Code Along 8
Aaric Adams
2024-11-14
Relational data
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(nycflights13)Mutating joins
flights2 <- flights %>%
select(year:day, hour, origin, dest, tailnum, carrier)
flights2## # A tibble: 336,776 × 8
## year month day hour origin dest tailnum carrier
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr>
## 1 2013 1 1 5 EWR IAH N14228 UA
## 2 2013 1 1 5 LGA IAH N24211 UA
## 3 2013 1 1 5 JFK MIA N619AA AA
## 4 2013 1 1 5 JFK BQN N804JB B6
## 5 2013 1 1 6 LGA ATL N668DN DL
## 6 2013 1 1 5 EWR ORD N39463 UA
## 7 2013 1 1 6 EWR FLL N516JB B6
## 8 2013 1 1 6 LGA IAD N829AS EV
## 9 2013 1 1 6 JFK MCO N593JB B6
## 10 2013 1 1 6 LGA ORD N3ALAA AA
## # ℹ 336,766 more rows#> # A tibble: 336,776 × 8
#> year month day hour origin dest tailnum carrier
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA
#> 2 2013 1 1 5 LGA IAH N24211 UA
#> 3 2013 1 1 5 JFK MIA N619AA AA
#> 4 2013 1 1 5 JFK BQN N804JB B6
#> 5 2013 1 1 6 LGA ATL N668DN DL
#> 6 2013 1 1 5 EWR ORD N39463 UA
#> # ℹ 336,770 more rowsflights2 %>%
select(-origin, -dest) %>%
left_join(airlines, by = "carrier")## # A tibble: 336,776 × 7
## year month day hour tailnum carrier name
## <int> <int> <int> <dbl> <chr> <chr> <chr>
## 1 2013 1 1 5 N14228 UA United Air Lines Inc.
## 2 2013 1 1 5 N24211 UA United Air Lines Inc.
## 3 2013 1 1 5 N619AA AA American Airlines Inc.
## 4 2013 1 1 5 N804JB B6 JetBlue Airways
## 5 2013 1 1 6 N668DN DL Delta Air Lines Inc.
## 6 2013 1 1 5 N39463 UA United Air Lines Inc.
## 7 2013 1 1 6 N516JB B6 JetBlue Airways
## 8 2013 1 1 6 N829AS EV ExpressJet Airlines Inc.
## 9 2013 1 1 6 N593JB B6 JetBlue Airways
## 10 2013 1 1 6 N3ALAA AA American Airlines Inc.
## # ℹ 336,766 more rows#> # A tibble: 336,776 × 7
#> year month day hour tailnum carrier name
#> <int> <int> <int> <dbl> <chr> <chr> <chr>
#> 1 2013 1 1 5 N14228 UA United Air Lines Inc.
#> 2 2013 1 1 5 N24211 UA United Air Lines Inc.
#> 3 2013 1 1 5 N619AA AA American Airlines Inc.
#> 4 2013 1 1 5 N804JB B6 JetBlue Airways
#> 5 2013 1 1 6 N668DN DL Delta Air Lines Inc.
#> 6 2013 1 1 5 N39463 UA United Air Lines Inc.
#> # ℹ 336,770 more rowsflights2 %>%
select(-origin, -dest) %>%
mutate(name = airlines$name[match(carrier, airlines$carrier)])## # A tibble: 336,776 × 7
## year month day hour tailnum carrier name
## <int> <int> <int> <dbl> <chr> <chr> <chr>
## 1 2013 1 1 5 N14228 UA United Air Lines Inc.
## 2 2013 1 1 5 N24211 UA United Air Lines Inc.
## 3 2013 1 1 5 N619AA AA American Airlines Inc.
## 4 2013 1 1 5 N804JB B6 JetBlue Airways
## 5 2013 1 1 6 N668DN DL Delta Air Lines Inc.
## 6 2013 1 1 5 N39463 UA United Air Lines Inc.
## 7 2013 1 1 6 N516JB B6 JetBlue Airways
## 8 2013 1 1 6 N829AS EV ExpressJet Airlines Inc.
## 9 2013 1 1 6 N593JB B6 JetBlue Airways
## 10 2013 1 1 6 N3ALAA AA American Airlines Inc.
## # ℹ 336,766 more rows#> # A tibble: 336,776 × 7
#> year month day hour tailnum carrier name
#> <int> <int> <int> <dbl> <chr> <chr> <chr>
#> 1 2013 1 1 5 N14228 UA United Air Lines Inc.
