Wk 3 Assignment: Data Transformation from the Hands-on Programming
with R
title: "Untitled"
author: "Suma Pendyala"
date: "6/7/2020"
output: html_document
5.1.1 Prerequisites
library(nycflights13)
library(tidyverse)
## -- Attaching packages ------------------------------------------------------------------------------- tidyverse 1.3.0 --
## v ggplot2 3.3.1 v purrr 0.3.4
## v tibble 3.0.1 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0
## -- Conflicts ---------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
5.1.2 nycflights13
## # 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 1 517 515 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 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>
#> # 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 1 517 515 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
#> # . with 336,770 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>
5.2 Filter rows with filter()
filter(flights, month == 1, day == 1)
## # A tibble: 842 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 832 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>
#> # A tibble: 842 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
#> # . with 836 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>
jan1 <- filter(flights, month == 1, day == 1)
(dec25 <- filter(flights, month == 12, day == 25))
## # A tibble: 719 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 25 456 500 -4 649 651
## 2 2013 12 25 524 515 9 805 814
## 3 2013 12 25 542 540 2 832 850
## 4 2013 12 25 546 550 -4 1022 1027
## 5 2013 12 25 556 600 -4 730 745
## 6 2013 12 25 557 600 -3 743 752
## 7 2013 12 25 557 600 -3 818 831
## 8 2013 12 25 559 600 -1 855 856
## 9 2013 12 25 559 600 -1 849 855
## 10 2013 12 25 600 600 0 850 846
## # ... with 709 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>
#> # A tibble: 719 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 25 456 500 -4 649 651
#> 2 2013 12 25 524 515 9 805 814
#> 3 2013 12 25 542 540 2 832 850
#> 4 2013 12 25 546 550 -4 1022 1027
#> 5 2013 12 25 556 600 -4 730 745
#> 6 2013 12 25 557 600 -3 743 752
#> # . with 713 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>
5.2.1 Comparisons
filter(flights, month == 1)
## # A tibble: 27,004 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 26,994 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>
#> [1] FALSE
1 / 49 * 49 == 1
#> [1] TRUE
near(1 / 49 * 49, 1)
5.2.2 Logical operators
filter(flights, month == 11 | month == 12)
## # A tibble: 55,403 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 11 1 5 2359 6 352 345
## 2 2013 11 1 35 2250 105 123 2356
## 3 2013 11 1 455 500 -5 641 651
## 4 2013 11 1 539 545 -6 856 827
## 5 2013 11 1 542 545 -3 831 855
## 6 2013 11 1 549 600 -11 912 923
## 7 2013 11 1 550 600 -10 705 659
## 8 2013 11 1 554 600 -6 659 701
## 9 2013 11 1 554 600 -6 826 827
## 10 2013 11 1 554 600 -6 749 751
## # ... with 55,393 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>
nov_dec <- filter(flights, month %in% c(11, 12))
filter(flights, !(arr_delay > 120 | dep_delay > 120))
## # A tibble: 316,050 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 316,040 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>
filter(flights, arr_delay <= 120, dep_delay <= 120)
## # A tibble: 316,050 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 316,040 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>
5.2.3 Missing values
# Let x be Mary's age. We don't know how old she is.
x <- NA
# Let y be John's age. We don't know how old he is.
y <- NA
# Are John and Mary the same age?
x == y
#> [1] NA
# We don't know!
is.na(x)
df <- tibble(x = c(1, NA, 3))
filter(df, x > 1)
## # A tibble: 1 x 1
## x
## <dbl>
## 1 3
#> # A tibble: 1 x 1
#> x
#> <dbl>
#> 1 3
filter(df, is.na(x) | x > 1)
## # A tibble: 2 x 1
## x
## <dbl>
## 1 NA
## 2 3
#> # A tibble: 2 x 1
#> x
#> <dbl>
#> 1 NA
#> 2 3
5.3 Arrange rows with arrange()
arrange(flights, year, month, day)
## # 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 1 517 515 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 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>
#> # 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 1 517 515 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
#> # . with 336,770 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>
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>
#> # 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
#> # . with 336,770 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>
df <- tibble(x = c(5, 2, NA))
arrange(df, x)
## # A tibble: 3 x 1
## x
## <dbl>
## 1 2
## 2 5
## 3 NA
#> # A tibble: 3 x 1
#> x
#> <dbl>
#> 1 2
#> 2 5
#> 3 NA
arrange(df, desc(x))
## # A tibble: 3 x 1
## x
## <dbl>
## 1 5
## 2 2
## 3 NA
#> # A tibble: 3 x 1
#> x
#> <dbl>
#> 1 5
#> 2 2
#> 3 NA
5.4 Select columns with select()
# Select columns by name
select(flights, year, month, day)
## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # ... with 336,766 more rows
#> # A tibble: 336,776 x 3
#> year month day
#> <int> <int> <int>
#> 1 2013 1 1
#> 2 2013 1 1
#> 3 2013 1 1
#> 4 2013 1 1
#> 5 2013 1 1
#> 6 2013 1 1
#> # . with 336,770 more rows
# Select all columns between year and day (inclusive)
select(flights, year:day)
## # A tibble: 336,776 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
## 7 2013 1 1
## 8 2013 1 1
## 9 2013 1 1
## 10 2013 1 1
## # ... with 336,766 more rows
#> # A tibble: 336,776 x 3
#> year month day
#> <int> <int> <int>
#> 1 2013 1 1
#> 2 2013 1 1
#> 3 2013 1 1
#> 4 2013 1 1
#> 5 2013 1 1
#> 6 2013 1 1
#> # . with 336,770 more rows
