flights <- read.csv("Domestic2016.csv", header = TRUE, stringsAsFactors = FALSE)
head(flights)## FlightDate Carrier TailNum FlightNum Origin OriginCityName
## 1 1/15/2016 AA N3ALAA 56 DEN Denver, CO
## 2 1/15/2016 AA N3GUAA 208 SFO San Francisco, CA
## 3 1/10/2016 AA N3BVAA 210 LAS Las Vegas, NV
## 4 1/15/2016 AA N3HFAA 217 LAS Las Vegas, NV
## 5 1/10/2016 AA N796AA 34 LAX Los Angeles, CA
## 6 1/22/2016 AA N480AA 248 DFW Dallas/Fort Worth, TX
## OriginState Dest DestCityName DestState CRSDepTime DepTime WheelsOff
## 1 CO MIA Miami, FL FL 1045 1042 1059
## 2 CA MIA Miami, FL FL 640 638 656
## 3 NV JFK New York, NY NY 820 818 835
## 4 NV MIA Miami, FL FL 2359 2356 15
## 5 CA JFK New York, NY NY 800 759 813
## 6 TX BNA Nashville, TN TN 700 655 704
## WheelsOn CRSArrTime ArrTime Cancelled Diverted CRSElapsedTime
## 1 1852 1639 1902 0 1 234
## 2 1719 1458 1728 0 1 318
## 3 1852 1615 1923 0 1 295
## 4 1630 725 1642 0 1 266
## 5 1906 1629 1923 0 1 329
## 6 730 847 736 0 1 107
## ActualElapsedTime Distance
## 1 NA 1709
## 2 NA 2585
## 3 NA 2248
## 4 NA 2174
## 5 NA 2475
## 6 NA 631str(flights)## 'data.frame': 445827 obs. of 21 variables:
## $ FlightDate : chr "1/15/2016" "1/15/2016" "1/10/2016" "1/15/2016" ...
## $ Carrier : chr "AA" "AA" "AA" "AA" ...
## $ TailNum : chr "N3ALAA" "N3GUAA" "N3BVAA" "N3HFAA" ...
## $ FlightNum : int 56 208 210 217 34 248 256 260 88 94 ...
## $ Origin : chr "DEN" "SFO" "LAS" "LAS" ...
## $ OriginCityName : chr "Denver, CO" "San Francisco, CA" "Las Vegas, NV" "Las Vegas, NV" ...
## $ OriginState : chr "CO" "CA" "NV" "NV" ...
## $ Dest : chr "MIA" "MIA" "JFK" "MIA" ...
## $ DestCityName : chr "Miami, FL" "Miami, FL" "New York, NY" "Miami, FL" ...
## $ DestState : chr "FL" "FL" "NY" "FL" ...
## $ CRSDepTime : int 1045 640 820 2359 800 700 1330 35 1605 745 ...
## $ DepTime : int 1042 638 818 2356 759 655 1323 55 1624 740 ...
## $ WheelsOff : int 1059 656 835 15 813 704 1413 108 1640 753 ...
## $ WheelsOn : int 1852 1719 1852 1630 1906 730 1518 1134 NA 1821 ...
## $ CRSArrTime : int 1639 1458 1615 725 1629 847 2153 825 1856 1604 ...
## $ ArrTime : int 1902 1728 1923 1642 1923 736 1526 1147 NA 1831 ...
## $ Cancelled : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Diverted : int 1 1 1 1 1 1 1 1 1 1 ...
## $ CRSElapsedTime : int 234 318 295 266 329 107 323 290 171 319 ...
## $ ActualElapsedTime: int NA NA NA NA NA NA NA NA NA NA ...
## $ Distance : int 1709 2585 2248 2174 2475 631 2475 2342 1096 2446 ...kable(names(flights))| FlightDate |
| Carrier |
| TailNum |
| FlightNum |
| Origin |
| OriginCityName |
| OriginState |
| Dest |
| DestCityName |
| DestState |
| CRSDepTime |
| DepTime |
| WheelsOff |
| WheelsOn |
| CRSArrTime |
| ArrTime |
| Cancelled |
| Diverted |
| CRSElapsedTime |
| ActualElapsedTime |
| Distance |
#flights %>% filter(!complete.cases(.))
summary(flights) %>% na.omit## FlightDate Carrier TailNum FlightNum
## Length:445827 Length:445827 Length:445827 Min. : 1
## Class :character Class :character Class :character 1st Qu.: 702
## Mode :character Mode :character Mode :character Median :1594
## Origin OriginCityName OriginState
## Length:445827 Length:445827 Length:445827
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
## Dest DestCityName DestState CRSDepTime
## Length:445827 Length:445827 Length:445827 Min. : 1
## Class :character Class :character Class :character 1st Qu.: 920
## Mode :character Mode :character Mode :character Median :1325
## DepTime WheelsOff WheelsOn CRSArrTime
## Min. : 1 Min. : 1 Min. : 1 Min. : 1
## 1st Qu.: 924 1st Qu.: 939 1st Qu.:1104 1st Qu.:1118
## Median :1331 Median :1344 Median :1519 Median :1527
## ArrTime Cancelled Diverted CRSElapsedTime
## Min. : 1 Min. :0.00000 Min. :0.000000 Min. : 21.0
## 1st Qu.:1108 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.: 90.0
## Median :1522 Median :0.00000 Median :0.000000 Median :128.0
## ActualElapsedTime Distance
## Min. : 15.0 Min. : 31.0
## 1st Qu.: 85.0 1st Qu.: 391.0
## Median :122.0 Median : 679.0flights$FlightDate <- as.Date(flights$FlightDate, format = "%m/%d/%Y")flights %>% filter(Cancelled == 0) %>% summarize(maxcrsdep = max(CRSDepTime), maxdep = max(DepTime))## maxcrsdep maxdep
## 1 2359 2400flights <- flights %>%
mutate(new_CRSDepTime = paste(FlightDate, sprintf("%04d", CRSDepTime)))
flights$new_CRSDepTime <- as.POSIXct(flights$new_CRSDepTime, format="%Y-%m-%d %H%M")
#head(flights %>% select(new_CRSDepTime, CRSDepTime))flights <- flights %>%
mutate(new_CRSArrTime = paste(FlightDate, sprintf("%04d", CRSArrTime)))
flights$new_CRSArrTime <- as.POSIXct(flights$new_CRSArrTime, format="%Y-%m-%d %H%M")
#head(flights %>% select(new_CRSArrTime, CRSArrTime))flights <-flights %>% filter(Cancelled == 0) %>% mutate(new_DepTime = paste(FlightDate, sprintf("%04d", DepTime)), new_ArrTime = paste(FlightDate, sprintf("%04d", ArrTime)), new_WheelsOff = paste(FlightDate, sprintf("%04d", WheelsOff)), new_WheelsOn = paste(FlightDate, sprintf("%04d", WheelsOn)))
flights$new_DepTime <- as.POSIXct(flights$new_DepTime, format="%Y-%m-%d %H%M")
flights$new_ArrTime <- as.POSIXct(flights$new_ArrTime, format="%Y-%m-%d %H%M")
flights$new_WheelsOff <- as.POSIXct(flights$new_WheelsOff, format="%Y-%m-%d %H%M")
flights$new_WheelsOn <- as.POSIXct(flights$new_WheelsOn, format ="%Y-%m-%d %H%M")
#head(flights)
#str(flights)#flights %>% filter(!complete.cases(.))flights <- flights %>% filter(Cancelled == 0) %>% mutate(DepDelay = as.integer(difftime(new_DepTime, new_CRSDepTime, units = "mins")))
head(flights %>% select(CRSDepTime, DepTime, DepDelay))## CRSDepTime DepTime DepDelay
## 1 1045 1042 -3
## 2 640 638 -2
## 3 820 818 -2
## 4 2359 2356 -3
## 5 800 759 -1
## 6 700 655 -5flights <- flights %>% filter(Cancelled == 0) %>% mutate(DepDelayMinutes = ifelse(DepDelay < 0, 0, DepDelay), DepDel15 = ifelse(DepDelay >= 15, 1, 0))
head(flights %>% select(DepDelay, DepDelayMinutes, DepDel15))## DepDelay DepDelayMinutes DepDel15
## 1 -3 0 0
## 2 -2 0 0
## 3 -2 0 0
## 4 -3 0 0
## 5 -1 0 0
## 6 -5 0 0flights <- flights %>% filter(Cancelled == 0) %>% mutate(TaxiOut = as.integer(difftime(new_WheelsOff, new_DepTime, units = "mins")), TaxiIn = as.integer(difftime(new_ArrTime, new_WheelsOn, units = "mins")), ArrDelay = as.integer(difftime(new_ArrTime, new_CRSArrTime, units = "mins")), ArrDelayMinutes = ifelse(ArrDelay < 0, 0, ArrDelay), ArrDel15 = ifelse(ArrDelay >= 15, 1,0), FlightTimeBuffer = CRSElapsedTime - ActualElapsedTime)
flights <- flights %>% filter(Cancelled == 0) %>% mutate(AirTime = ActualElapsedTime - TaxiOut - TaxiIn)
flights <- flights %>% filter(Cancelled == 0) %>% mutate(AirSpeed = Distance / (AirTime/60))
