Analyzing SFO Landing

The sfo_stats dataset provides monthly statistics on San Francisco International Airport’s air traffic landing between July 2005 and September 2020. The following vignette demonstrate some approaches for exploring the dataset. As the structure of the sfo_stats is similar to the sfo_passengers dataset, we will repeat the same data prep steps as shown on the previous vignette. We will use the dplyr and plotly packages for data manipulation and visualization, respectively.

Data prep

For simplicity, let’s use a shorter name , d, for the dataset:

library(sfo)
library(dplyr)
library(plotly)

d <- sfo_stats

head(d)
#>   activity_period operating_airline operating_airline_iata_code
#> 1          202009   United Airlines                          UA
#> 2          202009   United Airlines                          UA
#> 3          202009   United Airlines                          UA
#> 4          202009   United Airlines                          UA
#> 5          202009   United Airlines                          UA
#> 6          202009   United Airlines                          UA
#>   published_airline published_airline_iata_code   geo_summary geo_region
#> 1   United Airlines                          UA International     Mexico
#> 2   United Airlines                          UA      Domestic         US
#> 3   United Airlines                          UA International     Canada
#> 4   United Airlines                          UA      Domestic         US
#> 5   United Airlines                          UA International     Mexico
#> 6   United Airlines                          UA      Domestic         US
#>   landing_aircraft_type aircraft_body_type aircraft_manufacturer aircraft_model
#> 1             Passenger        Narrow Body                Airbus           A320
#> 2             Passenger        Narrow Body                Boeing           B738
#> 3             Passenger        Narrow Body                Boeing           B738
#> 4             Passenger        Narrow Body                Boeing           B738
#> 5             Passenger        Narrow Body                Boeing           B738
#> 6             Passenger        Narrow Body                Boeing           B739
#>   aircraft_version landing_count total_landed_weight
#> 1                -            37             5261326
#> 2                -            14             2048200
#> 3                -             1              146300
#> 4                -           251            36721300
#> 5                -             3              438900
#> 6                -           553            86986900

Next, let’s reformat the period indicator, activity_period to a Date object, setting the first day of the month as the default day:

d$date <- as.Date(paste(substr(d$activity_period, 1,4), 
                        substr(d$activity_period, 5,6), 
                        "01", sep ="/"))

We can see, with the str command, the stucture of the dataset:

str(d)
#> 'data.frame':    25429 obs. of  15 variables:
#>  $ activity_period            : int  202009 202009 202009 202009 202009 202009 202009 202009 202009 202009 ...
#>  $ operating_airline          : chr  "United Airlines" "United Airlines" "United Airlines" "United Airlines" ...
#>  $ operating_airline_iata_code: chr  "UA" "UA" "UA" "UA" ...
#>  $ published_airline          : chr  "United Airlines" "United Airlines" "United Airlines" "United Airlines" ...
#>  $ published_airline_iata_code: chr  "UA" "UA" "UA" "UA" ...
#>  $ geo_summary                : chr  "International" "Domestic" "International" "Domestic" ...
#>  $ geo_region                 : chr  "Mexico" "US" "Canada" "US" ...
#>  $ landing_aircraft_type      : chr  "Passenger" "Passenger" "Passenger" "Passenger" ...
#>  $ aircraft_body_type         : chr  "Narrow Body" "Narrow Body" "Narrow Body" "Narrow Body" ...
#>  $ aircraft_manufacturer      : chr  "Airbus" "Boeing" "Boeing" "Boeing" ...
#>  $ aircraft_model             : chr  "A320" "B738" "B738" "B738" ...
#>  $ aircraft_version           : chr  "-" "-" "-" "-" ...
#>  $ landing_count              : int  37 14 1 251 3 553 1 13 52 102 ...
#>  $ total_landed_weight        : int  5261326 2048200 146300 36721300 438900 86986900 157300 2044900 10296000 22848000 ...
#>  $ date                       : Date, format: "2020-09-01" "2020-09-01" ...

