Week12_Assignment
2022-08-07
## Warning: package 'viridis' was built under R version 4.2.1## Warning: package 'fueleconomy' was built under R version 4.2.1
13.4 Mutating Joins
1. 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:
airports %>%
semi_join(flights, c("faa" = "dest")) %>%
ggplot(aes(lon, lat)) +
borders("state") +
geom_point() +
coord_quickmap()
avg_dest_delays <-
flights %>%
group_by(dest) %>%
# arrival delay NA's are cancelled flights
summarise(delay = mean(arr_delay, na.rm = TRUE)) %>%
inner_join(airports, by = c(dest = "faa"))
avg_dest_delays %>%
ggplot(aes(lon, lat, colour = delay)) +
borders("state") +
geom_point() +
coord_quickmap()
2. Add the location of the origin and destination (i.e. the lat and lon) to flights.
airport_locations <- airports %>%
select(faa, lat, lon)
flights %>%
select(year:day, hour, origin, dest) %>%
left_join(
airport_locations,
by = c("origin" = "faa")
) %>%
left_join(
airport_locations,
by = c("dest" = "faa"),
suffix = c("_origin", "_dest")
# existing lat and lon variables in tibble gain the _origin suffix
# new lat and lon variables are given _dest suffix
)## # A tibble: 336,776 × 10
## year month day hour origin dest lat_origin lon_origin lat_dest lon_dest
## <int> <int> <int> <dbl> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 5 EWR IAH 40.7 -74.2 30.0 -95.3
## 2 2013 1 1 5 LGA IAH 40.8 -73.9 30.0 -95.3
## 3 2013 1 1 5 JFK MIA 40.6 -73.8 25.8 -80.3
## 4 2013 1 1 5 JFK BQN 40.6 -73.8 NA NA
## 5 2013 1 1 6 LGA ATL 40.8 -73.9 33.6 -84.4
## 6 2013 1 1 5 EWR ORD 40.7 -74.2 42.0 -87.9
## 7 2013 1 1 6 EWR FLL 40.7 -74.2 26.1 -80.2
## 8 2013 1 1 6 LGA IAD 40.8 -73.9 38.9 -77.5
## 9 2013 1 1 6 JFK MCO 40.6 -73.8 28.4 -81.3
## 10 2013 1 1 6 LGA ORD 40.8 -73.9 42.0 -87.9
## # … with 336,766 more rows
5.What happened on June 13, 2013? Display the spatial pattern of delays, and then use Google to cross-reference with the weather.
flights %>%
filter(year == 2013, month == 6, day == 13) %>%
group_by(dest) %>%
summarise(delay = mean(arr_delay, na.rm = TRUE)) %>%
inner_join(airports, by = c("dest" = "faa")) %>%
ggplot(aes(y = lat, x = lon, size = delay, colour = delay)) +
borders("state") +
geom_point() +
coord_quickmap() +
scale_colour_viridis()## Warning: Removed 3 rows containing missing values (geom_point).
2. Filter flights to only show flights with planes that have flown at least 100 flights.
#Find all planes that have flown at least 100 flights.
planes_gte100 <- flights %>%
filter(!is.na(tailnum)) %>%
group_by(tailnum) %>%
count() %>%
filter(n >= 100)
#semi join the data frame of planes that have flown at least 100 flights to the data frame of flights to select the flights by those planes.
flights %>%
semi_join(planes_gte100, by = "tailnum")## # A tibble: 228,390 × 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 1 544 545 -1 1004 1022
## 4 2013 1 1 554 558 -4 740 728
## 5 2013 1 1 555 600 -5 913 854
## 6 2013 1 1 557 600 -3 709 723
## 7 2013 1 1 557 600 -3 838 846
## 8 2013 1 1 558 600 -2 849 851
## 9 2013 1 1 558 600 -2 853 856
## 10 2013 1 1 558 600 -2 923 937
## # … with 228,380 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>
3.Combine fueleconomy::vehicles and fueleconomy::common to find only the records for the most common models.
