Madison 2026-03-09
Data can be found at https://www.kaggle.com/datasets/zkskhurram/global-civil-aviation-disruption2026-iranus-war
flight_cancellations_raw <- read_csv("flight_cancellations.csv")
conflict_events_raw <- read_csv("conflict_events.csv")
airline_losses_raw <- read_csv("airline_losses_estimate.csv")
airport_disruptions_raw <- read_csv("airport_disruptions.csv")
airspace_closures_raw <- read_csv("airspace_closures.csv")
flight_reroutes_raw <- read_csv("flight_reroutes.csv")## Rows: 50
## Columns: 9
## $ date <date> 2026-02-28, 2026-02-28, 2026-02-28, 2026-02-28, 2…
## $ airport <chr> "Dubai International (DXB)", "Tehran Imam Khomeini…
## $ country <chr> "UAE", "Iran", "Israel", "Israel", "Qatar", "Qatar…
## $ airline <chr> "Emirates", "Iran Air", "El Al", "Wizz Air", "Qata…
## $ flight_number <chr> "EK0912", "IR0702", "LY0082", "W60001", "QR0502", …
## $ origin <chr> "DXB", "IKA", "TLV", "TLV", "DOH", "DOH", "DXB", "…
## $ destination <chr> "IKA", "DXB", "JFK", "LTN", "IKA", "THR", "IKA", "…
## $ cancellation_reason <chr> "Airspace closed - Iranian FIR shutdown", "Airspac…
## $ aircraft_type <chr> "Boeing 777-300ER", "Airbus A320-200", "Boeing 787…## date airport country airline
## Min. :2026-02-28 Length:50 Length:50 Length:50
## 1st Qu.:2026-03-01 Class :character Class :character Class :character
## Median :2026-03-02 Mode :character Mode :character Mode :character
## Mean :2026-03-02
## 3rd Qu.:2026-03-04
## Max. :2026-03-07
## flight_number origin destination cancellation_reason
## Length:50 Length:50 Length:50 Length:50
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## aircraft_type
## Length:50
## Class :character
## Mode :character
##
##
## ## date airport country airline
## 0 0 0 0
## flight_number origin destination cancellation_reason
## 0 0 0 0
## aircraft_type
## 0flight_cancellations <- flight_cancellations_raw %>%
# Parse date
mutate(date = ymd(date)) %>%
# Extract IATA code from airport name e.g. "Dubai International (DXB)" -> "DXB"
mutate(iata = str_extract(airport, "(?<=\\()\\w{3}(?=\\))"),
airport_name = str_trim(str_remove(airport, "\\s*\\(\\w{3}\\)"))) %>%
# Standardize airport name inconsistency: "Ataturk / Istanbul Airport" -> "Istanbul Airport"
mutate(airport_name = str_replace(airport_name, "Ataturk / Istanbul Airport", "Istanbul Airport")) %>%
# Standardize text columns to title case
mutate(across(c(country, airline, cancellation_reason, aircraft_type), str_to_title)) %>%
# Reorder columns cleanly
select(date, iata, airport_name, country, airline, flight_number,
origin, destination, cancellation_reason, aircraft_type) %>%
arrange(date, iata)## # A tibble: 20 × 2
## iata n
## <chr> <int>
## 1 DXB 10
## 2 DOH 6
## 3 IST 5
## 4 TLV 4
## 5 JED 3
## 6 LHR 3
## 7 MCT 3
## 8 FRA 2
## 9 RUH 2
## 10 SVO 2
## 11 AMM 1
## 12 AMS 1
## 13 BAH 1
## 14 CDG 1
## 15 DEL 1
## 16 GYD 1
## 17 IKA 1
## 18 KHI 1
## 19 KWI 1
## 20 LHE 1# Verify no remaining name inconsistencies
flight_cancellations %>% distinct(airport_name) %>% arrange(airport_name)## # A tibble: 20 × 1
