continuation character, +
seq(from = 1, to = 10)
## [1] 1 2 3 4 5 6 7 8 9 10
select(flights, year:dep_time)
## # A tibble: 336,776 × 4
## year month day dep_time
## <int> <int> <int> <int>
## 1 2013 1 1 517
## 2 2013 1 1 533
## 3 2013 1 1 542
## 4 2013 1 1 544
## 5 2013 1 1 554
## 6 2013 1 1 554
## 7 2013 1 1 555
## 8 2013 1 1 557
## 9 2013 1 1 557
## 10 2013 1 1 558
## # ℹ 336,766 more rows
select(flights, year, month, day, dep_time)
## # A tibble: 336,776 × 4
## year month day dep_time
## <int> <int> <int> <int>
## 1 2013 1 1 517
## 2 2013 1 1 533
## 3 2013 1 1 542
## 4 2013 1 1 544
## 5 2013 1 1 554
## 6 2013 1 1 554
## 7 2013 1 1 555
## 8 2013 1 1 557
## 9 2013 1 1 557
## 10 2013 1 1 558
## # ℹ 336,766 more rows
select(flights, year, month, day, dep_time, dep_delay)
## # A tibble: 336,776 × 5
## year month day dep_time dep_delay
## <int> <int> <int> <int> <dbl>
## 1 2013 1 1 517 2
## 2 2013 1 1 533 4
## 3 2013 1 1 542 2
## 4 2013 1 1 544 -1
## 5 2013 1 1 554 -6
## 6 2013 1 1 554 -4
## 7 2013 1 1 555 -5
## 8 2013 1 1 557 -3
## 9 2013 1 1 557 -3
## 10 2013 1 1 558 -2
## # ℹ 336,766 more rows
select(flights, year, month, day, starts_with("dep"))
## # A tibble: 336,776 × 5
## year month day dep_time dep_delay
## <int> <int> <int> <int> <dbl>
## 1 2013 1 1 517 2
## 2 2013 1 1 533 4
## 3 2013 1 1 542 2
## 4 2013 1 1 544 -1
## 5 2013 1 1 554 -6
## 6 2013 1 1 554 -4
## 7 2013 1 1 555 -5
## 8 2013 1 1 557 -3
## 9 2013 1 1 557 -3
## 10 2013 1 1 558 -2
## # ℹ 336,766 more rows
select(flights, year, month, day, contains("time"))
## # A tibble: 336,776 × 9
## year month day dep_time sched_dep_time arr_time sched_arr_time air_time
## <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 2013 1 1 517 515 830 819 227
## 2 2013 1 1 533 529 850 830 227
## 3 2013 1 1 542 540 923 850 160
## 4 2013 1 1 544 545 1004 1022 183
## 5 2013 1 1 554 600 812 837 116
## 6 2013 1 1 554 558 740 728 150
## 7 2013 1 1 555 600 913 854 158
## 8 2013 1 1 557 600 709 723 53
## 9 2013 1 1 557 600 838 846 140
## 10 2013 1 1 558 600 753 745 138
## # ℹ 336,766 more rows
## # ℹ 1 more variable: time_hour <dttm>
select(flights, year, month, day, ends_with("time"))
## # A tibble: 336,776 × 8
## year month day dep_time sched_dep_time arr_time sched_arr_time air_time
## <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 2013 1 1 517 515 830 819 227
## 2 2013 1 1 533 529 850 830 227
## 3 2013 1 1 542 540 923 850 160
## 4 2013 1 1 544 545 1004 1022 183
## 5 2013 1 1 554 600 812 837 116
## 6 2013 1 1 554 558 740 728 150
## 7 2013 1 1 555 600 913 854 158
## 8 2013 1 1 557 600 709 723 53
## 9 2013 1 1 557 600 838 846 140
## 10 2013 1 1 558 600 753 745 138
## # ℹ 336,766 more rows
select(flights, year, month, day, contains("time"), everything())
## # A tibble: 336,776 × 19
## year month day dep_time sched_dep_time arr_time sched_arr_time air_time
## <int> <int> <int> <int> <int> <int> <int> <dbl>
## 1 2013 1 1 517 515 830 819 227
## 2 2013 1 1 533 529 850 830 227
## 3 2013 1 1 542 540 923 850 160
## 4 2013 1 1 544 545 1004 1022 183
## 5 2013 1 1 554 600 812 837 116
## 6 2013 1 1 554 558 740 728 150
## 7 2013 1 1 555 600 913 854 158
## 8 2013 1 1 557 600 709 723 53
## 9 2013 1 1 557 600 838 846 140
## 10 2013 1 1 558 600 753 745 138
## # ℹ 336,766 more rows
## # ℹ 11 more variables: time_hour <dttm>, dep_delay <dbl>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # distance <dbl>, hour <dbl>, minute <dbl>
mutate(flights,
