Objectives

Previously… (1/2)

Date/Time Ambiguity and Formats

# year-month-day
ymd("2017-01-31")

## [1] "2017-01-31"

# month-day-year
mdy("January 31st, 2017")

## [1] "2017-01-31"

# day-month-year
dmy("31-Jan-2017")

## [1] "2017-01-31"

Previously… (2/2)

Time Granularity and Time-Zones

• Granularity Matters: The level of detail or precision used to represent time in a given context
  • Seconds, minutes, hours, days, weeks, months, years, decades, etc.
  • Semesters (fall, spring)
  • Seasons (winter, spring, summer, fall)
  • TV Show seasons or movie sequels
  • Election cycles
• Time Zones Shift Meaning: Be mindful of time-zones
  • A “morning sale” in New York City, USA isn’t morning in Manila, Philippines

Case Study I

New York City Flights

• The nycflights13 package: On-time data for all flights that departed NYC (i.e. JFK, LGA or EWR) in 2013
• The nycflights23 package: Similar to nycflights13 but in 2023

library(nycflights13)
library(nycflights23)

# bind the two data frames
flights <- nycflights13::flights %>% 
  rbind(nycflights23::flights)

# show size
dim(flights)

## [1] 772128     19

Variables of Interest

Each row in the flights tibble is a unique flight from NYC.

• date_time | date and time of departure, which can be decomposed into parts (year, month, day, hour)
• carrier | type of airline
• origin | origin airport in New York (JFK, LGA or EWR)
• dest | destination airport (e.g. ATL, LAX, PDX)
• dep_delay | Departure delays, in minutes. Negative times represent early departures/arrivals.

# view random sample
flights_sub |> 
  sample_n(3)

## # A tibble: 3 × 9
##   date_time           carrier origin dest  dep_delay  year month   day  hour
##   <dttm>              <chr>   <chr>  <chr>     <dbl> <int> <int> <int> <dbl>
## 1 2013-11-07 06:00:00 WN      LGA    ATL           0  2013    11     7     6
## 2 2013-10-30 12:00:00 AA      JFK    BOS           2  2013    10    30    12
## 3 2023-08-24 21:00:00 9E      LGA    PWM          34  2023     8    24    21

How to Get Started on Exploring Temporal Data

The main goal of time-series data is to analyze how a variable or set of variables evolve over time. This typically involves identifying patterns and trends.

How many flights occurred in 2013 and 2023?

Increase Granularity

What is the number of fights per month?

Incorporate Other Variables

What is median departure delay per month coming out of each NYC airport?

More Precision

What is the median daily delay during the summer months?

A Stragety on Visualizing Time-Series Data

Effective Methods for Analyzing Trends Over Time

1. Define the Objective

• What insights are you looking for?
• Are you analyzing trends, seasonality, anomalies, or forecasting?

2. Choose the Right Visualization Type

• Line graphs: Best for continuous trends over time
• Bar Plots: Good for comparing discrete time intervals
• Boxplots: Helps visualizing distributions for comparing discrete time intervals

3. Data Preprocessing

• Handle missing values.
• Aggregate data to appropriate time intervals (hourly, daily, monthly).
• Normalize or scale data if necessary.

4. Enhance Readability

• Use consistent time intervals on the x-axis.
• Add annotations for significant events.
• Utilize color coding for multiple time series.

Activity: Visualize Time-Series

1. Log-in to Posit Cloud and open the R Studio assignment MA15: Visualize Time-Series.
2. Make sure you are in the current working directory. Rename the .Rmd file by replacing [name] with your name using the format [First name][Last initial]. Then, open the .Rmd file.
3. Change the author in the YAML header.
4. Read the provided instructions.
5. Answer all exercise problems on the designated sections.