## airport_name
## <chr>
## 1 Amman Queen Alia
## 2 Amsterdam Schiphol
## 3 Bahrain International
## 4 Baku International
## 5 Ben Gurion International
## 6 Charles de Gaulle
## 7 Dubai International
## 8 Frankfurt
## 9 Hamad International
## 10 Heathrow
## 11 Indira Gandhi International
## 12 Istanbul Airport
## 13 Karachi Jinnah International
## 14 King Abdulaziz International
## 15 King Khalid International
## 16 Kuwait International
## 17 Lahore Allama Iqbal
## 18 Muscat International
## 19 Sheremetyevo International
## 20 Tehran Imam Khomeini## [1] "2026-02-28" "2026-03-07"## date iata airport_name country
## 0 0 0 0
## airline flight_number origin destination
## 0 0 0 0
## cancellation_reason aircraft_type
## 0 0## Rows: 28
## Columns: 9
## $ date <date> 2026-02-28, 2026-02-28, 2026-02-28, 2026-02-28, 2026-…
## $ time_utc <time> 21:02:00, 21:15:00, 21:45:00, 22:10:00, 22:30:00, 23:…
## $ location <chr> "Natanz Nuclear Facility (Isfahan)", "Fordow Nuclear F…
## $ latitude <dbl> 33.7225, 34.8846, 35.5233, 29.4752, 27.2220, 26.6000, …
## $ longitude <dbl> 51.7261, 50.5925, 51.7760, 52.5903, 56.3777, 56.4000, …
## $ event_type <chr> "US Airstrike - Precision Strike", "US Airstrike - Bun…
## $ aviation_impact <chr> "Immediate Iranian FIR closure", "Qom regional no-fly …
## $ severity <chr> "CRITICAL", "CRITICAL", "CRITICAL", "HIGH", "HIGH", "C…
## $ source <chr> "Reuters / AP / Flightradar24", "BBC World / FlightAwa…## date time_utc location latitude longitude
## 0 0 0 0 0
## event_type aviation_impact severity source
## 0 0 0 0conflict_events <- conflict_events_raw %>%
# Combine date + time_utc into single datetime
mutate(datetime_utc = ymd_hms(paste(date, time_utc)),
date = ymd(date)) %>%
# Standardize severity to ordered factor
mutate(severity = factor(severity,
levels = c("LOW", "MEDIUM", "HIGH", "CRITICAL"),
ordered = TRUE)) %>%
# Standardize event_type to title case
mutate(event_type = str_to_title(event_type)) %>%
# Flag the geographic outlier (UN Security Council = New York, not a conflict site)
mutate(is_diplomatic = if_else(str_detect(location, "UN Security Council"), TRUE, FALSE)) %>%
# Reorder columns
select(date, datetime_utc, location, latitude, longitude,
event_type, severity, aviation_impact, is_diplomatic, source) %>%
arrange(datetime_utc)## # A tibble: 4 × 2
## severity n
## <ord> <int>
## 1 LOW 1
## 2 MEDIUM 4
## 3 HIGH 15
## 4 CRITICAL 8# Confirm diplomatic outlier flagged
conflict_events %>% filter(is_diplomatic) %>% select(date, location, latitude, longitude)## # A tibble: 1 × 4
## date location latitude longitude
## <date> <chr> <dbl> <dbl>
## 1 2026-03-05 UN Security Council - Emergency Session 40.7 -74.0## [1] "2026-02-28" "2026-03-07"## date datetime_utc location latitude longitude
## 0 0 0 0 0
## event_type severity aviation_impact is_diplomatic source
## 0 0 0 0 0## Rows: 35
## Columns: 7
## $ airline <chr> "Emirates", "Qatar Airways", "Etihad Airways"…
## $ country <chr> "UAE", "Qatar", "UAE", "Turkey", "UK", "Germa…
## $ estimated_daily_loss_usd <dbl> 4200000, 3100000, 