between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?dep_time? What other variables are missing? What might these rows represent?NA ^ 0 not missing? Why is NA | TRUE not missing? Why is FALSE & NA not missing? Can you figure out the general rule? (NA * 0 is a tricky counterexample!)dep_time, dep_delay, arr_time, and arr_delay from flights.select() call?one_of() function do? Why might it be helpful in conjunction with this vector?1: Had an arrival delay of two or more hours
flights %>% filter(arr_delay >= 120)## # A tibble: 10,200 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 811 630 101 1047
## 2 2013 1 1 848 1835 853 1001
## 3 2013 1 1 957 733 144 1056
## 4 2013 1 1 1114 900 134 1447
## 5 2013 1 1 1505 1310 115 1638
## 6 2013 1 1 1525 1340 105 1831
## 7 2013 1 1 1549 1445 64 1912
## 8 2013 1 1 1558 1359 119 1718
## 9 2013 1 1 1732 1630 62 2028
## 10 2013 1 1 1803 1620 103 2008
## # … with 10,190 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>2: Flew to Houston (IAH or HOU)
flights %>% filter(dest == "IAH" | dest == "HOU")## # A tibble: 9,313 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 623 627 -4 933
## 4 2013 1 1 728 732 -4 1041
## 5 2013 1 1 739 739 0 1104
## 6 2013 1 1 908 908 0 1228
## 7 2013 1 1 1028 1026 2 1350
## 8 2013 1 1 1044 1045 -1 1352
## 9 2013 1 1 1114 900 134 1447
## 10 2013 1 1 1205 1200 5 1503
## # … with 9,303 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>3: Were operated by United, American, or Delta
flights %>% filter(carrier == "AA" | carrier == "DL" | carrier == "UA")## # A tibble: 139,504 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 554 600 -6 812
## 5 2013 1 1 554 558 -4 740
## 6 2013 1 1 558 600 -2 753
## 7 2013 1 1 558 600 -2 924
## 8 2013 1 1 558 600 -2 923
## 9 2013 1 1 559 600 -1 941
## 10 2013 1 1 559 600 -1 854
## # … with 139,494 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>4: Departed in summer (July, August, and September)
flights %>% filter(month == 7 | month == 8 | month == 9)## # A tibble: 86,326 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 7 1 1 2029 212 236
## 2 2013 7 1 2 2359 3 344
## 3 2013 7 1 29 2245 104 151
## 4 2013 7 1 43 2130 193 322
## 5 2013 7 1 44 2150 174 300
## 6 2013 7 1 46 2051 235 304
## 7 2013 7 1 48 2001 287 308
## 8 2013 7 1 58 2155 183 335
## 9 2013 7 1 100 2146 194 327
## 10 2013 7 1 100 2245 135 337
## # … with 86,316 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>5: Arrived more than two hours late, but didn’t leave late
flights %>% filter(arr_delay >= 120, dep_delay <= 0)## # A tibble: 29 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 27 1419 1420 -1 1754
## 2 2013 10 7 1350 1350 0 1736
## 3 2013 10 7 1357 1359 -2 1858
## 4 2013 10 16 657 700 -3 1258
## 5 2013 11 1 658 700 -2 1329
## 6 2013 3 18 1844 1847 -3 39
## 7 2013 4 17 1635 1640 -5 2049
## 8 2013 4 18 558 600 -2 1149
## 9 2013 4 18 655 700 -5 1213
## 10 2013 5 22 1827 1830 -3 2217
## # … with 19 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>6: Were delayed by at least an hour, but made up over 30 minutes in flight
flights %>% filter(arr_delay >= 60, dep_delay-arr_delay > 30)## # A tibble: 1,084 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 2205 1720 285 46