head(flights)## FlightDate Carrier TailNum FlightNum Origin OriginCityName
## 1 2016-01-15 AA N3ALAA 56 DEN Denver, CO
## 2 2016-01-15 AA N3GUAA 208 SFO San Francisco, CA
## 3 2016-01-10 AA N3BVAA 210 LAS Las Vegas, NV
## 4 2016-01-15 AA N3HFAA 217 LAS Las Vegas, NV
## 5 2016-01-10 AA N796AA 34 LAX Los Angeles, CA
## 6 2016-01-22 AA N480AA 248 DFW Dallas/Fort Worth, TX
## OriginState Dest DestCityName DestState CRSDepTime DepTime WheelsOff
## 1 CO MIA Miami, FL FL 1045 1042 1059
## 2 CA MIA Miami, FL FL 640 638 656
## 3 NV JFK New York, NY NY 820 818 835
## 4 NV MIA Miami, FL FL 2359 2356 15
## 5 CA JFK New York, NY NY 800 759 813
## 6 TX BNA Nashville, TN TN 700 655 704
## WheelsOn CRSArrTime ArrTime Cancelled Diverted CRSElapsedTime
## 1 1852 1639 1902 0 1 234
## 2 1719 1458 1728 0 1 318
## 3 1852 1615 1923 0 1 295
## 4 1630 725 1642 0 1 266
## 5 1906 1629 1923 0 1 329
## 6 730 847 736 0 1 107
## ActualElapsedTime Distance new_CRSDepTime new_CRSArrTime
## 1 NA 1709 2016-01-15 10:45:00 2016-01-15 16:39:00
## 2 NA 2585 2016-01-15 06:40:00 2016-01-15 14:58:00
## 3 NA 2248 2016-01-10 08:20:00 2016-01-10 16:15:00
## 4 NA 2174 2016-01-15 23:59:00 2016-01-15 07:25:00
## 5 NA 2475 2016-01-10 08:00:00 2016-01-10 16:29:00
## 6 NA 631 2016-01-22 07:00:00 2016-01-22 08:47:00
## new_DepTime new_ArrTime new_WheelsOff
## 1 2016-01-15 10:42:00 2016-01-15 19:02:00 2016-01-15 10:59:00
## 2 2016-01-15 06:38:00 2016-01-15 17:28:00 2016-01-15 06:56:00
## 3 2016-01-10 08:18:00 2016-01-10 19:23:00 2016-01-10 08:35:00
## 4 2016-01-15 23:56:00 2016-01-15 16:42:00 2016-01-15 00:15:00
## 5 2016-01-10 07:59:00 2016-01-10 19:23:00 2016-01-10 08:13:00
## 6 2016-01-22 06:55:00 2016-01-22 07:36:00 2016-01-22 07:04:00
## new_WheelsOn DepDelay DepDelayMinutes DepDel15 TaxiOut TaxiIn
## 1 2016-01-15 18:52:00 -3 0 0 17 10
## 2 2016-01-15 17:19:00 -2 0 0 18 9
## 3 2016-01-10 18:52:00 -2 0 0 17 31
## 4 2016-01-15 16:30:00 -3 0 0 -1421 12
## 5 2016-01-10 19:06:00 -1 0 0 14 17
## 6 2016-01-22 07:30:00 -5 0 0 9 6
## ArrDelay ArrDelayMinutes ArrDel15 FlightTimeBuffer AirTime AirSpeed
## 1 143 143 1 NA NA NA
## 2 150 150 1 NA NA NA
## 3 188 188 1 NA NA NA
## 4 557 557 1 NA NA NA
## 5 174 174 1 NA NA NA
## 6 -71 0 0 NA NA NAstr(flights)## 'data.frame': 434162 obs. of 38 variables:
## $ FlightDate : Date, format: "2016-01-15" "2016-01-15" ...
## $ Carrier : chr "AA" "AA" "AA" "AA" ...
## $ TailNum : chr "N3ALAA" "N3GUAA" "N3BVAA" "N3HFAA" ...
## $ FlightNum : int 56 208 210 217 34 248 256 260 88 94 ...
## $ Origin : chr "DEN" "SFO" "LAS" "LAS" ...
## $ OriginCityName : chr "Denver, CO" "San Francisco, CA" "Las Vegas, NV" "Las Vegas, NV" ...
## $ OriginState : chr "CO" "CA" "NV" "NV" ...
## $ Dest : chr "MIA" "MIA" "JFK" "MIA" ...
## $ DestCityName : chr "Miami, FL" "Miami, FL" "New York, NY" "Miami, FL" ...
## $ DestState : chr "FL" "FL" "NY" "FL" ...
## $ CRSDepTime : int 1045 640 820 2359 800 700 1330 35 1605 745 ...
## $ DepTime : int 1042 638 818 2356 759 655 1323 55 1624 740 ...
## $ WheelsOff : int 1059 656 835 15 813 704 1413 108 1640 753 ...
## $ WheelsOn : int 1852 1719 1852 1630 1906 730 1518 1134 NA 1821 ...
## $ CRSArrTime : int 1639 1458 1615 725 1629 847 2153 825 1856 1604 ...
## $ ArrTime : int 1902 1728 1923 1642 1923 736 1526 1147 NA 1831 ...
## $ Cancelled : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Diverted : int 1 1 1 1 1 1 1 1 1 1 ...
## $ CRSElapsedTime : int 234 318 295 266 329 107 323 290 171 319 ...
## $ ActualElapsedTime: int NA NA NA NA NA NA NA NA NA NA ...
## $ Distance : int 1709 2585 2248 2174 2475 631 2475 2342 1096 2446 ...
## $ new_CRSDepTime : POSIXct, format: "2016-01-15 10:45:00" "2016-01-15 06:40:00" ...
## $ new_CRSArrTime : POSIXct, format: "2016-01-15 16:39:00" "2016-01-15 14:58:00" ...
## $ new_DepTime : POSIXct, format: "2016-01-15 10:42:00" "2016-01-15 06:38:00" ...
## $ new_ArrTime : POSIXct, format: "2016-01-15 19:02:00" "2016-01-15 17:28:00" ...
## $ new_WheelsOff : POSIXct, format: "2016-01-15 10:59:00" "2016-01-15 06:56:00" ...
## $ new_WheelsOn : POSIXct, format: "2016-01-15 18:52:00" "2016-01-15 17:19:00" ...
## $ DepDelay : int -3 -2 -2 -3 -1 -5 -7 20 19 -5 ...
## $ DepDelayMinutes : num 0 0 0 0 0 0 0 20 19 0 ...
## $ DepDel15 : num 0 0 0 0 0 0 0 1 1 0 ...
## $ TaxiOut : int 17 18 17 -1421 14 9 50 13 16 13 ...
## $ TaxiIn : int 10 9 31 12 17 6 8 13 NA 10 ...
## $ ArrDelay : int 143 150 188 557 174 -71 -387 202 NA 147 ...
## $ ArrDelayMinutes : num 143 150 188 557 174 0 0 202 NA 147 ...
## $ ArrDel15 : num 1 1 1 1 1 0 0 1 NA 1 ...
## $ FlightTimeBuffer : int NA NA NA NA NA NA NA NA NA NA ...
## $ AirTime : int NA NA NA NA NA NA NA NA NA NA ...
## $ AirSpeed : num NA NA NA NA NA NA NA NA NA NA ...summary(flights)## FlightDate Carrier TailNum FlightNum
## Min. :2016-01-01 Length:434162 Length:434162 Min. : 1
## 1st Qu.:2016-01-08 Class :character Class :character 1st Qu.: 701
## Median :2016-01-15 Mode :character Mode :character Median :1591
## Mean :2016-01-15 Mean :2078
## 3rd Qu.:2016-01-24 3rd Qu.:2763
## Max. :2016-01-31 Max. :7438
##
## Origin OriginCityName OriginState
## Length:434162 Length:434162 Length:434162
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