1800000, 2400000, 1950000, …
## $ cancelled_flights <dbl> 18, 12, 8, 14, 6, 5, 4, 3, 7, 6, 4, 5, 6, 9, …
## $ rerouted_flights <dbl> 62, 41, 29, 38, 32, 28, 25, 21, 35, 31, 23, 2…
## $ additional_fuel_cost_usd <dbl> 2835200, 1765800, 1023400, 1432600, 1185600, …
## $ passengers_impacted <dbl> 9180, 6540, 4320, 7700, 4680, 3950, 3400, 280…## airline country estimated_daily_loss_usd
## 0 0 0
## cancelled_flights rerouted_flights additional_fuel_cost_usd
## 0 0 0
## passengers_impacted
## 0airline_losses <- airline_losses_raw %>%
# Standardize text columns
mutate(airline = str_to_title(airline),
country = str_to_title(country)) %>%
# Flag SAS "Scandinavia" country as non-standard
mutate(country_flag = if_else(country == "Scandinavia",
"Non-standard country value", NA_character_)) %>%
# Flag El Al zero reroutes (intentional — full airspace closure, not missing data)
mutate(zero_reroutes_note = if_else(airline == "El Al" & rerouted_flights == 0,
"Full airspace closure — reroutes not possible", NA_character_)) %>%
# Derive total loss estimate (daily loss is already provided; add cost-per-passenger metric)
mutate(loss_per_passenger_usd = round(estimated_daily_loss_usd / passengers_impacted, 2)) %>%
arrange(desc(estimated_daily_loss_usd))# Check flagged rows
airline_losses %>% filter(!is.na(country_flag) | !is.na(zero_reroutes_note)) %>%
select(airline, country, country_flag, zero_reroutes_note)## # A tibble: 2 × 4
## airline country country_flag zero_reroutes_note
## <chr> <chr> <chr> <chr>
## 1 El Al Israel <NA> Full airspace closure — rerout…
## 2 Sas Scandinavia Non-standard country value <NA># Top 5 airlines by daily loss
airline_losses %>% select(airline, country, estimated_daily_loss_usd) %>% head(5)## # A tibble: 5 × 3
## airline country estimated_daily_loss_usd
## <chr> <chr> <dbl>
## 1 Emirates Uae 4200000
## 2 Qatar Airways Qatar 3100000
## 3 El Al Israel 2800000
## 4 Turkish Airlines Turkey 2400000
## 5 Singapore Airlines Singapore 2100000## airline country estimated_daily_loss_usd
## 0 0 0
## cancelled_flights rerouted_flights additional_fuel_cost_usd
## 0 0 0
## passengers_impacted loss_per_passenger_usd
## 0 0## Rows: 35
## Columns: 10
## $ airport <chr> "Dubai International", "Hamad International", "Ben G…
## $ iata <chr> "DXB", "DOH", "TLV", "IST", "MCT", "LHR", "KWI", "BA…
## $ icao <chr> "OMDB", "OTHH", "LLBG", "LTFM", "OOMS", "EGLL", "OKB…
## $ country <chr> "UAE", "Qatar", "Israel", "Turkey", "Oman", "UK", "K…
## $ latitude <dbl> 25.2532, 25.2736, 32.0114, 41.2753, 23.5933, 51.4700…
## $ longitude <dbl> 55.3657, 51.6080, 34.8866, 28.7519, 58.2844, -0.4543…
## $ flights_cancelled <dbl> 87, 62, 128, 44, 38, 22, 46, 34, 52, 41, 28, 35, 22,…
## $ flights_delayed <dbl> 112, 78, 41, 67, 54, 44, 58, 48, 71, 58, 36, 44, 31,…
## $ flights_diverted <dbl> 24, 18, 0, 12, 10, 6, 14, 10, 15, 13, 8, 10, 7, 8, 5…
## $ runway_status <chr> "FULLY OPERATIONAL", "FULLY OPERATIONAL", "RESTRICTE…## airport iata icao country
## 0 0 0 0
## latitude longitude flights_cancelled flights_delayed
## 0 0 0 0
## flights_diverted runway_status
## 0 0airport_disruptions <- airport_disruptions_raw %>%
# Standardize text
mutate(country = str_to_title(country),