# Select all columns except those from year to day (inclusive)
select(flights, -(year:day))
## # A tibble: 336,776 x 16
## dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
## <int> <int> <dbl> <int> <int> <dbl> <chr>
## 1 517 515 2 830 819 11 UA
## 2 533 529 4 850 830 20 UA
## 3 542 540 2 923 850 33 AA
## 4 544 545 -1 1004 1022 -18 B6
## 5 554 600 -6 812 837 -25 DL
## 6 554 558 -4 740 728 12 UA
## 7 555 600 -5 913 854 19 B6
## 8 557 600 -3 709 723 -14 EV
## 9 557 600 -3 838 846 -8 B6
## 10 558 600 -2 753 745 8 AA
## # ... with 336,766 more rows, and 9 more variables: flight <int>,
## # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>,
## # hour <dbl>, minute <dbl>, time_hour <dttm>
#> # A tibble: 336,776 x 16
#> dep_time sched_dep_time dep_delay arr_time sched_arr_time arr_delay carrier
#> <int> <int> <dbl> <int> <int> <dbl> <chr>
#> 1 517 515 2 830 819 11 UA
#> 2 533 529 4 850 830 20 UA
#> 3 542 540 2 923 850 33 AA
#> 4 544 545 -1 1004 1022 -18 B6
#> 5 554 600 -6 812 837 -25 DL
#> 6 554 558 -4 740 728 12 UA
#> # . with 336,770 more rows, and 9 more variables: flight <int>, tailnum <chr>,
#> # origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
#> # minute <dbl>, time_hour <dttm>
rename(flights, tail_num = tailnum)
## # 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 1 517 515 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 336,766 more rows, and 11 more variables: arr_delay <dbl>,
## # carrier <chr>, flight <int>, tail_num <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>
#> # 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 1 517 515 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
#> # . with 336,770 more rows, and 11 more variables: arr_delay <dbl>,
#> # carrier <chr>, flight <int>, tail_num <chr>, origin <chr>, dest <chr>,
#> # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>, time_hour <dttm>
select(flights, time_hour, air_time, everything())
## # A tibble: 336,776 x 19
## time_hour air_time year month day dep_time sched_dep_time
## <dttm> <dbl> <int> <int> <int> <int> <int>
## 1 2013-01-01 05:00:00 227 2013 1 1 517 515
## 2 2013-01-01 05:00:00 227 2013 1 1 533 529
## 3 2013-01-01 05:00:00 160 2013 1 1 542 540
## 4 2013-01-01 05:00:00 183 2013 1 1 544 545
## 5 2013-01-01 06:00:00 116 2013 1 1 554 600
## 6 2013-01-01 05:00:00 150 2013 1 1 554 558
## 7 2013-01-01 06:00:00 158 2013 1 1 555 600
## 8 2013-01-01 06:00:00 53 2013 1 1 557 600
## 9 2013-01-01 06:00:00 140 2013 1 1 557 600
## 10 2013-01-01 06:00:00 138 2013 1 1 558 600
## # ... with 336,766 more rows, and 12 more variables: dep_delay <dbl>,
## # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
## # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
## # hour <dbl>, minute <dbl>
#> # A tibble: 336,776 x 19
#> time_hour air_time year month day dep_time sched_dep_time
#> <dttm> <dbl> <int> <int> <int> <int> <int>
#> 1 2013-01-01 05:00:00 227 2013 1 1 517 515
#> 2 2013-01-01 05:00:00 227 2013 1 1 533 529
#> 3 2013-01-01 05:00:00 160 2013 1 1 542 540
#> 4 2013-01-01 05:00:00 183 2013 1 1 544 545
#> 5 2013-01-01 06:00:00 116 2013 1 1 554 600
#> 6 2013-01-01 05:00:00 150 2013 1 1 554 558
#> # . with 336,770 more rows, and 12 more variables: dep_delay <dbl>,
#> # arr_time <int>, sched_arr_time <int>, arr_delay <dbl>, carrier <chr>,
#> # flight <int>, tailnum <chr>, origin <chr>, dest <chr>, distance <dbl>,
#> # hour <dbl>, minute <dbl>
5.5 Add new variables with mutate()
flights_sml <- select(flights,
year:day,
ends_with("delay"),
distance,
air_time
)
mutate(flights_sml,
gain = dep_delay - arr_delay,
speed = distance / air_time * 60
)
## # A tibble: 336,776 x 9
## year month day dep_delay arr_delay distance air_time gain speed
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227 -9 370.
## 2 2013 1 1 4 20 1416 227 -16 374.
## 3 2013 1 1 2 33 1089 160 -31 408.
## 4 2013 1 1 -1 -18 1576 183 17 517.
## 5 2013 1 1 -6 -25 762 116 19 394.
## 6 2013 1 1 -4 12 719 150 -16 288.
## 7 2013 1 1 -5 19 1065 158 -24 404.
## 8 2013 1 1 -3 -14 229 53 11 259.
## 9 2013 1 1 -3 -8 944 140 5 405.
## 10 2013 1 1 -2 8 733 138 -10 319.
## # ... with 336,766 more rows
#> # A tibble: 336,776 x 9
#> year month day dep_delay arr_delay distance air_time gain speed
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2013 1 1 2 11 1400 227 -9 370.
#> 2 2013 1 1 4 20 1416 227 -16 374.
#> 3 2013 1 1 2 33 1089 160 -31 408.
#> 4 2013 1 1 -1 -18 1576 183 17 517.
#> 5 2013 1 1 -6 -25 762 116 19 394.
#> 6 2013 1 1 -4 12 719 150 -16 288.
#> # . with 336,770 more rows
mutate(flights_sml,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)
## # A tibble: 336,776 x 10
## year month day dep_delay arr_delay distance air_time gain hours
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227 -9 3.78
## 2 2013 1 1 4 20 1416 227 -16 3.78
## 3 2013 1 1 2 33 1089 160 -31 2.67
## 4 2013 1 1 -1 -18 1576 183 17 3.05
## 5 2013 1 1 -6 -25 762 116 19 1.93
## 6 2013 1 1 -4 12 719 150 -16 2.5
## 7 2013 1 1 -5 19 1065 158 -24 2.63
## 8 2013 1 1 -3 -14 229 53 11 0.883
## 9 2013 1 1 -3 -8 944 140 5 2.33
## 10 2013 1 1 -2 8 733 138 -10 2.3
## # ... with 336,766 more rows, and 1 more variable: gain_per_hour <dbl>
#> # A tibble: 336,776 x 10
#> year month day dep_delay arr_delay distance air_time gain hours
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2013 1 1 2 11 1400 227 -9 3.78
#> 2 2013 1 1 4 20 1416 227 -16 3.78
#> 3 2013 1 1 2 33 1089 160 -31 2.67
#> 4 2013 1 1 -1 -18 1576 183 17 3.05
#> 5 2013 1 1 -6 -25 762 116 19 1.93
#> 6 2013 1 1 -4 12 719 150 -16 2.5
#> # . with 336,770 more rows, and 1 more variable: gain_per_hour <dbl>
