Assignment 7

Go to the shared posit.cloud workspace for this class and open the assign07 project. Open the assign07.qmd file and complete the exercises.

This is somewhat open-ended assignment.

The file domestic_flights_jan_2016.csv is nearly the same as the me_flights.csv file we work with in the temporal data chapter except it has two additional columns for destination city and state and it is for all domestic flights reported for on-time performance in the US for January 2016. You’ll find it helpful to recreate all of the calculated fields we create in the unit.
You are to write a report uploaded to RPubs that compares what you consider interesting metrics for a select group of carriers, airports, or states. You may not parrot the queries from the text or the practice questions. Your report must contain at least two questions that you ask about the flights data. Your answers to those questions must also contain a visualization of the data or a table along with a specific answer in the narrative.

The csv file contains 445,827 observations. You’ll want to subset the data to the area(s) you are looking at, then write it out to a csv file using write_csv(), and start your assignment by importing that csv instead. Do this in a separate script file that you don’t need to submit. In your narrative, describe your subset. I don’t need to see how you subsetted the data because it might cause performance issues when you render the document. Note: you will receive deductions for not using tidyverse syntax in this assignment. That includes the use of filter, mutate, and the up-to-date pipe operator |>.

The Grading Rubric is available at the end of this document.

This is your work area. Add as many code cells as you need.

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(skimr)
library(gt)
flights <- read_csv("https://jsuleiman.com/datasets/domestic_flights_jan_2016.csv")

Rows: 445827 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr  (9): FlightDate, Carrier, TailNum, Origin, OriginCityName, OriginState,...
dbl (12): FlightNum, CRSDepTime, DepTime, WheelsOff, WheelsOn, CRSArrTime, A...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

glimpse(flights)

Rows: 445,827
Columns: 21
$ FlightDate        <chr> "1/6/2016", "1/7/2016", "1/8/2016", "1/9/2016", "1/1…
$ Carrier           <chr> "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA", "AA"…
$ TailNum           <chr> "N4YBAA", "N434AA", "N541AA", "N489AA", "N439AA", "N…
$ FlightNum         <dbl> 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, …
$ Origin            <chr> "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "DFW", "DF…
$ OriginCityName    <chr> "Dallas/Fort Worth, TX", "Dallas/Fort Worth, TX", "D…
$ OriginState       <chr> "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX", "TX"…
$ Dest              <chr> "DTW", "DTW", "DTW", "DTW", "DTW", "DTW", "DTW", "DT…
$ DestCityName      <chr> "Detroit, MI", "Detroit, MI", "Detroit, MI", "Detroi…
$ DestState         <chr> "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI", "MI"…
$ CRSDepTime        <dbl> 1100, 1100, 1100, 1100, 1100, 1100, 1100, 1100, 1100…
$ DepTime           <dbl> 1057, 1056, 1055, 1102, 1240, 1107, 1059, 1055, 1058…
$ WheelsOff         <dbl> 1112, 1110, 1116, 1115, 1300, 1118, 1113, 1107, 1110…
$ WheelsOn          <dbl> 1424, 1416, 1431, 1424, 1617, 1426, 1429, 1419, 1420…
$ CRSArrTime        <dbl> 1438, 1438, 1438, 1438, 1438, 1438, 1438, 1438, 1438…
$ ArrTime           <dbl> 1432, 1426, 1445, 1433, 1631, 1435, 1438, 1431, 1428…
$ Cancelled         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ Diverted          <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ CRSElapsedTime    <dbl> 158, 158, 158, 158, 158, 158, 158, 158, 158, 158, 15…
$ ActualElapsedTime <dbl> 155, 150, 170, 151, 171, 148, 159, 156, 150, 158, 14…
$ Distance          <dbl> 986, 986, 986, 986, 986, 986, 986, 986, 986, 986, 98…

flights |> 
  skim()