runway_status = str_to_title(runway_status)) %>%
# Create simplified operational status category
mutate(status_category = case_when(
str_detect(runway_status, "Closed") ~ "Closed",
str_detect(runway_status, "Restricted|Partially") ~ "Restricted",
str_detect(runway_status, "Advisory") ~ "Advisory",
str_detect(runway_status, "Fully Operational") ~ "Operational",
TRUE ~ "Unknown"
)) %>%
# Flag the Princess Juliana / Curacao data entry error
mutate(data_quality_flag = if_else(
iata == "AUA" & str_detect(airport, "Curacao|Juliana"),
"Possible IATA/airport mismatch — verify AUA vs SXM",
NA_character_
)) %>%
# Derive total disruption metric
mutate(total_disruptions = flights_cancelled + flights_delayed + flights_diverted) %>%
arrange(desc(flights_cancelled))## # A tibble: 4 × 2
## status_category n
## <chr> <int>
## 1 Advisory 1
## 2 Closed 4
## 3 Operational 20
## 4 Restricted 10# Flagged data quality issues
airport_disruptions %>% filter(!is.na(data_quality_flag)) %>%
select(airport, iata, icao, country, data_quality_flag)## # A tibble: 1 × 5
## airport iata icao country data_quality_flag
## <chr> <chr> <chr> <chr> <chr>
## 1 Princess Juliana / Curacao AUA TNCA Aruba Possible IATA/airport mismatch…# Iranian airports: confirm 0 delays and 0 diversions
airport_disruptions %>%
filter(status_category == "Closed") %>%
select(airport, iata, flights_cancelled, flights_delayed, flights_diverted)## # A tibble: 4 × 5
## airport iata flights_cancelled flights_delayed flights_diverted
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 Tehran Imam Khomeini IKA 210 0 0
## 2 Mashhad International MHD 62 0 0
## 3 Isfahan International IFN 48 0 0
## 4 Shiraz International SYZ 36 0 0## airport iata icao country
## 0 0 0 0
## latitude longitude flights_cancelled flights_delayed
## 0 0 0 0
## flights_diverted runway_status status_category total_disruptions
## 0 0 0 0## Rows: 25
## Columns: 7
## $ country <chr> "Iran", "Iran", "Iraq", "Saudi Arabia", "UAE", "Kuw…
## $ region <chr> "Tehran FIR", "Esfahan FIR", "Baghdad FIR", "Jeddah…
## $ closure_start_time <dttm> 2026-02-28 21:00:00, 2026-02-28 21:00:00, 2026-03-…
## $ closure_end_time <dttm> 2026-03-07 23:59:00, 2026-03-07 23:59:00, 2026-03-…
## $ closure_reason <chr> "Active conflict - military operations", "Active co…
## $ authority <chr> "ICAO / CAOI", "CAOI", "GCAA Iraq", "GACA", "GCAA U…
## $ NOTAM_reference <chr> "A1420/26", "A1421/26", "A0330/26", "A0712/26", "A0…## country region closure_start_time closure_end_time
## 0 0 0 0
## closure_reason authority NOTAM_reference
## 0 0 0airspace_closures <- airspace_closures_raw %>%
# Parse ISO 8601 datetimes (already have timezone Z)
mutate(closure_start_time = ymd_hms(closure_start_time, tz = "UTC"),
closure_end_time = ymd_hms(closure_end_time, tz = "UTC")) %>%
# Engineer closure duration in hours
mutate(closure_duration_hours = as.numeric(difftime(closure_end_time,