##
## Dest DestCityName DestState CRSDepTime
## Length:434162 Length:434162 Length:434162 Min. : 1
## Class :character Class :character Class :character 1st Qu.: 920
## Mode :character Mode :character Mode :character Median :1325
## Mean :1330
## 3rd Qu.:1730
## Max. :2359
##
## DepTime WheelsOff WheelsOn CRSArrTime
## Min. : 1 Min. : 1 Min. : 1 Min. : 1
## 1st Qu.: 924 1st Qu.: 939 1st Qu.:1104 1st Qu.:1118
## Median :1331 Median :1344 Median :1519 Median :1527
## Mean :1334 Mean :1357 Mean :1483 Mean :1503
## 3rd Qu.:1736 3rd Qu.:1750 3rd Qu.:1914 3rd Qu.:1920
## Max. :2400 Max. :2400 Max. :2400 Max. :2359
## NA's :242
## ArrTime Cancelled Diverted CRSElapsedTime
## Min. : 1 Min. :0 Min. :0.00000 Min. : 21.0
## 1st Qu.:1108 1st Qu.:0 1st Qu.:0.00000 1st Qu.: 90.0
## Median :1522 Median :0 Median :0.00000 Median :128.0
## Mean :1488 Mean :0 Mean :0.00199 Mean :146.4
## 3rd Qu.:1919 3rd Qu.:0 3rd Qu.:0.00000 3rd Qu.:180.0
## Max. :2400 Max. :0 Max. :1.00000 Max. :705.0
## NA's :242
## ActualElapsedTime Distance new_CRSDepTime
## Min. : 15.0 Min. : 31 Min. :2016-01-01 00:10:00
## 1st Qu.: 85.0 1st Qu.: 391 1st Qu.:2016-01-08 11:20:00
## Median :122.0 Median : 679 Median :2016-01-15 20:45:00
## Mean :140.1 Mean : 844 Mean :2016-01-16 07:43:48
## 3rd Qu.:173.0 3rd Qu.:1089 3rd Qu.:2016-01-24 08:50:00
## Max. :721.0 Max. :4983 Max. :2016-01-31 23:59:00
## NA's :864
## new_CRSArrTime new_DepTime
## Min. :2016-01-01 00:01:00 Min. :2016-01-01 00:01:00
## 1st Qu.:2016-01-08 13:23:00 1st Qu.:2016-01-08 11:27:00
## Median :2016-01-15 22:27:00 Median :2016-01-15 20:59:00
## Mean :2016-01-16 09:28:08 Mean :2016-01-16 07:47:35
## 3rd Qu.:2016-01-24 10:32:00 3rd Qu.:2016-01-24 08:53:00
## Max. :2016-01-31 23:59:00 Max. :2016-02-01 00:00:00
##
## new_ArrTime new_WheelsOff
## Min. :2016-01-01 00:01:00 Min. :2016-01-01 00:01:00
## 1st Qu.:2016-01-08 13:17:00 1st Qu.:2016-01-08 11:43:00
## Median :2016-01-15 22:27:00 Median :2016-01-15 21:12:00
## Mean :2016-01-16 09:19:34 Mean :2016-01-16 08:00:25
## 3rd Qu.:2016-01-24 10:23:00 3rd Qu.:2016-01-24 09:07:00
## Max. :2016-02-01 00:00:00 Max. :2016-01-31 23:59:00
## NA's :242
## new_WheelsOn DepDelay DepDelayMinutes
## Min. :2016-01-01 00:01:00 Min. :-1438.000 Min. : 0.00
## 1st Qu.:2016-01-08 13:15:00 1st Qu.: -5.000 1st Qu.: 0.00
## Median :2016-01-15 22:25:00 Median : -2.000 Median : 0.00
## Mean :2016-01-16 09:16:46 Mean : 3.786 Mean : 10.31
## 3rd Qu.:2016-01-24 10:19:00 3rd Qu.: 5.000 3rd Qu.: 5.00
## Max. :2016-02-01 00:00:00 Max. : 1438.000 Max. :1438.00
## NA's :242
## DepDel15 TaxiOut TaxiIn
## Min. :0.0000 Min. :-1433.00 Min. :-1438.000
## 1st Qu.:0.0000 1st Qu.: 11.00 1st Qu.: 4.000
## Median :0.0000 Median : 14.00 Median : 6.000
## Mean :0.1611 Mean : 12.83 Mean : 2.811
## 3rd Qu.:0.0000 3rd Qu.: 19.00 3rd Qu.: 8.000
## Max. :1.0000 Max. : 158.00 Max. : 155.000
## NA's :242
## ArrDelay ArrDelayMinutes ArrDel15 FlightTimeBuffer
## Min. :-1438.000 Min. : 0.00 Min. :0.0000 Min. :-169.000
## 1st Qu.: -16.000 1st Qu.: 0.00 1st Qu.:0.0000 1st Qu.: 0.000
## Median : -7.000 Median : 0.00 Median :0.0000 Median : 7.000
## Mean : -8.493 Mean : 13.15 Mean :0.1587 Mean : 6.222
## 3rd Qu.: 5.000 3rd Qu.: 5.00 3rd Qu.:0.0000 3rd Qu.: 14.000
## Max. : 1439.000 Max. :1439.00 Max. :1.0000 Max. : 78.000
## NA's :242 NA's :242 NA's :242 NA's :864
## AirTime AirSpeed
## Min. : 8.0 Min. : 2.757
## 1st Qu.: 62.0 1st Qu.:364.054
## Median : 98.0 Median :412.586
## Mean : 124.5 Mean :407.001
## 3rd Qu.: 149.0 3rd Qu.:456.818
## Max. :2932.0 Max. :781.154
## NA's :864 NA's :864library(lubridate)##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## dateflights2 <- flights %>% filter(Cancelled == 0) q1 <- flights2 %>% group_by(day = wday(FlightDate, label = TRUE)) %>% mutate(delayed = ifelse(DepDelay > 0, 1, 0)) %>% summarize(perc_delay = sum(delayed)/ n())
q1 %>% ggvis(~day, ~perc_delay) %>% layer_bars()flights2 %>% mutate(delayed = ifelse(DepDelay > 0, 1, 0), time_part = ifelse(CRSDepTime > 1200, "Afternoon/Evening", "Morning")) %>% group_by(time_part) %>% summarize(perc_delay = sum(delayed) / n())## # A tibble: 2 × 2
