Evidencia 1
Luis Felipe Franco Rodríguez A00833894
2023-03-14
##Situación Problema ¿Como mejorar la posición competitiva en una de las aerolíneas mas grandes de Nueva York?
##Contexto: Te acabas de incorporar a una empresa consultora en Inteligencia de Negocios, actualmente están brindando servicios de análisis para la industria de la aviación y les interesa tener a la aerolínea American Airlines como cliente ya que es una de las aerolíneas líderes en los aeropuertos de Nueva York, motivo por el cuál te han contratado. Te han pedido que identifiques cómo puede dicha aerolínea mejorar su posición competitiva !! Para identificar oportunidades de mejorar la posición competitiva de la aerolínea American Airlines, necesitas realizar algunos análisis, para determinar si hay variaciones en la posición de liderazgo de dicha aerolínea.
library(nycflights13)
summary(flights)## year month day dep_time sched_dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1 Min. : 106
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907 1st Qu.: 906
## Median :2013 Median : 7.000 Median :16.00 Median :1401 Median :1359
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349 Mean :1344
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744 3rd Qu.:1729
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400 Max. :2359
## NA's :8255
## dep_delay arr_time sched_arr_time arr_delay
## Min. : -43.00 Min. : 1 Min. : 1 Min. : -86.000
## 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124 1st Qu.: -17.000
## Median : -2.00 Median :1535 Median :1556 Median : -5.000
## Mean : 12.64 Mean :1502 Mean :1536 Mean : 6.895
## 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945 3rd Qu.: 14.000
## Max. :1301.00 Max. :2400 Max. :2359 Max. :1272.000
## NA's :8255 NA's :8713 NA's :9430
## carrier flight tailnum origin
## Length:336776 Min. : 1 Length:336776 Length:336776
## Class :character 1st Qu.: 553 Class :character Class :character
## Mode :character Median :1496 Mode :character Mode :character
## Mean :1972
## 3rd Qu.:3465
## Max. :8500
##
## dest air_time distance hour
## Length:336776 Min. : 20.0 Min. : 17 Min. : 1.00
## Class :character 1st Qu.: 82.0 1st Qu.: 502 1st Qu.: 9.00
## Mode :character Median :129.0 Median : 872 Median :13.00
## Mean :150.7 Mean :1040 Mean :13.18
## 3rd Qu.:192.0 3rd Qu.:1389 3rd Qu.:17.00
## Max. :695.0 Max. :4983 Max. :23.00
## NA's :9430
## minute time_hour
## Min. : 0.00 Min. :2013-01-01 05:00:00.00
## 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00.00
## Median :29.00 Median :2013-07-03 10:00:00.00
## Mean :26.23 Mean :2013-07-03 05:22:54.64
## 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00.00
## Max. :59.00 Max. :2013-12-31 23:00:00.00
## Necesitas consultar para cada dataframe:
base de datos 1: Airlines
summary(airlines)## carrier name
## Length:16 Length:16
## Class :character Class :character
## Mode :character Mode :characterstr(airlines)## tibble [16 × 2] (S3: tbl_df/tbl/data.frame)
## $ carrier: chr [1:16] "9E" "AA" "AS" "B6" ...
## $ name : chr [1:16] "Endeavor Air Inc." "American Airlines Inc." "Alaska Airlines Inc." "JetBlue Airways" ...Base de datos 2: Airports