transmute(flights,
gain = dep_delay - arr_delay,
hours = air_time / 60,
gain_per_hour = gain / hours
)
## # A tibble: 336,776 x 3
## gain hours gain_per_hour
## <dbl> <dbl> <dbl>
## 1 -9 3.78 -2.38
## 2 -16 3.78 -4.23
## 3 -31 2.67 -11.6
## 4 17 3.05 5.57
## 5 19 1.93 9.83
## 6 -16 2.5 -6.4
## 7 -24 2.63 -9.11
## 8 11 0.883 12.5
## 9 5 2.33 2.14
## 10 -10 2.3 -4.35
## # ... with 336,766 more rows
#> # A tibble: 336,776 x 3
#> gain hours gain_per_hour
#> <dbl> <dbl> <dbl>
#> 1 -9 3.78 -2.38
#> 2 -16 3.78 -4.23
#> 3 -31 2.67 -11.6
#> 4 17 3.05 5.57
#> 5 19 1.93 9.83
#> 6 -16 2.5 -6.4
#> # . with 336,770 more rows
5.5.1 Useful creation functions
transmute(flights,
dep_time,
hour = dep_time %/% 100,
minute = dep_time %% 100
)
## # A tibble: 336,776 x 3
## dep_time hour minute
## <int> <dbl> <dbl>
## 1 517 5 17
## 2 533 5 33
## 3 542 5 42
## 4 544 5 44
## 5 554 5 54
## 6 554 5 54
## 7 555 5 55
## 8 557 5 57
## 9 557 5 57
## 10 558 5 58
## # ... with 336,766 more rows
#> # A tibble: 336,776 x 3
#> dep_time hour minute
#> <int> <dbl> <dbl>
#> 1 517 5 17
#> 2 533 5 33
#> 3 542 5 42
#> 4 544 5 44
#> 5 554 5 54
#> 6 554 5 54
#> # . with 336,770 more rows
## [1] 1 2 3 4 5 6 7 8 9 10
#> [1] 1 2 3 4 5 6 7 8 9 10
lag(x)
## [1] NA 1 2 3 4 5 6 7 8 9
#> [1] NA 1 2 3 4 5 6 7 8 9
lead(x)
## [1] 2 3 4 5 6 7 8 9 10 NA
#> [1] 2 3 4 5 6 7 8 9 10 NA
## [1] 1 2 3 4 5 6 7 8 9 10
#> [1] 1 2 3 4 5 6 7 8 9 10
cumsum(x)
## [1] 1 3 6 10 15 21 28 36 45 55
#> [1] 1 3 6 10 15 21 28 36 45 55
cummean(x)
## [1] 1.000000 1.000000 1.333333 1.750000 2.200000 2.666667 3.142857 3.625000
## [9] 4.111111 4.600000
#> [1] 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
y <- c(1, 2, 2, NA, 3, 4)
min_rank(y)
#> [1] 1 2 2 NA 4 5
min_rank(desc(y))
#> [1] 1 2 3 NA 4 5
dense_rank(y)
#> [1] 1 2 2 NA 3 4
percent_rank(y)
## [1] 0.00 0.25 0.25 NA 0.75 1.00
#> [1] 0.00 0.25 0.25 NA 0.75 1.00
cume_dist(y)
## [1] 0.2 0.6 0.6 NA 0.8 1.0
#> [1] 0.2 0.6 0.6 NA 0.8 1.0
summarise(flights, delay = mean(dep_delay, na.rm = TRUE))
## # A tibble: 1 x 1
## delay
## <dbl>
## 1 12.6
#> # A tibble: 1 x 1
#> delay
#> <dbl>
#> 1 12.6
by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day delay
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.55
## 9 2013 1 9 2.28
## 10 2013 1 10 2.84
## # ... with 355 more rows
#> # A tibble: 365 x 4
#> # Groups: year, month [12]
#> year month day delay
#> <int> <int> <int> <dbl>
#> 1 2013 1 1 11.5
#> 2 2013 1 2 13.9
#> 3 2013 1 3 11.0
#> 4 2013 1 4 8.95
#> 5 2013 1 5 5.73
#> 6 2013 1 6 7.15
#> # . with 359 more rows
5.6.1 Combining multiple operations with the pipe
by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
)
## `summarise()` ungrouping output (override with `.groups` argument)
delay <- filter(delay, count > 20, dest != "HNL")
# It looks like delays increase with distance up to ~750 miles
# and then decrease. Maybe as flights get longer there's more
# ability to make up delays in the air?
ggplot(data = delay, mapping = aes(x = dist, y = delay)) +
geom_point(aes(size = count), alpha = 1/3) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#> `geom_smooth()` using method = 'loess' and formula 'y ~ x'
delays <- flights %>%
group_by(dest) %>%
summarise(
count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)
) %>%
filter(count > 20, dest != "HNL")
## `summarise()` ungrouping output (override with `.groups` argument)
5.6.2 Missing values
flights %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay))
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 NA
## 2 2013 1 2 NA
## 3 2013 1 3 NA
## 4 2013 1 4 NA
## 5 2013 1 5 NA
## 6 2013 1 6 NA
## 7 2013 1 7 NA
## 8 2013 1 8 NA
## 9 2013 1 9 NA
## 10 2013 1 10 NA
## # ... with 355 more rows
#> # A tibble: 365 x 4
#> # Groups: year, month [12]
#> year month day mean
#> <int> <int> <int> <dbl>
#> 1 2013 1 1 NA
#> 2 2013 1 2 NA
#> 3 2013 1 3 NA
#> 4 2013 1 4 NA
#> 5 2013 1 5 NA
#> 6 2013 1 6 NA
#> # . with 359 more rows
flights %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay, na.rm = TRUE))
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.55
## 9 2013 1 9 2.28
## 10 2013 1 10 2.84
## # ... with 355 more rows
#> # A tibble: 365 x 4
#> # Groups: year, month [12]
#> year month day mean
#> <int> <int> <int> <dbl>
#> 1 2013 1 1 11.5
#> 2 2013 1 2 13.9
#> 3 2013 1 3 11.0
#> 4 2013 1 4 8.95
#> 5 2013 1 5 5.73
#> 6 2013 1 6 7.15
#> # . with 359 more rows
not_cancelled <- flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay))
not_cancelled %>%
group_by(year, month, day) %>%
summarise(mean = mean(dep_delay))
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day mean
## <int> <int> <int> <dbl>
## 1 2013 1 1 11.4
## 2 2013 1 2 13.7
## 3 2013 1 3 10.9
## 4 2013 1 4 8.97
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
## 7 2013 1 7 5.42
## 8 2013 1 8 2.56
## 9 2013 1 9 2.30
## 10 2013 1 10 2.84
## # ... with 355 more rows
#> # A tibble: 365 x 4
#> # Groups: year, month [12]
#> year month day mean
#> <int> <int> <int> <dbl>
#> 1 2013 1 1 11.4
#> 2 2013 1 2 13.7
#> 3 2013 1 3 10.9
#> 4 2013 1 4 8.97
#> 5 2013 1 5 5.73
#> 6 2013 1 6 7.15
#> # . with 359 more rows
5.6.3 Counts
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
)
## `summarise()` ungrouping output (override with `.groups` argument)
ggplot(data = delays, mapping = aes(x = delay)) +
geom_freqpoly(binwidth = 10)
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay, na.rm = TRUE),
n = n()
)
## `summarise()` ungrouping output (override with `.groups` argument)
ggplot(data = delays, mapping = aes(x = n, y = delay)) +
geom_point(alpha = 1/10)
delays %>%
filter(n > 25) %>%
ggplot(mapping = aes(x = n, y = delay)) +