## time_part perc_delay
## <chr> <dbl>
## 1 Afternoon/Evening 0.3965497
## 2 Morning 0.2622473library(chron)##
## Attaching package: 'chron'## The following objects are masked from 'package:lubridate':
##
## days, hours, minutes, seconds, yearsflights2 %>%group_by(is.weekend(FlightDate)) %>% mutate(delayed = ifelse(DepDelay > 0,1,0)) %>% summarize(perc_delay = sum(delayed)/ n())## # A tibble: 2 × 2
## `is.weekend(FlightDate)` perc_delay
## <lgl> <dbl>
## 1 FALSE 0.3354482
## 2 TRUE 0.3528838flights120 <- flights %>% filter(Cancelled == 0) %>% mutate(DepDelayMinutes = ifelse(DepDelay < 0, 0, DepDelay), DepDel120 = ifelse(DepDelay >= 120, 1, 0))
JanDelayedFlights <- flights120 %>% filter(DepDelay >= 120) %>% select(FlightDate, Carrier,Origin, OriginCityName, CRSDepTime, DepTime, WheelsOff, CRSArrTime, ArrTime, WheelsOn, Dest, DestCityName, DepDelayMinutes)
head(JanDelayedFlights)## FlightDate Carrier Origin OriginCityName CRSDepTime DepTime
## 1 2016-01-04 AS DFW Dallas/Fort Worth, TX 1320 1844
## 2 2016-01-02 AS OGG Kahului, HI 1240 1445
## 3 2016-01-31 AS JFK New York, NY 715 1356
## 4 2016-01-06 B6 OAK Oakland, CA 1850 2224
## 5 2016-01-24 B6 FLL Fort Lauderdale, FL 1327 1549
## 6 2016-01-24 B6 SJU San Juan, PR 1500 1704
## WheelsOff CRSArrTime ArrTime WheelsOn Dest DestCityName
## 1 1858 1540 2252 2244 SEA Seattle, WA
## 2 1500 1958 8 5 SAN San Diego, CA
## 3 1411 1034 1839 1831 SEA Seattle, WA
## 4 2235 2007 NA NA LGB Long Beach, CA
## 5 1607 1622 NA NA EWR Newark, NJ
## 6 1714 1811 NA NA EWR Newark, NJ
## DepDelayMinutes
## 1 324
## 2 125
## 3 401
## 4 214
## 5 142
## 6 124write.csv(JanDelayedFlights, "JanDelayedFlights2.csv", row.names = TRUE)sampleDelays <- sample_n(flights, 250)
sampleDelays## FlightDate Carrier TailNum FlightNum Origin
## 98938 2016-01-20 B6 N249JB 1507 JFK
## 266005 2016-01-15 WN N252WN 486 LAS
## 410247 2016-01-09 EV N11193 4555 IAH
## 144068 2016-01-20 OO N441SW 4752 SLC
## 321356 2016-01-12 WN N916WN 1167 BWI
## 70727 2016-01-04 AS N762AS 50 ANC
## 54288 2016-01-14 AA N521UW 621 PHX
## 118224 2016-01-21 HA N476HA 278 HNL
## 143941 2016-01-21 OO N225AG 3451 BUR
## 337530 2016-01-07 DL N340NB 1511 DTW
## 332009 2016-01-29 B6 N949JB 84 MCO
## 385714 2016-01-29 DL N940DL 2058 MCO
## 346418 2016-01-12 DL N304DQ 1598 ATL
## 305857 2016-01-26 WN N251WN 1165 PIT
## 159737 2016-01-24 OO N554CA 4851 BZN
## 78542 2016-01-03 AS N552AS 484 SEA
## 155860 2016-01-18 OO N187PQ 4797 SLC
## 247141 2016-01-07 WN N7811F 899 MSY
## 101356 2016-01-22 B6 N657JB 433 DCA
## 126353 2016-01-21 NK N637NK 571 TPA
## 66315 2016-01-28 AA N154UW 640 PHX
## 366542 2016-01-20 DL N944DL 1861 LIT
## 308397 2016-01-28 WN N7735A 473 ONT
## 62899 2016-01-25 AA N535UW 1841 DCA
## 169001 2016-01-02 OO N924SW 6283 DEN
## 415364 2016-01-31 EV N228PQ 4891 ATL
## 226963 2016-01-03 WN N554WN 4440 MDW
## 6803 2016-01-25 AA N789AA 293 JFK
## 415421 2016-01-15 EV N146PQ 4893 DTW
## 167070 2016-01-27 OO N973SW 6213 GRB
## 306062 2016-01-26 WN N569WN 413 SAN
## 211308 2016-01-03 UA N26960 1963 LAX
## 96170 2016-01-16 B6 N570JB 22 PBI
## 295684 2016-01-23 WN N930WN 3727 AUS
## 266176 2016-01-15 WN N7739A 1434 LAX
## 198446 2016-01-13 UA N36476 1107 EWR
## 31201 2016-01-01 AA N3FGAA 2203 MIA
## 203848 2016-01-10 UA N41140 1551 DEN
## 162256 2016-01-04 OO N114SY 5267 TUS
## 113509 2016-01-20 DL N806DN 1164 MSP
## 388512 2016-01-31 DL N992AT 1306 ATL
## 129495 2016-01-20 NK N587NK 936 RSW
## 176883 2016-01-31 UA N33266 1842 IAH
## 252736 2016-01-10 WN N241WN 1350 SAN
## 193815 2016-01-19 UA N815UA 1594 DCA
## 432793 2016-01-07 F9 N211FR 333 MSP
## 270719 2016-01-16 WN N248WN 3623 DAL
## 126588 2016-01-11 NK N634NK 606 RSW
## 31761 2016-01-03 AA N3GWAA 2218 DCA
## 231201 2016-01-04 WN N401WN 2880 BUF
## 120324 2016-01-04 HA N479HA 344 LIH
## 138161 2016-01-24 OO N975SW 5538 SFO
## 3459 2016-01-25 AA N865AA 162 HNL
## 400260 2016-01-05 DL N946DN 2646 MSY
## 384346 2016-01-29 DL N913DN 2266 ATL
## 256810 2016-01-12 WN N644SW 2437 PVD
## 422697 2016-01-10 EV N873AS 5485 ATL
## 212703 2016-01-02 UA N56859 1149 IAH
## 308193 2016-01-28 WN N8311Q 1356 MSP
## 172549 2016-01-19 OO N941SW 6240 ORD
## 303409 2016-01-07 WN N8623F 1017 ATL
## 107537 2016-01-01 DL N893AT 547 SYR
## 392908 2016-01-09 EV N909EV 2775 DFW
## 410325 2016-01-19 EV N11551 4556 ORD
## 328431 2016-01-04 DL N983DL 1711 ATL
## 86618 2016-01-05 B6 N198JB 260 PHL
## 124473 2016-01-01 NK N603NK 146 BOS
## 169801 2016-01-05 OO N133SY 6303 ORD
## 133049 2016-01-21 NK N614NK 906 PHX
## 424780 2016-01-23 EV N716EV 5220 ATL
## 161830 2016-01-26 OO N712SK 5252 CVG
## 50549 2016-01-08 AA N150UW 454 LAX
## 128535 2016-01-22 NK N626NK 736 LAX
## 51174 2016-01-12 AA N914UY 1747 CLT
## 259077 2016-01-16 UA N36272 360 IAD
## 149015 2016-01-11 OO N217AG 3490 SEA
## 93167 2016-01-11 B6 N796JB 1212 MCO
## 153251 2016-01-06 EV N11150 4731 ORD
## 354355 2016-01-17 DL N944DL 1337 LGA
## 346663 2016-01-29 AA N578UW 672 LAS
## 78113 2016-01-18 AS N435AS 345 OAK