gain = dep_delay - arr_delay) %>%
#select year, month, day, and gain
select(year:day, gain)
## # A tibble: 336,776 × 4
## year month day gain
## <int> <int> <int> <dbl>
## 1 2013 1 1 -9
## 2 2013 1 1 -16
## 3 2013 1 1 -31
## 4 2013 1 1 17
## 5 2013 1 1 19
## 6 2013 1 1 -16
## 7 2013 1 1 -24
## 8 2013 1 1 11
## 9 2013 1 1 5
## 10 2013 1 1 -10
## # ℹ 336,766 more rows
# alternative using transmute()
transmute(flights,
gain = dep_delay - arr_delay)
## # A tibble: 336,776 × 1
## gain
## <dbl>
## 1 -9
## 2 -16
## 3 -31
## 4 17
## 5 19
## 6 -16
## 7 -24
## 8 11
## 9 5
## 10 -10
## # ℹ 336,766 more rows
#lag()
select(flights, dep_time)
## # A tibble: 336,776 × 1
## dep_time
## <int>
## 1 517
## 2 533
## 3 542
## 4 544
## 5 554
## 6 554
## 7 555
## 8 557
## 9 557
## 10 558
## # ℹ 336,766 more rows
mutate(flights, dep_time_lag1 = lag(dep_time))
## # A tibble: 336,776 × 20
## 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
## # ℹ 336,766 more rows
## # ℹ 12 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_lag1 <int>
select(flights, minute) %>%
mutate(minute_cumsum = cumsum(minute))
## # A tibble: 336,776 × 2
## minute minute_cumsum
## <dbl> <dbl>
## 1 15 15
## 2 29 44
## 3 40 84
## 4 45 129
## 5 0 129
## 6 58 187
## 7 0 187
## 8 0 187
## 9 0 187
## 10 0 187
## # ℹ 336,766 more rows
Printing to screen
y <- seq(from = 1, to = 10)
y
## [1] 1 2 3 4 5 6 7 8 9 10
flights
## # A tibble: 336,776 × 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
## # ℹ 336,766 more rows
## # ℹ 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>
summarize(flights, avg_delay = mean(dep_delay, na.ra = TRUE))
## # A tibble: 1 × 1
## avg_delay
## <dbl>
## 1 NA
flights %>%
# Group by airlines
group_by(carrier) %>%
# Calculate average departure delay
summarize(delay = mean(dep_delay, na.rm = TRUE)) %>%
# Sort it
arrange(delay)
## # A tibble: 16 × 2
## carrier delay
## <chr> <dbl>
## 1 US 3.78
## 2 HA 4.90
## 3 AS 5.80
## 4 AA 8.59
## 5 DL 9.26
## 6 MQ 10.6
## 7 UA 12.1
## 8 OO 12.6
## 9 VX 12.9
## 10 B6 13.0
## 11 9E 16.7
## 12 WN 17.7
## 13 FL 18.7
## 14 YV 19.0
## 15 EV 20.0
## 16 F9 20.2
flights %>%
group_by(dest) %>%
summarise(count = n(),
dist = mean(distance, na.rm = TRUE),
delay = mean(arr_delay, na.rm = TRUE)) %>%
# plot
ggplot(mapping = aes(x = dist, y = delay))+ geom_point(aes(size= count), alpha = 0.3) + geom_smooth(se = FALSE)
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
flights %>%
#remove missing values
filter(is.na(dep_delay))
## # A tibble: 8,255 × 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
## # ℹ 8,245 more rows
## # ℹ 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>
flights %>%
group_by(year) %>%
summarize(count = n())
## # A tibble: 1 × 2
## year count
## <int> <int>
## 1 2013 336776