summary(airports)## faa name lat lon
## Length:1458 Length:1458 Min. :19.72 Min. :-176.65
## Class :character Class :character 1st Qu.:34.26 1st Qu.:-119.19
## Mode :character Mode :character Median :40.09 Median : -94.66
## Mean :41.65 Mean :-103.39
## 3rd Qu.:45.07 3rd Qu.: -82.52
## Max. :72.27 Max. : 174.11
## alt tz dst tzone
## Min. : -54.00 Min. :-10.000 Length:1458 Length:1458
## 1st Qu.: 70.25 1st Qu.: -8.000 Class :character Class :character
## Median : 473.00 Median : -6.000 Mode :character Mode :character
## Mean :1001.42 Mean : -6.519
## 3rd Qu.:1062.50 3rd Qu.: -5.000
## Max. :9078.00 Max. : 8.000str(airports)## tibble [1,458 × 8] (S3: tbl_df/tbl/data.frame)
## $ faa : chr [1:1458] "04G" "06A" "06C" "06N" ...
## $ name : chr [1:1458] "Lansdowne Airport" "Moton Field Municipal Airport" "Schaumburg Regional" "Randall Airport" ...
## $ lat : num [1:1458] 41.1 32.5 42 41.4 31.1 ...
## $ lon : num [1:1458] -80.6 -85.7 -88.1 -74.4 -81.4 ...
## $ alt : num [1:1458] 1044 264 801 523 11 ...
## $ tz : num [1:1458] -5 -6 -6 -5 -5 -5 -5 -5 -5 -8 ...
## $ dst : chr [1:1458] "A" "A" "A" "A" ...
## $ tzone: chr [1:1458] "America/New_York" "America/Chicago" "America/Chicago" "America/New_York" ...
## - attr(*, "spec")=List of 3
## ..$ cols :List of 12
## .. ..$ id : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ name : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ city : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ country: list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ faa : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ icao : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ lat : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ lon : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ alt : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ tz : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_double" "collector"
## .. ..$ dst : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## .. ..$ tzone : list()
## .. .. ..- attr(*, "class")= chr [1:2] "collector_character" "collector"
## ..$ default: list()
## .. ..- attr(*, "class")= chr [1:2] "collector_guess" "collector"
## ..$ skip : num 0
## ..- attr(*, "class")= chr "col_spec"Base de datos 3: Flights
summary(flights)## year month day dep_time sched_dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1 Min. : 106
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907 1st Qu.: 906
## Median :2013 Median : 7.000 Median :16.00 Median :1401 Median :1359
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349 Mean :1344
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744 3rd Qu.:1729
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400 Max. :2359
## NA's :8255
## dep_delay arr_time sched_arr_time arr_delay
## Min. : -43.00 Min. : 1 Min. : 1 Min. : -86.000
## 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124 1st Qu.: -17.000
## Median : -2.00 Median :1535 Median :1556 Median : -5.000
## Mean : 12.64 Mean :1502 Mean :1536 Mean : 6.895
## 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945 3rd Qu.: 14.000
## Max. :1301.00 Max. :2400 Max. :2359 Max. :1272.000
## NA's :8255 NA's :8713 NA's :9430
## carrier flight tailnum origin
## Length:336776 Min. : 1 Length:336776 Length:336776
## Class :character 1st Qu.: 553 Class :character Class :character
## Mode :character Median :1496 Mode :character Mode :character
## Mean :1972
## 3rd Qu.:3465
## Max. :8500
##
## dest air_time distance hour
## Length:336776 Min. : 20.0 Min. : 17 Min. : 1.00
## Class :character 1st Qu.: 82.0 1st Qu.: 502 1st Qu.: 9.00
## Mode :character Median :129.0 Median : 872 Median :13.00
## Mean :150.7 Mean :1040 Mean :13.18
## 3rd Qu.:192.0 3rd Qu.:1389 3rd Qu.:17.00
## Max. :695.0 Max. :4983 Max. :23.00
## NA's :9430
## minute time_hour
## Min. : 0.00 Min. :2013-01-01 05:00:00.00
## 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00.00
## Median :29.00 Median :2013-07-03 10:00:00.00
## Mean :26.23 Mean :2013-07-03 05:22:54.64
## 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00.00
## Max. :59.00 Max. :2013-12-31 23:00:00.00
## str(flights)## tibble [336,776 × 19] (S3: tbl_df/tbl/data.frame)
## $ year : int [1:336776] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ month : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ day : int [1:336776] 1 1 1 1 1 1 1 1 1 1 ...
