NYCflights13Homework
Samuel Kalibala
6/17/2021
Load the libraries and view the “flights” dataset
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.1.2 v dplyr 1.0.6
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(nycflights13)
library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alphaview(flights)
describe(flights)## Warning in FUN(newX[, i], ...): no non-missing arguments to min; returning Inf## Warning in FUN(newX[, i], ...): no non-missing arguments to max; returning -Inf## vars n mean sd median trimmed mad min max
## year 1 336776 2013.00 0.00 2013 2013.00 0.00 2013 2013
## month 2 336776 6.55 3.41 7 6.56 4.45 1 12
## day 3 336776 15.71 8.77 16 15.70 11.86 1 31
## dep_time 4 328521 1349.11 488.28 1401 1346.82 634.55 1 2400
## sched_dep_time 5 336776 1344.25 467.34 1359 1341.60 613.80 106 2359
## dep_delay 6 328521 12.64 40.21 -2 3.32 5.93 -43 1301
## arr_time 7 328063 1502.05 533.26 1535 1526.42 619.73 1 2400
## sched_arr_time 8 336776 1536.38 497.46 1556 1550.67 618.24 1 2359
## arr_delay 9 327346 6.90 44.63 -5 -1.03 20.76 -86 1272
## carrier* 10 336776 7.14 4.14 6 7.00 5.93 1 16
## flight 11 336776 1971.92 1632.47 1496 1830.51 1608.62 1 8500
## tailnum* 12 334264 1814.32 1199.75 1798 1778.21 1587.86 1 4043
## origin* 13 336776 1.95 0.82 2 1.94 1.48 1 3
## dest* 14 336776 50.03 28.12 50 49.56 32.62 1 105
## air_time 15 327346 150.69 93.69 129 140.03 75.61 20 695
## distance 16 336776 1039.91 733.23 872 955.27 569.32 17 4983
## hour 17 336776 13.18 4.66 13 13.15 5.93 1 23
## minute 18 336776 26.23 19.30 29 25.64 23.72 0 59
## time_hour 19 336776 NaN NA NA NaN NA Inf -Inf
## range skew kurtosis se
## year 0 NaN NaN 0.00
## month 11 -0.01 -1.19 0.01
## day 30 0.01 -1.19 0.02
## dep_time 2399 -0.02 -1.09 0.85
## sched_dep_time 2253 -0.01 -1.20 0.81
## dep_delay 1344 4.80 43.95 0.07
## arr_time 2399 -0.47 -0.19 0.93
## sched_arr_time 2358 -0.35 -0.38 0.86
## arr_delay 1358 3.72 29.23 0.08
## carrier* 15 0.36 -1.21 0.01
## flight 8499 0.66 -0.85 2.81
## tailnum* 4042 0.17 -1.24 2.08
## origin* 2 0.09 -1.50 0.00
## dest* 104 0.13 -1.08 0.05
## air_time 675 1.07 0.86 0.16
## distance 4966 1.13 1.19 1.26
## hour 22 0.00 -1.21 0.01
## minute 59 0.09 -1.24 0.03
## time_hour -Inf NA NA NAview(flights)Analysis question: how does arrival delay differ between winter months like January and summer months like July?
Use str to examine the structure of the data
str(flights)## tibble [336,776 x 19] (S3: tbl_df/tbl/data.frame)
## $ year : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ month : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ day : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ dep_time : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ...