## 313680 2016-01-29 WN N755SA 744 BWI
## 356484 2016-01-16 DL N978DL 2128 DTW
## 56840 2016-01-19 AA N192UW 1995 PHL
## 247220 2016-01-07 WN N290WN 153 OAK
## 186802 2016-01-24 UA N803UA 1938 SAT
## 432118 2016-01-01 F9 N938FR 701 ATL
## 131722 2016-01-16 OO N812SK 4794 PHX
## 341840 2016-01-09 DL N994AT 829 IAH
## 210351 2016-01-05 UA N69888 483 ORD
## 384790 2016-01-29 DL N593NW 1291 SEA
## 246555 2016-01-09 WN N904WN 1530 OAK
## 76960 2016-01-11 AS N556AS 427 LAX
## 125907 2016-01-15 NK N660NK 705 LGA
## 66736 2016-01-28 AA N831AW 1839 GSO
## 59139 2016-01-16 AA N581UW 796 LAX
## 250701 2016-01-10 WN N445WN 487 LAX
## 47536 2016-01-05 AA N952UW 2162 BOS
## 170304 2016-01-15 OO N957SW 6385 SMF
## 73242 2016-01-09 AS N491AS 612 SEA
## 273632 2016-01-18 WN N606SW 551 BOS
## 118715 2016-01-28 HA N493HA 306 HNL
## 229807 2016-01-04 WN N404WN 1137 IND
## 103237 2016-01-27 B6 N239JB 1170 MCO
## 361597 2016-01-18 DL N944AT 2017 ORD
## 317932 2016-01-27 WN N707SA 1794 BUR
## 173399 2016-01-05 OO N970SW 6484 SFO
## 244196 2016-01-08 WN N400WN 2595 CLT
## 359479 2016-01-20 DL N705TW 418 LAX
## 85591 2016-01-01 B6 N187JB 207 PWM
## 387058 2016-01-30 DL N554NW 1827 ATL
## 328221 2016-01-04 DL N926AT 1529 ATL
## 242759 2016-01-29 UA N851UA 701 AUS
## 114980 2016-01-19 F9 N935FR 1265 RSW
## 379624 2016-01-26 DL N336NW 2288 ATL
## 412820 2016-01-12 DL N675DL 2820 ATL
## 29252 2016-01-20 AA N4YLAA 2275 DSM
## 92324 2016-01-13 B6 N665JB 1853 FLL
## 123026 2016-01-03 HA N489HA 394 LIH
## 174132 2016-01-26 OO N135SY 6523 LAX
## 142253 2016-01-21 OO N960SW 5709 ORD
## 248722 2016-01-10 WN N737JW 901 HOU
## 222781 2016-01-02 WN N8624J 4883 LAS
## 434016 2016-01-03 F9 N223FR 575 DEN
## 351964 2016-01-14 DL N314NB 1330 DTW
## 414823 2016-01-19 EV N923EV 4968 ATL
## 422255 2016-01-05 EV N695CA 5250 DTW
## 182437 2016-01-28 UA N54241 1967 CLE
## 412748 2016-01-12 DL N986AT 2672 LGA
## 339287 2016-01-09 DL N836DN 903 ATL
## 338730 2016-01-08 DL N342NB 671 MSP
## 428231 2016-01-29 EV N13914 5821 ORD
## 243940 2016-01-08 WN N7740A 242 BUF
## 185800 2016-01-23 UA N451UA 1789 IAH
## 245771 2016-01-08 WN N8619F 2179 PHX
## 220430 2016-01-01 WN N387SW 1458 HOU
## 44174 2016-01-04 AA N757UW 460 PHX
## 265842 2016-01-14 WN N360SW 2272 SAN
## 237890 2016-01-06 WN N268WN 2776 PHX
## 428403 2016-01-01 EV N15574 5878 XNA
## 243811 2016-01-08 WN N223WN 389 MSY
## 396924 2016-01-01 EV N17115 4119 EWR
## 2446 2016-01-04 AA N3CSAA 224 LAS
## 21076 2016-01-03 AA N3MBAA 1409 DCA
## 210453 2016-01-05 UA N33266 570 DFW
## 186820 2016-01-24 UA N69839 1980 IAH
## 247442 2016-01-08 WN N919WN 2423 EWR
## 57395 2016-01-15 AA N170US 862 DCA
## 404453 2016-01-06 EV N12167 4398 CLE
## 251386 2016-01-11 WN N963WN 346 DTW
## 3637 2016-01-19 AA N4XHAA 168 ORD
## 217592 2016-01-29 VX N843VA 948 SFO
## 316246 2016-01-31 WN N271LV 35 DAL
## 163325 2016-01-03 OO N133SY 5204 ORD
## 143975 2016-01-12 OO N217AG 3453 HDN
## 201846 2016-01-08 UA N69806 554 ORD
## 401722 2016-01-19 EV N13992 4365 ORD
## 324251 2016-01-30 WN N374SW 879 STL
## 75455 2016-01-07 AS N796AS 717 SAN
## 417625 2016-01-29 EV N921EV 5098 CHA
## 41706 2016-01-29 AA N484AA 2375 ORD
## 132484 2016-01-18 NK N532NK 866 PDX
## 332976 2016-01-06 DL N970DL 1545 ATL
## 429230 2016-01-25 EV N717EV 5495 CVG
## 32919 2016-01-30 AA N4XUAA 2335 DFW
## 20569 2016-01-30 AA N3JNAA 1389 LGA
## 309016 2016-01-28 WN N557WN 582 BWI
## 50168 2016-01-10 AA N190UW 1830 PDX
## 84625 2016-01-02 B6 N198JB 2113 HPN
## 62329 2016-01-25 AA N647AW 449 STL
## 159582 2016-01-27 OO N928SW 5306 LAX
## 415136 2016-01-07 EV N12996 4699 ORD
## 121174 2016-01-26 DL N607AT 2415 ATL
## 27871 2016-01-02 AA N501AA 1646 DFW
## 14746 2016-01-23 AA N3DXAA 1266 RDU
## 74637 2016-01-14 AS N581AS 800 OGG
## 112033 2016-01-23 F9 N922FR 223 AUS
## 359201 2016-01-19 DL N936DN 1661 ATL
## 400046 2016-01-05 DL N839DN 2413 SEA
## 20854 2016-01-27 AA N3CMAA 1402 DFW
## 374924 2016-01-25 DL N394DA 1327 CMH
## 280520 2016-01-20 WN N207WN 588 ATL
## 36015 2016-01-29 AA N3GBAA 1599 DFW
## 339282 2016-01-09 DL N349NB 899 MSP
## 265108 2016-01-14 WN N232WN 413 MDW
## 195906 2016-01-15 UA N36207 1456 LGA
## 136850 2016-01-08 OO N161PQ 4485 SFO
## 361775 2016-01-19 DL N934DL 1834 ATL
## 98956 2016-01-20 B6 N784JB 1580 FLL
## 353114 2016-01-15 DL N370NW 1868 LAX
## 305704 2016-01-26 WN N417WN 611 PHX
## 304361 2016-01-27 WN N774SW 331 CMH
## 326392 2016-01-02 DL N346NB 2328 ATL
## 204785 2016-01-07 UA N61886 434 EWR
## 238837 2016-01-07 WN N737JW 913 MDW
## 40505 2016-01-20 AA N3JKAA 2425 DFW
## 256034 2016-01-12 WN N949WN 1570 ABQ
## 303532 2016-01-07 WN N786SW 2626 BHM
## 313200 2016-01-29 WN N441WN 2597 LAS
## 103976 2016-01-04 B6 N615JB 1903 JFK
## 64596 2016-01-26 AA N107US 1769 CLT
## 94056 2016-01-15 B6 N231JB 1035 