Copyright @ Fathir97 @ Prof.Dr. Suhartono M.Kom @ Magister Informatika @ UIN Maulana Malik Ibrahim @ UIN Malang
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.3 v purrr 0.3.4
## v tibble 3.0.6 v dplyr 1.0.4
## v tidyr 1.1.2 v stringr 1.4.0
## v readr 1.4.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(nycflights13)airlines## # A tibble: 16 x 2
## carrier name
## <chr> <chr>
## 1 9E Endeavor Air Inc.
## 2 AA American Airlines Inc.
## 3 AS Alaska Airlines Inc.
## 4 B6 JetBlue Airways
## 5 DL Delta Air Lines Inc.
## 6 EV ExpressJet Airlines Inc.
## 7 F9 Frontier Airlines Inc.
## 8 FL AirTran Airways Corporation
## 9 HA Hawaiian Airlines Inc.
## 10 MQ Envoy Air
## 11 OO SkyWest Airlines Inc.
## 12 UA United Air Lines Inc.
## 13 US US Airways Inc.
## 14 VX Virgin America
## 15 WN Southwest Airlines Co.
## 16 YV Mesa Airlines Inc.airports## # A tibble: 1,458 x 8
## faa name lat lon alt tz dst tzone
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 04G Lansdowne Airport 41.1 -80.6 1044 -5 A America/New_Yo~
## 2 06A Moton Field Municipal A~ 32.5 -85.7 264 -6 A America/Chicago
## 3 06C Schaumburg Regional 42.0 -88.1 801 -6 A America/Chicago
## 4 06N Randall Airport 41.4 -74.4 523 -5 A America/New_Yo~
## 5 09J Jekyll Island Airport 31.1 -81.4 11 -5 A America/New_Yo~
## 6 0A9 Elizabethton Municipal ~ 36.4 -82.2 1593 -5 A America/New_Yo~
## 7 0G6 Williams County Airport 41.5 -84.5 730 -5 A America/New_Yo~
## 8 0G7 Finger Lakes Regional A~ 42.9 -76.8 492 -5 A America/New_Yo~
## 9 0P2 Shoestring Aviation Air~ 39.8 -76.6 1000 -5 U America/New_Yo~
## 10 0S9 Jefferson County Intl 48.1 -123. 108 -8 A America/Los_An~
## # ... with 1,448 more rowsplanes## # A tibble: 3,322 x 9
## tailnum year type manufacturer model engines seats speed engine
## <chr> <int> <chr> <chr> <chr> <int> <int> <int> <chr>
## 1 N10156 2004 Fixed wing m~ EMBRAER EMB-1~ 2 55 NA Turbo-~
## 2 N102UW 1998 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 3 N103US 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 4 N104UW 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 5 N10575 2002 Fixed wing m~ EMBRAER EMB-1~ 2 55 NA Turbo-~
## 6 N105UW 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 7 N107US 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 8 N108UW 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 9 N109UW 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## 10 N110UW 1999 Fixed wing m~ AIRBUS INDUST~ A320-~ 2 182 NA Turbo-~
## # ... with 3,312 more rowsweather## # A tibble: 26,115 x 15
## origin year month day hour temp dewp humid wind_dir wind_speed
## <chr> <int> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 EWR 2013 1 1 1 39.0 26.1 59.4 270 10.4
## 2 EWR 2013 1 1 2 39.0 27.0 61.6 250 8.06
## 3 EWR 2013 1 1 3 39.0 28.0 64.4 240 11.5
## 4 EWR 2013 1 1 4 39.9 28.0 62.2 250 12.7
## 5 EWR 2013 1 1 5 39.0 28.0 64.4 260 12.7
## 6 EWR 2013 1 1 6 37.9 28.0 67.2 240 11.5
## 7 EWR 2013 1 1 7 39.0 28.0 64.4 240 15.0
## 8 EWR 2013 1 1 8 39.9 28.0 62.2 250 10.4
## 9 EWR 2013 1 1 9 39.9 28.0 62.2 260 15.0
## 10 EWR 2013 1 1 10 41 28.0 59.6 260 13.8
## # ... with 26,105 more rows, and 5 more variables: wind_gust <dbl>,
## # precip <dbl>, pressure <dbl>, visib <dbl>, time_hour <dttm>planes %>%
count(tailnum) %>%
filter(n > 1)## # A tibble: 0 x 2
## # ... with 2 variables: tailnum <chr>, n <int>weather %>%
count(year, month, day, hour, origin) %>%
filter(n > 1)## # A tibble: 3 x 6
## year month day hour origin n
## <int> <int> <int> <int> <chr> <int>
## 1 2013 11 3 1 EWR 2
## 2 2013 11 3 1 JFK 2
## 3 2013 11 3 1 LGA 2flights %>%
count(year, month, day, flight) %>%
filter(n > 1)## # A tibble: 29,768 x 5
## year month day flight n
## <int> <int> <int> <int> <int>
## 1 2013 1 1 1 2
## 2 2013 1 1 3 2
## 3 2013 1 1 4 2
## 4 2013 1 1 11 3
## 5 2013 1 1 15 2
## 6 2013 1 1 21 2
## 7 2013 1 1 27 4
## 8 2013 1 1 31 2
## 9 2013 1 1 32 2
## 10 2013 1 1 35 2
## # ... with 29,758 more rowsflights %>%
count(year, month, day, tailnum) %>%
filter(n > 1)## # A tibble: 64,928 x 5
## year month day tailnum n
## <int> <int> <int> <chr> <int>
## 1 2013 1 1 N0EGMQ 2
## 2 2013 1 1 N11189 2
## 3 2013 1 1 N11536 2
## 4 2013 1 1 N11544 3
## 5 2013 1 1 N11551 2
## 6 2013 1 1 N12540 2
## 7 2013 1 1 N12567 2
## 8 2013 1 1 N13123 2
## 9 2013 1 1 N13538 3
## 10 2013 1 1 N13566 3
## # ... with 64,918 more rowsflights2 <- flights %>%
select(year:day, hour, origin, dest, tailnum, carrier)
flights2## # A tibble: 336,776 x 8
## year month day hour origin dest tailnum carrier
## <int> <int> <int> <dbl> <chr> <chr> <chr> <chr>
## 1 2013 1 1 5 EWR IAH N14228 UA
## 2 2013 1 1 5 LGA IAH N24211 UA
## 3 2013 1 1 5 JFK MIA N619AA AA
## 4 2013 1 1 5 JFK BQN N804JB B6
## 5 2013 1 1 6 LGA ATL N668DN DL
## 6 2013 1 1 5 EWR ORD N39463 UA
## 7 2013 1 1 6 EWR FLL N516JB B6
## 8 2013 1 1 6 LGA IAD N829AS EV
## 9 2013 1 1 6 JFK MCO N593JB B6
## 10 2013 1 1 6 LGA ORD N3ALAA AA
## # ... with 336,766 more rows