Exercises: 1-3 (Pgs. 49-50); 2-4 (Pg. 51); 2,4 (Pg. 54); 1-4 (Pg. 58)
Submission: Submit via an electronic document on Sakai. Must be submitted as a HTML file generated in RStudio. All assigned problems are chosen according to the textbook R for Data Science. You do not need R code to answer every question. If you answer without using R code, delete the code chunk. If the question requires R code, make sure you display R code. If the question requires a figure, make sure you display a figure. A lot of the questions can be answered in written response, but require R code and/or figures for understanding and explaining.
A. Flights that had an arrival delay of two or more hours:
filter(flights, arr_delay >= 120)## # A tibble: 10,200 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 811 630 101 1047 830
## 2 2013 1 1 848 1835 853 1001 1950
## 3 2013 1 1 957 733 144 1056 853
## 4 2013 1 1 1114 900 134 1447 1222
## 5 2013 1 1 1505 1310 115 1638 1431
## 6 2013 1 1 1525 1340 105 1831 1626
## 7 2013 1 1 1549 1445 64 1912 1656
## 8 2013 1 1 1558 1359 119 1718 1515
## 9 2013 1 1 1732 1630 62 2028 1825
## 10 2013 1 1 1803 1620 103 2008 1750
## # … with 10,190 more rows, and 11 more variables: 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>B. Flights that flew to Houston (IAH or HOU):
filter(flights, dest %in% c("IAH", "HOU"))## # A tibble: 9,313 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 623 627 -4 933 932
## 4 2013 1 1 728 732 -4 1041 1038
## 5 2013 1 1 739 739 0 1104 1038
## 6 2013 1 1 908 908 0 1228 1219
## 7 2013 1 1 1028 1026 2 1350 1339
## 8 2013 1 1 1044 1045 -1 1352 1351
## 9 2013 1 1 1114 900 134 1447 1222
## 10 2013 1 1 1205 1200 5 1503 1505
## # … with 9,303 more rows, and 11 more variables: 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>C. Flights that were operated by United, American, or Delta:
filter(flights, carrier %in% c("UA", "AA", "DA"))## # A tibble: 91,394 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 554 558 -4 740 728
## 5 2013 1 1 558 600 -2 753 745
## 6 2013 1 1 558 600 -2 924 917
## 7 2013 1 1 558 600 -2 923 937
## 8 2013 1 1 559 600 -1 941 910
## 9 2013 1 1 559 600 -1 854 902
## 10 2013 1 1 606 610 -4 858 910
## # … with 91,384 more rows, and 11 more variables: 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>D. Flights that departed in summer (July, August, and September):
filter(flights, month %in% c(7, 8, 9))## # A tibble: 86,326 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 7 1 1 2029 212 236 2359
## 2 2013 7 1 2 2359 3 344 344
## 3 2013 7 1 29 2245 104 151 1
## 4 2013 7 1 43 2130 193 322 14
## 5 2013 7 1 44 2150 174 300 100
## 6 2013 7 1 46 2051 235 304 2358
## 7 2013 7 1 48 2001 287 308 2305
## 8 2013 7 1 58 2155 183 335 43
## 9 2013 7 1 100 2146 194 327 30
## 10 2013 7 1 100 2245 135 337 135
## # … with 86,316 more rows, and 11 more variables: 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>E. Flights that arrived more than two hours late, but didn’t leave late:
filter(flights, arr_delay > 120 & dep_delay == 0)## # A tibble: 3 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 10 7 1350 1350 0 1736 1526
## 2 2013 5 23 1810 1810 0 2208 2000
## 3 2013 7 1 905 905 0 1443 1223
## # … with 11 more variables: 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>F. Flights that were delayed by at least an hour, but made up over 30 minutes in flight:
filter(flights, dep_delay >= 60 & dep_delay - arr_delay > 30)## # A tibble: 1,844 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 2205 1720 285 46 2040
## 2 2013 1 1 2326 2130 116 131 18
## 3 2013 1 3 1503 1221 162 1803 1555
## 4 2013 1 3 1839 1700 99 2056 1950
## 5 2013 1 3 1850 1745 65 2148 2120
## 6 2013 1 3 1941 1759 102 2246 2139
## 7 2013 1 3 1950 1845 65 2228 2227
## 8 2013 1 3 2015 1915 60 2135 2111
## 9 2013 1 3 2257 2000 177 45 2224
## 10 2013 1 4 1917 1700 137 2135 1950
## # … with 1,834 more rows, and 11 more variables: 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>G. Flights that departed between midnight and 6 a.m. (inclusive):
filter(flights, dep_time >= 2400 | dep_time <= 600)## # A tibble: 9,373 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## 7 2013 1 1 555 600 -5 913 854
## 8 2013 1 1 557 600 -3 709 723
## 9 2013 1 1 557 600 -3 838 846
## 10 2013 1 1 558 600 -2 753 745
## # … with 9,363 more rows, and 11 more variables: 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>The dplyr filtering helper between() is a shorter way of writing an inequality that has a minimum and maximum. It can be used to simplify previous challenges like Part G in Exercise 1.
