{r modifying-date-time-components-1, exercise = TRUE}

```

```{r modifying-date-time-components-1-hint-1, eval = FALSE}
datetime <- ymd_hms("...")
```

```{r modifying-date-time-components-1-test, include = FALSE}
datetime <- ymd_hms("2026-07-08 12:34:56")
```

### Exercise 2

Let's modify the date by using `year()` on `datetime` and setting it to 2030. Type `datetime` after to see whether it has been changed.

```{r modifying-date-time-components-2, exercise = TRUE}

```

```{r modifying-date-time-components-2-hint-1, eval = FALSE}
year(...) <- 2030
datetime
```

```{r modifying-date-time-components-2-test, include = FALSE}
year(datetime) <- 2030
datetime
```

### 

We see that everything else about the date is the same except the year, which is now 2030 instead of 2026.

### Exercise 3

Now, we're going to modify the date by using `month()`. Run `month()` on `datetime` and setting it to 2030. Type `datetime` after to see whether it has been changed.

```{r modifying-date-time-components-3, exercise = TRUE}

```

```{r modifying-date-time-components-3-hint-1, eval = FALSE}
month(...) <- 01
...
```

```{r modifying-date-time-components-3-test, include = FALSE}
month(datetime) <- 01
datetime
```

### 

Once again, everything is the same about the date except for the month. 

### Exercise 4

Run `hour(datetime) <- hour(datetime) + 1`. Print `datetime` to see the changes.

```{r modifying-date-time-components-4, exercise = TRUE}

```

```{r modifying-date-time-components-4-hint-1, eval = FALSE}
hour(...) <- hour(...) + 1
...
```

```{r modifying-date-time-components-4-test, include = FALSE}
hour(datetime) <- hour(datetime) + 1
datetime
```

### 

We can see that the hour has gone up by one. We first recieve the hour of `datetime`, we add one to it, and we set that value to `datetime`. 

With the `ymd_hms()` functions we can automatically assign the Universal Coordinated Time Zone (UTC) to the parsed date.

### Exercise 5

Run `update()` with the arguments `datetime`, `year = 2030`, `month = 2`, `mday = 2`, `hour = 2`

```{r modifying-date-time-components-5, exercise = TRUE}

```

```{r modifying-date-time-components-5-hint-1, eval = FALSE}
update(..., year = ..., month = ..., mday = ..., hour = ...)
```

```{r modifying-date-time-components-5-test, include = FALSE}
update(datetime, year = 2030, month = 2, mday = 2, hour = 2)
```

### 

Alternatively, rather than modifying an existing variable, you can create a new date-time with `update()`. This also allows you to set multiple values in one step as shown above. Check out [`update()`](https://lubridate.tidyverse.org/reference/DateTimeUpdate.html) for more information

### Exercise 6

Run `update()` with the arguments `ymd("2023-02-01")` and  `mday = 30`

```{r modifying-date-time-components-6, exercise = TRUE}

```

```{r modifying-date-time-components-6-hint-1, eval = FALSE}
update(ymd("..."), mday = ...)
```

```{r modifying-date-time-components-6-test, include = FALSE}
update(ymd("2023-02-01"), mday = 30)
```

### 

If values are too big they will roll over.


## Time Spans with Duration
### 

Next you’ll learn about how arithmetic with dates works, including subtraction, addition, and division. Along the way, you’ll learn about three important classes that represent time spans:

**- Durations**, which represent an exact number of seconds.
**- Periods**, which represent human units like weeks and months.
**- Intervals**, which represent a starting and ending point.

How do you pick between duration, periods, and intervals? As always, pick the simplest data structure that solves your problem. If you only care about physical time, use a duration; if you need to add human times, use a period; if you need to figure out how long a span is in human units, use an interval.

### Exercise 1

Run `today()` and subtract `ymd("1979-10-14")` from it. Set the resulting object to `h_age`. Print out `h_age`

```{r time-spans-with-duration-1, exercise = TRUE}


```

```{r time-spans-with-duration-1-hint-1, eval = FALSE}
h_age <- ...() - ymd("...")
...
```

```{r time-spans-with-duration-1-test, include = FALSE}
h_age <- today() - ymd("1979-10-14")
h_age
```

### 

You should get a time difference of 16354 days.

In R, when you subtract two dates, you get a difftime object.

A `difftime` class object records a time span of seconds, minutes, hours, days, or weeks. This ambiguity can make difftimes a little painful to work with, so lubridate provides an alternative which always uses seconds: the **duration.**

### Exercise 2

Run `as.duration` on `h_age`

```{r time-spans-with-duration-2, exercise = TRUE}

```

```{r time-spans-with-duration-2-hint-1, eval = FALSE}
as.duration(...)
```

```{r time-spans-with-duration-2-test, include = FALSE}
as.duration(h_age)
```

### 

The resulting output should be in seconds. `as.duration()` gets rid of the ambiguity of different types of time spans and always uses seconds.

### Exercise 3

Durations come with a bunch of convenient constructors:

Run the code below.