## 2 2013 1 1 2326 2130 116 131
## 3 2013 1 3 1503 1221 162 1803
## 4 2013 1 3 1839 1700 99 2056
## 5 2013 1 3 1941 1759 102 2246
## 6 2013 1 3 2257 2000 177 45
## 7 2013 1 4 1917 1700 137 2135
## 8 2013 1 4 2010 1745 145 2257
## 9 2013 1 4 2058 1730 208 2
## 10 2013 1 4 2100 1920 100 2224
## # … with 1,074 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>7: Departed between midnight and 6am (inclusive)
flights %>% filter(dep_time >= 2400 | dep_time <= 600)## # A tibble: 9,373 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # … with 9,363 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?flights %>% filter(between(month, 7, 9))## # A tibble: 86,326 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 7 1 1 2029 212 236
## 2 2013 7 1 2 2359 3 344
## 3 2013 7 1 29 2245 104 151
## 4 2013 7 1 43 2130 193 322
## 5 2013 7 1 44 2150 174 300
## 6 2013 7 1 46 2051 235 304
## 7 2013 7 1 48 2001 287 308
## 8 2013 7 1 58 2155 183 335
## 9 2013 7 1 100 2146 194 327
## 10 2013 7 1 100 2245 135 337
## # … with 86,316 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>flights %>% filter(!between(dep_time, 601, 2359))## # A tibble: 9,373 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # … with 9,363 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>dep_time? What other variables are missing? What might these rows represent?summary(flights)## year month day dep_time
## Min. :2013 Min. : 1.000 Min. : 1.00 Min. : 1
## 1st Qu.:2013 1st Qu.: 4.000 1st Qu.: 8.00 1st Qu.: 907
## Median :2013 Median : 7.000 Median :16.00 Median :1401
## Mean :2013 Mean : 6.549 Mean :15.71 Mean :1349
## 3rd Qu.:2013 3rd Qu.:10.000 3rd Qu.:23.00 3rd Qu.:1744
## Max. :2013 Max. :12.000 Max. :31.00 Max. :2400
## NA's :8255
## sched_dep_time dep_delay arr_time sched_arr_time
## Min. : 106 Min. : -43.00 Min. : 1 Min. : 1
## 1st Qu.: 906 1st Qu.: -5.00 1st Qu.:1104 1st Qu.:1124
## Median :1359 Median : -2.00 Median :1535 Median :1556
## Mean :1344 Mean : 12.64 Mean :1502 Mean :1536
## 3rd Qu.:1729 3rd Qu.: 11.00 3rd Qu.:1940 3rd Qu.:1945
## Max. :2359 Max. :1301.00 Max. :2400 Max. :2359
## NA's :8255 NA's :8713
## arr_delay carrier flight tailnum
## Min. : -86.000 Length:336776 Min. : 1 Length:336776
## 1st Qu.: -17.000 Class :character 1st Qu.: 553 Class :character
## Median : -5.000 Mode :character Median :1496 Mode :character
## Mean : 6.895 Mean :1972
## 3rd Qu.: 14.000 3rd Qu.:3465
## Max. :1272.000 Max. :8500
## NA's :9430
## origin dest air_time distance
## Length:336776 Length:336776 Min. : 20.0 Min. : 17
## Class :character Class :character 1st Qu.: 82.0 1st Qu.: 502
## Mode :character Mode :character Median :129.0 Median : 872
## Mean :150.7 Mean :1040
## 3rd Qu.:192.0 3rd Qu.:1389
## Max. :695.0 Max. :4983
## NA's :9430
## hour minute time_hour
## Min. : 1.00 Min. : 0.00 Min. :2013-01-01 05:00:00
## 1st Qu.: 9.00 1st Qu.: 8.00 1st Qu.:2013-04-04 13:00:00
## Median :13.00 Median :29.00 Median :2013-07-03 10:00:00
## Mean :13.18 Mean :26.23 Mean :2013-07-03 05:22:54
## 3rd Qu.:17.00 3rd Qu.:44.00 3rd Qu.:2013-10-01 07:00:00
## Max. :23.00 Max. :59.00 Max. :2013-12-31 23:00:00
## flights %>% filter(is.na(dep_time) | dep_time >= 0)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## 7 2013 1 1 555 600 -5 913
## 8 2013 1 1 557 600 -3 709
## 9 2013 1 1 557 600 -3 838
## 10 2013 1 1 558 600 -2 753
## # … with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>NA ^ 0 not missing? Why is NA | TRUE not missing? Why is FALSE & NA not missing? Can you figure out the general rule? (NA * 0 is a tricky counterexample!)arrange() to sort all missing values to the start? (Hint: use is.na()).flights %>% arrange(!is.na(dep_time), dep_time)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 NA 1630 NA NA
## 2 2013 1 1 NA 1935 NA NA
## 3 2013 1 1 NA 1500 NA NA
## 4 2013 1 1 NA 600 NA NA
## 