library(nycflights13)
library(tidyverse)
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str(flights)
## tibble [336,776 × 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" ...
PHL_monthly_flights <- flights %>%
#Eliminate the NAs
filter(!is.na(arr_delay)) %>%
filter(dest == "PHL" & arr_delay<= 12 & year == 2013)
PHL_monthly_flights
## # A tibble: 1,076 × 19
## year month day dep_time sched_de…¹ dep_d…² arr_t…³ sched…⁴ arr_d…⁵ carrier
## <int> <int> <int> <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 2013 1 1 908 915 -7 1004 1033 -29 US
## 2 2013 1 1 1600 1610 -10 1712 1729 -17 9E
## 3 2013 1 1 2000 2000 0 2054 2110 -16 9E
## 4 2013 1 2 602 600 2 646 659 -13 US
## 5 2013 1 2 743 745 -2 858 857 1 9E
## 6 2013 1 2 1606 1610 -4 1730 1729 1 9E
## 7 2013 1 2 2003 2015 -12 2102 2125 -23 9E
## 8 2013 1 3 556 600 -4 700 659 1 US
## 9 2013 1 3 949 955 -6 1051 1100 -9 9E
## 10 2013 1 3 1250 1300 -10 1346 1406 -20 9E
## # … with 1,066 more rows, 9 more variables: flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, and abbreviated variable names
## # ¹sched_dep_time, ²dep_delay, ³arr_time, ⁴sched_arr_time, ⁵arr_delay
flights_barplot <- PHL_monthly_flights %>%
ggplot() +
geom_bar(mapping =aes(x=month, y = arr_delay, fill = month, color = "orange"),
position = "dodge", stat = "identity") +
ggtitle("PHL's Flight Arrival Delays in 2013") +
ylab("Number of Flight arrival delays") +
labs(fill = "Flight Delays - Months")
flights_barplot
In reviewing the dataset I noticed that Philadelphia International
Airport (PHL) had a large number of flights that arrived early or on
time during 2013. By filtering the data,1076 rows, there were no real
outliers when comparing arrivals from the three NYC airports (JFK, EWR,
and LGA). Higher arrival times appear in month 3,6,7,8,9,10,11,and 12.
The use of a second color (orange) helps to illustrate the limited
number of flights that arrived late. The use of the second color also
helps the reader see the large and short gaps, by month, where flights
arrived either late, on-time, or early.