```{r time-spans-with-duration-3, exercise = TRUE}
dseconds(15)
dminutes(10)
dhours(c(12, 24))
ddays(0:5)
dweeks(3)
dyears(1)
```

```{r time-spans-with-duration-3-test, include = FALSE}
dseconds(15)
dminutes(10)
dhours(c(12, 24))
ddays(0:5)
dweeks(3)
dyears(1)
```

### 

Durations always record the time span in seconds. Larger units are created by converting minutes, hours, days, weeks, and years to seconds: 60 seconds in a minute, 60 minutes in an hour, 24 hours in a day, and 7 days in a week. Larger time units are more problematic. A year uses the “average” number of days in a year, i.e. 365.25. There’s no way to convert a month to a duration, because there’s just too much variation.

You can also add use `c()` and `:` to run multiple arguments at once.

### Exercise 4

Multiply `2` with `dyears(1)`. Add `dweeks(12)`

```{r time-spans-with-duration-4, exercise = TRUE}

```

```{r time-spans-with-duration-4-hint-1, eval = FALSE}
2 * dyears(...) + dweeks(...)
```

```{r time-spans-with-duration-4-test, include = FALSE}
2 * dyears(1) + dweeks(12)
```

### 

You can add and multiply durations as well. This is as simple as it looks, everything is converted to seconds, then the addition/multiplication happens and the final value is returned in seconds and years.

### Exercise 5

Add `today()` to `ddays(1)`. On the next line subtract `dyears(1)` from `today()`

```{r time-spans-with-duration-5, exercise = TRUE}


```

```{r time-spans-with-duration-5-hint-1, eval = FALSE}
...() + ddays(...)
...() - dyears(...)
```

```{r time-spans-with-duration-5-test, include = FALSE}
today() + ddays(1)
today() - dyears(1)
```

### 

You can add and subtract durations to and from dates. Notice you get the date for tomorrow and todays date last year.

### Exercise 6

Create a new object named `one_am`. Set `one_am` to `ymd_hms("2026-03-08 01:00:00", tz = "America/New_York")`

```{r time-spans-with-duration-6, exercise = TRUE}

```

```{r time-spans-with-duration-6-hint-1, eval = FALSE}
one_am <- ...
```

```{r time-spans-with-duration-6-test, include = FALSE}
one_am <- ymd_hms("2026-03-08 01:00:00", tz = "America/New_York")
```

### 

However, because durations represent an exact number of seconds, sometimes you might get an unexpected result

### Exercise 7

Print `one_am`, then on the next line add `ddays(1)` to `one_am`.

```{r time-spans-with-duration-7, exercise = TRUE}


```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r time-spans-with-duration-7-hint-1, eval = FALSE}
one_am
one_am + ...
```

```{r time-spans-with-duration-7-test, include = FALSE}
one_am
one_am + ddays(1)
```

### 

Why is one day after 1am March 8, 2am March 9? If you look carefully at the date you might also notice that the time zones have changed. March 8 only has 23 hours because it’s when DST starts, so if we add a full days worth of seconds we end up with a different time.

### 

With durations, we can add, divide, subtract, and multiply with other functions to get a new desired output. With duration we can use `dseconds()` (seconds), `dminutes()` (minutes), `dhours()` (hours), `ddays()` (days), `dweeks()` (weeks), `dyears()` (years). Check out all of the other functions here: [**Duration**](https://lubridate.tidyverse.org/reference/duration.html)

## Time Spans with Periods
### 

To solve the problem from Exercise 7 of the last section, lubridate provides **periods.** Periods are time spans but don’t have a fixed length in seconds, instead they work with “human” times, like days and months. That allows them to work in a more intuitive way

### Exercise 1

Run the code below

```{r time-spans-with-periods-1, exercise = TRUE}
hours(c(12, 24))
days(7)
months(1:6)
```

```{r time-spans-with-periods-1-test, include = FALSE}
hours(c(12, 24))
days(7)
months(1:6)
```

### 

Like durations, periods can be created with a number of friendly constructor functions. 

Instead of returning an output in seconds, the value returned can be in hours, days or months, it just depends on the function used.