STT
## 19056 2016-01-24 AA N3HVAA 1327 DFW
## 27953 2016-01-23 AA N3BAAA 1568 MCI
## 137228 2016-01-11 NK N509NK 448 DFW
## 93757 2016-01-15 B6 N947JB 323 JFK
## 147111 2016-01-04 OO N919SW 6346 MRY
## 103672 2016-01-30 B6 N329JB 2807 PWM
## 133420 2016-01-25 OO N967SW 6422 SFO
## 323965 2016-01-31 WN N663SW 1261 PDX
## 420717 2016-01-06 EV N538CA 5084 ATL
## 195823 2016-01-15 UA N75854 1221 HNL
## 103440 2016-01-27 B6 N789JB 2094 FLL
## 183356 2016-01-25 UA N37408 527 RSW
## 23366 2016-01-05 AA N3GUAA 1440 DFW
## 314678 2016-01-29 WN N8309C 2179 TPA
## 369493 2016-01-21 DL N950DL 1372 DTW
## 86008 2016-01-04 B6 N236JB 395 HPN
## 238558 2016-01-07 WN N361SW 1991 LAX
## 354867 2016-01-15 DL N812DN 2134 SEA
## 23752 2016-01-02 AA N861AA 1454 SFO
## 134778 2016-01-21 NK N506NK 479 MSY
## 352444 2016-01-15 DL N944AT 1318 GSO
## 222498 2016-01-01 WN N946WN 375 PHX
## 36210 2016-01-24 AA N865AA 2460 DFW
## 342916 2016-01-11 DL N553NW 1718 MCO
## 64275 2016-01-26 AA N178US 593 PHX
## 266111 2016-01-15 WN N928WN 553 LAX
## 410588 2016-01-19 EV N11193 4576 GRR
## 75034 2016-01-06 AS N563AS 804 KOA
## 408078 2016-01-25 EV N14203 4213 ORD
## 165982 2016-01-19 EV N36915 5781 IAH
## 399760 2016-01-05 DL N301NB 2136 ELP
## 326750 2016-01-02 DL N593NW 2814 MCO
## 325232 2016-01-31 WN N249WN 790 SNA
## 418484 2016-01-24 EV N851AS 5034 ATL
## 216505 2016-01-26 VX N282VA 39 SFO
## 127503 2016-01-16 NK N620NK 847 ORD
## 18124 2016-01-12 AA N3MHAA 1296 ORD
## 431803 2016-01-14 EV N13989 6171 SBN
## 226874 2016-01-03 WN N8606C 4117 MCO
## 109849 2016-01-02 F9 N902FR 901 TTN
## 374383 2016-01-24 DL N370NW 1158 MCO
## 109415 2016-01-16 F9 N919FR 164 DEN
## 26551 2016-01-31 AA N3AYAA 310 MIA
## 123920 2016-01-27 NK N659NK 403 ATL
## 339424 2016-01-09 DL N947DL 1126 CVG
## 93680 2016-01-15 B6 N606JB 149 JFK
## 267886 2016-01-15 WN N733SA 525 SAT
## 330037 2016-01-04 DL N934AT 2821 JFK
## OriginCityName OriginState Dest
## 98938 New York, NY NY IAD
## 266005 Las Vegas, NV NV OKC
## 410247 Houston, TX TX BRO
## 144068 Salt Lake City, UT UT EUG
## 321356 Baltimore, MD MD MCO
## 70727 Anchorage, AK AK SCC
## 54288 Phoenix, AZ AZ SNA
## 118224 Honolulu, HI HI KOA
## 143941 Burbank, CA CA PDX
## 337530 Detroit, MI MI DEN
## 332009 Orlando, FL FL JFK
## 385714 Orlando, FL FL JFK
## 346418 Atlanta, GA GA CHS
## 305857 Pittsburgh, PA PA MCO
## 159737 Bozeman, MT MT SLC
## 78542 Seattle, WA WA SAN
## 155860 Salt Lake City, UT UT SNA
## 247141 New Orleans, LA LA SAT
## 101356 Washington, DC VA PBI
## 126353 Tampa, FL FL FLL
## 66315 Phoenix, AZ AZ LAX
## 366542 Little Rock, AR AR ATL
## 308397 Ontario, CA CA LAS
## 62899 Washington, DC VA TPA
## 169001 Denver, CO CO GJT
## 415364 Atlanta, GA GA MLI
## 226963 Chicago, IL IL DEN
## 6803 New York, NY NY LAX
## 415421 Detroit, MI MI CLE
## 167070 Green Bay, WI WI ORD
## 306062 San Diego, CA CA TUS
## 211308 Los Angeles, CA CA IAH
## 96170 West Palm Beach/Palm Beach, FL FL BOS
## 295684 Austin, TX TX PHX
## 266176 Los Angeles, CA CA RNO
## 198446 Newark, NJ NJ PDX
## 31201 Miami, FL FL LAS
## 203848 Denver, CO CO EWR
## 162256 Tucson, AZ AZ ORD
## 113509 Minneapolis, MN MN RSW
## 388512 Atlanta, GA GA TUL
## 129495 Fort Myers, FL FL ACY
## 176883 Houston, TX TX BOS
## 252736 San Diego, CA CA ATL
## 193815 Washington, DC VA IAH
## 432793 Minneapolis, MN MN DEN
## 270719 Dallas, TX TX DCA
## 126588 Fort Myers, FL FL DTW
## 31761 Washington, DC VA DFW
## 231201 Buffalo, NY NY MCO
## 120324 Lihue, HI HI HNL
## 138161 San Francisco, CA CA MRY
## 3459 Honolulu, HI HI LAX
## 400260 New Orleans, LA LA ATL
## 384346 Atlanta, GA GA DEN
## 256810 Providence, RI RI TPA
## 422697 Atlanta, GA GA AEX
## 212703 Houston, TX TX SFO
## 308193 Minneapolis, MN MN DEN
## 172549 Chicago, IL IL SGF
## 303409 Atlanta, GA GA MDW
## 107537 Syracuse, NY NY ATL
## 392908 Dallas/Fort Worth, TX TX BTR
## 410325 Chicago, IL IL CAK
## 328431 Atlanta, GA GA DFW
## 86618 Philadelphia, PA PA BOS
## 124473 Boston, MA MA ATL
## 169801 Chicago, IL IL DAY
## 133049 Phoenix, AZ AZ DEN
## 424780 Atlanta, GA GA EYW
## 161830 Cincinnati, OH KY IAH
## 50549 Los Angeles, CA CA PHX
## 128535 Los Angeles, CA CA ORD
## 51174 Charlotte, NC NC PIT
## 259077 Washington, DC VA TPA
## 149015 Seattle, WA WA FAT
## 93167 Orlando, FL FL BWI
## 153251 Chicago, IL IL DAY
## 354355 New York, NY NY ATL
## 346663 Las Vegas, NV NV DFW
## 78113 Oakland, CA CA SEA
## 313680 Baltimore, MD MD BOS
## 356484 Detroit, MI MI MCO
## 56840 Philadelphia, PA PA ORD
## 247220 Oakland, CA CA SAN
## 186802 San Antonio, TX TX IAH
## 432118 Atlanta, GA GA MSY
## 131722 Phoenix, AZ AZ LAX
## 341840 Houston, TX TX ATL
## 210351 Chicago, IL IL PHX
## 384790 Seattle, WA WA ATL
## 246555 Oakland, CA CA SEA
## 76960 Los Angeles, CA CA SEA