## $ dep_time : int [1:336776] 517 533 542 544 554 554 555 557 557 558 ...
## $ sched_dep_time: int [1:336776] 515 529 540 545 600 558 600 600 600 600 ...
## $ dep_delay : num [1:336776] 2 4 2 -1 -6 -4 -5 -3 -3 -2 ...
## $ arr_time : int [1:336776] 830 850 923 1004 812 740 913 709 838 753 ...
## $ sched_arr_time: int [1:336776] 819 830 850 1022 837 728 854 723 846 745 ...
## $ arr_delay : num [1:336776] 11 20 33 -18 -25 12 19 -14 -8 8 ...
## $ carrier : chr [1:336776] "UA" "UA" "AA" "B6" ...
## $ flight : int [1:336776] 1545 1714 1141 725 461 1696 507 5708 79 301 ...
## $ tailnum : chr [1:336776] "N14228" "N24211" "N619AA" "N804JB" ...
## $ origin : chr [1:336776] "EWR" "LGA" "JFK" "JFK" ...
## $ dest : chr [1:336776] "IAH" "IAH" "MIA" "BQN" ...
## $ air_time : num [1:336776] 227 227 160 183 116 150 158 53 140 138 ...
## $ distance : num [1:336776] 1400 1416 1089 1576 762 ...
## $ hour : num [1:336776] 5 5 5 5 6 5 6 6 6 6 ...
## $ minute : num [1:336776] 15 29 40 45 0 58 0 0 0 0 ...
## $ time_hour : POSIXct[1:336776], format: "2013-01-01 05:00:00" "2013-01-01 05:00:00" ...Base de datos 4: Planes
summary(planes)## tailnum year type manufacturer
## Length:3322 Min. :1956 Length:3322 Length:3322
## Class :character 1st Qu.:1997 Class :character Class :character
## Mode :character Median :2001 Mode :character Mode :character
## Mean :2000
## 3rd Qu.:2005
## Max. :2013
## NA's :70
## model engines seats speed
## Length:3322 Min. :1.000 Min. : 2.0 Min. : 90.0
## Class :character 1st Qu.:2.000 1st Qu.:140.0 1st Qu.:107.5
## Mode :character Median :2.000 Median :149.0 Median :162.0
## Mean :1.995 Mean :154.3 Mean :236.8
## 3rd Qu.:2.000 3rd Qu.:182.0 3rd Qu.:432.0
## Max. :4.000 Max. :450.0 Max. :432.0
## NA's :3299
## engine
## Length:3322
## Class :character
## Mode :character
##
##
##
## str(planes)## tibble [3,322 × 9] (S3: tbl_df/tbl/data.frame)
## $ tailnum : chr [1:3322] "N10156" "N102UW" "N103US" "N104UW" ...
## $ year : int [1:3322] 2004 1998 1999 1999 2002 1999 1999 1999 1999 1999 ...
## $ type : chr [1:3322] "Fixed wing multi engine" "Fixed wing multi engine" "Fixed wing multi engine" "Fixed wing multi engine" ...