### Exercise 2

Add `months(6)` and `days(1)`. Multiply the resulting value by `10`.

```{r time-spans-with-periods-2, exercise = TRUE}

```

```{r time-spans-with-periods-2-hint-1, eval = FALSE}
10 * (months(...) + days(...))
```

```{r time-spans-with-periods-2-test, include = FALSE}
10 * (months(6) + days(1))
```

### 

You can add and multiply periods similar to how we do with durations.

### Exercise 3

Add `dyears(1)` to `ymd("2024-01-01")`. Do the same thing on the next line but change `dyears()` to just `years()`

```{r time-spans-with-periods-3, exercise = TRUE}


```

```{r time-spans-with-periods-3-hint-1, eval = FALSE}
ymd("...") + dyears(...)
ymd("...") + years(...)
```

```{r time-spans-with-periods-3-test, include = FALSE}
ymd("2024-01-01") + dyears(1)
ymd("2024-01-01") + years(1)
```

### 

We can of course add periods to dates.

### Exercise 4

Add `ddays(1)` and `days(1)` to `one_am` on separate lines.

```{r time-spans-with-periods-4, exercise = TRUE}


```

```{r time-spans-with-periods-4-hint-1, eval = FALSE}
one_am + ddays(...)
one_am + days(...)
```

```{r time-spans-with-periods-4-test, include = FALSE}
one_am + ddays(1)
one_am + days(1)
```

### 

We can safely say that compared to durations, periods are more likely to do what you expect.

### Exercise 5

Let’s use periods to fix an oddity related to our flight dates. Some planes appear to have arrived at their destination **before** they departed from New York City.

```{r}
flights_dt |> 
  filter(arr_time < dep_time) 
```

### 

Pipe `mutate()` to the function. Create a new variable, `overnight`, and set it equal to `arr_time < dep_time` 

```{r time-spans-with-periods-5, exercise = TRUE}
flights_dt2 <- flights_dt
```

```{r time-spans-with-periods-5-hint-1, eval = FALSE}
... |>
  mutate(... = ...)
```

```{r time-spans-with-periods-5-test, include = FALSE}
flights_dt2 <- flights_dt |> 
  mutate(
    overnight = arr_time < dep_time)
```

### 

These are overnight flights. We used the same date information for both the departure and the arrival times, but these flights arrived on the following day. We can fix this by adding `days(1)` to the arrival time of each overnight flight.

### Exercise 6

Modify the `arr_time` and `sched_arr_time` variables by adding `days(overnight)` inside of `mutate()`

```{r time-spans-with-periods-6, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r time-spans-with-periods-6-hint-1, eval = FALSE}
... |>
  mutate(...,
         arr_time = arr_time + ...,
         sched_arr_time = sched_arr_time + ...)
```

```{r time-spans-with-periods-6-test, include = FALSE}
flights_dt2 <- flights_dt |> 
  mutate(
    overnight = arr_time < dep_time,
    arr_time = arr_time + days(overnight),
    sched_arr_time = sched_arr_time + days(overnight)
  )
```

### Exercise 7

Pipe `flights_dt2` to `filter()`, with the argument `arr_time < dep_time`

```{r time-spans-with-periods-7, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r time-spans-with-periods-7-hint-1, eval = FALSE}
flights_dt2 |> 
  filter(... < ...)
```

```{r time-spans-with-periods-7-test, include = FALSE}
flights_dt2 |> 
  filter(arr_time < dep_time)
```

### 

We see that there are no flights that departed after they arrived, since that is not possible! All of our flights finally obey the laws of physics.

## Time Spans with Intervals
### 

### Exercise 1

Run `dyears(1) / ddays(365)`

```{r time-spans-with-intervals-1, exercise = TRUE}

```

```{r time-spans-with-intervals-1-hint-1, eval = FALSE}
dyears(...) / ddays(...)
```

```{r time-spans-with-intervals-1-test, include = FALSE}
dyears(1) / ddays(365)
```

### 

You may be wondering why the answer is not 1. Well, there are techinically 365.25 days in a year, divided by 365 gets you a little over one.

### Exercise 2

Run `years(1) / days(1)`

```{r time-spans-with-intervals-2, exercise = TRUE}

```

```{r time-spans-with-intervals-2-hint-1, eval = FALSE}
years(...) / days(...)
```

```{r time-spans-with-intervals-2-test, include = FALSE}
years(1) / days(1)
```

### 

Initially you may ask what this returns. Well, if the year was 2015 it should return 365, but if it was 2016, it should return 366! There’s not quite enough information for lubridate to give a single clear answer. What it does instead is give an estimate.