geom_point(alpha = 1/10)
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay)
)
## `summarise()` ungrouping output (override with `.groups` argument)
ggplot(data = delays, mapping = aes(x = delay)) +
geom_freqpoly(binwidth = 10)
delays <- not_cancelled %>%
group_by(tailnum) %>%
summarise(
delay = mean(arr_delay, na.rm = TRUE),
n = n()
)
## `summarise()` ungrouping output (override with `.groups` argument)
ggplot(data = delays, mapping = aes(x = n, y = delay)) +
geom_point(alpha = 1/10)
delays %>%
filter(n > 25) %>%
ggplot(mapping = aes(x = n, y = delay)) +
geom_point(alpha = 1/10)
# Convert to a tibble so it prints nicely
batting <- as_tibble(Lahman::Batting)
## Error in loadNamespace(name): there is no package called 'Lahman'
batters <- batting %>%
group_by(playerID) %>%
summarise(
ba = sum(H, na.rm = TRUE) / sum(AB, na.rm = TRUE),
ab = sum(AB, na.rm = TRUE)
)
## Error in eval(lhs, parent, parent): object 'batting' not found
batters %>%
filter(ab > 100) %>%
ggplot(mapping = aes(x = ab, y = ba)) +
geom_point() +
geom_smooth(se = FALSE)
## Error in eval(lhs, parent, parent): object 'batters' not found
#> `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
5.6.4 Useful summary functions
not_cancelled %>%
group_by(year, month, day) %>%
summarise(
avg_delay1 = mean(arr_delay),
avg_delay2 = mean(arr_delay[arr_delay > 0]) # the average positive delay
)
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 5
## # Groups: year, month [12]
## year month day avg_delay1 avg_delay2
## <int> <int> <int> <dbl> <dbl>
## 1 2013 1 1 12.7 32.5
## 2 2013 1 2 12.7 32.0
## 3 2013 1 3 5.73 27.7
## 4 2013 1 4 -1.93 28.3
## 5 2013 1 5 -1.53 22.6
## 6 2013 1 6 4.24 24.4
## 7 2013 1 7 -4.95 27.8
## 8 2013 1 8 -3.23 20.8
## 9 2013 1 9 -0.264 25.6
## 10 2013 1 10 -5.90 27.3
## # ... with 355 more rows
#> # A tibble: 365 x 5
#> # Groups: year, month [12]
#> year month day avg_delay1 avg_delay2
#> <int> <int> <int> <dbl> <dbl>
#> 1 2013 1 1 12.7 32.5
#> 2 2013 1 2 12.7 32.0
#> 3 2013 1 3 5.73 27.7
#> 4 2013 1 4 -1.93 28.3
#> 5 2013 1 5 -1.53 22.6
#> 6 2013 1 6 4.24 24.4
#> # . with 359 more rows
# Why is distance to some destinations more variable than to others?
not_cancelled %>%
group_by(dest) %>%
summarise(distance_sd = sd(distance)) %>%
arrange(desc(distance_sd))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 104 x 2
## dest distance_sd
## <chr> <dbl>
## 1 EGE 10.5
## 2 SAN 10.4
## 3 SFO 10.2
## 4 HNL 10.0
## 5 SEA 9.98
## 6 LAS 9.91
## 7 PDX 9.87
## 8 PHX 9.86
## 9 LAX 9.66
## 10 IND 9.46
## # ... with 94 more rows
#> # A tibble: 104 x 2
#> dest distance_sd
#> <chr> <dbl>
#> 1 EGE 10.5
#> 2 SAN 10.4
#> 3 SFO 10.2
#> 4 HNL 10.0
#> 5 SEA 9.98
#> 6 LAS 9.91
#> # . with 98 more rows
# When do the first and last flights leave each day?
not_cancelled %>%
group_by(year, month, day) %>%
summarise(
first = min(dep_time),
last = max(dep_time)
)
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 5
## # Groups: year, month [12]
## year month day first last
## <int> <int> <int> <int> <int>
## 1 2013 1 1 517 2356
## 2 2013 1 2 42 2354
## 3 2013 1 3 32 2349
## 4 2013 1 4 25 2358
## 5 2013 1 5 14 2357
## 6 2013 1 6 16 2355
## 7 2013 1 7 49 2359
## 8 2013 1 8 454 2351
## 9 2013 1 9 2 2252
## 10 2013 1 10 3 2320
## # ... with 355 more rows
#> # A tibble: 365 x 5
#> # Groups: year, month [12]
#> year month day first last
#> <int> <int> <int> <int> <int>
#> 1 2013 1 1 517 2356
#> 2 2013 1 2 42 2354
#> 3 2013 1 3 32 2349
#> 4 2013 1 4 25 2358
#> 5 2013 1 5 14 2357
#> 6 2013 1 6 16 2355
#> # . with 359 more rows
# When do the first and last flights leave each day?
not_cancelled %>%
group_by(year, month, day) %>%
summarise(
first = min(dep_time),
last = max(dep_time)
)
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 5
## # Groups: year, month [12]
## year month day first last
## <int> <int> <int> <int> <int>
## 1 2013 1 1 517 2356
## 2 2013 1 2 42 2354
## 3 2013 1 3 32 2349
## 4 2013 1 4 25 2358
## 5 2013 1 5 14 2357
## 6 2013 1 6 16 2355
## 7 2013 1 7 49 2359
## 8 2013 1 8 454 2351
## 9 2013 1 9 2 2252
## 10 2013 1 10 3 2320
## # ... with 355 more rows
#> # A tibble: 365 x 5
#> # Groups: year, month [12]
#> year month day first last
#> <int> <int> <int> <int> <int>
#> 1 2013 1 1 517 2356
#> 2 2013 1 2 42 2354
#> 3 2013 1 3 32 2349
#> 4 2013 1 4 25 2358
#> 5 2013 1 5 14 2357
#> 6 2013 1 6 16 2355
#> # . with 359 more rows
5.6.5 Grouping by multiple variables
daily <- group_by(flights, year, month, day)
(per_day <- summarise(daily, flights = n()))
## `summarise()` regrouping output by 'year', 'month' (override with `.groups` argument)
## # A tibble: 365 x 4
## # Groups: year, month [12]
## year month day flights
## <int> <int> <int> <int>
## 1 2013 1 1 842
## 2 2013 1 2 943
## 3 2013 1 3 914
## 4 2013 1 4 915
## 5 2013 1 5 720
## 6 2013 1 6 832
## 7 2013 1 7 933
## 8 2013 1 8 899
## 9 2013 1 9 902
## 10 2013 1 10 932
## # ... with 355 more rows
#> # A tibble: 365 x 4
#> # Groups: year, month [12]
#> year month day flights
#> <int> <int> <int> <int>
#> 1 2013 1 1 842
#> 2 2013 1 2 943
#> 3 2013 1 3 914
#> 4 2013 1 4 915
#> 5 2013 1 5 720
#> 6 2013 1 6 832
#> # . with 359 more rows
(per_month <- summarise(per_day, flights = sum(flights)))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
## # A tibble: 12 x 3
## # Groups: year [1]
## year month flights
## <int> <int> <int>
## 1 2013 1 27004
## 2 2013 2 24951
## 3 2013 3 28834
## 4 2013 4 28330
## 5 2013 5 28796
## 6 2013 6 28243
## 7 2013 7 29425
## 8 2013 8 29327
## 9 2013 9 27574
## 10 2013 10 28889
## 11 2013 11 27268
## 12 2013 12 28135
#> # A tibble: 12 x 3
