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Sample Flight Results Dataset

Source: R/data.R
sample_flight_results.Rd

A comprehensive sample dataset containing flight data from 5 Indian origins (BOM, DEL, VNS, PAT, GAY) to JFK spanning from December 18, 2025 to January 5, 2026. This dataset demonstrates realistic pricing patterns including a Christmas/New Year price spike, making it ideal for testing and demonstrating flight analysis functions.

Usage

sample_flight_results

Format

A flight_results object (S3 class) with the following structure:

data

A data frame with 95 rows (5 origins × 19 days) containing:

  • departure_date: Character, departure date in "YYYY-MM-DD" format

  • departure_time: Character, departure time in "HH:MM" format

  • arrival_date: Character, arrival date in "YYYY-MM-DD" format

  • arrival_time: Character, arrival time in "HH:MM" format

  • origin: Character, origin airport code (BOM, DEL, VNS, PAT, GAY)

  • destination: Character, destination airport code (JFK)

  • airlines: Character, airline name

  • travel_time: Character, total travel time in "XX hr YY min" format

  • price: Numeric, ticket price in USD

  • num_stops: Integer, number of stops (0-2)

  • layover: Character, layover information (if applicable)

  • access_date: Character, timestamp when data was accessed

  • co2_emission_kg: Numeric, estimated CO2 emissions in kg

  • emission_diff_pct: Numeric, emission difference percentage

BOM, DEL, VNS, PAT, GAY

Query objects for each origin containing the data subset and query parameters

Details

The dataset features:

  • Realistic travel times varying by origin (15.5-18.5 hours)

  • Base prices varying by origin ($580-$700)

  • Christmas/New Year price spike (Dec 23 - Jan 3) with 1.3x-4.5x multiplier

  • Peak prices around January 1-2

  • Weekend price adjustments (10% increase)

  • Random variation to simulate real-world data

This dataset is particularly useful for:

See also

sample_flights for a simpler data frame example, fa_plot_prices for plotting functions, fa_summarize_prices for price summary tables

Examples

# Load and examine the dataset
head(sample_flight_results$data)
#>   departure_date departure_time arrival_date arrival_time origin destination
#> 1     2025-12-18          08:15   2025-12-18        16:00    BOM         JFK
#> 2     2025-12-19          14:00   2025-12-19        08:45    BOM         JFK
#> 3     2025-12-20          17:30   2025-12-20        15:00    BOM         JFK
#> 4     2025-12-21          11:00   2025-12-21        07:00    BOM         JFK
#> 5     2025-12-22          20:00   2025-12-22        20:15    BOM         JFK
#> 6     2025-12-23          21:00   2025-12-23        22:15    BOM         JFK
#>           airlines  travel_time price num_stops         layover
#> 1         Emirates 15 hr 25 min   624         1 3 hr 15 min FRA
#> 2           United  15 hr 3 min   587         1 4 hr 15 min FRA
#> 3        Air India  15 hr 2 min   658         0            <NA>
#> 4 Turkish Airlines 16 hr 17 min   609         1 5 hr 45 min DXB
#> 5         Emirates 16 hr 13 min   625         1 4 hr 45 min IST
#> 6         Emirates 16 hr 10 min   679         2  2 hr 0 min LHR
#>           access_date co2_emission_kg emission_diff_pct
#> 1 2025-11-16 16:19:56             838              -0.1
#> 2 2025-11-16 16:19:56             878              10.9
#> 3 2025-11-16 16:19:56             837               0.3
#> 4 2025-11-16 16:19:56             907              11.2
#> 5 2025-11-16 16:19:56             873               3.4
#> 6 2025-11-16 16:19:56             884               7.0

if (FALSE) { # \dontrun{
# Plot with automatic Christmas spike visualization
fa_plot_prices(sample_flight_results)

# Size points by travel time
fa_plot_prices(sample_flight_results, size_by = "travel_time")

# Create price summary table
fa_summarize_prices(sample_flight_results)

# Find best dates
fa_find_best_dates(sample_flight_results, n = 5)
} # }