Flight Analysis: Find the best flights

An R package for analyzing, forecasting, and collecting flight data and prices from Google Flights.

Features

• Detailed scraping and querying tools for Google Flights using chromote
• Support for multiple trip types: one-way, round-trip, chain-trip, and perfect-chain
• Flexible date search across multiple airports and date ranges
• Summary tables showing prices by city and date
• Automatic identification of cheapest travel dates
• Visualization functions for price trends and best dates

Installation

You can install the development version of flightanalysis from GitHub:

install.packages('flightanalysis', 
  repos = c('https://rempsyc.r-universe.dev', 'https://cloud.r-project.org'))

# Or if you need the version from the last hour, install through `remotes`
# install.packages("remotes")
remotes::install_github("rempsyc/flightanalysis")

Usage

Loading the Package

library(flightanalysis)

Creating Flight Queries and Fetching the Data

The main scraping function that makes up the backbone of most functionalities is fa_define_query(). It serves as a data object, preserving the flight information as well as meta-data from your query. fa_fetch_flights() then fetches flight information from that query. Origin and destination airports can be specified as a mix of Airport 3-letter codes, city 3-letter codes, or city names.

# Round-trip
query <- fa_define_query("NYC", "London", "2025-12-20", "2026-01-05")

# Same as:
query <- fa_define_query("New York", "LON", "2025-12-20", "2026-01-05")
query
#> Flight Query( {Not Yet Fetched}
#> 2025-12-20: NYC --> LON
#> 2026-01-05: LON --> NYC
#> )

# Fetch the flight data
flights <- fa_fetch_flights(query)
#>   Segment 1/2: NYC -> LON on 2025-12-20
#>   [OK] Successfully parsed 7 flights
#>   Segment 2/2: LON -> NYC on 2026-01-05
#>   [OK] Successfully parsed 18 flights
#>   [OK] Total flights retrieved: 25

# View the flight data
head(flights$data[1:11]) |>
  knitr::kable()

departure_date	departure_time	arrival_date	arrival_time	origin	origin_city	destination	destination_city	airlines	travel_time	price
2025-12-20	22:40	2025-12-21	11:05	EWR	Newark	LHR	London	United, Lufthansa, Austrian, Brussels Airlines	7 hr 25 min	844
2025-12-20	18:05	2025-12-21	06:00	JFK	New York	LGW	London	Price graph	6 hr 55 min	859
2025-12-20	19:45	2025-12-22	07:10	LGA	New York	LHR	London	Air Canada, United	30 hr 25 min	354
2025-12-20	19:20	2025-12-22	09:00	JFK	New York	LGW	London	American, British Airways	32 hr 40 min	480
2025-12-20	06:55	2025-12-21	07:10	EWR	Newark	LHR	London	Air Canada, United	19 hr 15 min	728
2025-12-20	15:04	2025-12-21	12:40	LGA	New York	LCY	London	American, Aer Lingus, British Airways, Iberia, BA Cityflyer	16 hr 36 min	854

The package supports multiple trip types:

• One-way: fa_define_query("JFK", "IST", "2025-07-20")
• Round-trip: fa_define_query("JFK", "IST", "2025-07-20", "2025-08-20")
• Chain-trip: fa_define_query("JFK", "IST", "2025-08-20", "RDU", "LGA", "2025-12-25")
• Perfect-chain: fa_define_query("JFK", "2025-09-20", "IST", "2025-09-25", "JFK")

Flexible Date Search

The package supports flexible date search across multiple airports and dates:

# Create query objects for multiple origins and dates
queries <- fa_define_query_range(
  origin = c("BOM", "DEL"),
  dest = "JFK",
  date_min = "2025-12-18",
  date_max = "2025-12-22"
)

# Fetch all flights
flights <- fa_fetch_flights(queries, verbose = FALSE)

# Create summary table (City × Date with prices)
fa_summarize_prices(flights) |>
  knitr::kable()

City	Origin	2025-12-18	2025-12-19	2025-12-20	2025-12-21	2025-12-22	Average_Price
Mumbai	BOM	$419	$503	$539	$465	$442	$474
Delhi	DEL	$420	$420	$493	$510	$445	$458
Best	Day	X

# Find the cheapest dates
fa_find_best_dates(
  flights, 
  n = 5,
  by = "min",
  price_max = 1400,
  max_stops = 1,
  travel_time_max = 26  # 26 hours (numeric = hours, or use "26 hr" format)
  ) |>
  knitr::kable()

departure_date	departure_time	arrival_date	arrival_time	origin	price	num_stops	layover	travel_time	co2_emission_kg	airlines	n_routes
2025-12-18	10:40	2025-12-18	19:30	BOM	442	1	1 hr LHR	19 hr 20 min	792	Virgin Atlantic, Delta	1
2025-12-19	19:10	2025-12-19	06:00	BOM	434	1	2 hr 40 min DEL	21 hr 20 min	991	IndiGo, American	1
2025-12-19	20:55	2025-12-19	09:00	DEL	451	1	3 hr 30 min AUH	22 hr 35 min	843	Etihad	1
2025-12-21	18:00	2025-12-22	06:00	BOM	465	1	3 hr 40 min DEL	22 hr 30 min	991	IndiGo, American	1
2025-12-22	10:40	2025-12-22	22:55	BOM	442	1	4 hr 25 min LHR	22 hr 45 min	796	Virgin Atlantic, Delta	1

Visualizing Price Data

The package includes plotting functions to visualize price trends and best dates:

# Plot price trends across dates
fa_plot_prices(flights, 
               title = "Flight Prices: BOM/DEL to JFK",
               size_by = "travel_time",
               annotate_col = "travel_time")

# Plot best travel dates
fa_plot_best_dates(flights)

Original Python Package

Credits: This package is an R implementation inspired by the original Python package google-flight-analysis by Kaya Celebi.