## $ manufacturer: chr [1:3322] "EMBRAER" "AIRBUS INDUSTRIE" "AIRBUS INDUSTRIE" "AIRBUS INDUSTRIE" ...
## $ model : chr [1:3322] "EMB-145XR" "A320-214" "A320-214" "A320-214" ...
## $ engines : int [1:3322] 2 2 2 2 2 2 2 2 2 2 ...
## $ seats : int [1:3322] 55 182 182 182 55 182 182 182 182 182 ...
## $ speed : int [1:3322] NA NA NA NA NA NA NA NA NA NA ...
## $ engine : chr [1:3322] "Turbo-fan" "Turbo-fan" "Turbo-fan" "Turbo-fan" ...Base de datos 5: Weather
summary(weather)## origin year month day
## Length:26115 Min. :2013 Min. : 1.000 Min. : 1.00
## Class :character 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00
## Mode :character Median :2013 Median : 7.000 Median :16.00
## Mean :2013 Mean : 6.504 Mean :15.68
## 3rd Qu.:2013 3rd Qu.: 9.000 3rd Qu.:23.00
## Max. :2013 Max. :12.000 Max. :31.00
##
## hour temp dewp humid
## Min. : 0.00 Min. : 10.94 Min. :-9.94 Min. : 12.74
## 1st Qu.: 6.00 1st Qu.: 39.92 1st Qu.:26.06 1st Qu.: 47.05
## Median :11.00 Median : 55.40 Median :42.08 Median : 61.79
## Mean :11.49 Mean : 55.26 Mean :41.44 Mean : 62.53
## 3rd Qu.:17.00 3rd Qu.: 69.98 3rd Qu.:57.92 3rd Qu.: 78.79
## Max. :23.00 Max. :100.04 Max. :78.08 Max. :100.00
## NA's :1 NA's :1 NA's :1
## wind_dir wind_speed wind_gust precip
## Min. : 0.0 Min. : 0.000 Min. :16.11 Min. :0.000000
## 1st Qu.:120.0 1st Qu.: 6.905 1st Qu.:20.71 1st Qu.:0.000000
## Median :220.0 Median : 10.357 Median :24.17 Median :0.000000
## Mean :199.8 Mean : 10.518 Mean :25.49 Mean :0.004469
## 3rd Qu.:290.0 3rd Qu.: 13.809 3rd Qu.:28.77 3rd Qu.:0.000000
## Max. :360.0 Max. :1048.361 Max. :66.75 Max. :1.210000
## NA's :460 NA's :4 NA's :20778
## pressure visib time_hour
## Min. : 983.8 Min. : 0.000 Min. :2013-01-01 01:00:00.0
## 1st Qu.:1012.9 1st Qu.:10.000 1st Qu.:2013-04-01 21:30:00.0
## Median :1017.6 Median :10.000 Median :2013-07-01 14:00:00.0
## Mean :1017.9 Mean : 9.255 Mean :2013-07-01 18:26:37.7
## 3rd Qu.:1023.0 3rd Qu.:10.000 3rd Qu.:2013-09-30 13:00:00.0
## Max. :1042.1 Max. :10.000 Max. :2013-12-30 18:00:00.0
## NA's :2729str(weather)## tibble [26,115 × 15] (S3: tbl_df/tbl/data.frame)
## $ origin : chr [1:26115] "EWR" "EWR" "EWR" "EWR" ...
## $ year : int [1:26115] 2013 2013 2013 2013 2013 2013 2013 2013 2013 2013 ...
## $ month : int [1:26115] 1 1 1 1 1 1 1 1 1 1 ...
## $ day : int [1:26115] 1 1 1 1 1 1 1 1 1 1 ...
## $ hour : int [1:26115] 1 2 3 4 5 6 7 8 9 10 ...
## $ temp : num [1:26115] 39 39 39 39.9 39 ...
## $ dewp : num [1:26115] 26.1 27 28 28 28 ...
## $ humid : num [1:26115] 59.4 61.6 64.4 62.2 64.4 ...
## $ wind_dir : num [1:26115] 270 250 240 250 260 240 240 250 260 260 ...
## $ wind_speed: num [1:26115] 10.36 8.06 11.51 12.66 12.66 ...
## $ wind_gust : num [1:26115] NA NA NA NA NA NA NA NA NA NA ...
## $ precip : num [1:26115] 0 0 0 0 0 0 0 0 0 0 ...
## $ pressure : num [1:26115] 1012 1012 1012 1012 1012 ...
## $ visib : num [1:26115] 10 10 10 10 10 10 10 10 10 10 ...
## $ time_hour : POSIXct[1:26115], format: "2013-01-01 01:00:00" "2013-01-01 02:00:00" ...Identifica los diferentes tipos de datos y explica en qué consiste cada uno de ellos y cuál es la diferencia entre uno y otro, incluyendo los siguientes tipos de datos: int, dbl, chr, dttm.
-Integer
-double
-character
-data time
Base de datos 1: Planes
data(planes)
str(planes)## tibble [3,322 × 9] (S3: tbl_df/tbl/data.frame)
## $ tailnum : chr [1:3322] "N10156" "N102UW" "N103US" "N104UW" ...
## $ year : int [1:3322] 2004 1998 1999 1999 2002 1999 1999 1999 1999 1999 ...
## $ type : chr [1:3322] "Fixed wing multi engine" "Fixed wing multi engine" "Fixed wing multi engine" "Fixed wing multi engine" ...