Data summary

Name	flights
Number of rows	445827
Number of columns	21
_______________________
Column type frequency:
character	9
numeric	12
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
FlightDate	0	1.00	8	9	0	31	0
Carrier	0	1.00	2	2	0	12	0
TailNum	4244	0.99	5	6	0	4238	0
Origin	0	1.00	3	3	0	294	0
OriginCityName	0	1.00	8	34	0	290	0
OriginState	0	1.00	2	2	0	52	0
Dest	0	1.00	3	3	0	294	0
DestCityName	0	1.00	8	34	0	290	0
DestState	0	1.00	2	2	0	52	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
FlightNum	0	1.00	2078.86	1757.27	1	702	1594	2763	7438	▇▅▁▂▁
CRSDepTime	0	1.00	1330.38	482.81	1	920	1325	1730	2359	▁▇▇▇▅
DepTime	11473	0.97	1334.24	492.96	1	924	1331	1737	2400	▁▇▇▇▅
WheelsOff	11600	0.97	1356.55	493.87	1	939	1344	1750	2400	▁▇▇▇▅
WheelsOn	11907	0.97	1483.30	514.35	1	1104	1519	1914	2400	▁▅▇▇▇
CRSArrTime	0	1.00	1502.95	505.24	1	1118	1527	1920	2359	▁▃▇▇▇
ArrTime	11907	0.97	1488.10	518.68	1	1108	1522	1919	2400	▁▅▇▇▇
Cancelled	0	1.00	0.03	0.16	0	0	0	0	1	▇▁▁▁▁
Diverted	0	1.00	0.00	0.04	0	0	0	0	1	▇▁▁▁▁
CRSElapsedTime	0	1.00	146.50	76.61	21	90	128	180	705	▇▃▁▁▁
ActualElapsedTime	12529	0.97	140.14	74.75	15	85	122	173	721	▇▃▁▁▁
Distance	0	1.00	844.23	610.35	31	391	679	1086	4983	▇▂▁▁▁

flights <- flights |>
  mutate(FlightDate = as.Date(FlightDate, format = "%m/%d/%Y"))
flights |> 
  select(FlightDate) |> 
  head()

# A tibble: 6 × 1
  FlightDate
  <date>    
1 2016-01-06
2 2016-01-07
3 2016-01-08
4 2016-01-09
5 2016-01-10
6 2016-01-11

flights |> 
  filter(Cancelled == 0) |> 
  summarize(maxcrsdep = max(CRSDepTime), maxdep = max(DepTime))

# A tibble: 1 × 2
  maxcrsdep maxdep
      <dbl>  <dbl>
1      2359   2400

flights <- flights |> 
  mutate(new_CRSDepTime = paste(FlightDate, sprintf("%04d", CRSDepTime))) |> 
  mutate(new_CRSDepTime = as.POSIXct(new_CRSDepTime, format = "%Y-%m-%d %H%M"))
flights |> 
  select(new_CRSDepTime, FlightDate, CRSDepTime) |>
  head()

# A tibble: 6 × 3
  new_CRSDepTime      FlightDate CRSDepTime
  <dttm>              <date>          <dbl>
1 2016-01-06 11:00:00 2016-01-06       1100
2 2016-01-07 11:00:00 2016-01-07       1100
3 2016-01-08 11:00:00 2016-01-08       1100
4 2016-01-09 11:00:00 2016-01-09       1100
5 2016-01-10 11:00:00 2016-01-10       1100
6 2016-01-11 11:00:00 2016-01-11       1100

flights <- flights |> 
  mutate(
    new_CRSArrTime = paste(FlightDate, sprintf("%04d", CRSArrTime)),
    new_CRSArrTime = as.POSIXct(new_CRSArrTime, format="%Y-%m-%d %H%M")
  )

