18 Manipulate
18.1 dplyr::arrange
nov_dep_delays <- nov_dep_delays %>%
arrange(
dest, # Ascending destination name
-dep_delay # Descending delay
)
nov_dep_delays[1:3, ]## # A tibble: 3 x 6
## origin dest dep_delay year month day
## <chr> <chr> <dbl> <int> <int> <int>
## 1 JFK ABQ 25 2013 11 29
## 2 JFK ABQ 21 2013 11 22
## 3 JFK ABQ 17 2013 11 2118.2 dplyr::summarise
summarise: calculate aggregated values (e.g., mean, max, etc)
aggr_dep_delays_nov <- nov_dep_delays %>%
# Need to filter out rows where delay is NA
filter(!is.na(dep_delay)) %>%
# Create two aggregated columns
summarize(
avg_dep_delay = mean(dep_delay),
tot_dep_delay = sum(dep_delay)
)
aggr_dep_delays_nov## # A tibble: 1 x 2
## avg_dep_delay tot_dep_delay
## <dbl> <dbl>
## 1 5.44 14694518.3 dplyr::group_by
dest_dep_delays_nov <- nov_dep_delays %>%
# Need to filter out rows where delay is NA
filter(!is.na(dep_delay)) %>%
# Frist group by same destination
group_by(dest) %>%
# Then caluculate aggregated value
summarize(
tot_dep_delay = sum(dep_delay)
)
dest_dep_delays_nov[1:3, ]## # A tibble: 3 x 2
## dest tot_dep_delay
## <chr> <dbl>
## 1 ABQ -66
## 2 ALB 636
## 3 ATL 818418.4 dplyr::mutate
dest_dep_delays_nov <- dest_dep_delays_nov %>%
mutate(
tot_dep_delay_days = ((tot_dep_delay / 60) /24)
)
dest_dep_delays_nov[1:3, ]## # A tibble: 3 x 3
## dest tot_dep_delay tot_dep_delay_days
## <chr> <dbl> <dbl>
## 1 ABQ -66 -0.0458
## 2 ALB 636 0.442
## 3 ATL 8184 5.6818.5 Full pipe example
dest_dep_delays_nov <- nycflights13::flights %>%
select(origin, dest, dep_delay, arr_delay, year:day) %>%
select(-arr_delay) %>%
filter(month == 11) %>%
filter(!is.na(dep_delay)) %>%
arrange(dest, -dep_delay) %>%
group_by(dest) %>%
summarize(tot_dep_delay = sum(dep_delay)) %>%
mutate(tot_dep_delay_days = ((tot_dep_delay / 60) /24))
dest_dep_delays_nov[1:3, ]## # A tibble: 3 x 3
## dest tot_dep_delay tot_dep_delay_days
## <chr> <dbl> <dbl>
## 1 ABQ -66 -0.0458
## 2 ALB 636 0.442
## 3 ATL 8184 5.68