## $ manufacturer: chr [1:3322] "EMBRAER" "AIRBUS INDUSTRIE" "AIRBUS INDUSTRIE" "AIRBUS INDUSTRIE" ...
## $ model : chr [1:3322] "EMB-145XR" "A320-214" "A320-214" "A320-214" ...
## $ engines : int [1:3322] 2 2 2 2 2 2 2 2 2 2 ...
## $ seats : int [1:3322] 55 182 182 182 55 182 182 182 182 182 ...
## $ speed : int [1:3322] NA NA NA NA NA NA NA NA NA NA ...
## $ engine : chr [1:3322] "Turbo-fan" "Turbo-fan" "Turbo-fan" "Turbo-fan" ...ncol(planes)## [1] 9nrow(planes)## [1] 3322dim(planes)## [1] 3322 9head(planes)## # A tibble: 6 × 9
## tailnum year type manuf…¹ model engines seats speed engine
## <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
## 1 N10156 2004 Fixed wing multi engine EMBRAER EMB-… 2 55 NA Turbo…
## 2 N102UW 1998 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
## 3 N103US 1999 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
## 4 N104UW 1999 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
## 5 N10575 2002 Fixed wing multi engine EMBRAER EMB-… 2 55 NA Turbo…
## 6 N105UW 1999 Fixed wing multi engine AIRBUS… A320… 2 182 NA Turbo…
## # … with abbreviated variable name ¹manufacturertail(planes)## # A tibble: 6 × 9
## tailnum year type manuf…¹ model engines seats speed engine
## <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
## 1 N996DL 1991 Fixed wing multi engine MCDONN… MD-88 2 142 NA Turbo…
## 2 N997AT 2002 Fixed wing multi engine BOEING 717-… 2 100 NA Turbo…
## 3 N997DL 1992 Fixed wing multi engine MCDONN… MD-88 2 142 NA Turbo…
## 4 N998AT 2002 Fixed wing multi engine BOEING 717-… 2 100 NA Turbo…
## 5 N998DL 1992 Fixed wing multi engine MCDONN… MD-88 2 142 NA Turbo…
## 6 N999DN 1992 Fixed wing multi engine MCDONN… MD-88 2 142 NA Turbo…
## # … with abbreviated variable name ¹manufacturersummary (planes)## tailnum year type manufacturer
## Length:3322 Min. :1956 Length:3322 Length:3322
## Class :character 1st Qu.:1997 Class :character Class :character
## Mode :character Median :2001 Mode :character Mode :character
## Mean :2000
## 3rd Qu.:2005
## Max. :2013
## NA's :70
## model engines seats speed
## Length:3322 Min. :1.000 Min. : 2.0 Min. : 90.0
## Class :character 1st Qu.:2.000 1st Qu.:140.0 1st Qu.:107.5
## Mode :character Median :2.000 Median :149.0 Median :162.0
## Mean :1.995 Mean :154.3 Mean :236.8
## 3rd Qu.:2.000 3rd Qu.:182.0 3rd Qu.:432.0
## Max. :4.000 Max. :450.0 Max. :432.0
## NA's :3299
## engine
## Length:3322
## Class :character
## Mode :character
##
##
##
## Hallazgos
Mediante la exploración de datos pudimos identificar las variables los “dataframes”. Podemos observar si hay algunas variables con NA’s. O si tenemos preguntas para aclararar algo de la manipulación.
Se te ha solicitado consultar cuáles son las aerolíneas de mayor tráfico aéreo en origen y destino. Cuentas con un data frame llamado flights que contiene toda la información de los vuelos de todos los aeropuertos de New York. Para lograrlo considera las funciones sugeridas en los siguientes pasos:
Consulta el data frame flights para recordar su contenido.
View(flights)Define un criterio para encontrar las aerolíneas que han recorrido más distancia (en millas) y crea un nuevo data frame que filtre solamente a las aeorlíneas que han recorrido una distancia superior a la media, se desean ver los campos carrier, distance, origin, dest en forma descendente por distancia.
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionmas_distancia <- select(flights, carrier, distance, origin, dest)
head(mas_distancia)## # A tibble: 6 × 4
## carrier distance origin dest
## <chr> <dbl> <chr> <chr>
## 1 UA 1400 EWR IAH
## 2 UA 1416 LGA IAH
## 3 AA 1089 JFK MIA
## 4 B6 1576 JFK BQN
## 5 DL 762 LGA ATL
## 6 UA 719 EWR ORDmayor_media <- filter(mas_distancia, distance>1040)
head(mayor_media)## # A tibble: 6 × 4
## carrier distance origin dest
## <chr> <dbl> <chr> <chr>
## 1 UA 1400 EWR IAH
## 2 UA 1416 LGA IAH
## 3 AA 1089 JFK MIA
## 4 B6 1576 JFK BQN
## 5 B6 1065 EWR FLL
## 6 UA 2475 JFK LAXorden <- arrange(mayor_media,desc(distance))
head(orden)## # A tibble: 6 × 4
