Module 8-1 Demonstration

class: center, middle, inverse, title-slide

.title[
# Module 8-1 Demonstration
]
.subtitle[
## Special Operations: Dealing with date/time
]

---

# Date-time Basics

- Base R has functions to get the current date and time.

- The `lubridate` package offers fast and user-friendly parsing of date-time data.

.pull-left[

``` r
# get date information

Sys.Date()
```

```
## [1] "2025-10-02"
```

``` r
# get current time

Sys.time()
```

```
## [1] "2025-10-02 13:53:20 AEST"
```
]
.pull-right[

``` r
#install.packages("lubridate")
library(lubridate)
```

``` r
# get current time using `lubridate`

now()
```

```
## [1] "2025-10-02 13:53:20 AEST"
```
]

---
# Date-time manipulations

- When date and time data are imported into R they will often default to:

- a **factor** or **character** due to symbols such as `-`, `:` and `/`

.pull-left[

``` r
candy <- read.csv("../data/candy_production.csv")

head(candy,3)
```

```
##   observation_date IPG3113N
## 1       1972-01-01  85.6945
## 2       1972-02-01  71.8200
## 3       1972-03-01  66.0229
```

``` r
class(candy$observation_date)
```

```
## [1] "character"
```
]
.pull-right[

``` r
candy <- read.csv("../data/candy_production.csv", stringsAsFactors = TRUE)

head(candy,3)
```

```
##   observation_date IPG3113N
## 1       1972-01-01  85.6945
## 2       1972-02-01  71.8200
## 3       1972-03-01  66.0229
```

``` r
class(candy$observation_date)
```

```
## [1] "factor"
```
]

---

# Date-time manipulations Cont.

- Or sometimes they are converted into a **numeric (integer)** format:

``` r
library(lubridate)
head(lakers[, 1:4], 3)
```

```
##       date opponent game_type  time
## 1 20081028      POR      home 12:00
## 2 20081028      POR      home 11:39
## 3 20081028      POR      home 11:37
```

``` r
class(lakers$date)
```

```
## [1] "integer"
```

---

# Date-time manipulations Cont.

- If this is the case, we need to convert these to proper date format.

- We can use different strategies to convert them to a date format:

- `as.Date()` function under Base R.
    - `lubridate` package functions

---

# Convert factors to date with `as.Date()`

``` r
candy <- read.csv("../data/candy_production.csv", stringsAsFactors = TRUE)

head(candy,3)
```

```
##   observation_date IPG3113N
## 1       1972-01-01  85.6945
## 2       1972-02-01  71.8200
## 3       1972-03-01  66.0229
```

``` r
class(candy$observation_date)
```

```
## [1] "factor"
```

``` r
* candy_date <- as.Date(candy$observation_date)

class(candy_date)
```

```
## [1] "Date"
```

---

# Convert strings to date with `as.Date()`

``` r
candy <- read.csv("../data/candy_production.csv", stringsAsFactors = FALSE)

head(candy,3)
```

```
##   observation_date IPG3113N
## 1       1972-01-01  85.6945
## 2       1972-02-01  71.8200
## 3       1972-03-01  66.0229
```

``` r
class(candy$observation_date)
```

```
## [1] "character"
```

``` r
* candy_date <- as.Date(candy$observation_date)

class(candy_date)
```

```
## [1] "Date"
```

---

# Date-time manipulations Cont.

- Note that the default date format is **YYYY-MM-DD**; therefore, if your string is of different format you must incorporate the `format` argument.

- Have a look at these two examples:

``` r
x <- c("08/03/2018", "23/03/2016", "30/01/2018")
y <- c("08.03.2018", "23.03.2016", "30.01.2018")
```

- This time the string format is **DD/MM/YYYY** for `x` and **DD.MM.YYYY** for `y`; therefore, we need to specify the `format` argument explicitly.

``` r
* x_date <- as.Date(x, format = "%d/%m/%Y")

* y_date <- as.Date(y, format = "%d.%m.%Y") 
```

- [Useful link to Dates and Times in R](https://www.stat.berkeley.edu/~s133/dates.html) 
---

# Date-time manipulations with `lubridate`

- The `lubridate` package can automatically recognise the common separators used when recording dates (`-`, `/`, `.`, and ` `).