#> 2 2013 1 1 5 N24211 UA United Air Lines Inc.
#> 3 2013 1 1 5 N619AA AA American Airlines Inc.
#> 4 2013 1 1 5 N804JB B6 JetBlue Airways
#> 5 2013 1 1 6 N668DN DL Delta Air Lines Inc.
#> 6 2013 1 1 5 N39463 UA United Air Lines Inc.
#> # ℹ 336,770 more rowsx <- tribble(
~key, ~val_x,
1, "x1",
2, "x2",
3, "x3"
)
y <- tribble(
~key, ~val_y,
1, "y1",
2, "y2",
4, "y3"
)x %>%
inner_join(y, by = "key")## # A tibble: 2 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2#> # A tibble: 2 × 3
#> key val_x val_y
#> <dbl> <chr> <chr>
#> 1 1 x1 y1
#> 2 2 x2 y2x <- tribble(
~key, ~val_x,
1, "x1",
2, "x2",
2, "x3",
1, "x4"
)
y <- tribble(
~key, ~val_y,
1, "y1",
2, "y2"
)
left_join(x, y, by = "key")## # A tibble: 4 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 2 x3 y2
## 4 1 x4 y1#> # A tibble: 4 × 3
#> key val_x val_y
#> <dbl> <chr> <chr>
#> 1 1 x1 y1
#> 2 2 x2 y2
#> 3 2 x3 y2
#> 4 1 x4 y1x <- tribble(
~key, ~val_x,
1, "x1",
2, "x2",
2, "x3",
3, "x4"
)
y <- tribble(
~key, ~val_y,
1, "y1",
2, "y2",
2, "y3",
3, "y4"
)
left_join(x, y, by = "key")## Warning in left_join(x, y, by = "key"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 2 of `x` matches multiple rows in `y`.
## ℹ Row 2 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.## # A tibble: 6 × 3
## key val_x val_y
## <dbl> <chr> <chr>
## 1 1 x1 y1
## 2 2 x2 y2
## 3 2 x2 y3
## 4 2 x3 y2
## 5 2 x3 y3
## 6 3 x4 y4#> Warning in left_join(x, y, by = "key"): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 2 of `x` matches multiple rows in `y`.
#> ℹ Row 2 of `y` matches multiple rows in `x`.
#> ℹ If a many-to-many relationship is expected, set `relationship =
#> "many-to-many"` to silence this warning.
#> # A tibble: 6 × 3
#> key val_x val_y
#> <dbl> <chr> <chr>
#> 1 1 x1 y1
#> 2 2 x2 y2
#> 3 2 x2 y3
#> 4 2 x3 y2
#> 5 2 x3 y3
#> 6 3 x4 y4flights2 %>%
left_join(weather)## Joining with `by = join_by(year, month, day, hour, origin)`## # A tibble: 336,776 × 18
## year month day hour origin dest tailnum carrier temp dewp humid
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2013 1 1 5 EWR IAH N14228 UA 39.0 28.0 64.4
## 2 2013 1 1 5 LGA IAH N24211 UA 39.9 25.0 54.8
## 3 2013 1 1 5 JFK MIA N619AA AA 39.0 27.0 61.6
## 4 2013 1 1 5 JFK BQN N804JB B6 39.0 27.0 61.6
## 5 2013 1 1 6 LGA ATL N668DN DL 39.9 25.0 54.8
## 6 2013 1 1 5 EWR ORD N39463 UA 39.0 28.0 64.4
## 7 2013 1 1 6 EWR FLL N516JB B6 37.9 28.0 67.2
## 8 2013 1 1 6 LGA IAD N829AS EV 39.9 25.0 54.8
## 9 2013 1 1 6 JFK MCO N593JB B6 37.9 27.0 64.3
## 10 2013 1 1 6 LGA ORD N3ALAA AA 39.9 25.0 54.8
## # ℹ 336,766 more rows
## # ℹ 7 more variables: wind_dir <dbl>, wind_speed <dbl>, wind_gust <dbl>,
## # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>#> Joining with `by = join_by(year, month, day, hour, origin)`
#> # A tibble: 336,776 × 18
#> year month day hour origin dest tailnum carrier temp dewp humid
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA 39.0 28.0 64.4
#> 2 2013 1 1 5 LGA IAH N24211 UA 39.9 25.0 54.8
