Joins

Counting distinct observations

airlines %>%
  count(carrier) %>% 
  filter(n > 1)

## # A tibble: 0 x 2
## # ... with 2 variables: carrier <chr>, n <int>

airports %>%
  count(faa) %>%
  filter(n > 1)

## # A tibble: 0 x 2
## # ... with 2 variables: faa <chr>, n <int>

Destination by airline

flights %>% 
  filter(distance < 300) %>%
  left_join(airlines, by = "carrier") %>%
  left_join(airports, by = c("dest" = "faa"))

## # A tibble: 51,287 x 27
##     year month   day dep_time sched_dep_time dep_delay arr_time
##    <int> <int> <int>    <int>          <int>     <dbl>    <int>
##  1  2013     1     1      557            600        -3      709
##  2  2013     1     1      559            559         0      702
##  3  2013     1     1      629            630        -1      721
##  4  2013     1     1      632            608        24      740
##  5  2013     1     1      639            640        -1      739
##  6  2013     1     1      732            735        -3      857
##  7  2013     1     1      733            736        -3      854
##  8  2013     1     1      801            805        -4      900
##  9  2013     1     1      803            810        -7      903
## 10  2013     1     1      820            830       -10      940
## # ... with 51,277 more rows, and 20 more variables: sched_arr_time <int>,
## #   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.x <chr>, name.y <chr>, lat <dbl>,
## #   lon <dbl>, alt <int>, tz <dbl>, dst <chr>, tzone <chr>

Destination by airline, wide form

flights %>% 
  filter(distance < 300) %>%
  left_join(airlines, by = "carrier") %>%
  rename(carrier_name = name) %>%
  left_join(airports, by = c("dest" = "faa")) %>% 
  rename(airport_name = name) %>%
  count(carrier_name, airport_name) %>%
  spread(carrier_name, n)

## # A tibble: 19 x 12
##    airport_name `American Airli… `Delta Air Line… `Endeavor Air I…
##    <chr>                   <int>            <int>            <int>
##  1 Albany Intl                NA               NA               NA
##  2 Baltimore W…               NA               NA              856
##  3 Bradley Intl               NA               NA               NA
##  4 Buffalo Nia…               NA                3               54
##  5 Burlington …               NA               NA                2
##  6 General Edw…             1455              972              914
##  7 Greater Roc…               NA               NA              281
##  8 La Guardia                 NA               NA               NA
##  9 Manchester …               NA               NA               11
## 10 "Martha\\\\…               NA               NA               71
## 11 Nantucket M…               NA               NA               NA
## 12 Norfolk Intl               NA               NA              402
## 13 Philadelphi…               NA                2              940
## 14 Portland In…               NA              235               NA
## 15 Richmond In…               NA               NA              340
## 16 Ronald Reag…               NA                2             1074
## 17 Syracuse Ha…               NA               NA              170
## 18 Theodore Fr…               NA               NA               NA
## 19 Washington …               NA               NA              664
## # ... with 8 more variables: `Envoy Air` <int>, `ExpressJet Airlines
## #   Inc.` <int>, `JetBlue Airways` <int>, `Mesa Airlines Inc.` <int>,
## #   `SkyWest Airlines Inc.` <int>, `Southwest Airlines Co.` <int>, `United
## #   Air Lines Inc.` <int>, `US Airways Inc.` <int>

Destination by airline, economic join

airline_names <-
  airlines %>%
  rename(carrier_name = name)

dest_airport_names <-
  airports %>%
  select(dest = faa, airport_name = name)

verbose_destinations_by_carrier <-
  flights %>% 
  filter(distance < 300) %>%
  count(carrier, dest) %>% 
  left_join(airline_names, by = "carrier") %>%
  select(-carrier) %>% 
  left_join(dest_airport_names, by = "dest") %>% 
  select(-dest)

verbose_destinations_by_carrier

## # A tibble: 58 x 3
##        n carrier_name      airport_name                      
##    <int> <chr>             <chr>                             
##  1   914 Endeavor Air Inc. General Edward Lawrence Logan Intl
##  2     2 Endeavor Air Inc. Burlington Intl                   
##  3    54 Endeavor Air Inc. Buffalo Niagara Intl              
##  4   856 Endeavor Air Inc. Baltimore Washington Intl         
##  5  1074 Endeavor Air Inc. Ronald Reagan Washington Natl     
##  6   664 Endeavor Air Inc. Washington Dulles Intl            
##  7    11 Endeavor Air Inc. Manchester Regional Airport       
##  8    71 Endeavor Air Inc. "Martha\\\\'s Vineyard"           
##  9   402 Endeavor Air Inc. Norfolk Intl                      
## 10   940 Endeavor Air Inc. Philadelphia Intl                 
## # ... with 48 more rows

Heat map

verbose_destinations_by_carrier %>%
  ggplot() +
  geom_raster(aes(airport_name, carrier_name, fill = n)) +
  ggpubr::rotate_x_text()

  # theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))