filter(flights, between(dep_time, 600, 2400))## # A tibble: 319,791 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 600 600 0 851 858
## 2 2013 1 1 600 600 0 837 825
## 3 2013 1 1 601 600 1 844 850
## 4 2013 1 1 602 610 -8 812 820
## 5 2013 1 1 602 605 -3 821 805
## 6 2013 1 1 606 610 -4 858 910
## 7 2013 1 1 606 610 -4 837 845
## 8 2013 1 1 607 607 0 858 915
## 9 2013 1 1 608 600 8 807 735
## 10 2013 1 1 611 600 11 945 931
## # … with 319,781 more rows, and 11 more variables: 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>sum(is.na(flights$dep_time))## [1] 8255filter(flights, is.na(dep_time))## # A tibble: 8,255 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 NA 1630 NA NA 1815
## 2 2013 1 1 NA 1935 NA NA 2240
## 3 2013 1 1 NA 1500 NA NA 1825
## 4 2013 1 1 NA 600 NA NA 901
## 5 2013 1 2 NA 1540 NA NA 1747
## 6 2013 1 2 NA 1620 NA NA 1746
## 7 2013 1 2 NA 1355 NA NA 1459
## 8 2013 1 2 NA 1420 NA NA 1644
## 9 2013 1 2 NA 1321 NA NA 1536
## 10 2013 1 2 NA 1545 NA NA 1910
## # … with 8,245 more rows, and 11 more variables: 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>There are 8,255 flights with a missing dep_time. Flights missing a dep_time are also missing dep_delay, arr_time, arr_delay, and air_time. This missing information represents flights that were probably cancelled.
The code below sorts flights to find the most delayed:
arrange(flights, desc(dep_delay))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## 6 2013 4 10 1100 1900 960 1342 2211
## 7 2013 3 17 2321 810 911 135 1020
## 8 2013 6 27 959 1900 899 1236 2226
## 9 2013 7 22 2257 759 898 121 1026
## 10 2013 12 5 756 1700 896 1058 2020
## # … with 336,766 more rows, and 11 more variables: 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>The code below finds the flights that left the earliest:
arrange(flights, dep_delay)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 12 7 2040 2123 -43 40 2352
## 2 2013 2 3 2022 2055 -33 2240 2338
## 3 2013 11 10 1408 1440 -32 1549 1559
## 4 2013 1 11 1900 1930 -30 2233 2243
## 5 2013 1 29 1703 1730 -27 1947 1957
## 6 2013 8 9 729 755 -26 1002 955
## 7 2013 10 23 1907 1932 -25 2143 2143
## 8 2013 3 30 2030 2055 -25 2213 2250
## 9 2013 3 2 1431 1455 -24 1601 1631
## 10 2013 5 5 934 958 -24 1225 1309
## # … with 336,766 more rows, and 11 more variables: 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>The code below finds the fastest flights:
arrange(flights, air_time)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 16 1355 1315 40 1442 1411
## 2 2013 4 13 537 527 10 622 628
## 3 2013 12 6 922 851 31 1021 954
## 4 2013 2 3 2153 2129 24 2247 2224
## 5 2013 2 5 1303 1315 -12 1342 1411
## 6 2013 2 12 2123 2130 -7 2211 2225
## 7 2013 3 2 1450 1500 -10 1547 1608
## 8 2013 3 8 2026 1935 51 2131 2056
## 9 2013 3 18 1456 1329 87 1533 1426
## 10 2013 3 19 2226 2145 41 2305 2246
## # … with 336,766 more rows, and 11 more variables: 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>The code below finds the flights that traveled the longest:
arrange(flights, desc(distance))## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 1 857 900 -3 1516 1530