#> 3 2013 1 1 5 JFK MIA N619AA AA 39.0 27.0 61.6
#> 4 2013 1 1 5 JFK BQN N804JB B6 39.0 27.0 61.6
#> 5 2013 1 1 6 LGA ATL N668DN DL 39.9 25.0 54.8
#> 6 2013 1 1 5 EWR ORD N39463 UA 39.0 28.0 64.4
#> # ℹ 336,770 more rows
#> # ℹ 7 more variables: wind_dir <dbl>, wind_speed <dbl>, wind_gust <dbl>,
#> # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>flights2 %>%
left_join(planes, by = "tailnum")## # A tibble: 336,776 × 16
## year.x month day hour origin dest tailnum carrier year.y type
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <int> <chr>
## 1 2013 1 1 5 EWR IAH N14228 UA 1999 Fixed wing mult…
## 2 2013 1 1 5 LGA IAH N24211 UA 1998 Fixed wing mult…
## 3 2013 1 1 5 JFK MIA N619AA AA 1990 Fixed wing mult…
## 4 2013 1 1 5 JFK BQN N804JB B6 2012 Fixed wing mult…
## 5 2013 1 1 6 LGA ATL N668DN DL 1991 Fixed wing mult…
## 6 2013 1 1 5 EWR ORD N39463 UA 2012 Fixed wing mult…
## 7 2013 1 1 6 EWR FLL N516JB B6 2000 Fixed wing mult…
## 8 2013 1 1 6 LGA IAD N829AS EV 1998 Fixed wing mult…
## 9 2013 1 1 6 JFK MCO N593JB B6 2004 Fixed wing mult…
## 10 2013 1 1 6 LGA ORD N3ALAA AA NA <NA>
## # ℹ 336,766 more rows
## # ℹ 6 more variables: manufacturer <chr>, model <chr>, engines <int>,
## # seats <int>, speed <int>, engine <chr>#> # A tibble: 336,776 × 16
#> year.x month day hour origin dest tailnum carrier year.y type
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <int> <chr>
#> 1 2013 1 1 5 EWR IAH N14228 UA 1999 Fixed wing multi…
#> 2 2013 1 1 5 LGA IAH N24211 UA 1998 Fixed wing multi…
#> 3 2013 1 1 5 JFK MIA N619AA AA 1990 Fixed wing multi…
#> 4 2013 1 1 5 JFK BQN N804JB B6 2012 Fixed wing multi…
#> 5 2013 1 1 6 LGA ATL N668DN DL 1991 Fixed wing multi…
#> 6 2013 1 1 5 EWR ORD N39463 UA 2012 Fixed wing multi…
#> # ℹ 336,770 more rows
#> # ℹ 6 more variables: manufacturer <chr>, model <chr>, engines <int>,
#> # seats <int>, speed <int>, engine <chr>flights2 %>%
left_join(airports, c("dest" = "faa"))## # A tibble: 336,776 × 15
## year month day hour origin dest tailnum carrier name lat lon alt
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2013 1 1 5 EWR IAH N14228 UA Georg… 30.0 -95.3 97
## 2 2013 1 1 5 LGA IAH N24211 UA Georg… 30.0 -95.3 97
## 3 2013 1 1 5 JFK MIA N619AA AA Miami… 25.8 -80.3 8
## 4 2013 1 1 5 JFK BQN N804JB B6 <NA> NA NA NA
## 5 2013 1 1 6 LGA ATL N668DN DL Harts… 33.6 -84.4 1026
## 6 2013 1 1 5 EWR ORD N39463 UA Chica… 42.0 -87.9 668
## 7 2013 1 1 6 EWR FLL N516JB B6 Fort … 26.1 -80.2 9
## 8 2013 1 1 6 LGA IAD N829AS EV Washi… 38.9 -77.5 313
## 9 2013 1 1 6 JFK MCO N593JB B6 Orlan… 28.4 -81.3 96
## 10 2013 1 1 6 LGA ORD N3ALAA AA Chica… 42.0 -87.9 668
## # ℹ 336,766 more rows
## # ℹ 3 more variables: tz <dbl>, dst <chr>, tzone <chr>#> # A tibble: 336,776 × 15
#> year month day hour origin dest tailnum carrier name lat lon alt
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA George… 30.0 -95.3 97
#> 2 2013 1 1 5 LGA IAH N24211 UA George… 30.0 -95.3 97