closure_start_time,
units = "hours"))) %>%
# Standardize text
mutate(country = str_to_title(country),
region = str_to_title(region)) %>%
# Classify closure severity tier based on duration
mutate(duration_tier = case_when(
closure_duration_hours >= 100 ~ "Extended (100h+)",
closure_duration_hours >= 48 ~ "Prolonged (48-100h)",
closure_duration_hours >= 12 ~ "Significant (12-48h)",
TRUE ~ "Short (<12h)"
)) %>%
arrange(desc(closure_duration_hours))## # A tibble: 4 × 2
## duration_tier n
## <chr> <int>
## 1 Extended (100h+) 5
## 2 Prolonged (48-100h) 7
## 3 Short (<12h) 4
## 4 Significant (12-48h) 9# Longest closures
airspace_closures %>%
select(country, region, closure_duration_hours, duration_tier) %>%
head(5)## # A tibble: 5 × 4
## country region closure_duration_hours duration_tier
## <chr> <chr> <dbl> <chr>
## 1 Iran Tehran Fir 171. Extended (100h+)
## 2 Iran Esfahan Fir 171. Extended (100h+)
## 3 Israel Tel Aviv Fir 170. Extended (100h+)
## 4 Afghanistan Kabul Fir 136. Extended (100h+)
## 5 Iraq Baghdad Fir 108. Extended (100h+)## [1] NA NA## country region closure_start_time
## 0 0 3
## closure_end_time closure_reason authority
## 1 0 0
## NOTAM_reference closure_duration_hours duration_tier
## 0 4 0## Rows: 45
## Columns: 7
## $ flight_id <chr> "EK0201-20260301", "EK0203-20260301", "BA0105…
## $ airline <chr> "Emirates", "Emirates", "British Airways", "B…
## $ original_route <chr> "LHR-DXB via standard Gulf", "LHR-DXB via sta…
## $ diverted_route <chr> "LHR-DXB via Southern Red Sea", "LHR-DXB via …
## $ additional_distance_km <dbl> 620, 620, 980, 980, 870, 870, 850, 850, 410, …
## $ additional_fuel_cost_usd <dbl> 48400, 48400, 76440, 76440, 67860, 67860, 663…
## $ delay_minutes <dbl> 55, 52, 88, 91, 77, 80, 75, 72, 38, 35, 42, 4…## flight_id airline original_route
## 0 0 0
## diverted_route additional_distance_km additional_fuel_cost_usd
## 0 0 0
## delay_minutes
## 0flight_reroutes <- flight_reroutes_raw %>%
# Parse flight_id into components: airline code, flight number, date
mutate(
flight_code = str_extract(flight_id, "^[A-Z0-9]+(?=\\d{4}-)"),
flight_num = str_extract(flight_id, "\\d{4}(?=-)"),
flight_date = ymd(str_extract(flight_id, "\\d{8}$"))
) %>%
# Fix known typo: "DXC-BEY" -> "DXB-BEY" in diverted_route
mutate(diverted_route = str_replace(diverted_route, "DXC-BEY", "DXB-BEY"),
typo_corrected = if_else(str_detect(flight_id, "FZ0103"), TRUE, FALSE)) %>%
# Standardize airline name
mutate(airline = str_to_title(airline)) %>%
# Derive fuel cost per km efficiency metric
mutate(fuel_cost_per_km = round(additional_fuel_cost_usd / additional_distance_km, 2)) %>%
# Note: original_route and diverted_route are free-text labels — keep as-is, do not parse
select(flight_id, flight_date, airline, original_route, diverted_route,
additional_distance_km, additional_fuel_cost_usd, fuel_cost_per_km,
delay_minutes, typo_corrected) %>%
arrange(desc(additional_distance_km))# Confirm typo fix
flight_reroutes %>% filter(typo_corrected) %>%
select(flight_id, diverted_route, typo_corrected)## # A tibble: 1 × 3