- As a result, you only need to focus on specifying the order of the date elements. For example:

``` r
a <- c("26/03/2016", "08/12/2011", "05/01/2013")
```

- In order to convert `a` into a date, we can use `dmy()` (Day, Month, Year)

``` r
library(lubridate)

* a_date <- dmy(a)

a_date
```

```
## [1] "2016-03-26" "2011-12-08" "2013-01-05"
```

---

# Date-time manipulations with `lubridate`

- Here is the list of lubridate functions used for this purpose:

Function | Order of elements in date-time 
---------|--------------------------------
ymd() | year, month, day
ydm() | year, day, month
mdy() | month, day, year
dmy() | day, month, year
hm() | hour, minute
hms() | hour, minute, second
ymd_hms() | year, month, day, hour, minute, second

---

# Your turn! Activity

- Convert the following strings into a date format first using `as.Date()` and then `lubridate` functions.

``` r
task1 <- c("08032018", "29062017", "23032016", "30012018")
```

``` r
task2 <- c("08}03}2018", "29}06}2017", "23}03}2016", "30}01}2018")
```

``` r
task3 <- c("08.03.2018", "29062017", "23/03/2016", "30-01-2018")
```

???

``` r
task1 <- c("08032018", "29062017", "23032016", "30012018")

# using as.Date

date_task1 <- as.Date(task1, format = "%d%m%Y")
class(date_task1)
```

```
## [1] "Date"
```

``` r
# using lubridate

date_task1 <- dmy(task1)
class(date_task1)
```

```
## [1] "Date"
```

``` r
task2 <- c("08}03}2018", "29}06}2017", "23}03}2016", "30}01}2018")

# using as.Date

date_task2 <- as.Date(task2, format = "%d}%m}%Y")
class(date_task2)
```

```
## [1] "Date"
```

``` r
# using lubridate
date_task2 <- dmy(task2)
class(date_task2)
```

```
## [1] "Date"
```

``` r
task3 <- c("08.03.2018", "29062017", "23/03/2016", "30-01-2018")

# using as.Date

date_task3 <- as.Date(task3, format = c("%d.%m.%Y", "%d%m%Y", "%d/%m/%Y", "%d-%m-%Y"))
date_task3
```

```
## [1] "2018-03-08" "2017-06-29" "2016-03-23" "2018-01-30"
```

``` r
class(date_task3)
```

```
## [1] "Date"
```

``` r
# using lubridate
date_task3 <- dmy(task3)
class(date_task3)
```

```
## [1] "Date"
```

- As seen, lubridate is very flexible in recognising separators.

---

# Extract & manipulate parts of dates

- Sometimes, instead of a single string, we will have date-time spread across multiple columns.

- Remember the flights data in the `nycflights13` package.

``` r
library(nycflights13)

head(flights[,c(1:3, 17:18)])
```

```
## # A tibble: 6 × 5
##    year month   day  hour minute
##   <int> <int> <int> <dbl>  <dbl>
## 1  2013     1     1     5     15
## 2  2013     1     1     5     29
## 3  2013     1     1     5     40
## 4  2013     1     1     5     45
## 5  2013     1     1     6      0
## 6  2013     1     1     5     58
```

- Components of the date information is given in multiple columns.

---

# Extract & manipulate parts of dates Cont.

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

``` r
*flights_new<- flights %>% mutate(departure = make_datetime(year, month, day, hour, minute))

head(flights_new$departure)
```

```
## [1] "2013-01-01 05:15:00 UTC" "2013-01-01 05:29:00 UTC"
## [3] "2013-01-01 05:40:00 UTC" "2013-01-01 05:45:00 UTC"
## [5] "2013-01-01 06:00:00 UTC" "2013-01-01 05:58:00 UTC"
```

---

# Extract & manipulate parts of dates Cont.

- Conversely, we can extract individual parts of the date with the accessor functions in `lubridate`. Here is the list of available functions:

Accessor Function | Extracts 
------------------ | ----------
year() | year
month() | month
mday() | day of the month
yday() | day of the year
wday() | day of the week
hour() | hour 
minute() | minute
second() | second

---

# Extract & manipulate parts of dates Cont.

- To extract the year information of the `flights_new$departure` column we can use:

``` r
flights_new$departure %>% year() %>% head()
```

```
## [1] 2013 2013 2013 2013 2013 2013
```

- We can also use accessor functions to change the components of a date/time:

``` r
# create a date
datetime <- ymd_hms("2016-07-12 12:34:56")

# replace the year component with 2025
year(datetime) <- 2025

# replace the month component with January
month(datetime) <- 05

datetime
```

```
## [1] "2025-05-12 12:34:56 UTC"
```

---

# Date arithmetic

- We may require to compute a new variable from the date - time information.