### Exercise 3

Connect `ymd("2023-01-01")` and `ymd("2024-01-01")` with a `%--%`. Set it equal to a new object called `y2023`. Print `y2023`

```{r time-spans-with-intervals-3, exercise = TRUE}

```

```{r time-spans-with-intervals-3-hint-1, eval = FALSE}
y2023 <- ymd("...") %--% ymd("...")
```

```{r time-spans-with-intervals-3-test, include = FALSE}
y2023 <- ymd("2023-01-01") %--% ymd("2024-01-01")
y2023
```

### 

If you want a more accurate measurement, you’ll have to use an **interval.** An interval is a pair of starting and ending date times, or you can think of it as a duration with a starting point.

You can create an interval by writing `start %--% end`:

### Exercise 4

Do the same thing, but for the year 2024. Name this new object `y2024`

```{r time-spans-with-intervals-4, exercise = TRUE}

```

```{r time-spans-with-intervals-4-hint-1, eval = FALSE}
y2024 <- ymd("...") %--% ymd("...")
```

```{r time-spans-with-intervals-4-test, include = FALSE}
y2024 <- ymd("2024-01-01") %--% ymd("2025-01-01")
y2024
```

### Exercise 5

Divide `y2023` and `y2024` by `days(1)`

```{r time-spans-with-intervals-5, exercise = TRUE}


```

```{r time-spans-with-intervals-5-hint-1, eval = FALSE}
y2023 / days(...)
y2024 / days(...)
```

```{r time-spans-with-intervals-5-test, include = FALSE}
y2023 / days(1)
y2024 / days(1)
```

### 

Here we see that now we either get 365 or 366. Instead of getting an estimate, we get the exact number of days for that year.

### 

Whenever possible we attempt to use duration, periods, and interval respectively based on the situation because of the accuracy it provides us.

### Exercise 6

Subtract `today()` from your birthday using `ymd()`

```{r time-spans-with-intervals-6, exercise = TRUE}

```

```{r time-spans-with-intervals-6-hint-1, eval = FALSE}
today() - ymd("...")
```

```{r time-spans-with-intervals-6-test, include = FALSE}
age <- today() - ymd("2000-1-30") 
# My birthday
```

### 

The output may seem familiar to you, since this is your age!


## Time Zones
### 

Time zones are an enormously complicated topic because of their interaction with geopolitical entities. Fortunately we don’t need to dig into all the details as they’re not all important for data analysis, but there are a few challenges we’ll need to tackle head on.

### 

The first challenge is that everyday names of time zones tend to be ambiguous. For example, if you’re American you’re probably familiar with EST, or Eastern Standard Time. However, both Australia and Canada also have EST! To avoid confusion, R uses the international standard IANA time zones. These use a consistent naming scheme `{area}/{location}`, typically in the form `{continent}/{city}` or `{ocean}/{city}`. Examples include “America/New_York”, “Europe/Paris”, and “Pacific/Auckland”.

### 

You might wonder why the time zone uses a city, when typically you think of time zones as associated with a country or region within a country. This is because the IANA database has to record decades worth of time zone rules. Over the course of decades, countries change names (or break apart) fairly frequently, but city names tend to stay the same. Another problem is that the name needs to reflect not only the current behavior, but also the complete history. For example, there are time zones for both “America/New_York” and “America/Detroit”. These cities both currently use Eastern Standard Time but in 1969-1972 Michigan (the state in which Detroit is located), did not follow DST, so it needs a different name. It’s worth reading the raw time zone database (available at [**https://www.iana.org/time-zones**](https://www.iana.org/time-zones)) just to read some of these stories!

### Exercise 1

Type `Sys.timezone()` and run in the code chunk below.

```{r time-zones-1, exercise = TRUE}

```

```{r time-zones-1-hint-1, eval = FALSE}
...()
```

```{r time-zones-1-test, include = FALSE}
Sys.timezone()
```

### 

We can find out what R thinks our current time zone is with `Sys.timezone()`. If R doesn't know, you will get an `NA`

### Exercise 2

Run `OlsonNames()`

```{r time-zones-2, exercise = TRUE}

```

```{r time-zones-2-hint-1, eval = FALSE}
OlsonNames()
```

```{r time-zones-2-test, include = FALSE}
OlsonNames()
```

### 

`OlsonNames()` gives us the complete list of all time zone names.

