Data wrangling - I

Lecture 3

Dr. Greg Chism

University of Arizona
INFO 526 - Fall 2024

Announcements

  • HW 1 due Friday, Sept 13 at 5:00pm
  • Project proposal peer review (Monday, Sept 16)

    • You’ll be assessed on the quality (and participation) of your peer review

    • Updates based on peer review are due Monday, Sept 19 at 5:00pm

    • Full group attendance is mandatory

    • Your proposal will be “graded” after that, by me

Setup

# load packages
if(!require(pacman))
  install.packages("pacman")

pacman::p_load(tidyverse,
               glue,
               scales,
               countdown)

# set theme for ggplot2
ggplot2::theme_set(ggplot2::theme_minimal(base_size = 14))

# set width of code output
options(width = 65)

# set figure parameters for knitr
knitr::opts_chunk$set(
  fig.width = 7,        # 7" width
  fig.asp = 0.618,      # the golden ratio
  fig.retina = 3,       # dpi multiplier for displaying HTML output on retina
  fig.align = "center", # center align figures
  dpi = 300             # higher dpi, sharper image
)

Transforming and reshaping a single data frame

Data: Hotel bookings

  • Data from two hotels: one resort and one city hotel
  • Observations: Each row represents a hotel booking
hotels <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-02-11/hotels.csv")

Scenario 1

We…

have a single data frame

want to slice it, and dice it, and juice it, and process it, so we can plot it

dplyr 101

Which of the following (if any) are unfamiliar to you?

Average cost of daily stay

Let’s recreate this visualization!

Livecoding

Reveal below for code developed during live coding session.

Code
hotels |>
  mutate(
    arrival_date = glue::glue("{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(
    legend.position = c(0.15, 0.9),
    legend.box.background = element_rect(fill = "white",
                                         color = "white"),
    plot.subtitle = element_text(color = "cornsilk4"),
    plot.caption = element_text(color = "cornsilk4")
  )

🔗 datavizaz.org