- 1 Load packages
- 2 Load data
- 3 Exercises/questions
- 4 Solutions
- 4.1 Plot the distribution of the delays. Describe your insights.
- 4.2 Plot the distribution of the delays per origin airport.
- 4.3 Visualize the assocation of delay and time of the day. Find a way to reduce overplotting.
- 4.4 Visualize the assocation of delay and distance to destination. Separate by origin and month.
- 4.5 Visualize the assocation of delay and time of the day. Only include the three airlines where the delay is highest.
- 4.6 Visualize the proportion of delayed flights per origin.
- 4.7 Visualize the proportion of delayed flights per time of the day
- 4.8 Visualize the proportion of delayed flights per week day
- 5 Reproducibility
1 Load packages
library(tidyverse) # data wrangling2 Load data
library(tidyverse)
library(nycflights13)
data("flights")3 Exercises/questions
See here
4 Solutions
4.1 Plot the distribution of the delays. Describe your insights.
flights %>%
ggplot() +
aes(x = dep_delay) +
geom_histogram()
Alternatively:
flights %>%
ggplot() +
aes(x = dep_delay) +
geom_density()
The distribution is skewed to the right. Some flights are extremely lated compared to the majority.
4.2 Plot the distribution of the delays per origin airport.
flights %>%
ggplot() +
aes(x = dep_delay) +
geom_density() +
facet_wrap(~ origin)
4.3 Visualize the assocation of delay and time of the day. Find a way to reduce overplotting.
Hint: Try out geom_bind2d() or geom_density2d() instead of using a scatter plot.
flights %>%
ggplot() +
aes(x = dep_time, dep_delay) +
geom_density2d()
Adding a “smoother” line:
flights %>%
ggplot() +
aes(x = dep_time, dep_delay) +
geom_density2d() +
geom_smooth() # smoothing line
Alternatively:
flights %>%
ggplot() +
aes(x = dep_time, dep_delay) +
geom_bin2d() +
geom_smooth(method = "lm") # smoothing line
4.4 Visualize the assocation of delay and distance to destination. Separate by origin and month.
flights %>%
ggplot() +
aes(x = distance, dep_delay) +
geom_density2d() +
facet_grid(origin ~ month)
4.5 Visualize the assocation of delay and time of the day. Only include the three airlines where the delay is highest.
Reduce overplotting.
flights %>%
group_by(carrier) %>%
summarise(dep_delay_carrier = mean(dep_delay, na.rm = TRUE)) %>%
arrange(-dep_delay_carrier) %>%
slice(1:3)
#> # A tibble: 3 x 2
#> carrier dep_delay_carrier
#> <chr> <dbl>
#> 1 F9 20.2
#> 2 EV 20.0
#> 3 YV 19.0flights %>%
filter(carrier %in% c("F9", "EV", "YV")) %>%
ggplot() +
aes(x = dep_time, dep_delay) +
geom_density2d()
4.6 Visualize the proportion of delayed flights per origin.
flights %>%
mutate(is_delayed = dep_delay > 0) %>%
group_by(origin) %>%
summarise(delay_n = sum(is_delayed == TRUE, na.rm = TRUE),
delay_prop = delay_n / n()) %>%
ggplot() +
aes(x = origin, y = delay_prop) +
geom_col()
Alternatively:
flights %>%
mutate(is_delayed = dep_delay > 0) %>%
group_by(origin) %>%
ggplot() +
aes(x = origin, fill = is_delayed) +
geom_bar()
Or even this way:
flights %>%
mutate(is_delayed = dep_delay > 0) %>%
group_by(origin) %>%
ggplot() +
aes(x = origin, fill = is_delayed) +
geom_bar(position = "fill")
4.7 Visualize the proportion of delayed flights per time of the day
flights %>%
mutate(is_delayed = dep_delay > 0) %>%
group_by(origin) %>%
drop_na(is_delayed, origin) %>%
ggplot() +
aes(x = origin, fill = is_delayed) +
geom_bar(position = "fill") +
facet_wrap(~ hour) +
scale_fill_viridis_d() # d as in "discrete"
4.8 Visualize the proportion of delayed flights per week day
There’s a package that does the weight lifting for us when it comes to working with times and dates:
library(lubridate)flights %>%
mutate(is_delayed = dep_delay > 0) %>%
mutate(day_of_week = wday(time_hour)) %>% # Wochentag
group_by(origin) %>%
drop_na(is_delayed, origin) %>%
ggplot() +
aes(x = origin, fill = is_delayed) +
geom_bar(position = "fill") +
facet_wrap(~ day_of_week) +
scale_fill_viridis_d() # d wie "discrete"
5 Reproducibility
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