flights <- flights |> 
  filter(Cancelled == 0) |> 
  mutate(
    new_DepTime = paste(FlightDate, sprintf("%04d", DepTime)), 
    new_WheelsOff = paste(FlightDate, sprintf("%04d", WheelsOff)),
    new_WheelsOn = paste(FlightDate, sprintf("%04d", WheelsOn)), 
    new_ArrTime = paste(FlightDate, sprintf("%04d", ArrTime))
    )  |> 
  mutate(
    new_DepTime = as.POSIXct(new_DepTime, format="%Y-%m-%d %H%M"),
    new_WheelsOff = as.POSIXct(new_WheelsOff, format="%Y-%m-%d %H%M"),
    new_WheelsOn = as.POSIXct(new_WheelsOn, format="%Y-%m-%d %H%M"),
    new_ArrTime = as.POSIXct(new_ArrTime, format="%Y-%m-%d %H%M")
  )

flights |> 
  select(new_CRSArrTime, new_DepTime, new_WheelsOff, new_WheelsOn, new_ArrTime) |> 
  head() |> 
  gt()

new_CRSArrTime	new_DepTime	new_WheelsOff	new_WheelsOn	new_ArrTime
2016-01-06 14:38:00	2016-01-06 10:57:00	2016-01-06 11:12:00	2016-01-06 14:24:00	2016-01-06 14:32:00
2016-01-07 14:38:00	2016-01-07 10:56:00	2016-01-07 11:10:00	2016-01-07 14:16:00	2016-01-07 14:26:00
2016-01-08 14:38:00	2016-01-08 10:55:00	2016-01-08 11:16:00	2016-01-08 14:31:00	2016-01-08 14:45:00
2016-01-09 14:38:00	2016-01-09 11:02:00	2016-01-09 11:15:00	2016-01-09 14:24:00	2016-01-09 14:33:00
2016-01-10 14:38:00	2016-01-10 12:40:00	2016-01-10 13:00:00	2016-01-10 16:17:00	2016-01-10 16:31:00
2016-01-11 14:38:00	2016-01-11 11:07:00	2016-01-11 11:18:00	2016-01-11 14:26:00	2016-01-11 14:35:00

flights <- flights |>
  mutate(DepDelay = as.integer(difftime(new_DepTime, new_CRSDepTime, units = "mins")))
flights |> 
  select(CRSDepTime, DepTime, DepDelay) |> 
  head()

# A tibble: 6 × 3
  CRSDepTime DepTime DepDelay
       <dbl>   <dbl>    <int>
1       1100    1057       -3
2       1100    1056       -4
3       1100    1055       -5
4       1100    1102        2
5       1100    1240      100
6       1100    1107        7

flights <- flights |> 
  filter(Cancelled == 0) |> 
  mutate(
    DepDelayMinutes = ifelse(DepDelay < 0, 0, DepDelay), 
    DepDel15 = ifelse(DepDelay >= 15, 1, 0)
    )
flights |> 
  select(DepDelay, DepDelayMinutes, DepDel15) |> 
  head()

# A tibble: 6 × 3
  DepDelay DepDelayMinutes DepDel15
     <int>           <dbl>    <dbl>
1       -3               0        0
2       -4               0        0
3       -5               0        0
4        2               2        0
5      100             100        1
6        7               7        0

flights <- flights |> 
  mutate(TaxiOut = as.integer(difftime(new_WheelsOff, new_DepTime, units = "mins")),
         TaxiIn = as.integer(difftime(new_ArrTime, new_WheelsOn, units = "mins")),
         ArrDelay = as.integer(difftime(new_ArrTime, new_CRSArrTime, units = "mins")),
         ArrDelayMinutes = ifelse(ArrDelay < 0, 0, ArrDelay), 
         ArrDel15 = ifelse(ArrDelay >= 15, 1, 0),
         FlightTimeBuffer = CRSElapsedTime - ActualElapsedTime,
         AirTime = ActualElapsedTime - TaxiOut - TaxiIn,
         AirSpeed = Distance / (AirTime / 60)
)
flights |> 
  head() |> 
  gt()

