sfo
The sfo package provides summary statistics of the monthly passengers and landing in San Francisco International Airport (SFO).
Data source: San Francisco data portal - DataSF API
Installation
Install the stable version from CRAN:
install.packages("sfo")or install the development version from Github:
# install.packages("devtools")
devtools::install_github("RamiKrispin/sfo", ref = "main")Datasets
The sfo package provides the following two datasets:
sfo_passengers- air traffic passengers statisticssfo_stats- air traffic landing statistics
More information about the datasets available on the following vignette.
Examples
The sfo_passengers dataset provides a monthly summary of the number of
passengers in SFO airport by different categories (such as terminal,
geo, type, etc.):
library(sfo)
data("sfo_passengers")
head(sfo_passengers)
#> activity_period operating_airline operating_airline_iata_code published_airline published_airline_iata_code geo_summary geo_region activity_type_code price_category_code terminal
#> 1 202009 United Airlines UA United Airlines UA International Mexico Enplaned Other Terminal 3
#> 2 202009 United Airlines UA United Airlines UA International Mexico Enplaned Other Terminal 3
#> 3 202009 United Airlines UA United Airlines UA International Mexico Enplaned Other International
#> 4 202009 United Airlines UA United Airlines UA International Mexico Deplaned Other International
#> 5 202009 United Airlines UA United Airlines UA International Europe Enplaned Other International
#> 6 202009 United Airlines UA United Airlines UA International Europe Deplaned Other International
#> boarding_area passenger_count
#> 1 F 6712
#> 2 E 396
#> 3 G 376
#> 4 G 6817
#> 5 G 3851
#> 6 G 3700The sfo_stats dataset provides a monthly statistics on the air traffic
landing at SFO airport:
data("sfo_stats")
head(sfo_stats)
#> activity_period operating_airline operating_airline_iata_code published_airline published_airline_iata_code geo_summary geo_region landing_aircraft_type aircraft_body_type aircraft_manufacturer
#> 1 202009 United Airlines UA United Airlines UA International Mexico Passenger Narrow Body Airbus
#> 2 202009 United Airlines UA United Airlines UA Domestic US Passenger Narrow Body Boeing
#> 3 202009 United Airlines UA United Airlines UA International Canada Passenger Narrow Body Boeing
#> 4 202009 United Airlines UA United Airlines UA Domestic US Passenger Narrow Body Boeing
#> 5 202009 United Airlines UA United Airlines UA International Mexico Passenger Narrow Body Boeing
#> 6 202009 United Airlines UA United Airlines UA Domestic US Passenger Narrow Body Boeing
#> aircraft_model aircraft_version landing_count total_landed_weight
#> 1 A320 - 37 5261326
#> 2 B738 - 14 2048200
#> 3 B738 - 1 146300
#> 4 B738 - 251 36721300
#> 5 B738 - 3 438900
#> 6 B739 - 553 86986900Total number of passngers
The total number of passengers in most recent month by
activity_type_code and geo_region:
library(dplyr)
sfo_passengers %>%
filter(activity_period == max(activity_period)) %>%
group_by(activity_type_code, geo_region) %>%
summarise(total = sum(passenger_count), .groups = "drop")
#> # A tibble: 16 x 3
#> activity_type_code geo_region total
#> <chr> <chr> <int>
#> 1 Deplaned Asia 15775
#> 2 Deplaned Australia / Oceania 2942
#> 3 Deplaned Canada 4021
#> 4 Deplaned Central America 505
#> 5 Deplaned Europe 12031
#> 6 Deplaned Mexico 11642
#> 7 Deplaned Middle East 1490
#> 8 Deplaned US 394036
#> 9 Enplaned Asia 20123
#> 10 Enplaned Australia / Oceania 3347
#> 11 Enplaned Canada 4291
#> 12 Enplaned Central America 328
#> 13 Enplaned Europe 12271
#> 14 Enplaned Mexico 12430
#> 15 Enplaned Middle East 1176
#> 16 Enplaned US 409584The sankey_ly function enables us to plot the distribution of a
numeric variable by multiple categorical variables. The following
example shows the distribution of the total United Airlines passengers
during 2019 by terminal, travel type (domestic and international), geo,
and travel direction (deplaned, enplaned, and transit):
sfo_passengers %>%
filter(operating_airline == "United Airlines",
activity_period >= 201901 & activity_period < 202001) %>%
mutate(terminal = ifelse(terminal == "International", "international", terminal)) %>%
group_by(operating_airline,activity_type_code, geo_summary, geo_region, terminal) %>%
summarise(total = sum(passenger_count), .groups = "drop") %>%
sankey_ly(cat_cols = c("operating_airline", "terminal","geo_summary", "geo_region", "activity_type_code"),
num_col = "total",
title = "Dist. of United Airlines Passengers at SFO During 2019")Total number of landing
The total number of landing in most recent month by activity_type_code
and aircraft_manufacturer:
sfo_stats %>%
filter(activity_period == max(activity_period),
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 Sep 2020")The following Sankey plot demonstrate the distribution of number of landing in SFO by region and aircraft type, manufacturer, and body type during Sep 2020:
sfo_stats %>%
filter(activity_period == max(activity_period)) %>%
group_by(geo_summary, geo_region, landing_aircraft_type, aircraft_manufacturer, aircraft_body_type) %>%
summarise(total_landing = sum(landing_count),
groups = "drop") %>%
sankey_ly(cat_cols = c("geo_summary", "geo_region",
"landing_aircraft_type",
"aircraft_manufacturer",
"aircraft_body_type"),
num_col = "total_landing",
title = "Landing Summary by Geo Region and Aircraft Type During Sep 2020")