#> 3 2013 1 1 5 JFK MIA N619AA AA Miami … 25.8 -80.3 8
#> 4 2013 1 1 5 JFK BQN N804JB B6 <NA> NA NA NA
#> 5 2013 1 1 6 LGA ATL N668DN DL Hartsf… 33.6 -84.4 1026
#> 6 2013 1 1 5 EWR ORD N39463 UA Chicag… 42.0 -87.9 668
#> # ℹ 336,770 more rows
#> # ℹ 3 more variables: tz <dbl>, dst <chr>, tzone <chr>
flights2 %>%
left_join(airports, c("origin" = "faa"))## # A tibble: 336,776 × 15
## year month day hour origin dest tailnum carrier name lat lon alt
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 2013 1 1 5 EWR IAH N14228 UA Newar… 40.7 -74.2 18
## 2 2013 1 1 5 LGA IAH N24211 UA La Gu… 40.8 -73.9 22
## 3 2013 1 1 5 JFK MIA N619AA AA John … 40.6 -73.8 13
## 4 2013 1 1 5 JFK BQN N804JB B6 John … 40.6 -73.8 13
## 5 2013 1 1 6 LGA ATL N668DN DL La Gu… 40.8 -73.9 22
## 6 2013 1 1 5 EWR ORD N39463 UA Newar… 40.7 -74.2 18
## 7 2013 1 1 6 EWR FLL N516JB B6 Newar… 40.7 -74.2 18
## 8 2013 1 1 6 LGA IAD N829AS EV La Gu… 40.8 -73.9 22
## 9 2013 1 1 6 JFK MCO N593JB B6 John … 40.6 -73.8 13
## 10 2013 1 1 6 LGA ORD N3ALAA AA La Gu… 40.8 -73.9 22
## # ℹ 336,766 more rows
## # ℹ 3 more variables: tz <dbl>, dst <chr>, tzone <chr>#> # A tibble: 336,776 × 15
#> year month day hour origin dest tailnum carrier name lat lon alt
#> <int> <int> <int> <dbl> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 2013 1 1 5 EWR IAH N14228 UA Newark… 40.7 -74.2 18
#> 2 2013 1 1 5 LGA IAH N24211 UA La Gua… 40.8 -73.9 22
#> 3 2013 1 1 5 JFK MIA N619AA AA John F… 40.6 -73.8 13
#> 4 2013 1 1 5 JFK BQN N804JB B6 John F… 40.6 -73.8 13
#> 5 2013 1 1 6 LGA ATL N668DN DL La Gua… 40.8 -73.9 22
#> 6 2013 1 1 5 EWR ORD N39463 UA Newark… 40.7 -74.2 18
#> # ℹ 336,770 more rows
#> # ℹ 3 more variables: tz <dbl>, dst <chr>, tzone <chr>Exercises
- 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()
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"))
#> `summarise()` ungrouping output (override with `.groups` argument)avg_dest_delays %>%
ggplot(aes(lon, lat, colour = delay)) +
borders("state") +
geom_point() +
coord_quickmap()
- 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")
)## # A tibble: 336,776 × 10
## year month day hour origin dest lat.x lon.x lat.y lon.y
## <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
## # ℹ 336,766 more rows#> # A tibble: 336,776 x 10
#> year month day hour origin dest lat.x lon.x lat.y lon.y
#> <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
#> # … with 336,770 more rowsairport_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
## # ℹ 336,766 more rows#> # A tibble: 336,776 x 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
#> # … with 336,770 more rows3.Is there a relationship between the age of a plane and its delays? To compare the age of the plane to flights delay, I merge flights with the planes, which contains a variable plane_year, with the year in which the plane was built. To look at the relationship between plane age and departure delay
plane_cohorts <- inner_join(flights,
select(planes, tailnum, plane_year = year),