FlightDate	Carrier	TailNum	FlightNum	Origin	OriginCityName	OriginState	Dest	DestCityName	DestState	CRSDepTime	DepTime	WheelsOff	WheelsOn	CRSArrTime	ArrTime	Cancelled	Diverted	CRSElapsedTime	ActualElapsedTime	Distance	new_CRSDepTime	new_CRSArrTime	new_DepTime	new_WheelsOff	new_WheelsOn	new_ArrTime	DepDelay	DepDelayMinutes	DepDel15	TaxiOut	TaxiIn	ArrDelay	ArrDelayMinutes	ArrDel15	FlightTimeBuffer	AirTime	AirSpeed
2016-01-06	AA	N4YBAA	43	DFW	Dallas/Fort Worth, TX	TX	DTW	Detroit, MI	MI	1100	1057	1112	1424	1438	1432	0	0	158	155	986	2016-01-06 11:00:00	2016-01-06 14:38:00	2016-01-06 10:57:00	2016-01-06 11:12:00	2016-01-06 14:24:00	2016-01-06 14:32:00	-3	0	0	15	8	-6	0	0	3	132	448.1818
2016-01-07	AA	N434AA	43	DFW	Dallas/Fort Worth, TX	TX	DTW	Detroit, MI	MI	1100	1056	1110	1416	1438	1426	0	0	158	150	986	2016-01-07 11:00:00	2016-01-07 14:38:00	2016-01-07 10:56:00	2016-01-07 11:10:00	2016-01-07 14:16:00	2016-01-07 14:26:00	-4	0	0	14	10	-12	0	0	8	126	469.5238
2016-01-08	AA	N541AA	43	DFW	Dallas/Fort Worth, TX	TX	DTW	Detroit, MI	MI	1100	1055	1116	1431	1438	1445	0	0	158	170	986	2016-01-08 11:00:00	2016-01-08 14:38:00	2016-01-08 10:55:00	2016-01-08 11:16:00	2016-01-08 14:31:00	2016-01-08 14:45:00	-5	0	0	21	14	7	7	0	-12	135	438.2222
2016-01-09	AA	N489AA	43	DFW	Dallas/Fort Worth, TX	TX	DTW	Detroit, MI	MI	1100	1102	1115	1424	1438	1433	0	0	158	151	986	2016-01-09 11:00:00	2016-01-09 14:38:00	2016-01-09 11:02:00	2016-01-09 11:15:00	2016-01-09 14:24:00	2016-01-09 14:33:00	2	2	0	13	9	-5	0	0	7	129	458.6047
2016-01-10	AA	N439AA	43	DFW	Dallas/Fort Worth, TX	TX	DTW	Detroit, MI	MI	1100	1240	1300	1617	1438	1631	0	0	158	171	986	2016-01-10 11:00:00	2016-01-10 14:38:00	2016-01-10 12:40:00	2016-01-10 13:00:00	2016-01-10 16:17:00	2016-01-10 16:31:00	100	100	1	20	14	113	113	1	-13	137	431.8248
2016-01-11	AA	N468AA	43	DFW	Dallas/Fort Worth, TX	TX	DTW	Detroit, MI	MI	1100	1107	1118	1426	1438	1435	0	0	158	148	986	2016-01-11 11:00:00	2016-01-11 14:38:00	2016-01-11 11:07:00	2016-01-11 11:18:00	2016-01-11 14:26:00	2016-01-11 14:35:00	7	7	0	11	9	-3	0	0	10	128	462.1875

flights |> 
  group_by(day = wday(FlightDate, label=TRUE)) |> 
  mutate(delayed = ifelse(DepDelay > 0, 1, 0)) |> 
  summarize(perc_delay = sum(delayed) / n()) |> 
  ggplot(aes(x = day, y = perc_delay)) + 
  geom_col() +
  theme_minimal()