#> # Groups: year [1]
#> year month flights
#> <int> <int> <int>
#> 1 2013 1 27004
#> 2 2013 2 24951
#> 3 2013 3 28834
#> 4 2013 4 28330
#> 5 2013 5 28796
#> 6 2013 6 28243
#> # . with 6 more rows
(per_year <- summarise(per_month, flights = sum(flights)))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 1 x 2
## year flights
## <int> <int>
## 1 2013 336776
#> # A tibble: 1 x 2
#> year flights
#> <int> <int>
#> 1 2013 336776
5.6.6 Ungrouping
daily %>%
ungroup() %>% # no longer grouped by date
summarise(flights = n()) # all flights
## # A tibble: 1 x 1
## flights
## <int>
## 1 336776
#> # A tibble: 1 x 1
#> flights
#> <int>
#> 1 336776
5.7 Grouped mutates (and filters)
flights_sml %>%
group_by(year, month, day) %>%
filter(rank(desc(arr_delay)) < 10)
## # A tibble: 3,306 x 7
## # Groups: year, month, day [365]
## year month day dep_delay arr_delay distance air_time
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 853 851 184 41
## 2 2013 1 1 290 338 1134 213
## 3 2013 1 1 260 263 266 46
## 4 2013 1 1 157 174 213 60
## 5 2013 1 1 216 222 708 121
## 6 2013 1 1 255 250 589 115
## 7 2013 1 1 285 246 1085 146
## 8 2013 1 1 192 191 199 44
## 9 2013 1 1 379 456 1092 222
## 10 2013 1 2 224 207 550 94
## # ... with 3,296 more rows
#> # A tibble: 3,306 x 7
#> # Groups: year, month, day [365]
#> year month day dep_delay arr_delay distance air_time
#> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
#> 1 2013 1 1 853 851 184 41
#> 2 2013 1 1 290 338 1134 213
#> 3 2013 1 1 260 263 266 46
#> 4 2013 1 1 157 174 213 60
#> 5 2013 1 1 216 222 708 121
#> 6 2013 1 1 255 250 589 115
#> # . with 3,300 more rows
popular_dests <- flights %>%
group_by(dest) %>%
filter(n() > 365)
popular_dests
## # A tibble: 332,577 x 19
## # Groups: dest [77]
## 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 332,567 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>
#> # A tibble: 332,577 x 19
#> # Groups: dest [77]
#> 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
#> # . with 332,571 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>
popular_dests %>%
filter(arr_delay > 0) %>%
mutate(prop_delay = arr_delay / sum(arr_delay)) %>%
select(year:day, dest, arr_delay, prop_delay)
## # A tibble: 131,106 x 6
## # Groups: dest [77]
## year month day dest arr_delay prop_delay
## <int> <int> <int> <chr> <dbl> <dbl>
## 1 2013 1 1 IAH 11 0.000111
## 2 2013 1 1 IAH 20 0.000201
## 3 2013 1 1 MIA 33 0.000235
## 4 2013 1 1 ORD 12 0.0000424
## 5 2013 1 1 FLL 19 0.0000938
## 6 2013 1 1 ORD 8 0.0000283
## 7 2013 1 1 LAX 7 0.0000344
## 8 2013 1 1 DFW 31 0.000282
## 9 2013 1 1 ATL 12 0.0000400
## 10 2013 1 1 DTW 16 0.000116
## # ... with 131,096 more rows
#> # A tibble: 131,106 x 6
#> # Groups: dest [77]
#> year month day dest arr_delay prop_delay
#> <int> <int> <int> <chr> <dbl> <dbl>
#> 1 2013 1 1 IAH 11 0.000111
#> 2 2013 1 1 IAH 20 0.000201
#> 3 2013 1 1 MIA 33 0.000235
#> 4 2013 1 1 ORD 12 0.0000424
#> 5 2013 1 1 FLL 19 0.0000938
#> 6 2013 1 1 ORD 8 0.0000283
#> # . with 131,100 more rows
5.2.4 Exercises
1 - Find all flights that
1. Had an arrival delay of two or more hours
library(tidyverse)
flights <- nycflights13::flights
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>
2. 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>
3. Were operated by United, American, or Delta
filter(flights, carrier %in% c('UA','AA','DL'))
## # A tibble: 139,504 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 1 554 600 -6 812 837
## 5 2013 1 1 554 558 -4 740 728
## 6 2013 1 1 558 600 -2 753 745
## 7 2013 1 1 558 600 -2 924 917
## 8 2013 1 1 558 600 -2 923 937
## 9 2013 1 1 559 600 -1 941 910
## 10 2013 1 1 559 600 -1 854 902
## # ... with 139,494 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>
filter(flights, month %in% c(7,8,9))
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>
5. Arrived more than two hours late, but didn't leave late
filter(flights, dep_delay <= 0, arr_delay >= 120)
## # A tibble: 29 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 27 1419 1420 -1 1754 1550
## 2 2013 10 7 1350 1350 0 1736 1526
## 3 2013 10 7 1357 1359 -2 1858 1654
## 4 2013 10 16 657 700 -3 1258 1056
## 5 2013 11 1 658 700 -2 1329 1015
## 6 2013 3 18 1844 1847 -3 39 2219
## 7 2013 4 17 1635 1640 -5 2049 1845
## 8 2013 4 18 558 600 -2 1149 850
## 9 2013 4 18 655 700 -5 1213 950
## 10 2013 5 22 1827 1830 -3 2217 2010
## # ... with 19 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>
6. 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>
7. Departed between midnight and 6am (inclusive)
filter(flights, dep_time >= 0, dep_time <= 600)
## # A tibble: 9,344 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 9,334 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>
2 - Another useful dplyr filtering helper is between(). What does it do? Can you use it to simplify the code needed to answer the previous challenges?
filter(flights, between(dep_time, 0 , 600))
## # A tibble: 9,344 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 9,334 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>
3 - How many flights have a missing dep_time? What other variables are missing? What might these rows represent?
sum(is.na(flights$dep_time))
filter(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>
4 - Why is 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!)
NA * 0 gives NA
5.3.1 Exercises
1 -How could you use arrange() to sort all missing values to the start? (Hint: use is.na()).
2 -Sort flights to find the most delayed flights. Find the flights that left earliest.
3 - Sort flights to find the fastest (highest speed) flights.