by = "tailnum"
) %>%
mutate(age = year - plane_year) %>%
filter(!is.na(age)) %>%
mutate(age = if_else(age > 25, 25L, age)) %>%
group_by(age) %>%
summarise(
dep_delay_mean = mean(dep_delay, na.rm = TRUE),
dep_delay_sd = sd(dep_delay, na.rm = TRUE),
arr_delay_mean = mean(arr_delay, na.rm = TRUE),
arr_delay_sd = sd(arr_delay, na.rm = TRUE),
n_arr_delay = sum(!is.na(arr_delay)),
n_dep_delay = sum(!is.na(dep_delay))
)
#> `summarise()` ungrouping output (override with `.groups` argument)ggplot(plane_cohorts, aes(x = age, y = dep_delay_mean)) +
geom_point() +
scale_x_continuous("Age of plane (years)", breaks = seq(0, 30, by = 10)) +
scale_y_continuous("Mean Departure Delay (minutes)")
ggplot(plane_cohorts, aes(x = age, y = arr_delay_mean)) +
geom_point() +
scale_x_continuous("Age of Plane (years)", breaks = seq(0, 30, by = 10)) +
scale_y_continuous("Mean Arrival Delay (minutes)")
- What weather conditions make it more likely to see a delay? Almost any amount of precipitation is associated with a delay. However, there is not a strong a trend above 0.02 in. of precipitation.
flight_weather <-
flights %>%
inner_join(weather, by = c(
"origin" = "origin",
"year" = "year",
"month" = "month",
"day" = "day",
"hour" = "hour"
))flight_weather %>%
group_by(precip) %>%
summarise(delay = mean(dep_delay, na.rm = TRUE)) %>%
ggplot(aes(x = precip, y = delay)) +
geom_line() + geom_point()
#> `summarise()` ungrouping output (override with `.groups` argument)flight_weather %>%
ungroup() %>%
mutate(visib_cat = cut_interval(visib, n = 10)) %>%
group_by(visib_cat) %>%
summarise(dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
ggplot(aes(x = visib_cat, y = dep_delay)) +
geom_point()
#> `summarise()` ungrouping output (override with `.groups` argument)- What happened on June 13, 2013? Display the spatial pattern of delays, and then use Google to cross-reference with the weather. There was a large series of storms (derechos) in the southeastern US
##Filtering Joins
top_dest <- flights %>%
count(dest, sort = TRUE) %>%
head(10)
top_dest## # A tibble: 10 × 2
## dest n
## <chr> <int>
## 1 ORD 17283
## 2 ATL 17215
## 3 LAX 16174
## 4 BOS 15508
## 5 MCO 14082
## 6 CLT 14064
## 7 SFO 13331
## 8 FLL 12055
## 9 MIA 11728
## 10 DCA 9705#> # A tibble: 10 × 2
#> dest n
#> <chr> <int>
#> 1 ORD 17283
#> 2 ATL 17215
#> 3 LAX 16174
#> 4 BOS 15508
#> 5 MCO 14082
#> 6 CLT 14064
#> # ℹ 4 more rowsflights %>%
filter(dest %in% top_dest$dest)## # A tibble: 141,145 × 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 1 542 540 2 923 850
## 2 2013 1 1 554 600 -6 812 837
## 3 2013 1 1 554 558 -4 740 728
## 4 2013 1 1 555 600 -5 913 854
## 5 2013 1 1 557 600 -3 838 846
## 6 2013 1 1 558 600 -2 753 745
## 7 2013 1 1 558 600 -2 924 917
## 8 2013 1 1 558 600 -2 923 937
## 9 2013 1 1 559 559 0 702 706
## 10 2013 1 1 600 600 0 851 858
## # ℹ 141,135 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>#> # A tibble: 141,145 × 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 1 542 540 2 923 850
#> 2 2013 1 1 554 600 -6 812 837
