Lab 04 - La Quinta is Spanish for next to Denny’s, Pt. 1

Visualizing spatial data

Have you ever taken a road trip in the US and thought to yourself “I wonder what La Quinta means”. Well, the late comedian Mitch Hedberg thinks it’s Spanish for next to Denny’s.

If you’re not familiar with these two establishments, Denny’s is a casual diner chain that is open 24 hours and La Quinta Inn and Suites is a hotel chain.

These two establishments tend to be clustered together, or at least this observation is a joke made famous by Mitch Hedberg. In this lab we explore the validity of this joke and along the way learn some more data wrangling and tips for visualizing spatial data.

The inspiration for this lab comes from a blog post by John Reiser on his new jersey geographer blog. You can read that analysis here. Reiser’s blog post focuses on scraping data from Denny’s and La Quinta Inn and Suites websites using Python. In this lab we focus on visualization and analysis of these data. However note that the data scraping was also done in R, and we we will discuss web scraping using R later in the course. But for now we focus on the data that has already been scraped and tidied for you.

Learning goals

• Visualising spatial data
• Joining data frames

Getting started

Go to the course GitHub organization and locate your homework repo, clone it in RStudio and open the R Markdown document. Knit the document to make sure it compiles without errors.

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

• Update the YAML, changing the author name to your name, and knit the document.
• Commit your changes with a meaningful commit message.
• Push your changes to GitHub.
• Go to your repo on GitHub and confirm that your changes are visible in your Rmd and md files. If anything is missing, commit and push again.

Packages

We’ll use the tidyverse package for much of the data wrangling and visualisation and the data lives in the dsbox package. These packages are already installed for you. You can load them by running the following in your Console:

library(tidyverse)
library(dsbox) 

Data

The datasets we’ll use are called dennys and laquinta from the dsbox package. Note that these data were scraped from here and here, respectively.

The datasets we’ll use are called dennys and laquinta from the dsbox package. Since the datasets are distributed with the package, we don’t need to load them separately; they become available to us when we load the package. You can find out more about the datasets by inspecting their documentation, which you can access by running ?dennys and ?laquinta in the Console or using the Help menu in RStudio to search for dennys or laquinta. You can also find this information here and here.

To help with our analysis we will also use a dataset on US states, which is located in your repository’s data folder.

states <- read_csv("data/states.csv")

Each observation in this dataset represents a state, including DC. Along with the name of the state we have the two-letter abbreviation and we have the geographic area of the state (in square miles).

Exercises

1. What are the dimensions of the Denny’s dataset? (Hint: Use inline R code and functions like nrow and ncol to compose your answer.) What does each row in the dataset represent? What are the variables?

2. What are the dimensions of the La Quinta’s dataset? What does each row in the dataset represent? What are the variables?

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

We would like to limit our analysis to Denny’s and La Quinta locations in the United States.

1. Take a look at the websites that the data come from (linked above). Are there any La Quinta’s locations outside of the US? If so, which countries? What about Denny’s?

2. Now take a look at the data. What would be some ways of determining whether or not either establishment has any locations outside the US using just the data (and not the websites). Don’t worry about whether you know how to implement this, just brainstorm some ideas. Write down at least one as your answer, but you’re welcomed to write down a few options too.

We will determine whether or not the establishment has a location outside the US using the state variable in the dn and lq datasets. We know exactly which states are in the US, and we have this information in the states dataframe we loaded.

1. Find the Denny’s locations that are outside the US, if any. To do so, filter the Denny’s locations for observations where state is not in states$abbreviation. The code for this is given below. Note that the %in% operator matches the states listed in the state variable to those listed in states$abbreviation. The ! operator means not. Are there any Denny’s locations outside the US?

“Filter for states that are not in states$abbreviation.” dn %>% filter(!(state %in% states$abbreviation))
1. Add a country variable to the Denny’s dataset and set all observations equal to "United States". Remember, you can use the mutate function for adding a variable. Make sure to save the result of this as dn again so that the stored data frame contains the new variable going forward.

We don’t need to tell R how many times to repeat the character string “United States” to fill in the data for all observations, R takes care of that automatically.

dn %>%
mutate(country = "United States")
1. Find the La Quinta locations that are outside the US, and figure out which country they are in. This might require some googling. Take notes, you will need to use this information in the next exercise.

2. Add a country variable to the La Quinta dataset. Use the case_when function to populate this variable. You’ll need to refer to your notes from Exercise 7 about which country the non-US locations are in. Here is some starter code to get you going:

lq %>%
mutate(country = case_when(
state %in% state.abb     ~ "United States",
state %in% c("ON", "BC") ~ "Canada",
state == "ANT"           ~ "Colombia",
...                      # fill in the rest
))

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

Going forward we will work with the data from the United States only. All Denny’s locations are in the United States, so we don’t need to worry about them. However we do need to filter the La Quinta dataset for locations in United States.

lq <- lq %>%
filter(country == "United States")
1. Which states have the most and fewest Denny’s locations? What about La Quinta? Is this surprising? Why or why not?

Next, let’s calculate which states have the most Denny’s locations per thousand square miles. This requires joinining information from the frequency tables you created in Exercise 8 with information from the states data frame.

First, we count how many observations are in each state, which will give us a data frame with two variables: state and n. Then, we join this data frame with the states data frame. However note that the variables in the states data frame that has the two-letter abbreviations is called abbreviation. So when we’re joining the two data frames we specify that the state variable from the Denny’s data should be matched by the abbreviation variable from the states data:

dn %>%
count(state) %>%
inner_join(states, by = c("state" = "abbreviation"))

Before you move on the the next question, run the code above and take a look at the output. In the next exercise you will need to build on this pipe.

1. Which states have the most Denny’s locations per thousand square miles? What about La Quinta?

Next, we put the two datasets together into a single data frame. However before we do so, we need to add an identifier variable. We’ll call this establishment and set the value to "Denny's" and "La Quinta" for the dn and lq data frames, respectively.

dn <- dn %>%
mutate(establishment = "Denny's")
lq <- lq %>%
mutate(establishment = "La Quinta")

Since the two data frames have the same columns, we can easily bind them with the bind_rows function:

dn_lq <- bind_rows(dn, lq)

We can plot the locations of the two establishments using a scatter plot, and color the points by the establishment type. Note that the latitude is plotted on the x-axis and the longitude on the y-axis.

ggplot(dn_lq, mapping = aes(x = longitude, y = latitude, color = establishment)) +
geom_point()

The following two questions ask you to create visualizations. These should follow best practices you learned in class, such as informative titles, axis labels, etc. See http://ggplot2.tidyverse.org/reference/labs.html for help with the syntax. You can also choose different themes to change the overall look of your plots, see http://ggplot2.tidyverse.org/reference/ggtheme.html for help with these.

1. Filter the data for observations in North Carolina only, and recreate the plot. You should also adjust the transparency of the points, by setting the alpha level, so that it’s easier to see the overplotted ones. Visually, does Mitch Hedberg’s joke appear to hold here?

2. Now filter the data for observations in Texas only, and recreate the plot, with an appropriate alpha level. Visually, does Mitch Hedberg’s joke appear to hold here?

That’s it for now! In the next lab we will take a more quantitative approach to answering these questions.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with an appropriate commit message. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards and review the md document on GitHub to make sure you’re happy with the final state of your work.