SNCF Trains and US Flights Project
Richard Loeur & Arnaud Lemoine & Antoine Cremel
18/12/2019
The URL of the deployed shiny app : https://arnaud-lemoinefr.shinyapps.io/Project/
The URL of the pushed code on github : https://github.com/arnaudhjh/RProject
The URL of the presentation on Rpubs :
Transport Project
Our datasets:
- SNCF train dataset (they used to have train I guess)
This dataset contain data that describe a train line so we have the train departure and arrival stations, the journey time (in minutes) and some more information like delay, cancelation…
- 2015 US flights
This dataset is representing all the fligths in the US for the year 2015. It contains the flight number, the airline, the departure and arrival airport as well as the date and information about the distance or delay and more.
Summary of the Train Dataset
year month service
Min. :2015 Min. : 1.000 International: 432
1st Qu.:2016 1st Qu.: 3.000 National :3600
Median :2017 Median : 6.000 NA's :1430
Mean :2017 Mean : 6.369
3rd Qu.:2018 3rd Qu.: 9.000
Max. :2018 Max. :12.000
departure_station arrival_station journey_time_avg
PARIS LYON :1139 PARIS LYON :1139 Min. : 45.96
PARIS MONTPARNASSE : 752 PARIS MONTPARNASSE : 752 1st Qu.:100.77
PARIS EST : 282 PARIS EST : 282 Median :160.84
LYON PART DIEU : 246 LYON PART DIEU : 246 Mean :165.39
PARIS NORD : 188 PARIS NORD : 188 3rd Qu.:205.70
MARSEILLE ST CHARLES: 174 MARSEILLE ST CHARLES: 174 Max. :481.00
(Other) :2681 (Other) :2681
total_num_trips num_of_canceled_trains comment_cancellations
Min. : 6.0 Min. : 0.000 Mode:logical
1st Qu.:181.0 1st Qu.: 0.000 NA's:5462
Median :238.0 Median : 1.000
Mean :281.1 Mean : 7.737
3rd Qu.:390.0 3rd Qu.: 4.000
Max. :878.0 Max. :279.000
num_late_at_departure avg_delay_late_at_departure avg_delay_all_departing
Min. : 0.00 Min. : 0.00 Min. : -4.468
1st Qu.: 10.00 1st Qu.: 11.98 1st Qu.: 0.896
Median : 23.00 Median : 15.84 Median : 1.783
Mean : 41.58 Mean : 16.81 Mean : 2.539
3rd Qu.: 51.75 3rd Qu.: 20.28 3rd Qu.: 3.243
Max. :451.00 Max. :173.57 Max. :173.571
comment_delays_at_departure num_arriving_late avg_delay_late_on_arrival
Mode:logical Min. : 0.00 Min. : 0.00
NA's:5462 1st Qu.: 17.00 1st Qu.: 23.81
Median : 30.00 Median : 30.76
Mean : 38.03 Mean : 32.45
3rd Qu.: 50.00 3rd Qu.: 38.77
Max. :235.00 Max. :258.00
NA's :9 NA's :9
avg_delay_all_arriving
Min. :-143.969
1st Qu.: 2.706
Median : 4.581
Mean : 5.287
3rd Qu.: 7.252
Max. : 36.817
comment_delays_on_arrival
Ce mois-ci, l'OD a été touchée par les incidents suivants: \nLe 3 : Avarie à la caténaire à lâ\200\231entrée de la gare de Paris Montparnasse (46 TGV ; 476mn)\nLe 6 : épisode neigeux sur toute la France (187 TGV ; 18980mn ; 23 suppressions)\nLe 7 : épisode neigeux sur toute la France (134 TGV ; 6294mn ; 77 suppressions)\nLe 8 : épisode neigeux sur toute la France (166 TGV ; 2814mn ; 30 suppressions)\nLe 9 : épisode neigeux sur toute la France (151 TGV ; 2830mn ; 15 suppressions)\nLe 15 : Incident caténaire à lâ\200\231entrée de la gare de Paris Montparnasse (78 TGV ; 2349mn ; 15 suppressions)\nLe 15 : Dérangement dâ\200\231installation sur la ligne grande vitesse (22 TGV ; 672mn)\nLe 21 : Présences de Chèvres aux abords de la ligne grande vitesse à Marcoussis (15 TGV ; 292mn)\nLe 21 : Avarie Matérielle sur la ligne grande vitesse au niveau de St Leger (62 TGV ; 2150mn)\nLe 28 : Bâche dans la caténaire à lâ\200\231entrée de la gare de Paris Montparnasse (28 TGV ; 605mn): 34