## flight_id diverted_route typo_corrected
## <chr> <chr> <lgl>
## 1 FZ0103-20260302 DXB-BEY via Egyptian route TRUE# Longest reroutes
flight_reroutes %>%
select(flight_id, airline, additional_distance_km, additional_fuel_cost_usd, delay_minutes) %>%
head(5)## # A tibble: 5 × 5
## flight_id airline additional_distance_km additional_fuel_cost…¹ delay_minutes
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 CX0253-20… Cathay… 1350 105300 122
## 2 CX0255-20… Cathay… 1350 105300 118
## 3 SQ0317-20… Singap… 1200 93600 108
## 4 SQ0319-20… Singap… 1200 93600 105
## 5 AI0910-20… Air In… 1140 88920 102
## # ℹ abbreviated name: ¹additional_fuel_cost_usd## [1] "2026-03-01" "2026-03-03"## flight_id flight_date airline
## 0 0 0
## original_route diverted_route additional_distance_km
## 0 0 0
## additional_fuel_cost_usd fuel_cost_per_km delay_minutes
## 0 0 0# --- Airline name consistency: flight_cancellations <-> airline_losses ---
cancellation_airlines <- flight_cancellations %>% distinct(airline) %>% pull(airline)
losses_airlines <- airline_losses %>% distinct(airline) %>% pull(airline)
cat("Airlines in cancellations NOT in losses:\n")## Airlines in cancellations NOT in losses:## [1] "Iran Air" "Wizz Air" "Kuwait Airways" "Azal"##
## Airlines in losses NOT in cancellations:## [1] "Singapore Airlines" "Cathay Pacific" "Air China"
## [4] "Korean Air" "China Eastern" "China Southern"
## [7] "Japan Airlines" "Ana" "Malaysia Airlines"
## [10] "Asiana Airlines" "Thai Airways" "Iberia"
## [13] "Swiss International" "Sas" "Austrian Airlines"
## [16] "Finnair" "Lot Polish Airlines" "Egyptair"# --- IATA consistency: flight_cancellations <-> airport_disruptions ---
cancellation_iatas <- flight_cancellations %>% distinct(iata) %>% pull(iata)
disruption_iatas <- airport_disruptions %>% distinct(iata) %>% pull(iata)
cat("\nIATAs in cancellations NOT in airport_disruptions:\n")##
## IATAs in cancellations NOT in airport_disruptions:## character(0)##
## IATAs in airport_disruptions NOT in cancellations:## [1] "MHD" "IFN" "AUH" "SYZ" "SHJ" "ICN" "DME" "NRT" "EVN" "HKG" "SIN" "CAI"
## [13] "LCA" "TBS" "AUA"# --- Country consistency: airspace_closures <-> airport_disruptions ---
closure_countries <- airspace_closures %>% distinct(country) %>% pull(country)
disruption_countries <- airport_disruptions %>% distinct(country) %>% pull(country)
cat("\nCountries in closures NOT in airport_disruptions:\n")##
## Countries in closures NOT in airport_disruptions:## [1] "Afghanistan" "Iraq" "Turkmenistan" "Kazakhstan" "Greece"
## [6] "Yemen"# Primary analysis table: cancellations enriched with airport + airline loss data
analysis_main <- flight_cancellations %>%
# Join airport metadata (lat/lon, status, total disruptions)
left_join(
airport_disruptions %>%
select(iata, latitude, longitude, status_category, total_disruptions),
by = "iata"
) %>%
# Join airline financial impact
left_join(
airline_losses %>%
select(airline, country, estimated_daily_loss_usd, passengers_impacted,
loss_per_passenger_usd),
by = "airline"
)
