1 Load packages
library(tidyverse) # data wrangling2 Get the data
We’ll be analyzing the data set flights, describing the flights which started from NYC in 2013.
Here’s how to get the data set:
library(tidyverse)
library(nycflights13)
data("flights")2.1 Alternative way to get the data
Alternatively, import the data from a csv file:
flights_url <- "https://vincentarelbundock.github.io/Rdatasets/csv/nycflights13/flights.csv"
flights <- read_csv(flights_url)3 Exercises
Plot the distribution of the delays. Describe your insights.
Plot the distribution of the delay per origin airport.
Visualize the assocation of delay and time of the day. Find a way to reduce overplotting. Hint: Try out
geom_bind2d()instead of using a scatter plot.Visualize the assocation of delay and distance to destination. Separate by origin and month.
Visualize the assocation of delay and time of the day. Only include the three airlines where the delay is highest. Reduce overplotting.
Visualize the proportion of delayed flights per origin.
Visualize the proportion of delayed flights per time.
Visualize the proportion of delayed flights per week day.
4 Solutions
Check out the solutions here. Only after trying really hard 😀.
5 Reproducibility
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