#> 3 2013 1 1 554 558 -4 740 728
#> 4 2013 1 1 555 600 -5 913 854
#> 5 2013 1 1 557 600 -3 838 846
#> 6 2013 1 1 558 600 -2 753 745
#> # ℹ 141,139 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>flights %>%
semi_join(top_dest)## Joining with `by = join_by(dest)`## # A tibble: 141,145 × 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 1 542 540 2 923 850
## 2 2013 1 1 554 600 -6 812 837
## 3 2013 1 1 554 558 -4 740 728
## 4 2013 1 1 555 600 -5 913 854
## 5 2013 1 1 557 600 -3 838 846
## 6 2013 1 1 558 600 -2 753 745
## 7 2013 1 1 558 600 -2 924 917
## 8 2013 1 1 558 600 -2 923 937
## 9 2013 1 1 559 559 0 702 706
## 10 2013 1 1 600 600 0 851 858
## # ℹ 141,135 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>#> Joining with `by = join_by(dest)`
#> # A tibble: 141,145 × 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 1 542 540 2 923 850
#> 2 2013 1 1 554 600 -6 812 837
#> 3 2013 1 1 554 558 -4 740 728
#> 4 2013 1 1 555 600 -5 913 854
#> 5 2013 1 1 557 600 -3 838 846
#> 6 2013 1 1 558 600 -2 753 745
#> # ℹ 141,139 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>flights %>%
semi_join(top_dest)## Joining with `by = join_by(dest)`## # A tibble: 141,145 × 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 1 542 540 2 923 850
## 2 2013 1 1 554 600 -6 812 837
## 3 2013 1 1 554 558 -4 740 728
## 4 2013 1 1 555 600 -5 913 854
## 5 2013 1 1 557 600 -3 838 846
## 6 2013 1 1 558 600 -2 753 745
## 7 2013 1 1 558 600 -2 924 917
## 8 2013 1 1 558 600 -2 923 937
## 9 2013 1 1 559 559 0 702 706
## 10 2013 1 1 600 600 0 851 858
## # ℹ 141,135 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>#> Joining with `by = join_by(dest)`
#> # A tibble: 141,145 × 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 1 542 540 2 923 850
#> 2 2013 1 1 554 600 -6 812 837
#> 3 2013 1 1 554 558 -4 740 728
#> 4 2013 1 1 555 600 -5 913 854
#> 5 2013 1 1 557 600 -3 838 846
#> 6 2013 1 1 558 600 -2 753 745
#> # ℹ 141,139 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>flights %>%
anti_join(planes, by = "tailnum") %>%
count(tailnum, sort = TRUE)## # A tibble: 722 × 2
## tailnum n
## <chr> <int>
## 1 <NA> 2512
## 2 N725MQ 575
## 3 N722MQ 513
## 4 N723MQ 507
## 5 N713MQ 483
## 6 N735MQ 396
## 7 N0EGMQ 371
## 8 N534MQ 364
## 9 N542MQ 363
## 10 N531MQ 349
## # ℹ 712 more rows#> # A tibble: 722 × 2
#> tailnum n
#> <chr> <int>
#> 1 <NA> 2512
#> 2 N725MQ 575
#> 3 N722MQ 513
#> 4 N723MQ 507
#> 5 N713MQ 483
#> 6 N735MQ 396
#> # ℹ 716 more rowsExercises
- 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.) Flights that have a missing tailnum all have missing values of arr_time, meaning that the flight was canceled. Many of the tail numbers that don’t have a matching value in planes are registered to American Airlines (AA) or Envoy Airlines (MQ). The documentation for planes states
2.Filter flights to only show flights with planes that have flown at least 100 flights.