5 2013 1 2 NA 1540 NA NA
## 6 2013 1 2 NA 1620 NA NA
## 7 2013 1 2 NA 1355 NA NA
## 8 2013 1 2 NA 1420 NA NA
## 9 2013 1 2 NA 1321 NA NA
## 10 2013 1 2 NA 1545 NA NA
## # … with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>flights to find the most delayed flights. Find the flights that left earliest.flights %>% arrange(desc(arr_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 9 641 900 1301 1242
## 2 2013 6 15 1432 1935 1137 1607
## 3 2013 1 10 1121 1635 1126 1239
## 4 2013 9 20 1139 1845 1014 1457
## 5 2013 7 22 845 1600 1005 1044
## 6 2013 4 10 1100 1900 960 1342
## 7 2013 3 17 2321 810 911 135
## 8 2013 7 22 2257 759 898 121
## 9 2013 12 5 756 1700 896 1058
## 10 2013 5 3 1133 2055 878 1250
## # … with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>flights %>% arrange(dep_delay)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 12 7 2040 2123 -43 40
## 2 2013 2 3 2022 2055 -33 2240
## 3 2013 11 10 1408 1440 -32 1549
## 4 2013 1 11 1900 1930 -30 2233
## 5 2013 1 29 1703 1730 -27 1947
## 6 2013 8 9 729 755 -26 1002
## 7 2013 10 23 1907 1932 -25 2143
## 8 2013 3 30 2030 2055 -25 2213
## 9 2013 3 2 1431 1455 -24 1601
## 10 2013 5 5 934 958 -24 1225
## # … with 336,766 more rows, and 12 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>flights %>% mutate(flight_time =
ifelse(((arr_time%/%100 * 60 + arr_time%%100)-
(dep_time%/%100 * 60 + dep_time%%100)) < 0,
((arr_time%/%100 * 60 + arr_time%%100)-
(dep_time%/%100 * 60 + dep_time%%100))+1440,
((arr_time%/%100 * 60 + arr_time%%100)-
(dep_time%/%100 * 60 + dep_time%%100)))) %>%
arrange(flight_time)## # A tibble: 336,776 x 20
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 8 14 1133 1148 -15 1206
## 2 2013 1 5 1323 1325 -2 1358
## 3 2013 2 22 1312 1316 -4 1347
## 4 2013 9 3 1203 1153 10 1238
## 5 2013 3 19 1455 1329 86 1531
## 6 2013 8 14 722 730 -8 758
## 7 2013 8 18 722 729 -7 758
## 8 2013 9 24 718 725 -7 754
## 9 2013 1 15 1326 1315 11 1403
## 10 2013 3 18 1456 1329 87 1533
## # … with 336,766 more rows, and 13 more variables: sched_arr_time <int>,
## # arr_delay <dbl>, carrier <chr>, flight <int>, tailnum <chr>,
## # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, flight_time <dbl>longest
(flights %>% arrange(desc(distance)))[1,]## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 857 900 -3 1516
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>shortest
(flights %>% arrange(distance))[1,]## # A tibble: 1 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 7 27 NA 106 NA NA
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>dep_time, dep_delay, arr_time, and arr_delay from flights.select(flights, dep_time, dep_delay, arr_time, arr_delay)
select(flights, c(dep_time, dep_delay, arr_time, arr_delay))
flights %>% select(dep_time, dep_delay, arr_time, arr_delay)
flights %>% select(c(dep_time, dep_delay, arr_time, arr_delay))select() call?flights %>% select(dep_time, dep_time)## # A tibble: 336,776 x 1
## dep_time
## <int>
## 1 517
## 2 533
## 3 542
## 4 544
## 5 554
## 6 554
## 7 555
## 8 557
## 9 557
## 10 558
## # … with 336,766 more rowsone_of() function do? Why might it be helpful in conjunction with this vector?## # A tibble: 336,776 x 5
## year month day dep_delay arr_delay
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 2 11
## 2 2013 1 1 4 20
## 3 2013 1 1 2 33
## 4 2013 1 1 -1 -18
## 5 2013 1 1 -6 -25
## 6 2013 1 1 -4 12
## 7 2013 1 1 -5 19
## 8 2013 1 1 -3 -14
## 9 2013 1 1 -3 -8
## 10 2013 1 1 -2 8
## # … with 336,766 more rowsselect(flights, contains("TIME"))