Using lubridate to Work with Dates

Thursday, October 24

Today we will…

• Reminder About Lab 3 / Challenge 3 Revisions
• Debrief Lab 4
  • Lab 4 Peer Code Review
• Midterm Portfolio
  • What to Expect
  • How to Get Started
• New Material
  • Working with Date & Time Variables
• PA 5.2: Jewel Heist

Lab 3 / Challenge 3 Revisions

Lab 3 / Challenge 3 Revisions

Revisions are due by Friday

Any problem receiving a “Growing” can be revised and submitted for additional feedback

Lab 4

Common Mishaps

• Q1: Who collected these data? When were the data collected?
• Q4: Column titles of 2008 and 2018 are not descriptive!
  • Creating column names that describe the values stored in those columns!
  • The names_prefix = argument to pivot_wider() can help you make better column names!
  • DVS-6: I can create tables which make my summaries clear to the reader
• Q4: Unless you specify .groups = "drop" within summarize() your table still is grouped!
  • group_by() + summarize() only drops the first group.
  • If you have two variables inside group_by(), then the data will still be grouped by the second variable!
• Q7: The data description contains important information!
  • mc_toddler – Aggregated weekly, full-time median price charged for Center-based Care for toddlers.
  • mhi_2018 – Median household income expressed in 2018 dollars.

Recreating the Plot

DVS-2: I use plot modifications to make my visualizations clearer to the reader

• Facets ordered based on developmental stage not alphabetically
• Ordering colors in the legend so they appear in the same order as the lines in the plot.

DVS-3: I show creativity in my visualizations

• Exploring different color themes
  • Personally, I like the "Accent" palette from the RColorBrewer package, but you might like others!
  • Getting 10 colors is hard! I would recommend looking into the colorRampPalette() function to get more colors.

• Exploring different plot themes
  • Personally, I like theme_bw(), but you might like others!

Other “Big Picture” Code Feedback

I strongly recommend against nested functions, as they are difficult for people to understand what your code is doing. Having two lines is not less efficient and is more readable.

mutate(age_group = fct_relevel(fct_recode(age_group,
                                          "Infant" = "mc_infant",
                                          "Toddler" = "mc_toddler",
                                          "Preschool" = "mc_preschool"),
                                "Infant",
                                "Toddler",
                                "Preschool"))

Can you do this entire process in one pipeline?

ca_counties <- counties |>
  filter(state_name == "California")

ca_childcare <- childcare_costs |>
  inner_join(ca_counties)

Saving Objects That Aren’t Worth Saving

We should only save objects that we need to use later!

lowest_child_care_price_2018 <- ca_childcare |>
  filter(study_year == 2018) |>
  group_by(region) |>   
  summarise(median_infant_price = median(mc_infant)) |> 
  slice_min(order_by = median_infant_price)

lowest_child_care_price_2018

Don’t forget to complete your Lab 4 code review!

Don’t forget to complete your Lab 4 code review!

Make sure your feedback follows the code review guidelines.

Insert your review into the comment box!

Midterm Portfolios

What to Expect

You will create a Midterm Portfolio, with code covering the learning targets we have covered thus far in the course.

• Your code will be pulled from your Lab and Challenge assignments.

• You will write reflections on how you’ve:
  • revised your thinking
  • extended your thinking
  • supported and collaborated with your peers

Getting Started

1. Follow these directions to fork a copy of the Final Portfolio repository.

2. Clone the repository into RStudio.

3. Open the reflection_template.qmd file and give it a read!

4. Start finding excerpts of code you wrote for the Labs or Challenges that fit each learning target.

Date + Time Variables

Why are dates and times tricky?

When parsing dates and times, we have to consider complicating factors like…

• Daylight Savings Time.
  • One day a year is 23 hours; one day a year is 25 hours.
  • Some places use it, some don’t.
• Leap years – most years have 365 days, some have 366.
• Time zones.

lubridate

• Convert a date-like variable (“May 8, 1995”) to a date or date-time object.

• Find the weekday, month, year, etc from a date-time object.

• Convert between time zones.

Note

The lubridate package installs and loads with the tidyverse.

date-time Objects

There are multiple data types for dates and times.

• A date:
  • date or Date
• A date and a time (identifies a unique instant in time):
  • dtm
  • POSIXlt – stores date-times as the number of seconds since January 1, 1970 (“Unix Epoch”)
  • POSIXct – stores date-times as a list with elements for second, minute, hour, day, month, year, etc.

Creating date-time Objects

Create a date from individual components:

make_date(year = 1995, month = 05, day = 08)

[1] "1995-05-08"

Create a date-time Object from a String

mdy("August 29, 1991")

[1] "1991-08-29"

dmy("29-August-1991", 
    tz = "America/Denver")

[1] "1991-08-29 MDT"

dmy_hms("29-August-1991 9:32:12", 
        tz = "America/Denver")

[1] "1991-08-29 09:32:12 MDT"

as_datetime("91-08-29", 
            format = "%y-%m-%d")

[1] "1991-08-29 UTC"

parse_datetime("8/29/1991", 
               format = "%m/%d/%Y")

[1] "1991-08-29 UTC"

Creating date-time Objects

Common Mistake with Dates

as_datetime(2023-02-6)

[1] "1970-01-01 00:33:35 UTC"

my_date <- 2023-02-6
my_date

[1] 2015

What’s wrong here?