## $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ...
## $ dep_delay : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
## $ arr_time : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ...
## $ sched_arr_time: int [1:336776] 819 830 850 1022 837 728 854 723 846 745 ...
## $ arr_delay : num [1:336776] 11 20 33 -18 -25 12 19 -14 -8 8 ...
## $ carrier : chr [1:336776] "UA" "UA" "AA" "B6" ...
## $ flight : int [1:336776] 1545 1714 1141 725 461 1696 507 5708 79 301 ...
## $ tailnum : chr [1:336776] "N14228" "N24211" "N619AA" "N804JB" ...
## $ origin : chr [1:336776] "EWR" "LGA" "JFK" "JFK" ...
## $ dest : chr [1:336776] "IAH" "IAH" "MIA" "BQN" ...
## $ air_time : num [1:336776] 227 227 160 183 116 150 158 53 140 138 ...
## $ distance : num [1:336776] 1400 1416 1089 1576 762 ...
## $ hour : num [1:336776] 5 5 5 5 6 5 6 6 6 6 ...
## $ minute : num [1:336776] 15 29 40 45 0 58 0 0 0 0 ...
## $ time_hour : POSIXct[1:336776], format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...Filter for the top 10 arrival delays in January of 1 minute or more. Used >= 1 minute becuase the data has negative delays meaning some flights arrived ahead of schedule.
January_arrdelays <- flights %>%
filter(month == "1" & arr_delay >= 1) %>%
arrange(desc(arr_delay))%>%
head(10)
print(January_arrdelays)## # A tibble: 10 x 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 9 641 900 1301 1242 1530
## 2 2013 1 10 1121 1635 1126 1239 1810
## 3 2013 1 1 848 1835 853 1001 1950
## 4 2013 1 13 1809 810 599 2054 1042
## 5 2013 1 16 1622 800 502 1911 1054
## 6 2013 1 23 1551 753 478 1812 1006
## 7 2013 1 1 2343 1724 379 314 1938
## 8 2013 1 10 1525 900 385 1713 1039
## 9 2013 1 25 15 1815 360 208 1958
## 10 2013 1 2 1607 1030 337 2003 1355
## # ... with 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>The data shows that the top 10 arrival delays in Janaury ranged from 368 minutes to 1,272 minutes.
Filter for top 10 arrival delays in July of 1 minute or more
July_arrdelays <- flights %>%
filter(month == "7" & arr_delay >= 1) %>%
arrange(desc(arr_delay))%>%
head(10)
print(July_arrdelays)## # A tibble: 10 x 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 7 22 845 1600 1005 1044 1815
## 2 2013 7 22 2257 759 898 121 1026
## 3 2013 7 7 2059 1030 629 106 1350
## 4 2013 7 21 1555 615 580 1955 910
## 5 2013 7 7 2123 1030 653 17 1345
## 6 2013 7 27 1456 600 536 1649 712
## 7 2013 7 6 149 1600 589 456 1935
## 8 2013 7 10 2346 1410 576 141 1630
## 9 2013 7 28 6 1600 486 231 1815
## 10 2013 7 10 2238 1439 479 39 1644
## # ... with 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>The data shows that the top 10 arrival delays in the month of July ranged from 475 minutes to 989 minutes.
Do a plot of arrival delays in minutes by month.
plot1 <- flights %>%
ggplot(aes(month, arr_delay)) +
geom_point() +
ggtitle("Arrival delays in minutes by month")
plot1## Warning: Removed 9430 rows containing missing values (geom_point).
#Comment on the data
This data shows arrival time and departure time by flights to and from New York City in the year 2013 including arrival delays and departure delays shown in minutes. Delayed flights are a major concern of passengers and these tend to occur in periods of busy travel such as summer or in periods of bad weather such as winter. The data has a total of 336,776 cases entered for the twelve months of 2013. I decided to investigate how arrival delays differed between a winter month such as January and a summer month such as July. I filtered the data for the top 10 arrival delays in the month of January and found out that the delays ranged from 368 minutes to 1,272 minutes. I also filtered for the top 10 arrival delays in the month of July and discovered that the delays ranged from 475 minutes to 989 minutes. This suggested that there are probably longer arrival delays in January than July. To investigate further I decided to plot the entire data of arrival delay by month. The plot shows that across the months there was an almost similar proportion of flights that had a negative arrival delay, meaning they arrived ahead of schedule. The plot further shows that, excluding outliers, the months of June and July had the longest arrival delays, followed by December and January. Thus, suggesting that the busy summer months as well as the bad-weather winter months have the longest arrival delays. Other areas to be explored could include delays in departure; and delays in arrival and departure by type of carrier and by destination.