Exercise 02

Exercise due on 2023-09-11

ℹ️ See week 2 for related slides and readings

1 Overview

Practice these skills

• Build a layered plot using ggplot(), aes(), and different geom_ functions
• Explore the difference between discrete and continuous variables
• Use coord_sf() to modify a plot created with geom_sf()

Think about these questions

• What makes a scale better or worse at visualizing data?
• How do you write a clear and accurate title, legend, or caption?
• When is it appropriate to modify the geometry of your data when making a map?

2 Setup

This exercises uses the {ggplot2} and {dplyr} packages (both from the tidyverse family of packages) and the {sf} package:

library(ggplot2)
library(dplyr)
library(sf)

For this week’s exercise, we are also going to use data from the {rnaturalearth} package. Make sure to install those packages and re-start your session if these packages are not installed already:

# pak::pkg_install(c("rnaturalearth", "rnaturalearthdata"))
library(rnaturalearth)

We are going to use ne_download() to download the countries dataset and then use st_centroid() to make a version of this dataset where the features show the center of each country instead of the boundaries:

countries <- ne_download(scale = "medium", type = "countries", returnclass = "sf")

countries <- st_transform(countries, crs = 3857)

countries_center <- st_centroid(countries)

glimpse(countries)

Some of the following exercises don’t require a sf object. You can also use the mpg or storms dataset we looked at during this week’s lecture:

glimpse(mpg)

glimpse(storms)

One advantage of using {ggplot2} over a map-making focused package like {tmap} is the wide variety of extension packages created by the large community of users and developers making data visualizations (including maps) with {ggplot2}.

We aren’t going to use some of these extension packages (and others) but we won’t load them yet. Run the following code to install (and don’t forget to restart your session afterwards):

pak::pkg_install(c("patchwork", "plotly", "smoothr"))

If you finish this exercise but still want more practice, you can download this RMarkdown document with exercises shared by Thomas Lin Pedersen for his two-part 2020 online workshop on {ggplot2} (part 1 and part 2 are both available on YouTube).

3 Exercises

3.1 Plotting a single variable

Find a discrete variable in countries and then create a plot with geom_bar():

ggplot(data = countries) +
  geom_bar(mapping = aes(x = ____))

Next, find a continuous variable and make a plot with geom_histogram():

ggplot(data = countries) +
  geom_histogram(mapping = aes(x = ____))

Now, let’s make a map! Use countries_center and geom_sf() to make a map with a continuous variable mapped to size:

ggplot(data = countries_center) +
  geom_sf(aes(size = ____))

---

Next, make a map with geom_sf() with one discrete variable mapped to color:

ggplot(data = countries) +
  geom_sf(aes(color = ____))

Is that the map you expected? Try it again with the discrete variable mapped to fill:

ggplot(data = countries) +
  geom_sf(aes(fill = ____))

---

Now, make a plot using any geom function of your choice:

ggplot(data = countries) +
  ____

Explain in plain language. What does your map show? ____

3.2 Plotting two variables

For this next section, you can continue to use countries as your dataset or load a different dataset using {rnaturaleath}. You can see what vector data is available using rnaturalearth::ne_find_vector_data() then find the function you need to load the data into a new object:

rnaturalearth::ne_find_vector_data()

First, find two continuous variables and create a scatter plot with geom_point():

ggplot(data = ____) +
  geom_point(aes(____))

Next, look in your data for one discrete and one continuous variable then use aes() to set those variables for geom_col(). The geom_col() function is similar to geom_bar() but you must provide both an x and a y variable:

ggplot(data = ____) +
  geom_col(aes(____))

Now, use geom_sf() mapping your continous variable to fill or color and your discrete variable to facet_wrap():

ggplot(data = ____) +
  geom_sf(aes(____)) +
  facet_wrap(~ ____)

Finally, create a map using a different aesthetic that we haven’t tried yet. Options could include linewidth, size, alpha, or linetype:

ggplot(data = ____) +
  geom_sf(aes(____ = ____))

Is this aesthetic mapping an effective way of visualizing the variable? ____

If so, why do you think it works well? If not, why does it not work well? ____

3.3 Using scales and colors

{ggplot2} uses naming conventions to organize the scale functions. This isn’t the same for every function but they look something like: “scale_”. So, scale_fill_viridis_d() applies the Viridis color scale to a discrete variable mapped to the fill aesthetic.

Use the data to create a plot and take a look at the colors set when you use scale_color_viridis_c():

ggplot(____) +
  geom_dotplot(aes(____, _____, color = ____)) +
  scale_color_viridis_c()

The ColorBrewer scales are designed for use with thematic maps. Use ?scale_color_brewer() to pull up the documentation for this function and review the information on the type and palette parameters.

