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FAA Wildlife Strike Database

'The FAA Wildlife Strike Database contains records of reported wildlife strikes since 1990. Strike reporting is voluntary. Therefore, this database only represents the information we have received from airlines, airports, pilots, and other sources. Report on wildlife impacts". To keep things simple I just took data from the big 4: American Airlines, Delta, Southwest, and United as they account for ~ 70% of passengers in the USA but there are many many more available airlines.

A report on the full dataset.

Source
Full data dictionary

h/t to my colleague Katie Masiello for sharing this dataset!

Get the data!

wildlife_impacts <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-07-23/wildlife_impacts.csv")

Data Dictionary

wildlife_impacts.csv

variable class description
incident_date date Date of incident
state character State
airport_id character Airport ID
airport character Airport Name
operator character Operator/Airline
atype character Airline type
type_eng character Engine type
species_id character Species ID
species character Species
damage character Damage: N None M Minor, M Uncertain, S Substantial, D Destroyed
num_engs character Number of engines
incident_month double Incident month
incident_year double Incident year
time_of_day character Incident Time of day
time double Incident time
height double Plane height at impact
speed double Plane speed at impact
phase_of_flt character Phase of flight at impact
sky character Sky condition
precip character Precipitation
cost_repairs_infl_adj double Cost of repairs adjusted for inflation 
library(tidyverse)
library(here)
library(rvest)

df_aa <- readxl::read_excel(here("2019", "2019-07-23", "wildlife_aa.xlsx"))

df_delta <- readxl::read_excel(here("2019", "2019-07-23", "wildlife_delta.xlsx"))

df_southwest <- readxl::read_excel(here("2019", "2019-07-23", "wildlife_southwest.xlsx"))

df_united <- readxl::read_excel(here("2019", "2019-07-23", "wildlife_united.xlsx"))

df_all <- bind_rows(df_aa, df_delta) %>% 
  bind_rows(df_southwest) %>% 
  bind_rows(df_united) %>% 
  janitor::clean_names() %>% 
  select(incident_date:airport, operator:damage, num_engs,incident_month:time,
         height, speed, phase_of_flt, sky, precip, cost_repairs_infl_adj)

df_all %>% 
  write_csv(here("2019" , "2019-07-23", "wildlife_impacts.csv"))

# create dictionary
tomtom::create_dictionary(df_all)

# test plot
df_all %>% 
  ggplot(aes(x = incident_date)) +
  geom_histogram() +
  facet_wrap(~operator)

# scrape airline size
url <- "https://en.wikipedia.org/wiki/List_of_largest_airlines_in_North_America"

raw_html <- url %>% 
  read_html() %>% 
  html_table(fill = TRUE)

clean_pass <- raw_html %>% .[[1]] %>% 
  as_tibble() %>% 
  select(Rank:`2018`) %>% 
  rename("passengers" = `2018`) %>% 
  janitor::clean_names() %>% 
  mutate(passengers = parse_number(passengers),
         group = c(rep("big 4", 4), rep("Other", 13))) 

total_sum <- clean_pass %>% 
  pull(passengers) %>% 
  sum()

clean_pass %>% 
  group_by(group) %>% 
  summarize(sum = sum(passengers)) %>% 
  mutate(percent_of_total = paste0(round(sum/total_sum * 100, 1), "%"))

|group |       sum|percent_of_total |
|:-----|---------:|:----------------|
|big 4 | 718146104|70.3%            |
|Other | 304031622|29.7%            |