## 125907 New York, NY NY FLL
## 66736 Greensboro/High Point, NC NC CLT
## 59139 Los Angeles, CA CA PHL
## 250701 Los Angeles, CA CA HOU
## 47536 Boston, MA MA LGA
## 170304 Sacramento, CA CA DEN
## 73242 Seattle, WA WA LAS
## 273632 Boston, MA MA MCI
## 118715 Honolulu, HI HI OGG
## 229807 Indianapolis, IN IN FLL
## 103237 Orlando, FL FL RIC
## 361597 Chicago, IL IL DTW
## 317932 Burbank, CA CA SFO
## 173399 San Francisco, CA CA SBA
## 244196 Charlotte, NC NC DAL
## 359479 Los Angeles, CA CA JFK
## 85591 Portland, ME ME JFK
## 387058 Atlanta, GA GA FLL
## 328221 Atlanta, GA GA GPT
## 242759 Austin, TX TX DEN
## 114980 Fort Myers, FL FL ORD
## 379624 Atlanta, GA GA BHM
## 412820 Atlanta, GA GA LAS
## 29252 Des Moines, IA IA DFW
## 92324 Fort Lauderdale, FL FL SJU
## 123026 Lihue, HI HI HNL
## 174132 Los Angeles, CA CA AUS
## 142253 Chicago, IL IL CID
## 248722 Houston, TX TX DEN
## 222781 Las Vegas, NV NV STL
## 434016 Denver, CO CO SLC
## 351964 Detroit, MI MI CLT
## 414823 Atlanta, GA GA MGM
## 422255 Detroit, MI MI MDT
## 182437 Cleveland, OH OH EWR
## 412748 New York, NY NY BOS
## 339287 Atlanta, GA GA BOS
## 338730 Minneapolis, MN MN DCA
## 428231 Chicago, IL IL GSP
## 243940 Buffalo, NY NY MCO
## 185800 Houston, TX TX TPA
## 245771 Phoenix, AZ AZ ONT
## 220430 Houston, TX TX ABQ
## 44174 Phoenix, AZ AZ IND
## 265842 San Diego, CA CA OAK
## 237890 Phoenix, AZ AZ ABQ
## 428403 Fayetteville, AR AR IAH
## 243811 New Orleans, LA LA MCI
## 396924 Newark, NJ NJ RIC
## 2446 Las Vegas, NV NV ORD
## 21076 Washington, DC VA ORD
## 210453 Dallas/Fort Worth, TX TX IAH
## 186820 Houston, TX TX MCO
## 247442 Newark, NJ NJ DEN
## 57395 Washington, DC VA MCO
## 404453 Cleveland, OH OH DCA
## 251386 Detroit, MI MI STL
## 3637 Chicago, IL IL RDU
## 217592 San Francisco, CA CA LAX
## 316246 Dallas, TX TX HOU
## 163325 Chicago, IL IL DTW
## 143975 Hayden, CO CO SEA
## 201846 Chicago, IL IL SAN
## 401722 Chicago, IL IL SAV
## 324251 St. Louis, MO MO MCI
## 75455 San Diego, CA CA SLC
## 417625 Chattanooga, TN TN ATL
## 41706 Chicago, IL IL DFW
## 132484 Portland, OR OR LAS
## 332976 Atlanta, GA GA BOS
## 429230 Cincinnati, OH KY DTW
## 32919 Dallas/Fort Worth, TX TX ORD
## 20569 New York, NY NY MIA
## 309016 Baltimore, MD MD ORF
## 50168 Portland, OR OR CLT
## 84625 White Plains, NY NY FLL
## 62329 St. Louis, MO MO PHX
## 159582 Los Angeles, CA CA SBA
## 415136 Chicago, IL IL MDT
## 121174 Atlanta, GA GA EWR
## 27871 Dallas/Fort Worth, TX TX TUL
## 14746 Raleigh/Durham, NC NC MIA
## 74637 Kahului, HI HI SMF
## 112033 Austin, TX TX DEN
## 359201 Atlanta, GA GA PHL
## 400046 Seattle, WA WA JFK
## 20854 Dallas/Fort Worth, TX TX SEA
## 374924 Columbus, OH OH LAX
## 280520 Atlanta, GA GA DAL
## 36015 Dallas/Fort Worth, TX TX SNA
## 339282 Minneapolis, MN MN PHX
## 265108 Chicago, IL IL SAN
## 195906 New York, NY NY IAH
## 136850 San Francisco, CA CA SLC
## 361775 Atlanta, GA GA STL
## 98956 Fort Lauderdale, FL FL DCA
## 353114 Los Angeles, CA CA SLC
## 305704 Phoenix, AZ AZ BUR
## 304361 Columbus, OH OH ATL
## 326392 Atlanta, GA GA CHA
## 204785 Newark, NJ NJ MCO
## 238837 Chicago, IL IL DCA
## 40505 Dallas/Fort Worth, TX TX LAX
## 256034 Albuquerque, NM NM PHX
## 303532 Birmingham, AL AL MCO
## 313200 Las Vegas, NV NV LAX
## 103976 New York, NY NY SJU
## 64596 Charlotte, NC NC MCI
## 94056 Charlotte Amalie, VI VI SJU
## 19056 Dallas/Fort Worth, TX TX PHL
## 27953 Kansas City, MO MO DFW
## 137228 Dallas/Fort Worth, TX TX RSW
## 93757 New York, NY NY LAX
## 147111 Monterey, CA CA SFO
## 103672 Portland, ME ME JFK
## 133420 San Francisco, CA CA SMX
## 323965 Portland, OR OR OAK
## 420717 Atlanta, GA GA TUL
## 195823 Honolulu, HI HI LAX
## 103440 Fort Lauderdale, FL FL BWI
## 183356 Fort Myers, FL FL IAH
## 23366 Dallas/Fort Worth, TX TX AUS
## 314678 Tampa, FL FL PHX
## 369493 Detroit, MI MI BUF
## 86008 White Plains, NY NY MCO
## 238558 Los Angeles, CA CA TUS
## 354867 Seattle, WA WA ATL
## 23752 San Francisco, CA CA DFW
## 134778 New Orleans, LA LA LAS
## 352444 Greensboro/High Point, NC NC ATL
## 222498 Phoenix, AZ AZ RNO
## 36210 Dallas/Fort Worth, TX TX LAX
## 342916 Orlando, FL FL ATL
## 64275 Phoenix, AZ AZ ORD
## 266111 Los Angeles, CA CA BWI
## 410588 Grand Rapids, MI MI IAH
## 75034 Kona, HI HI SAN
## 408078 Chicago, IL IL HPN
## 165982 Houston, TX TX LFT
## 399760 El Paso, TX TX ATL
## 326750 Orlando, FL FL DTW
## 325232 Santa Ana, CA CA SMF
## 418484 Atlanta, GA GA VLD
## 216505 San Francisco, CA CA OGG
## 127503 Chicago, IL IL DEN
## 18124 Chicago, IL IL SAN
## 431803 South Bend, IN IN ORD
## 226874 Orlando, FL FL SJU
## 109849 Trenton, NJ NJ ATL
## 374383 Orlando, FL FL SLC
## 109415 Denver, CO CO MSN
## 26551 Miami, FL FL LGA
## 123920 Atlanta, GA GA FLL
## 339424 Cincinnati, OH