Now, map a variable to the color aesthetic for geom_sf() and assign an appropriate type and palette value:

ggplot(____) +
  geom_sf(aes(color = ____)) +
  scale_color_brewer(type = ____, palette = ____)

Switching from color to fill, try it again with a different type and palette value:

ggplot(____) +
  geom_sf(aes(fill = ____)) +
  scale_fill_brewer(type = ____, palette = ____)

One last time, but we’re using scale_fill_distiller():

ggplot(____) +
  geom_sf(aes(fill = ____)) +
  scale_fill_distiller(type = ____, palette = ____)

Note that this scale_fill_distiller() scale only works with continuous values. If you get an error, you may need to map a different variable to fill.

3.4 Adding labels, legends, and themes

Set the data for ggplot() and then use the labs() function to apply a title and caption that make sense:

ggplot(data = ____) +
  geom_sf(color = "black", fill = NA) +
  labs(
    title = ____,
    caption = ____
  )

Now, map fill to a variable in your data using aes() and then use labs() to assign a label for fill:

ggplot(data = ____) +
  geom_sf(aes(fill = ____)) +
  labs(
    ____
  )

Finally, put all of these elements together with a theme function. theme_minimal() and theme_void() are good themes to use for maps but you can explore all of the options in the ggplot2 documentation:

ggplot(data = ____) +
  geom_sf(mapping = aes(____)) +
  labs(
    title = ____,
    caption = ____,
    ____
  ) +
  ____

3.5 Interactive plots with {plotly}

We aren’t doing much with interactivity in this class (or exercise) but I did want to give you a chance to try it out using the ggplotly() function from the {plotly} package:

p <- ggplot(data = ____) +
  geom____(aes(____))
  
plotly::ggplotly(
  p = p
)

A directory of {ggplot2} extensions is available through the tidyverse website if you want to try more tools for animation or interactivity including {gganimate} or {ggiraph}.

3.6 Map making with {ggplot2}

By default, any map created with geom_sf() will show the graticulates on the map and axis labels with the coordinate values. Add data to this map and then hide these graticules by adding theme_void():

ggplot(data = ____) +
  geom_sf(color = "black", fill = NA) +
  ____

You can also hide or change graticules by using theme(). Try setting the panel.grid argument to element_blank() to hide the grid:

ggplot(data = ____) +
  geom_sf(color = "black", fill = NA) +
  theme(
    panel.grid = ____
    )

Now, try “zooming” into a selected area of your map using the xlim and ylim arguments for coord_sf():

ggplot(data = ____) +
  geom_sf(color = "black", fill = NA) +
  coord_sf(
    xlim = ____,
    ylim = ____
  )

If you have difficulty with this one, look back at our week 2 slides for an example showing how to use sf::st_bbox() to get xmin, xmax, ymin, and ymax values for the xlim and ylim parameter.

---

Using an inset map or “locator map” with a larger area and a zoomed in map showing a featured area is a common cartographic approach. You can use patchwork::inset_element() from the {patchwork} package to set this up:

area_map <- ggplot(data = ____) +
  geom_sf(color = "black", fill = NA) +
  coord_sf(
    xlim = ____,
    ylim = ____
  )

inset_map <- ggplot(data = ____) +
  geom_sf(color = "black", fill = NA)

area_map +
  patchwork::inset_element(
    p = inset_map,
    left = ____,
    bottom = ____,
    top = ____,
    right = ____
  )

Remember, this format of calling functions (<package name>::<function name>) is just a shortcut for using functions from packages that are installed but not loaded into your environment. If you have any difficulty with this part of the exercise, make sure you have {patchwork} installed.

---

There are some cases when you need to modify the geometry of your data as part of the process of making a map. The st_simplify() function is one way to do that. Try setting dTolerance to a low value, e.g. dTolerance = 10, and run the code block. Then try to run it again with dTolerance = 100000.

usa <- filter(countries, NAME == "United States of America")

simple_usa <- st_simplify(x = usa, dTolerance = ____)

ggplot() +
  geom_sf(
    data = usa,
    color = "orange"
    ) +
  geom_sf(
    data = simple_usa,
    color = "purple"
  ) +
  theme_void()

What happens when you increase the value of dTolerance? ____

Now, let’s try to same thing but smoothing features with smoothr::smooth() instead of simplifying with sf::st_simplify(). Start by setting smoothness to a small number, smoothness = 0.5, and then run again with higher and higher numbers:

smooth_usa <- smoothr::smooth(x = usa, method = "ksmooth", smoothness = ____)

ggplot() +
  geom_sf(
    data = usa,
    color = "orange"
    ) +
  geom_sf(
    data = smooth_usa,
    color = "purple"
  ) +
  theme_void()

What happens when you increase the value of smoothness? ____

Check the documentation for st_simplify() or smoothr::smooth() for more information on how these functions work to modify the geometry.

4 Bonus exercise

4.1 Creating maps with {tmap}

Pick one of the maps you created in the prior questions of this exercise and create a similar version using the {tmap} package.

You can install {tmap} the same as any other package:

# pak::pkg_install("tmap")

Then load the library:

library(tmap)

And make a map using data from {rnaturalearth} or another source of your choice:

____

What is the same about making a map with {tmap} compared to {ggplot2}? ____

What is different about making a map with {tmap} compared to {ggplot2}? ____

Do you have any preference between the two? ____