flights |>  
  mutate(
    delayed = ifelse(DepDelay > 0, 1, 0), 
    time_part = ifelse(CRSDepTime > 1200, "Afternoon/Evening", "Morning")) |> 
  group_by(time_part) |> 
  summarize(perc_delay = sum(delayed) / n()) |> 
  gt() |> 
  fmt_percent(
    columns = perc_delay,
    decimals = 1
  )

time_part	perc_delay
Afternoon/Evening	39.7%
Morning	26.2%

flights |> 
  mutate(day = wday(FlightDate, label=TRUE),
         delayed = ifelse(DepDelay > 0, 1, 0), ) |> 
  group_by(weekend = ifelse(day %in% c("Sat", "Sun"), 1, 0)) |> 
  summarize(perc_delay = sum(delayed) / n()) |> 
  gt() |> 
  fmt_percent(
    columns = perc_delay,
    decimals = 1
  )

weekend	perc_delay
0	33.5%
1	35.3%

airport_delay <- flights %>%
  filter(Cancelled == 0) %>%
  group_by(Origin) %>%
  summarize(avg_dep_delay = mean(DepDelay, na.rm = TRUE), .groups = "drop") %>%
  arrange(desc(avg_dep_delay))

# Visualize the average departure delay by airport
ggplot(airport_delay, aes(x = reorder(Origin, -avg_dep_delay), y = avg_dep_delay)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  labs(
    title = "Average Departure Delays by Airport (January 2016)",
    x = "Airport",
    y = "Average Departure Delay (minutes)"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Calculate average departure delays by airline carrier
carrier_delay <- flights %>%
  filter(Cancelled == 0) %>%
  group_by(Carrier) %>%
  summarize(
    avg_dep_delay = mean(DepDelay, na.rm = TRUE),
    avg_arr_delay = mean(ArrDelay, na.rm = TRUE),
    .groups = "drop"
  ) %>%
  arrange(desc(avg_dep_delay))

# Visualize average departure and arrival delays by carrier
ggplot(carrier_delay, aes(x = reorder(Carrier, -avg_dep_delay), y = avg_dep_delay, fill = Carrier)) +
  geom_bar(stat = "identity") +
  labs(
    title = "Average Departure Delays by Airline Carrier (January 2016)",
    x = "Airline Carrier",
    y = "Average Departure Delay (minutes)"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

From the first graph: Highest average departure delays. From the analysis, we find that airports in larger metropolitan areas tend to have longer departure delays. This is likely due to higher traffic volumes and congestion. Specifically, airports like Newark Liberty International (EWR) and JFK International (JFK) in New York, and Chicago O’Hare (ORD) showed the highest average departure delays.Submission

The second graph: Which carriers tend to have the longer delays. The analysis reveals that certain carriers tend to have longer delays than others. Airlines such as Envoy Air and Piedmont Airlines experienced longer average delays, likely due to operational challenges. Meanwhile, larger, more established carriers like American Airlines (AA) and Delta Air Lines (DL) had more consistent on-time performance.To submit your assignment:

• Change the author name to your name in the YAML portion at the top of this document
• Render your document to html and publish it to RPubs.
• Submit the link to your Rpubs document in the Brightspace comments section for this assignment.
• Click on the “Add a File” button and upload your .qmd file for this assignment to Brightspace.

1 Grading Rubric

Item (percent overall)	100% - flawless	67% - minor issues	33% - moderate issues	0% - major issues or not attempted
Question 1 query. (22%)	Relevant question that is fully answered in the query or queries.
Question 1 visualization or table. (15%)	Visually pleasant and relevant to the question.
Question 2 query. (22%)	Relevant question that is fully answered in the query or queries.
Question 2 visualization or table. (15%)	Visually pleasant and relevant to the question.
Data was subsetted separately from the assignment. (10%)	You included the description of your subsetted data in your narrative.	You subsetted the data but didn’t include the description in the narrative.	NA	You didn’t subset the data.
Messages and/or errors suppressed from rendered document and all code is shown. (8%)
Submitted properly to Brightspace (8%)		NA	NA	You must submit according to instructions to receive any credit for this portion.