| title | date |
|---|---|
LufthansaR |
`r Sys.Date()` |
LufthansaR is an API wrapper package for R. It enables programmers to access to Lufthansa Open API from R environment.
This document introduces you to LufthansaR's basic set of tools, and show how to use them. Once you have installed the package, read vignette("LufthansaR") to learn more.
To have access to Lufthansa Open API, one has to sign in to Mashery, Lufthansa's developer platform, and apply for a key. Please visit here.
Once you are registered, you will be given:
- a key and
- a secret
These two values can be exchanged for a short-lived access token. A valid access token must be sent with every API request while accessing any Lufthansa's API. In other words, every Lufthansa API requires you to pass Oauth token when getting the data from it.
You can install LufthansaR development version from GitHub
devtools::install_github("peter0083/LufthansaR")
CRAN version of the package will be scheduled to be added in the next version.
You can load LufthansaR as follows.
library(LufthansaR)
This will load the core lufthansaR functions.
You can store your client ID and secret in a ~/.Renviron file. R loads this file as
a system variable in each new session. The package uses these variables to request
new keys if needed. The .Renviron file should contain the lines
LUFTHANSA_API_CLIENT_ID = 'xxxxxxxxxxxxxxxxxxxxxxxx'
LUFTHANSA_API_CLIENT_SECRET = 'xxxxxxxxxx'
which specify the key and secret pair. This package does not remember the id or the secret.
NOTE: The name of the variables should be EXACTLY:
LUFTHANSA_API_CLIENT_IDLUFTHANSA_API_CLIENT_SECRET
Because tokens last for 1.5 days and to prevent the abuse of continuously requesting
new tokens, the package by default stores the token and its expiry in a file in the
working directory called .lufthansa-token. Caching the token provides a way of
using it across R sessions until it expires. Functions in the package use the get_token()
command to access the API. For more information about the function, see help(get_token).
Caching the token can be turned off by setting the following R option through
options(lufthansar_token_cache = FALSE)
Alternately, one can choose where to cache the token by using a filename instead
options(lufthansar_token_cache = 'path/to/.token-cache')
Users can see the token being used and its expiry by calling
LufthansaR::get_creds_from_env()
Get the current token being used by the package.
LufthansaR::get_token()
Each token is valid for a specified period of time. When the token is valid, LufthansaR uses the Client ID and Client Secret in your .Renviron.
This get_flight_status() function will print out the flight information
f_status <- LufthansaR::get_flight_status(flight_num ="LH493", verbose = TRUE)
The default is the flight status for today. However, you can call 5 days into the future by passing dep_date="2018-04-15" argument. The departure date (YYYY-MM-DD) in the local time of the departure airport. To supress the update message that is printed, pass the argument verbose = FALSE.
LufthansaR package utilizes httr to parse JSON content. You can access to different attributes of the already-parsed content:
# Departure Airport abbreviation
f_status$Departure$AirportCode
# Scheduled Departure Time (departure local time)
f_status$Departure$ScheduledTimeLocal$DateTime
# Departure Terminal
f_status$Departure$Terminal$Name
# Departure Status
f_status$Departure$TimeStatus$Definition
# Arrival Airport abbreviation
f_status$Arrival$AirportCode
# Scheduled Arrival Time (arrival local time)
f_status$Arrival$ScheduledTimeLocal$DateTime
# Arrival Terminal
f_status$Arrival$Terminal$Name
# Arrival Status
f_status$Arrival$TimeStatus$Definition
Let's load some packages that we will use below.
library(tidyverse)
library(lubridate)
To obtain the information about flights at the arrival airport,
get_flight_status_arrival(airport = "YVR", fromDateTime = "2018-04-13T00:00")
The output is the httr parsed content. The format of fromDateTime is YYYY-MM-DDTHH:MM. This is ISO-8601 date format.
Let's assume that we are interested in flights arriving at FRA around this time. And let's assume we are interested in showing some departure delays of those flights in a visualization.
# This to get the current local time at FRA and convert it to the ISO-8601 format.
tm <- as.POSIXlt(Sys.time(), tz="Europe/Berlin", "%Y-%m-%dT%H:%M")
tm_FRA <- strftime(tm, "%Y-%m-%dT%H:%M")
# to parse the content
parsed_content <- LufthansaR::get_flight_status_arrival(airport = "FRA", fromDateTime = tm_FRA)
You can see the content return by typing parsed_content. It is possible that there might not be any flight arriving at the time specified. Let's first see how many flights the API returns.
if (parsed_content$FlightStatusResource$Meta$TotalCount == 1){
(no_flight_returned <-parsed_content$FlightStatusResource$Meta$TotalCount)
} else{
(no_flight_returned <- summary(parsed_content$FlightStatusResource$Flights)[1])
}
In the following, a visualization is created by using the return content for departure delay for those flight arriving at FRA.