## carrier distance origin dest
## <chr> <dbl> <chr> <chr>
## 1 HA 4983 JFK HNL
## 2 HA 4983 JFK HNL
## 3 HA 4983 JFK HNL
## 4 HA 4983 JFK HNL
## 5 HA 4983 JFK HNL
## 6 HA 4983 JFK HNLcount(orden, carrier, sort=TRUE)## # A tibble: 13 × 2
## carrier n
## <chr> <int>
## 1 UA 39294
## 2 B6 24426
## 3 AA 23190
## 4 DL 21637
## 5 VX 5162
## 6 EV 3991
## 7 WN 3832
## 8 US 2271
## 9 9E 1377
## 10 MQ 744
## 11 AS 714
## 12 F9 685
## 13 HA 342Encuentra la suma y la media de las distancias recorridas por carrier, elimina los NA’S e interpreta que significa la suma y la media de las distancias recorridas. Función: group_by(), summarize(), mean(), sum(), na.rm=TRUE Ordena en forma descendente por distancia recorrida
distancias <- orden %>% group_by(carrier,origin,distance) %>%
summarize(suma_distancia=sum(distance, na.rm =TRUE),
promedio_distancia =mean(distance, na.rm =TRUE ))## `summarise()` has grouped output by 'carrier', 'origin'. You can override using
## the `.groups` argument.distancias## # A tibble: 150 × 5
## # Groups: carrier, origin [26]
## carrier origin distance suma_distancia promedio_distancia
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 9E JFK 1113 307188 1113
## 2 9E JFK 1182 515352 1182
## 3 9E JFK 1391 507715 1391
## 4 9E JFK 1521 3042 1521
## 5 9E JFK 1587 84111 1587
## 6 9E LGA 1047 81666 1047
## 7 9E LGA 1080 72360 1080
## 8 9E LGA 1107 94095 1107
## 9 9E LGA 1183 1183 1183
## 10 9E LGA 1389 19446 1389
## # … with 140 more rowsOrden descendiente por distancia recorrida
distancias_orden <- arrange(distancias, desc(suma_distancia))
head(distancias_orden)## # A tibble: 6 × 5
## # Groups: carrier, origin [5]
## carrier origin distance suma_distancia promedio_distancia
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 UA EWR 2565 11142360 2565
## 2 UA EWR 2454 9236856 2454
## 3 AA JFK 2475 7962075 2475
## 4 AA LGA 1389 6717204 1389
## 5 UA JFK 2586 6400350 2586
## 6 DL JFK 2475 6189975 2475• Identifica si las aerolíneas líderes son las mismas en los tres aeropuertos cuyo origen es Nueva York ( John F. Kennedy (JFK), LaGuardia (LGA) and Newark Liberty (EWR) ).
JFK <- distancias_orden %>%
filter(origin =="JFK")%>%
arrange(carrier,desc(suma_distancia))
JFK## # A tibble: 69 × 5
## # Groups: carrier, origin [8]
## carrier origin distance suma_distancia promedio_distancia
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 9E JFK 1182 515352 1182
## 2 9E JFK 1391 507715 1391
## 3 9E JFK 1113 307188 1113
## 4 9E JFK 1587 84111 1587
## 5 9E JFK 1521 3042 1521
## 6 AA JFK 2475 7962075 2475
## 7 AA JFK 2586 3677292 2586
## 8 AA JFK 1089 2418669 1089
## 9 AA JFK 1598 1756202 1598
## 10 AA JFK 2248 1436472 2248
## # … with 59 more rowsLGA <- distancias_orden %>%
filter(origin =="LGA")%>%
arrange(carrier,desc(suma_distancia))
LGA## # A tibble: 31 × 5
## # Groups: carrier, origin [9]
## carrier origin distance suma_distancia promedio_distancia
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 9E LGA 1107 94095 1107
## 2 9E LGA 1047 81666 1047
## 3 9E LGA 1080 72360 1080
## 4 9E LGA 1389 19446 1389
## 5 9E LGA 1183 1183 1183
## 6 AA LGA 1389 6717204 1389
## 7 AA LGA 1096 4323720 1096
## 8 B6 LGA 1076 2354288 1076
## 9 B6 LGA 1080 394200 1080
## 10 B6 LGA 1047 382155 1047
## # … with 21 more rowsEWR <- distancias_orden %>%
filter(origin =="EWR")%>%
arrange(carrier,desc(suma_distancia))
EWR## # A tibble: 50 × 5
## # Groups: carrier, origin [9]
## carrier origin distance suma_distancia promedio_distancia
## <chr> <chr> <dbl> <dbl> <dbl>
## 1 AA EWR 1372 2818088 1372
## 2 AA EWR 1085 1158780 1085
## 3 AA EWR 2454 895710 2454
## 4 AS EWR 2402 1715028 2402
## 5 B6 EWR 1065 1476090 1065
## 6 B6 EWR 1608 609432 1608
## 7 B6 EWR 1068 389820 1068
## 8 DL EWR 1969 697026 1969
## 9 EV EWR 1092 1480752 1092
## 10 EV EWR 1134 852768 1134
## # … with 40 more rows