Chapt 13
MT
3/4/2021
library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.0.4## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.5 v dplyr 1.0.3
## v tidyr 1.1.2 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)view(flights)
view(airports)
view(planes)Compute the average delay by destination, then join on the airports data frame so you can show the spatial distribution of delays. Here’s an easy way to draw a map of the United States:
flights1 <- flights %>%
select(dest, arr_delay) %>%
filter(arr_delay >0)flights2 <- flights1 %>%
group_by(dest) %>%
summarise(arr_delay = mean(arr_delay)) flights3 <- airports %>%
semi_join(flights2, c("faa" = "dest")) ggplot(flights3, aes(lon, lat)) +
borders("state") +
geom_point() +
coord_quickmap()
### Add the location of the origin and destination (i.e. the lat and lon) to flights.
flights %>%
left_join(airports, c("dest" = "faa"))## # A tibble: 336,776 x 26
## 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
## # ... with 336,766 more rows, and 18 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>,
## # name <chr>, lat <dbl>, lon <dbl>, alt <dbl>, tz <dbl>, dst <chr>,
## # tzone <chr>flights %>%
left_join(airports, c("origin" = "faa"))## # A tibble: 336,776 x 26
## 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
## # ... with 336,766 more rows, and 18 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>,
## # name <chr>, lat <dbl>, lon <dbl>, alt <dbl>, tz <dbl>, dst <chr>,
## # tzone <chr>Is there a relationship between the age of a plane and its delays? The first three highest delays are from airplanes from 1968, 1979, and 1959 69 min to 51 min; however newer planes also have delays greater than 30 minutes.
ageplane <-flights %>%
left_join(planes, by = "tailnum") %>%
select(year.y, tailnum, arr_delay) %>%
filter(arr_delay >0) ageplane1 <- ageplane %>%
group_by(year.y) %>%
summarise(arr_delay = mean(arr_delay)) What weather conditions make it more likely to see a delay?
Preassure seems to have impact on arrival delays. In the table, planes that had almost 20 hrs in arrival delay, saw air pressure at 1029 atm.
weather1 <- flights %>%
inner_join(weather, by = c ( "origin" = "origin",
"year" = "year",
"month" = "month",
"day" = "day",
"hour" = "hour"
))What happened on June 13 2013? Display the spatial pattern of delays, and then use Google to cross-reference with the weather.
On June 13, 2013, there were tornadoes.
weather2 <- flights %>%
filter(year == 2013, month == 6, day == 13) %>%
group_by(dest) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE))13.5.1 Exercises
what does it mean for a flight to have a missing tailnum? What do the tail numbers that don’t have a matching record in planes have in common? (Hint: one variable explains ~90% of the problems.)
The flights that have missing tailnumbers also have missing departure time, and arrival time. In addition, the flights are from year 213
fltail <- flights %>%
select(year, tailnum, dep_time, arr_time, arr_delay)Filter flights to only show flights with planes that have flown at least 100 flights.
plane100 <- flights %>%
filter(!is.na(tailnum)) %>%
group_by(tailnum) %>%
count() %>%
filter(n >= 100)What does anti_join(flights, airports, by = c(“dest” = “faa”)) tell you? What does anti_join(airports, flights, by = c(“faa” = “dest”)) tell you?
The list contains flights not in the FAA destinations.
anti_join(flights, airports, by = c("dest" = "faa"))## # A tibble: 7,602 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 1 544 545 -1 1004 1022
## 2 2013 1 1 615 615 0 1039 1100
## 3 2013 1 1 628 630 -2 1137 1140
## 4 2013 1 1 701 700 1 1123 1154
## 5 2013 1 1 711 715 -4 1151 1206
## 6 2013 1 1 820 820 0 1254 1310
## 7 2013 1 1 820 820 0 1249 1329
## 8 2013 1 1 840 845 -5 1311 1350
## 9 2013 1 1 909 810 59 1331 1315
## 10 2013 1 1 913 918 -5 1346 1416
## # ... with 7,592 more rows, and 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>anti_join(airports, flights, by = c("faa" = "dest"))## # A tibble: 1,357 x 8
## faa name lat lon alt tz dst tzone
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A America/New_Yo~
## 2 06A Moton Field Municipal A~ 32.5 -85.7 264 -6 A America/Chicago
## 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Chicago
## 4 06N Randall Airport 41.4 -74.4 523 -5 A America/New_Yo~
## 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/New_Yo~
## 6 0A9 Elizabethton Municipal ~ 36.4 -82.2 1593 -5 A America/New_Yo~
## 7 0G6 Williams County Airport 41.5 -84.5 730 -5 A America/New_Yo~
## 8 0G7 Finger Lakes Regional A~ 42.9 -76.8 492 -5 A America/New_Yo~
## 9 0P2 Shoestring Aviation Air~ 39.8 -76.6 1000 -5 U America/New_Yo~
## 10 0S9 Jefferson County Intl 48.1 -123. 108 -8 A America/Los_An~
## # ... with 1,347 more rowsYou might expect that there’s an implicit relationship between plane and airline, because each plane is flown by a single airline. Confirm or reject this hypothesis using the tools you’ve learned above.
Tailcarrier <- flights %>%
filter(!is.na(tailnum)) %>%
group_by(tailnum, carrier)```