4 - Which flights travelled the farthest? Which travelled the shortest?
head(arrange(flights, desc(is.na(dep_delay))))
## # A tibble: 6 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
## # ... 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>
head(arrange(flights, desc(dep_delay)))
## # A tibble: 6 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
## # ... 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>
head(arrange(flights, dep_delay))
## # A tibble: 6 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
## # ... 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>
head(arrange(flights, air_time))
## # A tibble: 6 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
## # ... 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>
head(arrange(flights, desc(distance)))
## # A tibble: 6 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
## # ... 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>
head(arrange(flights, distance))
## # A tibble: 6 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
## # ... 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>
5.4.1 Exercises
1 -Brainstorm as many ways as possible to select dep_time, dep_delay, arr_time, and arr_delay from flights.
2 -What happens if you include the name of a variable multiple times in a select() call?
3 -What does the one_of() function do? Why might it be helpful in conjunction with this vector?
vars <- c("year", "month", "day", "dep_delay", "arr_delay")
4- Does the result of running the following code surprise you? How do the select helpers deal with case by default? How can you change that default?
select(flights, contains("TIME"))
select(flights, dep_time, dep_delay, arr_time, arr_delay)
## # A tibble: 336,776 x 4
## dep_time dep_delay arr_time arr_delay
## <int> <dbl> <int> <dbl>
## 1 517 2 830 11
## 2 533 4 850 20
## 3 542 2 923 33
## 4 544 -1 1004 -18
## 5 554 -6 812 -25
## 6 554 -4 740 12
## 7 555 -5 913 19
## 8 557 -3 709 -14
## 9 557 -3 838 -8
## 10 558 -2 753 8
## # ... with 336,766 more rows
select(flights, starts_with('dep'), starts_with('arr'))
## # A tibble: 336,776 x 4
## dep_time dep_delay arr_time arr_delay
## <int> <dbl> <int> <dbl>
## 1 517 2 830 11
## 2 533 4 850 20
## 3 542 2 923 33
## 4 544 -1 1004 -18
## 5 554 -6 812 -25
## 6 554 -4 740 12
## 7 555 -5 913 19
## 8 557 -3 709 -14
## 9 557 -3 838 -8
## 10 558 -2 753 8
## # ... with 336,766 more rows
select(flights, dep_time, 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 rows
vars <- c("year", "month", "day", "dep_delay", "arr_delay")
select(flights, one_of(vars))
## # 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 rows
select(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 rows
select(flights, contains("TIME", ignore.case = FALSE))
## # A tibble: 336,776 x 0
5.5.2 Exercises
1 -Currently dep_time and sched_dep_time are convenient to look at, but hard to compute with because they're not really continuous numbers. Convert them to a more convenient representation of number of minutes since midnight.
2 -Compare air_time with arr_time - dep_time. What do you expect to see? What do you see? What do you need to do to fix it?
3 - Compare dep_time, sched_dep_time, and dep_delay. How would you expect those three numbers to be related?
4 -Find the 10 most delayed flights using a ranking function. How do you want to handle ties? Carefully read the documentation for min_rank().
5 -What does 1:3 + 1:10 return? Why?
6 -What trigonometric functions does R provide?
flights <- mutate(flights,
dep_time_mins = dep_time %/% 100 * 60 + dep_time %% 100,
sched_dep_time_mins = sched_dep_time %/% 100 * 60 +
sched_dep_time %% 100)
select(flights, starts_with('dep_time'), starts_with('sched'))
## # A tibble: 336,776 x 5
## dep_time dep_time_mins sched_dep_time sched_arr_time sched_dep_time_mins
## <int> <dbl> <int> <int> <dbl>
## 1 517 317 515 819 315
## 2 533 333 529 830 329
## 3 542 342 540 850 340
## 4 544 344 545 1022 345
## 5 554 354 600 837 360
## 6 554 354 558 728 358
## 7 555 355 600 854 360
## 8 557 357 600 723 360
## 9 557 357 600 846 360
## 10 558 358 600 745 360
## # ... with 336,766 more rows
flights %>% mutate(flight_time = arr_time - dep_time) %>%
select(air_time, flight_time)
## # A tibble: 336,776 x 2
## air_time flight_time
## <dbl> <int>
## 1 227 313
## 2 227 317
## 3 160 381
## 4 183 460
## 5 116 258
## 6 150 186
## 7 158 358
## 8 53 152
## 9 140 281
## 10 138 195
## # ... with 336,766 more rows
flights <- mutate(flights,
arr_time_mins = arr_time %/% 100 * 60 + arr_time %% 100)
flights <- mutate(flights, flight_time = arr_time_mins - dep_time_mins)
select(flights, air_time, flight_time)
## # A tibble: 336,776 x 2
## air_time flight_time
## <dbl> <dbl>
## 1 227 193
## 2 227 197
## 3 160 221
## 4 183 260
## 5 116 138
## 6 150 106
## 7 158 198
## 8 53 72
## 9 140 161
## 10 138 115
## # ... with 336,766 more rows
sum(flights$air_time == flights$flight_time, na.rm = TRUE)
select(flights, dep_time, sched_dep_time, dep_delay)
## # A tibble: 336,776 x 3
## dep_time sched_dep_time dep_delay
## <int> <int> <dbl>
## 1 517 515 2
## 2 533 529 4
## 3 542 540 2
## 4 544 545 -1
## 5 554 600 -6
## 6 554 558 -4
## 7 555 600 -5
## 8 557 600 -3
## 9 557 600 -3
## 10 558 600 -2
## # ... with 336,766 more rows
head(arrange(flights, min_rank(desc(dep_delay))), 10)
## # A tibble: 10 x 23
## 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 15 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>, dep_time_mins <dbl>,
## # sched_dep_time_mins <dbl>, arr_time_mins <dbl>, flight_time <dbl>
## # A tibble: 10 x 23
1:3 + 1:10
## Warning in 1:3 + 1:10: longer object length is not a multiple of shorter object
## length
## [1] 2 4 6 5 7 9 8 10 12 11
## starting httpd help server ... done
5.6.7 Exercises
1 - Brainstorm at least 5 different ways to assess the typical delay characteristics of a group of flights. Consider the following scenarios:
A flight is 15 minutes early 50% of the time, and 15 minutes late 50% of the time.
A flight is always 10 minutes late.
A flight is 30 minutes early 50% of the time, and 30 minutes late 50% of the time.
99% of the time a flight is on time. 1% of the time it's 2 hours late.
Which is more important: arrival delay or departure delay?
2 - Come up with another approach that will give you the same output as not_cancelled %>% count(dest) and not_cancelled %>% count(tailnum, wt = distance) (without using count()).
3 -Our definition of cancelled flights (is.na(dep_delay) | is.na(arr_delay) ) is slightly suboptimal. Why? Which is the most important column?
4 - Look at the number of cancelled flights per day. Is there a pattern? Is the proportion of cancelled flights related to the average delay?