The data set has 11 categorical variables and two numeric variables - landing_count and total_landed_weight.

Exploratory analysis

Let’s start with viewing the total monthly number of landing in SFO:

d %>% 
  group_by(date) %>%
  summarise(landing_count = sum(landing_count)) %>%
  plot_ly(x = ~ date, y = ~ landing_count,
          type = "scatter", mode = "lines") %>% 
  layout(title = "Montly Landing in SFO Airport",
         yaxis = list(title = "Number of Landing"),
         xaxis = list(title = "Source: San Francisco data portal (DataSF)"))

As can seen in the aggregate plot above, the data has:

We can use plotly’s fill plot to review the distribution of landing at SFO by geo region:

d %>% 
  group_by(date, geo_region) %>%
  summarise(landing_count = sum(landing_count)) %>%
  as.data.frame() %>%
plot_ly(x = ~ date, 
        y = ~ landing_count,
        # name = 'Food and Tobacco', 
        type = 'scatter', 
        mode = 'none', 
        stackgroup = 'one', 
        groupnorm = 'percent', fillcolor = ~ geo_region) %>%
  layout(title = "Dist. of Landing at SFO by Region",
         yaxis = list(title = "Percentage",
                      ticksuffix = "%"))

As expected, we can notice the change in geo’s landing distribution since March 2020 due to the Covid19 pandemic.

The aircraft_manufacturer column, as the name implies, provides the the aircraft manufacture. Let’s summarize the total landing during 2019, the most recent full calendar year, by the manufacturer type:

d %>% 
      filter(activity_period >= 201901 & activity_period < 202001,
             aircraft_manufacturer != "") %>%
      group_by(aircraft_manufacturer) %>%
      summarise(total_landing = sum(landing_count),
                `.groups` = "drop") %>%
      arrange(-total_landing) %>%
      plot_ly(labels = ~ aircraft_manufacturer,
              values = ~ total_landing) %>%
      add_pie(hole = 0.6) %>%
      layout(title = "Landing Distribution by Aircraft Manufacturer During 2019")

Similarly, we can add the aircract_body_type and get the distribution of landing airplans during 2019 by manufacturer and body type (e.g., wide, narrow, etc.):

d %>% 
      filter(activity_period >= 201901 & activity_period < 202001,
             aircraft_manufacturer != "") %>%
      group_by(aircraft_manufacturer, aircraft_body_type) %>%
      summarise(total_landing = sum(landing_count),
                `.groups` = "drop") %>%
      arrange(-total_landing)
#> # A tibble: 9 x 3
#>   aircraft_manufacturer aircraft_body_type total_landing
#>   <chr>                 <chr>                      <int>
#> 1 Boeing                Narrow Body                78143
#> 2 Airbus                Narrow Body                56148
#> 3 Boeing                Wide Body                  25950
#> 4 Embraer               Regional Jet               24324
#> 5 Bombardier            Regional Jet               20862
#> 6 Airbus                Wide Body                   5753
#> 7 Bombardier            Narrow Body                 1014
#> 8 McDonnell Douglas     Narrow Body                    3
#> 9 McDonnell Douglas     Wide Body                      1

A Sankey plot enables us to get a distribution flow of some numeric value by multiple categorical variables. In the following example, we will use the sankey_ly function to plot the distribution of landing during 2019 by geo, flight type, and aircraft details:

d %>%
  filter(activity_period >= 201901 & activity_period < 202001,
             aircraft_manufacturer != "") %>%
  group_by(geo_region, landing_aircraft_type, 
           aircraft_manufacturer, aircraft_model, 
           aircraft_body_type) %>%
  summarise(total_landing = sum(landing_count),
            groups = "drop") %>%
  sankey_ly(cat_cols = c("geo_region", 
                         "landing_aircraft_type", 
                         "aircraft_manufacturer",
                         "aircraft_model",
                         "aircraft_body_type"),
            num_col = "total_landing",
            title = "SFO Landing Summary by Geo Region and Aircraft Type During 2019")