flights %>%
filter(!is.na(tailnum)) %>%
group_by(tailnum) %>%
mutate(n = n()) %>%
filter(n >= 100)## # A tibble: 228,390 × 20
## # Groups: tailnum [1,217]
## 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
## # ℹ 228,380 more rows
## # ℹ 12 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>, n <int>#> # A tibble: 228,390 x 20
#> # Groups: tailnum [1,217]
#> 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
#> # … with 228,384 more rows, and 12 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>,
#> # n <int>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
## # ℹ 14,521 more rows#> # A tibble: 14,531 x 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 Integ… 1986 Subcom… Automa… Front-… 4 1.6 Regu… 28 22
#> 2 1834 Acura Integ… 1986 Subcom… Manual… Front-… 4 1.6 Regu… 28 23
#> 3 3037 Acura Integ… 1987 Subcom… Automa… Front-… 4 1.6 Regu… 28 22
#> 4 3038 Acura Integ… 1987 Subcom… Manual… Front-… 4 1.6 Regu… 28 23
#> 5 4183 Acura Integ… 1988 Subcom… Automa… Front-… 4 1.6 Regu… 27 22
#> 6 4184 Acura Integ… 1988 Subcom… Manual… Front-… 4 1.6 Regu… 28 23
#> # … with 14,525 more rowsfueleconomy::vehicles %>%
distinct(model, make) %>%
group_by(model) %>%
filter(n() > 1) %>%
arrange(model)## # A tibble: 126 × 2
## # Groups: model [60]
## model make
## <chr> <chr>
## 1 200 Audi
## 2 200 Chrysler
## 3 240 DL/240 GL Wagon Mcevoy Motors
## 4 240 DL/240 GL Wagon Volvo
## 5 300E Lambda Control Systems
## 6 300E Mercedes-Benz
## 7 300SL J.K. Motors
## 8 300SL Mercedes-Benz
## 9 500SE Mercedes-Benz
## 10 500SE Texas Coach Company
## # ℹ 116 more rows#> # A tibble: 126 x 2
#> # Groups: model [60]
#> make model
#> <chr> <chr>
#> 1 Audi 200
#> 2 Chrysler 200
#> 3 Mcevoy Motors 240 DL/240 GL Wagon
#> 4 Volvo 240 DL/240 GL Wagon
#> 5 Lambda Control Systems 300E
#> 6 Mercedes-Benz 300E
#> # … with 120 more rowsfueleconomy::common %>%
distinct(model, make) %>%
group_by(model) %>%
filter(n() > 1) %>%
arrange(model)## # A tibble: 8 × 2
## # Groups: model [3]
## model make
## <chr> <chr>
## 1 Colt Dodge
## 2 Colt Plymouth
## 3 Truck 2WD Mitsubishi
## 4 Truck 2WD Nissan
## 5 Truck 2WD Toyota
## 6 Truck 4WD Mitsubishi
## 7 Truck 4WD Nissan
## 8 Truck 4WD Toyota#> # A tibble: 8 x 2
#> # Groups: model [3]
#> make model
#> <chr> <chr>
#> 1 Dodge Colt
#> 2 Plymouth Colt
#> 3 Mitsubishi Truck 2WD
#> 4 Nissan Truck 2WD
#> 5 Toyota Truck 2WD
#> 6 Mitsubishi Truck 4WD
#> # … with 2 more rows4.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?
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.#> `summarise()` regrouping output by 'origin', 'year', 'month', 'day' (override with `.groups` argument)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 0#> # A tibble: 48 x 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()
5.What does anti_join(flights, airports, by = c(“dest” = “faa”)) tell you? What does anti_join(airports, flights, by = c(“faa” = “dest”)) tell you?
anti_join(flights, airports, by = c("dest" = "faa")) %>%
distinct(dest)## # A tibble: 4 × 1
## dest
## <chr>
## 1 BQN
## 2 SJU
## 3 STT
## 4 PSE#> # A tibble: 4 x 1
#> dest
#> <chr>
#> 1 BQN
#> 2 SJU
#> 3 STT
#> 4 PSEanti_join(airports, flights, by = c("faa" = "dest"))## # A tibble: 1,357 × 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/…
## 2 06A Moton Field Municipal Airport 32.5 -85.7 264 -6 A America/…
## 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/…
## 4 06N Randall Airport 41.4 -74.4 523 -5 A America/…
## 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/…
## 6 0A9 Elizabethton Municipal Airport 36.4 -82.2 1593 -5 A America/…
## 7 0G6 Williams County Airport 41.5 -84.5 730 -5 A America/…
## 8 0G7 Finger Lakes Regional Airport 42.9 -76.8 492 -5 A America/…
## 9 0P2 Shoestring Aviation Airfield 39.8 -76.6 1000 -5 U America/…
## 10 0S9 Jefferson County Intl 48.1 -123. 108 -8 A America/…
## # ℹ 1,347 more rows#> # 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_Y…
#> 2 06A Moton Field Municipal Airp… 32.5 -85.7 264 -6 A America/Chica…
#> 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Chica…
#> 4 06N Randall Airport 41.4 -74.4 523 -5 A America/New_Y…
#> 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/New_Y…
#> 6 0A9 Elizabethton Municipal Air… 36.4 -82.2 1593 -5 A America/New_Y…
#> # … with 1,351 more rows- You 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.
