Grammar of data wrangling

These are suggested answers to the application exercise. They may differ slightly from the code we developed in class and may not be complete. They’re provided after class to help review the application activity, but are not an alternative to working on the exercise and actively participating in class.

library(tidyverse)
library(scales)

hotels <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv")

Recreate the visualization from the slides.

hotels |>
  mutate(
    arrival_date = paste(arrival_date_year, arrival_date_month, arrival_date_day_of_month),
    arrival_date = ymd(arrival_date)
    ) |>
  group_by(hotel, arrival_date) |>
  summarise(mean_adr = mean(adr), .groups = "drop") |>
  ggplot(aes(x = arrival_date, y = mean_adr, group = hotel, color = hotel)) +
  geom_line() +
  scale_color_manual(values = c("cornsilk4", "deepskyblue3")) +
  scale_y_continuous(labels = label_dollar()) +
  labs(
    x = "Arrival date",
    y = "Mean average\ndaily rate (USD)",
    color = NULL,
    title = "Cost of daily hotel stay",
    subtitle = "July 2015 to August 2017",
    caption = "Source: Antonio, Almeida and Nunes (2019) | TidyTuesday"
  ) +
  theme_minimal() +
  theme(legend.position = c(0.15, 0.9))