Mois marqué par neuf accidents de personne et cinq heurts d'animaux, qui ont eu un fort impact sur l'ensemble des relations : 19
Ce mois-ci, l'OD a été touchée par: _x000D_\nLe 7 : Défaillance matérielle à la sortie de la gare de Paris Montparnasse (87 TGV ; 2202â\200\231)_x000D_\nLe 11 : Divergence entre les équipements au sol et les TGV en circulation sur la grande ceinture parisienne (36 TGV ; 1034â\200\231)_x000D_\nLe 13 : Dérangement dâ\200\231aiguille sur le tronc commun des lignes grandes vitesse à St Arnoult (19 TGV ; 289â\200\231)_x000D_\nLe 13 : Défaut dâ\200\231alimentation électrique à la sortie de la gare de Paris Montparnasse (30 TGV ; 422â\200\231)_x000D_\nLe 17 : Rupture caténaire en gare de Paris Montparnasse (75 TGV ; 4801â\200\231)_x000D_\nLe 19 : Personnes dans les voies à la sortie de la gare de Paris Montparnasse (24 TGV ; 363â\200\231)_x000D_\nLe 23 : Présence dâ\200\231objets pris dans la caténaire à la sortie du Mans (16 TGV ; 372â\200\231)_x000D_\nDu 29 au 31 : Dérangement du poste de Vanves (450 TGV ; 27110â\200\231) : 18
Des travaux de modernisation de l'infrastructure ont perturbé la régularité de cette relation en Juillet : 17
Des travaux de modernisation de l'infrastructure ont perturbé la régularité de cette relation en Juin : 16
(Other) :1437
NA's :3921
delay_cause_external_cause delay_cause_rail_infrastructure
Min. :0.0000 Min. :0.0000
1st Qu.:0.1667 1st Qu.:0.1515
Median :0.2571 Median :0.2353
Mean :0.2780 Mean :0.2518
3rd Qu.:0.3684 3rd Qu.:0.3333
Max. :1.0000 Max. :1.0000
NA's :170 NA's :170
delay_cause_traffic_management delay_cause_rolling_stock
Min. :0.0000 Min. :0.00000
1st Qu.:0.0800 1st Qu.:0.09292
Median :0.1613 Median :0.15843
Mean :0.1831 Mean :0.17877
3rd Qu.:0.2571 3rd Qu.:0.24000
Max. :1.0000 Max. :1.00000
NA's :170 NA's :170
delay_cause_station_management delay_cause_travelers num_greater_15_min_late
Min. :0.00000 Min. :0.00000 Min. : 0.00
1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.: 11.00
Median :0.05263 Median :0.02128 Median : 20.00
Mean :0.06999 Mean :0.03730 Mean : 26.09
3rd Qu.:0.10256 3rd Qu.:0.05769 3rd Qu.: 35.00
Max. :1.00000 Max. :0.66667 Max. :192.00
NA's :170 NA's :170 NA's :5
avg_delay_late_greater_15_min num_greater_30_min_late num_greater_60_min_late
Min. :-118.022 Min. : 0.00 Min. : 0.000
1st Qu.: 8.994 1st Qu.: 4.00 1st Qu.: 1.000
Median : 31.533 Median : 9.00 Median : 3.000
Mean : 28.984 Mean :11.65 Mean : 4.197
3rd Qu.: 41.000 3rd Qu.:16.00 3rd Qu.: 6.000
Max. : 258.000 Max. :91.00 Max. :36.000
NA's :5 NA's :5 NA's :5
Summary of the Flights Dataset
YEAR MONTH DAY DAY_OF_WEEK AIRLINE
Min. :2015 Min. :1 Min. :1 Min. :4 UA :40
1st Qu.:2015 1st Qu.:1 1st Qu.:1 1st Qu.:4 B6 :22
Median :2015 Median :1 Median :1 Median :4 OO :22
Mean :2015 Mean :1 Mean :1 Mean :4 AA :21
3rd Qu.:2015 3rd Qu.:1 3rd Qu.:1 3rd Qu.:4 EV :20
Max. :2015 Max. :1 Max. :1 Max. :4 DL :16
(Other):59
FLIGHT_NUMBER TAIL_NUMBER ORIGIN_AIRPORT DESTINATION_AIRPORT
Min. : 17 N107SY : 1 BOS : 12 IAH : 24
1st Qu.: 513 N11140 : 1 ANC : 10 DEN : 20
Median :1222 N11150 : 1 LAS : 10 DFW : 15
Mean :2000 N12142 : 1 LAX : 10 EWR : 13
3rd Qu.:2539 N12563 : 1 SFO : 9 MIA : 12
Max. :7419 N12967 : 1 PHX : 8 ATL : 11
(Other):194 (Other):141 (Other):105
SCHEDULED_DEPARTURE DEPARTURE_TIME DEPARTURE_DELAY TAXI_OUT
Min. : 5.0 Min. : 2.0 Min. :-18.000 Min. : 4.00