# The following is performed if the API returns some flight information
if(!(is.nan(no_flight_returned) | no_flight_returned <= 1)){
flight_departure_data <- data.frame(dept_airport = rep(NA, no_flight_returned),
scheduled_dept =rep(NA, no_flight_returned), actual_dept =rep(NA, no_flight_returned))
# wrangle the data
for (i in 1:no_flight_returned){
flight_departure_data$dept_airport[i] <-
parsed_content$FlightStatusResource$Flights[[1]][[i]]$Departure$AirportCode
flight_departure_data$scheduled_dept[i] <-
parsed_content$FlightStatusResource$Flights[[1]][[i]]$Departure$ScheduledTimeLocal$DateTime
flight_departure_data$actual_dept[i] <-
ifelse (is.null(parsed_content$FlightStatusResource$Flights[[1]][[i]]$Departure$ActualTimeLocal$DateTime), NA, parsed_content$FlightStatusResource$Flights[[1]][[i]]$Departure$ActualTimeLocal$DateTime)
}
# clean the json data
flight_departure_data$delay <-
-as.numeric(as.duration(interval(ymd_hm(flight_departure_data$actual_dept), ymd_hm(flight_departure_data$scheduled_dept))), "minutes")
flight_departure_data<- flight_departure_data %>%
mutate(status = ifelse(is.na(delay), "not departed",
ifelse(delay>0, "delayed departure", "early/on-time"))) %>%
mutate(delay = ifelse(is.na(delay), 1, delay))
# visualize the result
ggplot(data=flight_departure_data, aes(x=as.factor(dept_airport), y=delay)) +
geom_bar(stat="identity", aes(fill=status)) +
coord_flip() +
ggtitle(paste0("Delay Status at the Departure Airports for the Flights arriving at ", "FRA")) +
theme(legend.position = "bottom") +
xlab("Airport") +
ylab("Delay (minutes)")
} else {
print("No more than one flight information available at this time!")
}
To obtain the information about flights at the departure airport,
get_flight_status_departure(airport = "YVR", fromDateTime = "2018-04-13T00:00")
The output is the httr parsed content. The format of fromDateTime is YYYY-MM-DDTHH:MM. This is ISO-8601 date format.
Let's assume that we are interested in flights departing from FRA around this time.
# This to get the current local time at FRA and convert it to the ISO-8601 format.
tm <- as.POSIXlt(Sys.time(), tz="Europe/Berlin", "%Y-%m-%dT%H:%M")
tm_FRA <- strftime(tm, "%Y-%m-%dT%H:%M")
# to parse the content
parsed_content <- LufthansaR::get_flight_status_departure(airport = "FRA", fromDateTime = tm_FRA)
You can see the content return by typing parsed_content. It is possible that there might not be any flight arriving at the time specified. It is possible that there might not be any flight arriving at the time specified. Let's first see how many flights the API returns.
# to count the number of flights returned
if (parsed_content$FlightStatusResource$Meta$TotalCount == 1){
(no_flight_returned <-parsed_content$FlightStatusResource$Meta$TotalCount)
} else {
(no_flight_returned <- summary(parsed_content$FlightStatusResource$Flights)[1])
}
# The following is performed if the API returns more than one flight
if(!(is.nan(no_flight_returned) | no_flight_returned <= 1)){
flight_departure_data <- data.frame(flight_code = rep(NA, no_flight_returned),
scheduled_dept =rep(NA, no_flight_returned), destination_airport =rep(NA, no_flight_returned), arrival_time =rep(NA, no_flight_returned))
# data wrangling
for (i in 1:no_flight_returned){
flight_departure_data$flight_code[i] <-
paste0( parsed_content$FlightStatusResource$Flights[[1]][[i]]$MarketingCarrier$AirlineID,parsed_content$FlightStatusResource$Flights[[1]][[i]]$MarketingCarrier$FlightNumber)
flight_departure_data$scheduled_dept[i] <-
parsed_content$FlightStatusResource$Flights[[1]][[i]]$Departure$ScheduledTimeLocal$DateTime
flight_departure_data$destination_airport[i] <- parsed_content$FlightStatusResource$Flights[[1]][[i]]$Arrival$AirportCode
flight_departure_data$arrival_time[i] <- parsed_content$FlightStatusResource$Flights[[1]][[i]]$Arrival$ScheduledTimeLocal
}
flight_departure_data
} else {
print("No flight information available at this time!")
}
The following is a data frame of the departure data returned by Lufthansa API.
| flight_code | scheduled_dept | destination_airport | arrival_time | |
|---|---|---|---|---|
| 1 | LH1392 | 2018-04-15T09:00 | PRG | 2018-04-15T10:00 |
| 2 | LH1388 | 2018-04-15T09:00 | POZ | 2018-04-15T10:15 |
| 3 | OS262 | 2018-04-15T09:00 | SZG | 2018-04-15T09:55 |
| 4 | LH048 | 2018-04-15T09:00 | HAJ | 2018-04-15T09:50 |
| 5 | LH902 | 2018-04-15T09:00 | LHR | 2018-04-15T09:40 |
| 6 | LH1250 | 2018-04-15T09:00 | LNZ | 2018-04-15T10:00 |
| 7 | LH1214 | 2018-04-15T09:00 | GVA | 2018-04-15T10:05 |
| 8 | LH1336 | 2018-04-15T09:00 | BUD | 2018-04-15T10:30 |
| 9 | LH1298 | 2018-04-15T09:05 | IST | 2018-04-15T13:00 |
| 10 | LH260 | 2018-04-15T09:05 | GOA | 2018-04-15T10:30 |
| 11 | LH988 | 2018-04-15T09:05 | AMS | 2018-04-15T10:20 |
| 12 | LH248 | 2018-04-15T09:10 | MXP | 2018-04-15T10:20 |
| 13 | LH1358 | 2018-04-15T09:10 | WRO | 2018-04-15T10:25 |
| 14 | LH1158 | 2018-04-15T09:10 | PMI | 2018-04-15T11:15 |
| 15 | LH352 | 2018-04-15T09:10 | BRE | 2018-04-15T10:05 |
| 16 | LH1470 | 2018-04-15T09:10 | TSR | 2018-04-15T12:00 |
| 17 | LH836 | 2018-04-15T09:10 | BLL | 2018-04-15T10:25 |
| 18 | LH1148 | 2018-04-15T09:10 | AGP | 2018-04-15T12:05 |
| 19 | LH810 | 2018-04-15T09:10 | GOT | 2018-04-15T10:45 |
| 20 | LH074 | 2018-04-15T09:10 | DUS | 2018-04-15T10:00 |