Assignment 5
Jake Vaughan
2026-03-02
Question 1
Load Flights and dplyr for glimpse function to view all records and fields
pacman::p_load(nycflights13, dplyr)
head(flights) # this View() function opens lets you directly view the whole dataset## # A tibble: 6 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>glimpse(flights) # this glimpse() function provides a quick overview of the dataset## Rows: 336,776
## Columns: 19
## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ day <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ dep_time <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
## $ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
## $ dep_delay <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
## $ arr_time <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
## $ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
## $ arr_delay <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
## $ carrier <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
## $ flight <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
## $ tailnum <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
## $ origin <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
## $ dest <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
## $ air_time <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
## $ distance <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
## $ hour <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
## $ minute <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
## $ time_hour <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…summary(flights)## year month day dep_time sched_dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1 Min. : 106
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907 1st Qu.: 906
## Median :2013 Median : 7.000 Median :16.00 Median :1401 Median :1359
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349 Mean :1344
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744 3rd Qu.:1729
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400 Max. :2359
## NA's :8255
## dep_delay arr_time sched_arr_time arr_delay
## Min. : -43.00 Min. : 1 Min. : 1 Min. : -86.000
## 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124 1st Qu.: -17.000
## Median : -2.00 Median :1535 Median :1556 Median : -5.000
## Mean : 12.64 Mean :1502 Mean :1536 Mean : 6.895
## 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945 3rd Qu.: 14.000
## Max. :1301.00 Max. :2400 Max. :2359 Max. :1272.000
## NA's :8255 NA's :8713 NA's :9430
## carrier flight tailnum origin
## Length:336776 Min. : 1 Length:336776 Length:336776
## Class :character 1st Qu.: 553 Class :character Class :character
## Mode :character Median :1496 Mode :character Mode :character
## Mean :1972
## 3rd Qu.:3465
## Max. :8500
##
## dest air_time distance hour
## Length:336776 Min. : 20.0 Min. : 17 Min. : 1.00
## Class :character 1st Qu.: 82.0 1st Qu.: 502 1st Qu.: 9.00
## Mode :character Median :129.0 Median : 872 Median :13.00
## Mean :150.7 Mean :1040 Mean :13.18
## 3rd Qu.:192.0 3rd Qu.:1389 3rd Qu.:17.00
## Max. :695.0 Max. :4983 Max. :23.00
## NA's :9430
## minute time_hour
## Min. : 0.00 Min. :2013-01-01 05:00:00.00
## 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00.00
## Median :29.00 Median :2013-07-03 10:00:00.00
## Mean :26.23 Mean :2013-07-03 05:22:54.64
## 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00.00
## Max. :59.00 Max. :2013-12-31 23:00:00.00
## Question 2
Seeing which strings of code find the largest departure delay
## This method will tell you which row number is the flight with the maximum delay
maxdep <- max(flights$dep_delay, na.rm=TRUE)
maxdep## [1] 1301maxdep_id <- which(flights$dep_delay==maxdep)
flights[maxdep_id, 10:12]## # A tibble: 1 × 3
## carrier flight tailnum
## <chr> <int> <chr>
## 1 HA 51 N384HAmaxdep_id## [1] 7073## Shows a table organized by dep_delay
sortf <- arrange(flights,desc(dep_delay))
select(sortf, carrier, flight, tailnum, everything())## # A tibble: 336,776 × 19
## carrier flight tailnum year month day dep_time sched_dep_time dep_delay
## <chr> <int> <chr> <int> <int> <int> <int> <int> <dbl>
## 1 HA 51 N384HA 2013 1 9 641 900 1301
## 2 MQ 3535 N504MQ 2013 6 15 1432 1935 1137
## 3 MQ 3695 N517MQ 2013 1 10 1121 1635 1126
## 4 AA 177 N338AA 2013 9 20 1139 1845 1014
## 5 MQ 3075 N665MQ 2013 7 22 845 1600 1005
## 6 DL 2391 N959DL 2013 4 10 1100 1900 960
## 7 DL 2119 N927DA 2013 3 17 2321 810 911
## 8 DL 2007 N3762Y 2013 6 27 959 1900 899
## 9 DL 2047 N6716C 2013 7 22 2257 759 898
## 10 AA 172 N5DMAA 2013 12 5 756 1700 896
## # ℹ 336,766 more rows
## # ℹ 10 more variables: arr_time <int>, sched_arr_time <int>, arr_delay <dbl>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>Question 3
David is getting NA because mean returns NA if theres any NA
## Using na.rm removes NAs to get the means
flights %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay, na.rm = TRUE))## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by year, month, and day.
## ℹ Output is grouped by year and month.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(year, month, day))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## # A tibble: 365 × 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.55
## 9 2013 1 9 2.28
## 10 2013 1 10 2.84
## # ℹ 355 more rows## This almost works but doesn't group by day for the mean, however it does remove nas
not_cancelled <- flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay))
not_cancelled## # A tibble: 327,346 × 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # ℹ 327,336 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>## This doesn't work as it organizes and averages by tailnumber not day
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
)
delays## # A tibble: 4,037 × 2
## tailnum delay
## <chr> <dbl>
## 1 D942DN 31.5
## 2 N0EGMQ 9.98
## 3 N10156 12.7
## 4 N102UW 2.94
## 5 N103US -6.93
## 6 N104UW 1.80
## 7 N10575 20.7
## 8 N105UW -0.267
## 9 N107US -5.73
## 10 N108UW -1.25
## # ℹ 4,027 more rows## This works as it groups by day and summarizes mean, and removes nas
not_cancelled <- flights %>%
filter(!is.na(dep_delay))
not_cancelled %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay))## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by year, month, and day.