## 2 2013 1 2 909 900 9 1525 1530
## 3 2013 1 3 914 900 14 1504 1530
## 4 2013 1 4 900 900 0 1516 1530
## 5 2013 1 5 858 900 -2 1519 1530
## 6 2013 1 6 1019 900 79 1558 1530
## 7 2013 1 7 1042 900 102 1620 1530
## 8 2013 1 8 901 900 1 1504 1530
## 9 2013 1 9 641 900 1301 1242 1530
## 10 2013 1 10 859 900 -1 1449 1530
## # … with 336,766 more rows, and 11 more variables: 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>The code below finds the flights that traveled the shortest:
arrange(flights, distance)## # A tibble: 336,776 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 7 27 NA 106 NA NA 245
## 2 2013 1 3 2127 2129 -2 2222 2224
## 3 2013 1 4 1240 1200 40 1333 1306
## 4 2013 1 4 1829 1615 134 1937 1721
## 5 2013 1 4 2128 2129 -1 2218 2224
## 6 2013 1 5 1155 1200 -5 1241 1306
## 7 2013 1 6 2125 2129 -4 2224 2224
## 8 2013 1 7 2124 2129 -5 2212 2224
## 9 2013 1 8 2127 2130 -3 2304 2225
## 10 2013 1 9 2126 2129 -3 2217 2224
## # … with 336,766 more rows, and 11 more variables: 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>If you include the name of a variable multiple times in a select() call, the data for that variable is displayed once, but nothing else happens.
select(flights, air_time, air_time, air_time, air_time)## # A tibble: 336,776 x 1
## air_time
## <dbl>
## 1 227
## 2 227
## 3 160
## 4 183
## 5 116
## 6 150
## 7 158
## 8 53
## 9 140
## 10 138
## # … with 336,766 more rowsselect(flights, contains("TIME"))## # A tibble: 336,776 x 6
## dep_time sched_dep_time arr_time sched_arr_time air_time time_hour
## <int> <int> <int> <int> <dbl> <dttm>
## 1 517 515 830 819 227 2013-01-01 05:00:00
## 2 533 529 850 830 227 2013-01-01 05:00:00
## 3 542 540 923 850 160 2013-01-01 05:00:00
## 4 544 545 1004 1022 183 2013-01-01 05:00:00
## 5 554 600 812 837 116 2013-01-01 06:00:00
## 6 554 558 740 728 150 2013-01-01 05:00:00
## 7 555 600 913 854 158 2013-01-01 06:00:00
## 8 557 600 709 723 53 2013-01-01 06:00:00
## 9 557 600 838 846 140 2013-01-01 06:00:00
## 10 558 600 753 745 138 2013-01-01 06:00:00
## # … with 336,766 more rowsThe results do surprise me because even though our code searched for variables that contained “TIME”, the results showed variables that contained “time”. By default, the select helpers are not case sensitive. To change that default, you would include “ignore.case = FALSE”.
select(flights, contains("TIME", ignore.case = FALSE))## # A tibble: 336,776 x 0Nothing is returned because there are no variables that contain “TIME”.
The code below converts the dep_time to minutes.
transmute(flights, dep_time = (dep_time %/% 100) * 60 + (dep_time %% 100))## # A tibble: 336,776 x 1
## dep_time
## <dbl>
## 1 317
## 2 333
## 3 342
## 4 344
## 5 354
## 6 354
## 7 355
## 8 357
## 9 357
## 10 358
## # … with 336,766 more rowsThe code below converts sched_dep_time to minutes.
transmute(flights, sched_dep_time = (sched_dep_time %/% 100) * 60 + (sched_dep_time %% 100))## # A tibble: 336,776 x 1
## sched_dep_time
## <dbl>
## 1 315
## 2 329
## 3 340
## 4 345
## 5 360
## 6 358
## 7 360
## 8 360
## 9 360
## 10 360
## # … with 336,766 more rowsI expect air_time to be the difference between arr_time and dep_time => air_time = arr_time - dep_time. I see that air_time is in a minutes format, but arr_time and dep_time are both in a time format (hour:minute). I need to convert the arr_time and dep_time to minutes after midnight so I can test my expectation.