planes_carriers <-
flights %>%
filter(!is.na(tailnum)) %>%
distinct(tailnum, carrier)planes_carriers %>%
count(tailnum) %>%
filter(n > 1) %>%
nrow()## [1] 17#> [1] 17carrier_transfer_tbl <- planes_carriers %>%
# keep only planes which have flown for more than one airline
group_by(tailnum) %>%
filter(n() > 1) %>%
# join with airlines to get airline names
left_join(airlines, by = "carrier") %>%
arrange(tailnum, carrier)
carrier_transfer_tbl## # A tibble: 34 × 3
## # Groups: tailnum [17]
## tailnum carrier name
## <chr> <chr> <chr>
## 1 N146PQ 9E Endeavor Air Inc.
## 2 N146PQ EV ExpressJet Airlines Inc.
## 3 N153PQ 9E Endeavor Air Inc.
## 4 N153PQ EV ExpressJet Airlines Inc.
## 5 N176PQ 9E Endeavor Air Inc.
## 6 N176PQ EV ExpressJet Airlines Inc.
## 7 N181PQ 9E Endeavor Air Inc.
## 8 N181PQ EV ExpressJet Airlines Inc.
## 9 N197PQ 9E Endeavor Air Inc.
## 10 N197PQ EV ExpressJet Airlines Inc.
## # ℹ 24 more rows#> # A tibble: 34 x 3
#> # Groups: tailnum [17]
#> carrier tailnum name
#> <chr> <chr> <chr>
#> 1 9E N146PQ Endeavor Air Inc.
#> 2 EV N146PQ ExpressJet Airlines Inc.
#> 3 9E N153PQ Endeavor Air Inc.
#> 4 EV N153PQ ExpressJet Airlines Inc.
#> 5 9E N176PQ Endeavor Air Inc.
#> 6 EV N176PQ ExpressJet Airlines Inc.
#> # … with 28 more rowsJoin Problems
airports %>% count(alt, lon) %>% filter(n > 1)## # A tibble: 0 × 3
## # ℹ 3 variables: alt <dbl>, lon <dbl>, n <int>#> # A tibble: 0 × 3
#> # ℹ 3 variables: alt <dbl>, lon <dbl>, n <int>Set operations
df1 <- tribble(
~x, ~y,
1, 1,
2, 1
)
df2 <- tribble(
~x, ~y,
1, 1,
1, 2
)intersect(df1, df2)## # A tibble: 1 × 2
## x y
## <dbl> <dbl>
## 1 1 1#> # A tibble: 1 × 2
#> x y
#> <dbl> <dbl>
#> 1 1 1
# Note that we get 3 rows, not 4
union(df1, df2)## # A tibble: 3 × 2
## x y
## <dbl> <dbl>
## 1 1 1
## 2 2 1
## 3 1 2#> # A tibble: 3 × 2
#> x y
#> <dbl> <dbl>
#> 1 1 1
#> 2 2 1
#> 3 1 2
setdiff(df1, df2)## # A tibble: 1 × 2
## x y
## <dbl> <dbl>
## 1 2 1#> # A tibble: 1 × 2
#> x y
#> <dbl> <dbl>
#> 1 2 1
setdiff(df2, df1)## # A tibble: 1 × 2
## x y
## <dbl> <dbl>
## 1 1 2#> # A tibble: 1 × 2
#> x y
#> <dbl> <dbl>
#> 1 1 2Celtics 2025 nba champs