- To calculate **time difference**, simply subtract two dates:

``` r
my_age <- today() - ymd(19810529)
my_age
```

```
## Time difference of 16197 days
```

- Or use `difftime()` function

``` r
my_age <- difftime(today(), ymd(19810529))
my_age
```

```
## Time difference of 16197 days
```

- We get a **time intervals/differences object** (a.k.a `difftime` in R)

---

# Date arithmetic Cont.

- To change the time difference to another unit we can use `units` argument:

``` r
my_age_secs<-difftime(today(), ymd(19810529), units = "secs")
my_age_secs
```

```
## Time difference of 1399420800 secs
```

- `units` can be `"auto"`, `"secs"`, `"mins"`, `"hours"`, `"days"`, `"weeks"`.

- Note that `difftime` objects do not accept **'years'** as a value for 'units', due to "leap years".

- To get my age in years:

``` r
my_age_days<-difftime(today(), ymd(19810529), units = "days")

*my_age_years<- as.numeric(my_age_days/365)

my_age_years
```

```
## [1] 44.37534
```

---

# Date arithmetic Cont.

- A sequence of dates/times can be created using `seq()` function with specifying four arguments  `seq(from, to, by, length.out)`.

``` r
# create a sequence of years from 1980 to 2024 by 2

even_years <- seq(from = 1980, to=2024, by = 2)

even_years
```

```
##  [1] 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
## [16] 2010 2012 2014 2016 2018 2020 2022 2024
```

``` r
# create a sequence of hours from 1/1/2024 9:00 to 1/1/2024 12:00

hour_list <- seq(ymd_hm("2024-1-1 9:00"), ymd_hm("2024-1-1 12:00"), by = "hour")

hour_list
```

```
## [1] "2024-01-01 09:00:00 UTC" "2024-01-01 10:00:00 UTC"
## [3] "2024-01-01 11:00:00 UTC" "2024-01-01 12:00:00 UTC"
```

---

# Date arithmetic: durations

- A difftime class object records a time span of seconds, minutes, hours, days, or weeks. 
- `lubridate` provides an alternative which always uses seconds: the **duration**.

Duration Function | Usage 
------------------ | ----------
as.duration() | assigns duration in seconds
is.duration() | check if it is duration
dseconds | duration in seconds
dminutes | duration in minutes
dhours | duration in hours
ddays | duration in days
dweeks | duration in weeks
dyears | duration in years

- Minutes = 60 secs, hours = 3600 secs, days = 86400 secs, weeks = 604800 secs.

---

# Date arithmetic: durations Cont.

- With durations, date arithmetics would become easier:

``` r
# three weeks later
today() + dweeks(3)
```

```
## [1] "2025-10-23"
```

``` r
# tomorrow
today() + ddays(1)
```

```
## [1] "2025-10-03"
```

``` r
# last year
today() - dyears(1)
```

```
## [1] "2024-10-01 18:00:00 UTC"
```

---

# Date arithmetic: durations Cont.

``` r
# two years, 12 weeks and 15 hours before

today() - ( dyears(2) + dweeks(12) + dhours(15) )
```

```
## [1] "2023-07-09 21:00:00 UTC"
```

``` r
#same as above:

today() - ( 2*dyears(1) + dweeks(12) + dhours(15) )
```

```
## [1] "2023-07-09 21:00:00 UTC"
```

---

# Your turn!  Activity

- Task 1: Set up 10 weekly meeting with your manager (start it from today)

- Task 2: Find the weekday (i.e. Monday, Tuesday, Wednesday etc) of the day when you were born.

???

``` r
task1 <- today() + dweeks(0:9)
task1
```

```
##  [1] "2025-10-02" "2025-10-09" "2025-10-16" "2025-10-23" "2025-10-30"
##  [6] "2025-11-06" "2025-11-13" "2025-11-20" "2025-11-27" "2025-12-04"
```

``` r
task2 <- wday(dmy(29051981), label = T)
task2
```

```
## [1] Fri
## Levels: Sun < Mon < Tue < Wed < Thu < Fri < Sat
```

---

# Functions to Remember for Week 10

- Date/time manipulations using BaseR and `lubridate`
    - `as.Date()`, `ymd()`, `ydm()`, `mdy()`...etc.
    - `year()`, `month()`, `mday()`, `yday()`, `wday()`, ...etc.
    -  duration functions: `duration()`, `dminutes()` , `dhours()`, ..etc

- Practice!

---

# Your turn! Class Worksheet

- Working in small groups, complete the following class worksheet

[Module 8-1 Worksheet](../worksheets/Week_10_Worksheet.html)

- Once completed, feel free to work on your Assessments.
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