transmute(flights, arr_time, arr_time = (arr_time %/% 100) * 60 + (arr_time %% 100))## # A tibble: 336,776 x 1
## arr_time
## <dbl>
## 1 510
## 2 530
## 3 563
## 4 604
## 5 492
## 6 460
## 7 553
## 8 429
## 9 518
## 10 473
## # … with 336,766 more rowstransmute(flights, dep_time, dep_time = (dep_time %/% 100) * 60 + (dep_time %% 100))## # A tibble: 336,776 x 1
## dep_time
## <dbl>
## 1 317
## 2 333
## 3 342
## 4 344
## 5 354
## 6 354
## 7 355
## 8 357
## 9 357
## 10 358
## # … with 336,766 more rowstransmute(flights, difference = ((arr_time %/% 100) * 60 + (arr_time %% 100)) - ((dep_time %/% 100) * 60 + (dep_time %% 100)))## # A tibble: 336,776 x 1
## difference
## <dbl>
## 1 193
## 2 197
## 3 221
## 4 260
## 5 138
## 6 106
## 7 198
## 8 72
## 9 161
## 10 115
## # … with 336,766 more rowsselect(flights, air_time)## # A tibble: 336,776 x 1
## air_time
## <dbl>
## 1 227
## 2 227
## 3 160
## 4 183
## 5 116
## 6 150
## 7 158
## 8 53
## 9 140
## 10 138
## # … with 336,766 more rowsI converted the arr_time and dep_time to minutes after midnight and then I created a variable (difference) to find the difference between the two converted variables. The values for difference were not the same values for air_time. I am not quite sure why the difference would not match with air_time other than small factors like time on the terminal, time for people to board, time for people to deboard, etc. contributing to dep_time and arr_time.
I expect dep_time to be the sum of sched_dep_time and dep_delay => dep_time = sched_dep_time + dep_delay.
transmute(flights, sched_dep_time, sched_dep_time = (sched_dep_time %/% 100) * 60 + (sched_dep_time %% 100))## # A tibble: 336,776 x 1
## sched_dep_time
## <dbl>
## 1 315
## 2 329
## 3 340
## 4 345
## 5 360
## 6 358
## 7 360
## 8 360
## 9 360
## 10 360
## # … with 336,766 more rowstransmute(flights, sum = ((sched_dep_time %/% 100) * 60 + (sched_dep_time %% 100)) + dep_delay)## # A tibble: 336,776 x 1
## sum
## <dbl>
## 1 317
## 2 333
## 3 342
## 4 344
## 5 354
## 6 354
## 7 355
## 8 357
## 9 357
## 10 358
## # … with 336,766 more rowstransmute(flights, dep_time, dep_time = (dep_time %/% 100) * 60 + (dep_time %% 100))## # A tibble: 336,776 x 1
## dep_time
## <dbl>
## 1 317
## 2 333
## 3 342
## 4 344
## 5 354
## 6 354
## 7 355
## 8 357
## 9 357
## 10 358
## # … with 336,766 more rowsI did the same thing as I did in the previous problem. I converted the sched_dep_time and dep_time into minutes after midnight because dep_delay was already in a minutes format. I created a variable (sum) to calculate the sched_dep_time plus dep_delay. The values for sum were exactly the same values for dep_time.
filter(flights, min_rank(desc(dep_delay)) <= 10)## # A tibble: 10 x 19
## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## <int> <int> <int> <int> <int> <dbl> <int> <int>
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 1 10 1121 1635 1126 1239 1810
## 3 2013 12 5 756 1700 896 1058 2020
## 4 2013 3 17 2321 810 911 135 1020
## 5 2013 4 10 1100 1900 960 1342 2211
## 6 2013 6 15 1432 1935 1137 1607 2120
## 7 2013 6 27 959 1900 899 1236 2226
## 8 2013 7 22 845 1600 1005 1044 1815
## 9 2013 7 22 2257 759 898 121 1026
## 10 2013 9 20 1139 1845 1014 1457 2210
## # … with 11 more variables: 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>If there was a tie, I think there would be more than 10 results, so I would keep the code the same because 1) I haven’t learned how to deal with ties and 2) it’s probably better to show all of the results than to limit them.