Make sure you use quotes!

• 2,015 seconds \(\approx\) 33.5 minutes

Extracting date-time Components

bday <- ymd_hms("1989-01-14 12:03:12", 
                tz = "America/Denver")
bday

[1] "1989-01-14 12:03:12 MST"

year(bday)

[1] 1989

month(bday)

[1] 1

day(bday)

[1] 14

wday(bday)

[1] 7

wday(bday, 
     label = TRUE, 
     abbr = FALSE)

[1] Saturday
7 Levels: Sunday < Monday < Tuesday < Wednesday < Thursday < ... < Saturday

Subtraction with date-time Objects

Doing subtraction gives you a difftime object.

difftime objects do not always have the same units – it depends on the scale of the objects you are working with.

How old am I?

today() - mdy(01141989)

Time difference of 13085 days

How long did it take me to type this slide?

begin <- mdy_hms("10/21/2024 20:40:34")
finish <- mdy_hms("10/21/2024 20:43:11")

finish - begin

Time difference of 2.616667 mins

Durations and Periods

Durations will always give the time span in an exact number of seconds.

as.duration(
  today() - mdy(01141989)
            )

[1] "1130544000s (~35.82 years)"

Periods will give the time span in more approximate, but human readable times.

as.period(
  today() - mdy(01141989)
  )

[1] "13085d 0H 0M 0S"

Durations and Periods

We can also add time to date-time objects:

• days(), years(), etc. will add a period of time.
• ddays(), dyears(), etc. will add a duration of time.

Because durations use the exact number of seconds to represent days and years, you might get unexpected results.

When is is my 99th birthday?

mdy(01141989) + years(99)

[1] "2088-01-14"

mdy(01141989) + dyears(99)

[1] "2088-01-14 18:00:00 UTC"

Time Zones…

…are complicated!

Specify time zones in the form:

• {continent}/{city} – “America/Denver”, “Africa/Nairobi”
• {ocean}/{city} – “Pacific/Auckland”

What time zone does R think I’m in?

Sys.timezone()

[1] "America/Los_Angeles"

Time Zones

You can change the time zone of a date in two ways:

x <- ymd_hms("2024-10-24 18:00:00", 
             tz = "Europe/Copenhagen")

with_tz()

Keeps the instant in time the same, but changes the visual representation.

x |> 
  with_tz()

[1] "2024-10-24 09:00:00 PDT"

x |> 
  with_tz(tzone = "Asia/Kolkata")

[1] "2024-10-24 21:30:00 IST"

force_tz()

Changes the instant in time by forcing a time zone change.

x |> 
  force_tz()

[1] "2024-10-24 18:00:00 PDT"

x |> 
  force_tz(tzone = "Asia/Kolkata")

[1] "2024-10-24 18:00:00 IST"

Common Mistake with Dates

When you read data in or create a new date-time object, the default time zone (if not specified) is UTC (Universal Time Coordinated)*.

So, make sure you specify your desired time zone!

x <- mdy("11/20/1993")
tz(x)

[1] "UTC"

x <- mdy("11/20/1993", 
         tz = "America/Los_Angeles")
tz(x)

[1] "America/Los_Angeles"

*UTC is the same as GMT (Greenwich Mean Time)

PA 5.2: Jewel Heist

This activity will require knowledge of:

• Converting time zones
• Extracting components of a date
• Filtering based on date components
• Making intervals of time
• Filtering values that fall into an interval
• Finding the duration between two dates
• Modulus division

None of us have all these abilities. Each of us has some of these abilities.

lubridate Resources

Every group should have a lubridate cheatsheet!

On the Front:

• Middle Column – functions related to “getting components” of dates
• Bottom Right – functions related to time zones

On the Back:

• Left Column – functions related to adding / subtracting “periods” to / from dates
• Right Column – functions related to creating and using intervals of time

Task Card

Every group should have a task card!

On the Front

• the expectations of each role
• the norms of collaborating

On the Back

• lubridate functions for different tasks you may encounter

Getting Started

The partner who got the most sleep starts as the Developer (typing and listening to instructions from the Coder)!

• The Coder does not type.
  • The collaborative editing feature should allow you to track what is being typed.
• The Developer only types what they are told to type.

Submission

Submit the name of the thief to the Canvas Quiz.

• Each person will input the full name of the thief into the PA 5.2 quiz.
• The person who last occupied the role of Developer will download and submit the PA-5-dates.html file for the group.
  • Only one submission per group!

Lab 5: Murder in SQL City

To do…

• Lab 5: Murder in SQL City
  • due Sunday, October 27 at 11:59pm