## ℹ Output is grouped by year and month.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(year, month, day))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## # A tibble: 365 × 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.55
## 9 2013 1 9 2.28
## 10 2013 1 10 2.84
## # ℹ 355 more rowsQuestion 4
Use code from Q3 and then arrange by mean delay
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
) %>%
arrange(delay)
delays## # A tibble: 4,037 × 2
## tailnum delay
## <chr> <dbl>
## 1 N560AS -53
## 2 N315AS -51
## 3 N517AS -40.5
## 4 N7AYAA -35
## 5 N585AS -34.5
## 6 N915DN -34
## 7 N512AS -31.5
## 8 N594AS -31.3
## 9 N564AS -31
## 10 N593AS -30.5
## # ℹ 4,027 more rowsQuestion 5
added arrange last to the code to see which flights left latest on which days. The lowest value was 1728(5:28 pm) and the highest was 2400 (12:00 AM)
not_cancelled %>%
group_by(year, month, day) %>%
summarise(
first = min(dep_time),
last = max(dep_time)
) %>%
arrange(last)## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by year, month, and day.
## ℹ Output is grouped by year and month.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(year, month, day))` for per-operation grouping
## (`?dplyr::dplyr_by`) instead.## # A tibble: 365 × 5
## # Groups: year, month [12]
## year month day first last
## <int> <int> <int> <int> <int>
## 1 2013 2 8 458 1728
## 2 2013 9 7 20 2246
## 3 2013 1 9 2 2252
## 4 2013 8 26 13 2258
## 5 2013 9 4 146 2258
## 6 2013 1 11 11 2304
## 7 2013 9 21 8 2306
## 8 2013 10 18 9 2317
## 9 2013 1 10 3 2320
## 10 2013 3 17 17 2321
## # ℹ 355 more rowsQuestion 6
Using mean with a logical true false to find the average proportion of flights delayed over 60 min per month
flights %>%
group_by(month) %>%
summarise(
prop_over_60 = mean(dep_delay > 60, na.rm = TRUE),
n = sum(!is.na(dep_delay))
) %>%
arrange(desc(prop_over_60))## # A tibble: 12 × 3
## month prop_over_60 n
## <int> <dbl> <int>
## 1 7 0.134 28485
## 2 6 0.128 27234
## 3 12 0.0942 27110
## 4 4 0.0916 27662
## 5 3 0.0837 27973
## 6 5 0.0818 28233
## 7 8 0.0796 28841
## 8 2 0.0698 23690
## 9 1 0.0688 26483
## 10 9 0.0490 27122
## 11 10 0.0469 28653
## 12 11 0.0402 27035Question 7
Use group_by to group by destination, then used n_distinct to only show unique characters no repeats
flights %>%
group_by(dest) %>%
summarise(n_carriers = n_distinct(carrier)) %>%
arrange(desc(n_carriers))## # A tibble: 105 × 2
## dest n_carriers
## <chr> <int>
## 1 ATL 7
## 2 BOS 7
## 3 CLT 7
## 4 ORD 7
## 5 TPA 7
## 6 AUS 6
## 7 DCA 6
## 8 DTW 6
## 9 IAD 6
## 10 MSP 6
## # ℹ 95 more rowsQuestion 8
Testing each option
## Group Flights by Destination
by_dest <- group_by(flights, dest)
by_dest## # A tibble: 336,776 × 19
## # Groups: dest [105]
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # ℹ 336,766 more rows
## # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>## Summarise to compute distance, avg delay, and number of flights
delay <- summarise(by_dest,
count = n(),
dist =
mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE))
delay## # A tibble: 105 × 4
## dest count dist delay
## <chr> <int> <dbl> <dbl>
## 1 ABQ 254 1826 4.38
## 2 ACK 265 199 4.85
## 3 ALB 439 143 14.4
## 4 ANC 8 3370 -2.5
## 5 ATL 17215 757. 11.3
## 6 AUS 2439 1514. 6.02
## 7 AVL 275 584. 8.00
## 8 BDL 443 116 7.05
## 9 BGR 375 378 8.03
## 10 BHM 297 866. 16.9
## # ℹ 95 more rows## Filter to remove noisy points in Honolulu
delay <- filter(delay, count > 20, dest!= "HNL")
delay## # A tibble: 96 × 4
## dest count dist delay
## <chr> <int> <dbl> <dbl>
## 1 ABQ 254 1826 4.38
## 2 ACK 265 199 4.85
## 3 ALB 439 143 14.4
## 4 ATL 17215 757. 11.3
## 5 AUS 2439 1514. 6.02
## 6 AVL 275 584. 8.00
## 7 BDL 443 116 7.05
## 8 BGR 375 378 8.03
## 9 BHM 297 866. 16.9
## 10 BNA 6333 758. 11.8
## # ℹ 86 more rows## Plotting the relationship Plus adding ggplot2 to the library
library(ggplot2)
ggplot(data = delay, mapping = aes(x = dist, y =delay)) + geom_point(aes(size = count), alpha = 1/3) + geom_smooth(se = FALSE)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Question 9
acheieving the same as the previous code without the plotting aspect
delays <- flights %>%
group_by(dest) %>%
summarise(
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
) %>%
filter(count > 20, dest != "HNL")
delays## # A tibble: 96 × 4
## dest count dist delay
## <chr> <int> <dbl> <dbl>
## 1 ABQ 254 1826 4.38
## 2 ACK 265 199 4.85
## 3 ALB 439 143 14.4
## 4 ATL 17215 757. 11.3
## 5 AUS 2439 1514. 6.02
## 6 AVL 275 584. 8.00
## 7 BDL 443 116 7.05
## 8 BGR 375 378 8.03
## 9 BHM 297 866. 16.9
## 10 BNA 6333 758. 11.8
## # ℹ 86 more rows