Learning goals

• Visualizing spatial data
• Joining data frames

Getting started

Each member of the team should:

• Go to the course GitHub organization (or your team page) and locate your lab repo, which should be named lab-05-wrangling-YOUR_TEAM_NAME.
• Grab the URL of the repo, and clone it in RStudio by creating a new project from Version Control with Git.
• Open the R Markdown document lab-05.Rmd and Knit it. Make sure it compiles without errors. The output will be in the file markdown .md file with the same name.

library(tidyverse) 
library(dsbox)
library(ggmap)
library(maps)
library(sf)

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

Exercises

1. What are the dimensions of the Denny’s dataset? 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? 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?

🧶 ✅ ⬆️ 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.

3. 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?

4. 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 welcome 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 dennys and laquinta datasets. We know exactly which states are in the US, and we have this information in the states dataframe we loaded.

5. 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?

dennys %>%
  filter(!(state %in% states$abbreviation))

6. 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 dennys again so that the stored data frame contains the new variable going forward.

⊕Hint: You can use the mutate function with the argument country = “United States”. 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.

7. Find the La Quinta locations that are outside the US, and figure out which country they are in. (This will require some googling. Make sure to cite your source!) Take notes, you will need to use this information in the next exercise.

8. 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:

laquinta <- laquinta %>%
  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.

laquinta <- laquinta %>%
  filter(country == "United States")

9. 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 joining 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:

dennys %>%
  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.

10. 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 dennys and laquinta data frames, respectively.

dennys <- dennys %>%
  mutate(establishment = "Denny's")
laquinta <- laquinta %>%
  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(dennys, laquinta)

We can plot the locations of the two establishments using a scatterplot, 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, 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.

11. 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?

12. 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?

qmplot(longitude, latitude,
       data = dn_lq,
       maptype = "toner-lite") +
  geom_point(aes(color = establishment))

dn_lq_nc <- filter(dn_lq, state == "NC")

nc <- map('state',
          region = "north carolina",
          fill = TRUE,
          plot = FALSE) %>%
  st_as_sf()

ggplot() +
  geom_sf(data = nc) +
  geom_point(data = dn_lq_nc,
             aes(x = longitude, y = latitude,
                 color = establishment)) +
  coord_sf() # Ensures lat and long on same scale