1st Qu.:295.0 1st Qu.: 310.0 1st Qu.: -6.000 1st Qu.:11.00
Median :535.0 Median : 538.0 Median : -2.000 Median :14.00
Mean :433.3 Mean : 451.5 Mean : 5.654 Mean :16.15
3rd Qu.:550.0 3rd Qu.: 555.0 3rd Qu.: 3.500 3rd Qu.:19.00
Max. :600.0 Max. :2354.0 Max. :213.000 Max. :43.00
NA's :9 NA's :9 NA's :9
WHEELS_OFF SCHEDULED_TIME ELAPSED_TIME AIR_TIME
Min. : 14.0 Min. : 36.0 Min. : 35.0 Min. : 20.0
1st Qu.: 320.5 1st Qu.:105.0 1st Qu.:111.5 1st Qu.: 85.0
Median : 553.0 Median :161.5 Median :163.0 Median :138.0
Mean : 470.4 Mean :164.3 Mean :163.0 Mean :138.9
3rd Qu.: 610.0 3rd Qu.:210.0 3rd Qu.:201.5 3rd Qu.:182.0
Max. :1006.0 Max. :404.0 Max. :396.0 Max. :376.0
NA's :9 NA's :9 NA's :9
DISTANCE WHEELS_ON TAXI_IN SCHEDULED_ARRIVAL
Min. : 84.0 Min. : 254.0 Min. : 2.000 Min. : 320.0
1st Qu.: 518.2 1st Qu.: 619.0 1st Qu.: 5.000 1st Qu.: 630.0
Median : 989.0 Median : 730.0 Median : 7.000 Median : 736.5
Mean :1033.5 Mean : 750.4 Mean : 7.906 Mean : 757.8
3rd Qu.:1448.0 3rd Qu.: 846.5 3rd Qu.: 9.000 3rd Qu.: 850.0
Max. :2762.0 Max. :1344.0 Max. :52.000 Max. :1411.0
NA's :9 NA's :9
ARRIVAL_TIME ARRIVAL_DELAY DIVERTED CANCELLED
Min. : 259.0 Min. :-36.000 Min. :0 Min. :0.000
1st Qu.: 628.5 1st Qu.:-14.000 1st Qu.:0 1st Qu.:0.000
Median : 740.0 Median : -6.000 Median :0 Median :0.000
Mean : 762.5 Mean : 1.764 Mean :0 Mean :0.045
3rd Qu.: 858.0 3rd Qu.: 6.500 3rd Qu.:0 3rd Qu.:0.000
Max. :1357.0 Max. :226.000 Max. :0 Max. :1.000
NA's :9 NA's :9
CANCELLATION_REASON AIR_SYSTEM_DELAY SECURITY_DELAY AIRLINE_DELAY
:191 Min. : 0.00 Min. :0 Min. : 0.00
A: 3 1st Qu.: 0.00 1st Qu.:0 1st Qu.: 0.00
B: 6 Median : 9.50 Median :0 Median :15.00
Mean :10.54 Mean :0 Mean :23.21
3rd Qu.:16.25 3rd Qu.:0 3rd Qu.:53.25
Max. :43.00 Max. :0 Max. :85.00
NA's :172 NA's :172 NA's :172
LATE_AIRCRAFT_DELAY WEATHER_DELAY
Min. :0 Min. : 0.00
1st Qu.:0 1st Qu.: 0.00
Median :0 Median : 0.00
Mean :0 Mean : 23.93
3rd Qu.:0 3rd Qu.: 0.00
Max. :0 Max. :213.00
NA's :172 NA's :172
What we can learn from the Flights Dataset
We can see that the most popular Airlines in the US in 2015 where Southwest Airlines Co. with 1 261 855 fligths that year. The second one is American Airlines Inc. with 725 984 flights.
Unsurprinsing is the fact that Southwest Airlines is also the airline with the most delayed flights with 646 569.
Here we can see the application with a map showing the airport in th US, a radio button that enables us to choose between airline and departure airport for the data visualised, and a few tabs
The data shown from the Flights Dataset
[1] "Total number of flights per Airport"
Var1 Freq
1 AA 21
2 AS 11
3 B6 22
4 DL 16
5 EV 20
6 F9 4
7 HA 6
8 MQ 4
9 NK 12
10 OO 22
11 UA 40
12 US 14
13 WN 8
[1] "Mean time per airline"
Group.1 x
1 AA NA
2 AS NA
3 B6 158.90909
4 DL 166.68750
5 EV 96.10000
6 F9 140.00000
7 HA 99.83333
8 MQ NA
9 NK 165.00000
10 OO NA
11 UA 188.70000
12 US 197.14286
13 WN 149.37500
What we can learn from the Trains Dataset
The train Dataset shows us how the train per year and departure station are evolving (departure or arrival late) and the causes.
The fact that we have the causes enables us to drive some hypothetic conclusion on how this can impact the trains
We have two list button from which we can choose either the year or the departure train station, and the following data. also have a list to choose the cause that we want to have the percentage related.