glimpse(analysis_main)## Rows: 50
## Columns: 18
## $ date <date> 2026-02-28, 2026-02-28, 2026-02-28, 2026-02-…
## $ iata <chr> "DXB", "IKA", "TLV", "TLV", "BAH", "CDG", "DE…
## $ airport_name <chr> "Dubai International", "Tehran Imam Khomeini"…
## $ country.x <chr> "Uae", "Iran", "Israel", "Israel", "Bahrain",…
## $ airline <chr> "Emirates", "Iran Air", "El Al", "Wizz Air", …
## $ flight_number <chr> "EK0912", "IR0702", "LY0082", "W60001", "GF05…
## $ origin <chr> "DXB", "IKA", "TLV", "TLV", "BAH", "CDG", "DE…
## $ destination <chr> "IKA", "DXB", "JFK", "LTN", "IKA", "DXB", "DX…
## $ cancellation_reason <chr> "Airspace Closed - Iranian Fir Shutdown", "Ai…
## $ aircraft_type <chr> "Boeing 777-300er", "Airbus A320-200", "Boein…
## $ latitude <dbl> 25.2532, 35.4161, 32.0114, 32.0114, 26.2708, …
## $ longitude <dbl> 55.3657, 51.1522, 34.8866, 34.8866, 50.6336, …
## $ status_category <chr> "Operational", "Closed", "Restricted", "Restr…
## $ total_disruptions <dbl> 223, 210, 169, 169, 92, 48, 73, 158, 158, 223…
## $ country.y <chr> "Uae", NA, "Israel", NA, "Bahrain", "France",…
## $ estimated_daily_loss_usd <dbl> 4200000, NA, 2800000, NA, 680000, 1420000, 15…
## $ passengers_impacted <dbl> 9180, NA, 3980, NA, 2240, 3400, 3760, 6540, 6…
## $ loss_per_passenger_usd <dbl> 457.52, NA, 703.52, NA, 303.57, 417.65, 414.8…# Check join quality — how many rows lost?
cat("Rows before join:", nrow(flight_cancellations), "\n")## Rows before join: 50## Rows after join: 50cat("Unmatched airline rows (NA loss data):",
sum(is.na(analysis_main$estimated_daily_loss_usd)), "\n")## Unmatched airline rows (NA loss data): 4write_csv(flight_cancellations, "cleaned_flight_cancellations.csv")
write_csv(conflict_events, "cleaned_conflict_events.csv")
write_csv(airline_losses, "cleaned_airline_losses.csv")
write_csv(airport_disruptions, "cleaned_airport_disruptions.csv")
write_csv(airspace_closures, "cleaned_airspace_closures.csv")
write_csv(flight_reroutes, "cleaned_flight_reroutes.csv")
write_csv(analysis_main, "cleaned_analysis_main.csv")
cat("All cleaned files exported.\n")## All cleaned files exported.## R version 4.3.1 (2023-06-16 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: America/Los_Angeles
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lubridate_1.9.4 forcats_1.0.1 stringr_1.5.2 dplyr_1.1.4
## [5] purrr_1.0.4 readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
## [9] ggplot2_4.0.0 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] bit_4.6.0 gtable_0.3.6 crayon_1.5.3 compiler_4.3.1
## [5] tidyselect_1.2.1 parallel_4.3.1 scales_1.4.0 yaml_2.3.10
## [9] fastmap_1.2.0 R6_2.6.1 generics_0.1.4 knitr_1.50
## [13] pillar_1.11.1 RColorBrewer_1.1-3 tzdb_0.5.0 rlang_1.1.5
## [17] utf8_1.2.4 stringi_1.8.7 xfun_0.52 S7_0.2.0
## [21] bit64_4.6.0-1 timechange_0.3.0 cli_3.6.4 withr_3.0.2
## [25] magrittr_2.0.3 digest_0.6.37 grid_4.3.1 vroom_1.6.5
## [29] rstudioapi_0.17.1 hms_1.1.3 lifecycle_1.0.4 vctrs_0.6.5
## [33] evaluate_1.0.5 glue_1.8.0 farver_2.1.2 rmarkdown_2.30
## [37] tools_4.3.1 pkgconfig_2.0.3 htmltools_0.5.8.1