The sfo_stats dataset provides monthly statistics on San Francisco International Airport’s air traffic landing between July 2005 and December 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.
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 202012 United Airlines UA
#> 2 202012 United Airlines UA
#> 3 202012 United Airlines UA
#> 4 202012 United Airlines UA
#> 5 202012 United Airlines UA
#> 6 202012 United Airlines UA
#> published_airline published_airline_iata_code geo_summary geo_region
#> 1 United Airlines UA Domestic US
#> 2 United Airlines UA Domestic US
#> 3 United Airlines UA Domestic US
#> 4 United Airlines UA International Asia
#> 5 United Airlines UA International US
#> 6 United Airlines UA Domestic US
#> landing_aircraft_type aircraft_body_type aircraft_manufacturer aircraft_model
#> 1 Passenger Narrow Body Boeing B753
#> 2 Passenger Wide Body Boeing B763
#> 3 Passenger Wide Body Boeing B772
#> 4 Passenger Wide Body Boeing B772
#> 5 Passenger Wide Body Boeing B772
#> 6 Passenger Wide Body Boeing B773
#> aircraft_version landing_count total_landed_weight
#> 1 - 78 17472000
#> 2 - 19 6080000
#> 3 - 96 43500000
#> 4 - 1 460000
#> 5 - 8 3680000
#> 6 - 35 19390000Next, 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': 25765 obs. of 15 variables:
#> $ activity_period : int 202012 202012 202012 202012 202012 202012 202012 202012 202012 202012 ...
#> $ 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 "Domestic" "Domestic" "Domestic" "International" ...
#> $ geo_region : chr "US" "US" "US" "Asia" ...
#> $ landing_aircraft_type : chr "Passenger" "Passenger" "Passenger" "Passenger" ...
#> $ aircraft_body_type : chr "Narrow Body" "Wide Body" "Wide Body" "Wide Body" ...
#> $ aircraft_manufacturer : chr "Boeing" "Boeing" "Boeing" "Boeing" ...
#> $ aircraft_model : chr "B753" "B763" "B772" "B772" ...
#> $ aircraft_version : chr "-" "-" "-" "-" ...
#> $ landing_count : int 78 19 96 1 8 35 44 43 33 1 ...
#> $ total_landed_weight : int 17472000 6080000 43500000 460000 3680000 19390000 24376000 16340000 12540000 380000 ...
#> $ date : Date, format: "2020-12-01" "2020-12-01" ...The data set has 11 categorical variables and two numeric variables - landing_count and total_landed_weight.
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,
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 1A 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")