fueleconomy::vehicles %>%
semi_join(fueleconomy::common, by = c("make", "model"))## # A tibble: 14,531 × 12
## id make model year class trans drive cyl displ fuel hwy cty
## <dbl> <chr> <chr> <dbl> <chr> <chr> <chr> <dbl> <dbl> <chr> <dbl> <dbl>
## 1 1833 Acura Integra 1986 Subcompa… Auto… Fron… 4 1.6 Regu… 28 22
## 2 1834 Acura Integra 1986 Subcompa… Manu… Fron… 4 1.6 Regu… 28 23
## 3 3037 Acura Integra 1987 Subcompa… Auto… Fron… 4 1.6 Regu… 28 22
## 4 3038 Acura Integra 1987 Subcompa… Manu… Fron… 4 1.6 Regu… 28 23
## 5 4183 Acura Integra 1988 Subcompa… Auto… Fron… 4 1.6 Regu… 27 22
## 6 4184 Acura Integra 1988 Subcompa… Manu… Fron… 4 1.6 Regu… 28 23
## 7 5303 Acura Integra 1989 Subcompa… Auto… Fron… 4 1.6 Regu… 27 22
## 8 5304 Acura Integra 1989 Subcompa… Manu… Fron… 4 1.6 Regu… 28 23
## 9 6442 Acura Integra 1990 Subcompa… Auto… Fron… 4 1.8 Regu… 24 20
## 10 6443 Acura Integra 1990 Subcompa… Manu… Fron… 4 1.8 Regu… 26 21
## # … with 14,521 more rows
4.Find the 48 hours (over the course of the whole year) that have the worst delays. Cross-reference it with the weather data. Can you see any patterns?
#Group flights by hour of scheduled departure time and calculate the average delay. Then select the 48 observations (hours) with the highest average delay.
worst_hours <- flights %>%
mutate(hour = sched_dep_time %/% 100) %>%
group_by(origin, year, month, day, hour) %>%
summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
ungroup() %>%
arrange(desc(dep_delay)) %>%
slice(1:48)## `summarise()` has grouped output by 'origin', 'year', 'month', 'day'. You can
## override using the `.groups` argument.#Then use semi_join() to get the weather for these hours.
weather_most_delayed <- semi_join(weather, worst_hours,
by = c("origin", "year",
"month", "day", "hour"))select(weather_most_delayed, temp, wind_speed, precip) %>%
print(n = 48)## # A tibble: 48 × 3
## temp wind_speed precip
## <dbl> <dbl> <dbl>
## 1 27.0 13.8 0
## 2 28.0 19.6 0
## 3 28.9 28.8 0
## 4 33.8 9.21 0.06
## 5 34.0 8.06 0.05
## 6 80.1 8.06 0
## 7 86 13.8 0
## 8 73.4 6.90 0.08
## 9 84.0 5.75 0
## 10 78.8 18.4 0.23
## 11 53.6 0 0
## 12 60.8 31.1 0.11
## 13 55.4 17.3 0.14
## 14 53.1 9.21 0.01
## 15 55.9 11.5 0.1
## 16 55.4 8.06 0.15
## 17 57.0 29.9 0
## 18 33.8 20.7 0.02
## 19 34.0 19.6 0.01
## 20 36.0 21.9 0.01
## 21 37.9 16.1 0
## 22 32 13.8 0.12
## 23 60.1 33.4 0.14
## 24 60.8 11.5 0.02
## 25 62.1 17.3 0
## 26 66.9 10.4 0
## 27 66.9 13.8 0
## 28 79.0 10.4 0
## 29 77 16.1 0.07
## 30 75.9 13.8 0
## 31 82.4 8.06 0
## 32 86 9.21 0
## 33 80.1 9.21 0
## 34 80.6 11.5 0
## 35 78.1 6.90 0
## 36 75.2 10.4 0.01
## 37 73.9 5.75 0.03
## 38 73.9 8.06 0
## 39 75.0 4.60 0
## 40 75.0 4.60 0.01
## 41 80.1 0 0.01
## 42 80.1 0 0
## 43 77 10.4 0
## 44 82.0 10.4 0
## 45 72.0 13.8 0.3
## 46 72.0 4.60 0.03
## 47 51.1 4.60 0
## 48 54.0 6.90 0ggplot(weather_most_delayed, aes(x = precip, y = wind_speed, color = temp)) +
geom_point()