### Exercise 3

Create a new datetime. Use `ymd_hms()` with the argument "2024-06-01 12:00:00", set the `tz`to "America/New_York". Set the equal to a new object named `x1`. Print `x1`

```{r time-zones-3, exercise = TRUE}

```

```{r time-zones-3-hint-1, eval = FALSE}
x1 <- ymd_hms("...", tz = "...")
...
```

```{r time-zones-3-test, include = FALSE}
x1 <- ymd_hms("2024-06-01 12:00:00", tz = "America/New_York")
x1
```

### Exercise 4

Now, do the same thing again, but, change the datetime to "2024-06-01 18:00:00", and the `tz` to "Europe/Copenhagen". Set it equal to a new object called `x2`. Print `x2`

```{r time-zones-4, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r time-zones-4-hint-1, eval = FALSE}
x2 <- ymd_hms("...", tz = "...")
...
```

```{r time-zones-4-test, include = FALSE}
x2 <- ymd_hms("2024-06-01 18:00:00", tz = "Europe/Copenhagen")
x2
```

### Exercise 5

Do the same thing again, but, change the datetime to "2024-06-02 04:00:00", and the `tz` to  "Pacific/Auckland". Set it equal to a new object called `x3`. Print `x3`

```{r time-zones-5, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r time-zones-5-hint-1, eval = FALSE}
x3 <- ymd_hms("...", tz = "...")
...
```

```{r time-zones-5-test, include = FALSE}
x3 <- ymd_hms("2024-06-02 04:00:00", tz = "Pacific/Auckland")
x3
```

### 

In R, the time zone is an attribute of the date-time that only controls printing. For example, these three objects represent the same instant in time

### Exercise 6

Subtract `x2` and `x3` from `x1` on separate lines.

```{r time-zones-6, exercise = TRUE}


```

```{r time-zones-6-hint-1, eval = FALSE}
x1 - ...
x1 - ...
```

```{r time-zones-6-test, include = FALSE}
x1 - x2
x1 - x3
```

### 

We can verify that they’re the same time using subtraction, since the difference is 0.

### Exercise 7

Create a new object named `x4`. Set is equal to `x1`, `x2` and `x3` using a vector `c()`. Print `x4`

```{r time-zones-7, exercise = TRUE}


```

```{r time-zones-7-hint-1, eval = FALSE}
x4 <- c(..., ..., ...)
...
```

```{r time-zones-7-test, include = FALSE}
x4 <- c(x1, x2, x3)
x4
```

### 

Unless otherwise specified, lubridate always uses UTC. UTC (Coordinated Universal Time) is the standard time zone used by the scientific community and is roughly equivalent to GMT (Greenwich Mean Time). It does not have DST, which makes a convenient representation for computation. Operations that combine date-times, like `c()`, will often drop the time zone. In that case, the date-times will display in the time zone of the first element.

### 

You can change the time zone in two ways:

### Exercise 8

Use the `with_tz()` function with the arguments `x4` and `tzone = "Australia/Lord_Howe"`. Set this equal to a new object `x4a`, print it and subtract `x4` from it.

```{r time-zones-8, exercise = TRUE}



```

```{r time-zones-8-hint-1, eval = FALSE}
x4a <- with_tz(x4, tzone = "Australia/Lord_Howe")
x4a
x4a - x4
```

```{r time-zones-8-test, include = FALSE}
x4a <- with_tz(x4, tzone = "Australia/Lord_Howe")
x4a
x4a - x4
```

### 

Here we keep the instant in time the same, as shown by the the time difference of 0 seconds, using `with_tz`, but we change how it’s displayed. We use this when the instant is correct, but want a more natural display.


### Exercise 9

Use the `force_tz()` function with the arguments `x4` and `tzone = "Australia/Lord_Howe"`. Set this equal to a new object `x4b`, print it and subtract `x4` from it.

```{r time-zones-9, exercise = TRUE}

```

<button onclick = "transfer_code(this)">Copy previous code</button>

```{r time-zones-9-hint-1, eval = FALSE}
x4b <- force_tz(x4, tzone = "Australia/Lord_Howe")
x4b
x4b - x4
```

```{r time-zones-9-test, include = FALSE}
x4b <- force_tz(x4, tzone = "Australia/Lord_Howe")
x4b
x4b - x4
```

### 

Here, we change the underlying instant in time, as shown by the time difference of 14.5 hours, using `force_tz()`. We use this when we have an instant that has been labelled with the incorrect time zone, and need to fix it.


## Summary
### 

This tutorial covered [Chapter 17: Dates and times](https://r4ds.hadley.nz/datetimes.html) from [*R for Data Science (2e)*](https://r4ds.hadley.nz/) by Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund. You learned how to use the [**lubridate**](https://lubridate.tidyverse.org/) on the `flights` data from the [**nyc13flights**](https://nycflights13.tidyverse.org/) package.

```{r download-answers, child = system.file("child_documents/download_answers.Rmd", package = "tutorial.helpers")}
```