KY ATL
## 93680 New York, NY NY PSP
## 267886 San Antonio, TX TX LAS
## 330037 New York, NY NY PBI
## DestCityName DestState CRSDepTime DepTime
## 98938 Washington, DC VA 2131 2125
## 266005 Oklahoma City, OK OK 1535 1534
## 410247 Brownsville, TX TX 905 900
## 144068 Eugene, OR OR 2150 2147
## 321356 Orlando, FL FL 715 713
## 70727 Deadhorse, AK AK 1455 1449
## 54288 Santa Ana, CA CA 1120 1113
## 118224 Kona, HI HI 1627 1629
## 143941 Portland, OR OR 2055 2046
## 337530 Denver, CO CO 840 837
## 332009 New York, NY NY 1900 1859
## 385714 New York, NY NY 729 723
## 346418 Charleston, SC SC 1505 1518
## 305857 Orlando, FL FL 1130 1121
## 159737 Salt Lake City, UT UT 1310 1304
## 78542 San Diego, CA CA 1955 1957
## 155860 Santa Ana, CA CA 838 835
## 247141 San Antonio, TX TX 1610 1607
## 101356 West Palm Beach/Palm Beach, FL FL 943 1152
## 126353 Fort Lauderdale, FL FL 840 838
## 66315 Los Angeles, CA CA 2100 2056
## 366542 Atlanta, GA GA 1525 1525
## 308397 Las Vegas, NV NV 1735 1733
## 62899 Tampa, FL FL 1455 1622
## 169001 Grand Junction, CO CO 1428 1426
## 415364 Moline, IL IL 2130 2239
## 226963 Denver, CO CO 2000 2019
## 6803 Los Angeles, CA CA 2000 1958
## 415421 Cleveland, OH OH 1730 1728
## 167070 Chicago, IL IL 1858 1940
## 306062 Tucson, AZ AZ 1230 1224
## 211308 Houston, TX TX 1300 1252
## 96170 Boston, MA MA 720 716
## 295684 Phoenix, AZ AZ 915 919
## 266176 Reno, NV NV 2035 2102
## 198446 Portland, OR OR 1846 1841
## 31201 Las Vegas, NV NV 2144 2155
## 203848 Newark, NJ NJ 1247 1247
## 162256 Chicago, IL IL 1218 1228
## 113509 Fort Myers, FL FL 1950 1949
## 388512 Tulsa, OK OK 1630 1627
## 129495 Atlantic City, NJ NJ 1004 1001
## 176883 Boston, MA MA 1303 1259
## 252736 Atlanta, GA GA 1300 1259
## 193815 Houston, TX TX 1205 1206
## 432793 Denver, CO CO 530 531
## 270719 Washington, DC VA 1235 1231
## 126588 Detroit, MI MI 605 602
## 31761 Dallas/Fort Worth, TX TX 2055 2053
## 231201 Orlando, FL FL 625 619
## 120324 Honolulu, HI HI 1245 1243
## 138161 Monterey, CA CA 1558 1553
## 3459 Los Angeles, CA CA 1343 1339
## 400260 Atlanta, GA GA 1115 1114
## 384346 Denver, CO CO 1748 1747
## 256810 Tampa, FL FL 1445 1535
## 422697 Alexandria, LA LA 939 934
## 212703 San Francisco, CA CA 2330 4
## 308193 Denver, CO CO 625 622
## 172549 Springfield, MO MO 1555 1554
## 303409 Chicago, IL IL 1330 1341
## 107537 Atlanta, GA GA 1742 1751
## 392908 Baton Rouge, LA LA 1346 1358
## 410325 Akron, OH OH 751 744
## 328431 Dallas/Fort Worth, TX TX 2225 2225
## 86618 Boston, MA MA 1440 1430
## 124473 Atlanta, GA GA 1444 1448
## 169801 Dayton, OH OH 1522 1519
## 133049 Denver, CO CO 1237 1235
## 424780 Key West, FL FL 823 841
## 161830 Houston, TX TX 1520 1514
## 50549 Phoenix, AZ AZ 1745 1742
## 128535 Chicago, IL IL 1745 1746
## 51174 Pittsburgh, PA PA 1955 1953
## 259077 Tampa, FL FL 1735 1731
## 149015 Fresno, CA CA 1800 1755
## 93167 Baltimore, MD MD 1454 1447
## 153251 Dayton, OH OH 1734 1733
## 354355 Atlanta, GA GA 900 856
## 346663 Dallas/Fort Worth, TX TX 500 454
## 78113 Seattle, WA WA 600 552
## 313680 Boston, MA MA 1010 1010
## 356484 Orlando, FL FL 900 856
## 56840 Chicago, IL IL 1425 1423
## 247220 San Diego, CA CA 1450 1454
## 186802 Houston, TX TX 645 637
## 432118 New Orleans, LA LA 1745 1747
## 131722 Los Angeles, CA CA 1307 1304
## 341840 Atlanta, GA GA 1732 1744
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## 103237 Richmond, VA VA 1246 1239
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## 244196 Dallas, TX TX 1100 1059
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## 85591 New York, NY NY 1108 1104
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## 242759 Denver, CO CO 1635 1632
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## 412820 Las Vegas, NV NV 1340 1341
## 29252 Dallas/Fort Worth, TX TX 1031 1021
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## 142253 Cedar Rapids/Iowa City, IA IA 2235 2245
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## 428231 Greer, SC SC 710 714
## 243940 Orlando, FL FL 1940 1931
## 185800 Tampa, FL FL 1923 1923
## 245771 Ontario, CA CA 2105 2105
## 220430 Albuquerque, NM NM 1010 1011
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## 127503 20 123 433.17073
## 18124 5 237 436.20253
## 431803 8 24 210.00000
## 226874 6 142 502.39437
## 109849 -6 130 323.53846
## 374383 18 261 443.90805
## 109415 4 99 500.60606
## 26551 7 146 450.41096
## 123920 -5 95 366.94737
## 339424 4 76 294.47368
## 93680 22 315 452.95238
## 267886 10 156 411.15385
## 330037 19 159 387.92453q11 <- sampleDelays %>% group_by(day = wday(FlightDate, label = TRUE)) %>% mutate(delayed = ifelse(DepDelay > 0, 1, 0)) %>% summarize(perc_delay = sum(delayed)/ n())
q11 %>% ggvis(~day, ~perc_delay) %>% layer_bars()```