5 - Which carrier has the worst delays? Challenge: can you disentangle the effects of bad airports vs. bad carriers? Why/why not? (Hint: think about flights %>% group_by(carrier, dest) %>% summarise(n()))
6 -What does the sort argument to count() do. When might you use it?
flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay)) %>%
group_by(dest) %>% summarize(count = n())
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 104 x 2
## dest count
## <chr> <int>
## 1 ABQ 254
## 2 ACK 264
## 3 ALB 418
## 4 ANC 8
## 5 ATL 16837
## 6 AUS 2411
## 7 AVL 261
## 8 BDL 412
## 9 BGR 358
## 10 BHM 269
## # ... with 94 more rows
flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay)) %>%
group_by(tailnum) %>% summarize(count = sum(distance))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 4,037 x 2
## tailnum count
## <chr> <dbl>
## 1 D942DN 3418
## 2 N0EGMQ 239143
## 3 N10156 109664
## 4 N102UW 25722
## 5 N103US 24619
## 6 N104UW 24616
## 7 N10575 139903
## 8 N105UW 23618
## 9 N107US 21677
## 10 N108UW 32070
## # ... with 4,027 more rows
flights %>% select(starts_with("dep"), starts_with("arr")) %>%
sapply(function(x){sum(is.na(x))})
## dep_time dep_delay dep_time_mins arr_time arr_delay
## 8255 8255 8255 8713 9430
## arr_time_mins
## 8713
nrow(flights %>% filter(!is.na(dep_time), is.na(arr_time)))
flights %>% group_by(month, day) %>%
summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE),
prop_cancelled = sum(is.na(dep_time)/n())) %>%
ggplot(mapping = aes(x = avg_dep_delay, y = prop_cancelled)) +
geom_point() +
geom_smooth(method = 'lm', se = FALSE)
## `summarise()` regrouping output by 'month' (override with `.groups` argument)
## `geom_smooth()` using formula 'y ~ x'
worst <- flights %>% group_by(carrier) %>%
summarize(avg_arr_delay = mean(arr_delay, na.rm = TRUE),
avg_dep_delay = mean(dep_delay, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
arrange(worst, desc(avg_arr_delay))
## # A tibble: 16 x 3
## carrier avg_arr_delay avg_dep_delay
## <chr> <dbl> <dbl>
## 1 F9 21.9 20.2
## 2 FL 20.1 18.7
## 3 EV 15.8 20.0
## 4 YV 15.6 19.0
## 5 OO 11.9 12.6
## 6 MQ 10.8 10.6
## 7 WN 9.65 17.7
## 8 B6 9.46 13.0
## 9 9E 7.38 16.7
## 10 UA 3.56 12.1
## 11 US 2.13 3.78
## 12 VX 1.76 12.9
## 13 DL 1.64 9.26
## 14 AA 0.364 8.59
## 15 HA -6.92 4.90
## 16 AS -9.93 5.80
arrange(worst, desc(avg_dep_delay))
## # A tibble: 16 x 3
## carrier avg_arr_delay avg_dep_delay
## <chr> <dbl> <dbl>
## 1 F9 21.9 20.2
## 2 EV 15.8 20.0
## 3 YV 15.6 19.0
## 4 FL 20.1 18.7
## 5 WN 9.65 17.7
## 6 9E 7.38 16.7
## 7 B6 9.46 13.0
## 8 VX 1.76 12.9
## 9 OO 11.9 12.6
## 10 UA 3.56 12.1
## 11 MQ 10.8 10.6
## 12 DL 1.64 9.26
## 13 AA 0.364 8.59
## 14 AS -9.93 5.80
## 15 HA -6.92 4.90
## 16 US 2.13 3.78
flights %>% group_by(origin) %>%
summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 3 x 2
## origin avg_dep_delay
## <chr> <dbl>
## 1 EWR 15.1
## 2 JFK 12.1
## 3 LGA 10.3
flights %>% group_by(origin) %>%
summarize(avg_arr_delay = mean(arr_delay, na.rm = TRUE))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 3 x 2
## origin avg_arr_delay
## <chr> <dbl>
## 1 EWR 9.11
## 2 JFK 5.55
## 3 LGA 5.78
flights %>% group_by(carrier, origin) %>%
summarize(avg_dep_delay = mean(dep_delay, na.rm = TRUE)) %>%
filter(carrier == '9E')
## `summarise()` regrouping output by 'carrier' (override with `.groups` argument)
## # A tibble: 3 x 3
## # Groups: carrier [1]
## carrier origin avg_dep_delay
## <chr> <chr> <dbl>
## 1 9E EWR 5.95
## 2 9E JFK 19.0
## 3 9E LGA 8.89
flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay)) %>%
count(dest)
## # A tibble: 104 x 2
## dest n
## <chr> <int>
## 1 ABQ 254
## 2 ACK 264
## 3 ALB 418
## 4 ANC 8
## 5 ATL 16837
## 6 AUS 2411
## 7 AVL 261
## 8 BDL 412
## 9 BGR 358
## 10 BHM 269
## # ... with 94 more rows
flights %>%
filter(!is.na(dep_delay), !is.na(arr_delay)) %>%
count(dest, sort = TRUE)
## # A tibble: 104 x 2
## dest n
## <chr> <int>
## 1 ATL 16837
## 2 ORD 16566
## 3 LAX 16026
## 4 BOS 15022
## 5 MCO 13967
## 6 CLT 13674
## 7 SFO 13173
## 8 FLL 11897
## 9 MIA 11593
## 10 DCA 9111
## # ... with 94 more rows
5.7.1 Exercises
1-Refer back to the lists of useful mutate and filtering functions. Describe how each operation changes when you combine it with grouping.
2 - Which plane (tailnum) has the worst on-time record?
3 -What time of day should you fly if you want to avoid delays as much as possible?
4 - For each destination, compute the total minutes of delay. For each flight, compute the proportion of the total delay for its destination.
5 -Delays are typically temporally correlated: even once the problem that caused the initial delay has been resolved, later flights are delayed to allow earlier flights to leave. Using lag(), explore how the delay of a flight is related to the delay of the immediately preceding flight.
6 - Look at each destination. Can you find flights that are suspiciously fast? (i.e. flights that represent a potential data entry error). Compute the air time of a flight relative to the shortest flight to that destination. Which flights were most delayed in the air?
7 -Find all destinations that are flown by at least two carriers. Use that information to rank the carriers.
