library(tidyverse)
library(skimr)
Hotel bookings - factors
# From TidyTuesday: https://github.com/rfordatascience/tidytuesday/blob/master/data/2020/2020-02-11/readme.md
<- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv") hotels
First, knit the document and view the following visualisation. How are the months ordered? What would be a better order? Then, reorder the months on the x-axis (levels of arrival_date_month
) in a way that makes more sense. You will want to use a function from the forcats package, see https://forcats.tidyverse.org/reference/index.html for inspiration and help.
Stretch goal: If you finish the above task before time is up, change the y-axis label so the values are shown with dollar signs, e.g. $80 instead of 80. You will want to use a function from the scales package, see https://scales.r-lib.org/reference/index.html for inspiration and help.
%>%
hotels group_by(hotel, arrival_date_month) %>% # group by hotel type and arrival month
summarise(mean_adr = mean(adr)) %>% # calculate mean adr for each group
ggplot(aes(
x = arrival_date_month, # x-axis = arrival_date_month
y = mean_adr, # y-axis = mean_adr calculated above
group = hotel, # group lines by hotel type
color = hotel) # and color by hotel type
+
) geom_line() + # use lines to represent data
theme_minimal() + # use a minimal theme
labs(
x = "Arrival month", # customize labels
y = "Mean ADR (average daily rate)",
title = "Comparison of resort and city hotel prices across months",
subtitle = "Resort hotel prices soar in the summer while ciry hotel prices remain relatively constant throughout the year",
color = "Hotel type"
)
`summarise()` has grouped output by 'hotel'. You can override using the
`.groups` argument.