8 - For each plane, count the number of flights before the first delay of greater than 1 hour.
grouping, functions like mean(), median(), min(), or max()
flights %>% group_by(tailnum) %>%
filter(dep_delay > 0) %>%
summarize(avg_delay = mean(dep_delay)) %>%
arrange(desc(avg_delay))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 3,885 x 2
## tailnum avg_delay
## <chr> <dbl>
## 1 N844MH 297
## 2 N452UW 291
## 3 N922EV 274
## 4 N587NW 272
## 5 N911DA 268
## 6 N665MQ 268.
## 7 N673MQ 257
## 8 N851NW 233
## 9 N654UA 227
## 10 N550NW 212.
## # ... with 3,875 more rows
flights %>% filter (dep_delay > 0) %>%
group_by(hour) %>%
summarize(avg_delay = mean(dep_delay)) %>%
arrange(avg_delay)
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 19 x 2
## hour avg_delay
## <dbl> <dbl>
## 1 5 15.3
## 2 7 24.1
## 3 6 24.2
## 4 9 29.7
## 5 8 29.9
## 6 12 32.3
## 7 11 32.5
## 8 10 32.6
## 9 13 33.5
## 10 14 37.1
## 11 23 38.0
## 12 15 38.8
## 13 16 43.4
## 14 17 45.3
## 15 18 46.5
## 16 22 46.5
## 17 20 49.6
## 18 21 50.3
## 19 19 51.1
flights %>% select(dest, arr_delay) %>% group_by(dest) %>%
filter(arr_delay > 0) %>%
mutate(total_delay = sum(arr_delay, na.rm = TRUE),
prop_delay = arr_delay / total_delay)
## # A tibble: 133,004 x 4
## # Groups: dest [103]
## dest arr_delay total_delay prop_delay
## <chr> <dbl> <dbl> <dbl>
## 1 IAH 11 99391 0.000111
## 2 IAH 20 99391 0.000201
## 3 MIA 33 140424 0.000235
## 4 ORD 12 283046 0.0000424
## 5 FLL 19 202605 0.0000938
## 6 ORD 8 283046 0.0000283
## 7 LAX 7 203226 0.0000344
## 8 DFW 31 110009 0.000282
## 9 ATL 12 300299 0.0000400
## 10 DTW 16 138258 0.000116
## # ... with 132,994 more rows
flights %>% filter(origin == 'JFK') %>% filter(!is.na(dep_delay)) %>%
mutate(pre_dep_delay = lag(dep_delay, default = 0)) %>%
ggplot(mapping = aes(x = dep_delay, y= pre_dep_delay)) +
geom_point(alpha = .5)
flights <- flights %>% filter(origin == 'JFK') %>% filter(!is.na(dep_delay)) %>%
mutate(pre_dep_delay = lag(dep_delay, default = 0))
cor(flights$dep_delay, flights$pre_dep_delay)
flights %>% filter(!is.na(air_time)) %>% group_by(dest) %>%
mutate(air_time_mean = mean(air_time),
air_time_sd = sd(air_time),
z = (air_time - air_time_mean) / air_time_sd) %>%
select(z, air_time_mean, dest, everything()) %>%
arrange(z)
## # A tibble: 109,079 x 27
## # Groups: dest [70]
## z air_time_mean dest year month day dep_time sched_dep_time dep_delay
## <dbl> <dbl> <chr> <int> <int> <int> <int> <int> <dbl>
## 1 -4.10 57.1 BUF 2013 11 10 2307 2250 17
## 2 -3.80 51.9 ROC 2013 3 25 2340 2250 50
## 3 -3.55 329. SEA 2013 7 3 1533 1459 34
## 4 -3.51 52.6 ORF 2013 11 3 1720 1645 35
## 5 -3.40 66.5 PIT 2013 7 17 1504 1505 -1
## 6 -3.03 66.5 PIT 2013 5 8 1947 1859 48
## 7 -3.03 329. SEA 2013 5 6 1728 1730 -2
## 8 -2.99 331. PDX 2013 5 6 1753 1755 -2
## 9 -2.98 329. LAX 2013 9 6 2042 2025 17
## 10 -2.97 329. SEA 2013 5 6 1553 1557 -4
## # ... with 109,069 more rows, and 18 more variables: arr_time <int>,
## # sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, dep_time_mins <dbl>,
## # sched_dep_time_mins <dbl>, arr_time_mins <dbl>, flight_time <dbl>,
## # pre_dep_delay <dbl>, air_time_sd <dbl>
flights %>% filter(!is.na(air_time)) %>% group_by(dest) %>%
mutate(air_time_mean = mean(air_time),
air_time_sd = sd(air_time),
z = (air_time - air_time_mean) / air_time_sd) %>%
select(z, air_time_mean, dest, everything()) %>%
arrange(desc(z))
## # A tibble: 109,079 x 27
## # Groups: dest [70]
## z air_time_mean dest year month day dep_time sched_dep_time dep_delay
## <dbl> <dbl> <chr> <int> <int> <int> <int> <int> <dbl>
## 1 13.9 44.5 SYR 2013 9 1 2237 1711 326
## 2 12.2 42.1 ACK 2013 6 29 755 800 -5
## 3 11.8 38.5 BOS 2013 7 23 1617 1605 12
## 4 10.8 38.5 BOS 2013 7 23 1200 1200 0
## 5 10.4 71.9 RDU 2013 9 1 1719 1712 7
## 6 10.3 57.1 BUF 2013 12 16 923 925 -2
## 7 9.91 87.6 DTW 2013 7 27 1654 1620 34
## 8 9.74 38.5 BOS 2013 2 17 841 840 1
## 9 9.74 38.5 BOS 2013 7 23 1242 1245 -3
## 10 9.69 51.9 ROC 2013 7 10 2350 2030 200
## # ... with 109,069 more rows, and 18 more variables: arr_time <int>,
## # sched_arr_time <int>, arr_delay <dbl>, carrier <chr>, flight <int>,
## # tailnum <chr>, origin <chr>, air_time <dbl>, distance <dbl>, hour <dbl>,
## # minute <dbl>, time_hour <dttm>, dep_time_mins <dbl>,
## # sched_dep_time_mins <dbl>, arr_time_mins <dbl>, flight_time <dbl>,
## # pre_dep_delay <dbl>, air_time_sd <dbl>
flights %>% group_by(dest) %>%
summarise(num_carrier = length(unique(carrier))) %>%
filter(num_carrier >= 2) %>%
arrange(desc(num_carrier))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 45 x 2
## dest num_carrier
## <chr> <int>
## 1 LAX 5
## 2 SFO 5
## 3 TPA 5
## 4 AUS 4
## 5 BOS 4
## 6 LAS 4
## 7 PIT 4
## 8 ATL 3
## 9 BNA 3
## 10 CLT 3
## # ... with 35 more rows
flights %>% filter(!is.na(dep_delay)) %>% group_by(tailnum) %>%
mutate(max_delay = cummax(dep_delay),
less_one_hour = max_delay < 60) %>%
summarize(count = sum(less_one_hour)) %>%
arrange(desc(count))
## `summarise()` ungrouping output (override with `.groups` argument)
## # A tibble: 1,957 x 2
## tailnum count
## <chr> <int>
## 1 N705TW 159
## 2 N706TW 149
## 3 N713TW 128
## 4 N721TW 120
## 5 N5FAAA 117
## 6 N3769L 104
## 7 N804JB 104
## 8 N3748Y 103
## 9 N645JB 94
## 10 N3742C 91
## # ... with 1,947 more rows
