9 Making maps with R

library(sf)
library(terra)
library(dplyr)
library(spData)

These exercises rely on a new object, africa. Create it using the world and worldbank_df datasets from the spData package as follows:

library(spData)
africa = world |> 
  filter(continent == "Africa", !is.na(iso_a2)) |> 
  left_join(worldbank_df, by = "iso_a2") |> 
  select(name, subregion, gdpPercap, HDI, pop_growth) |> 
  st_transform("ESRI:102022")

We will also use zion and nlcd datasets from spDataLarge:

zion = st_read((system.file("vector/zion.gpkg", package = "spDataLarge")))
data(nlcd, package = "spDataLarge")

E1. Create a map showing the geographic distribution of the Human Development Index (HDI) across Africa with base graphics (hint: use plot()) and tmap packages (hint: use tm_shape(africa) + ...). - Name two advantages of each based on the experience. - Name three other mapping packages and an advantage of each. - Bonus: create three more maps of Africa using these three packages.

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E2. Extend the tmap created for the previous exercise so the legend has three bins: “High” (HDI above 0.7), “Medium” (HDI between 0.55 and 0.7) and “Low” (HDI below 0.55). - Bonus: improve the map aesthetics, for example by changing the legend title, class labels and color palette.

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E3. Represent africa’s subregions on the map. Change the default color palette and legend title. Next, combine this map and the map created in the previous exercise into a single plot.

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E4. Create a land cover map of the Zion National Park. - Change the default colors to match your perception of the land cover categories - Add a scale bar and north arrow and change the position of both to improve the map’s aesthetic appeal - Bonus: Add an inset map of Zion National Park’s location in the context of the Utah state. (Hint: an object representing Utah can be subset from the us_states dataset.)

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E5. Create facet maps of countries in Eastern Africa: - With one facet showing HDI and the other representing population growth (hint: using variables HDI and pop_growth, respectively) - With a ‘small multiple’ per country

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E6. Building on the previous facet map examples, create animated maps of East Africa: - Showing first the spatial distribution of HDI scores then population growth - Showing each country in order

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E7. Create an interactive map of Africa: - With tmap - With mapview - With leaflet - Bonus: For each approach, add a legend (if not automatically provided) and a scale bar

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E8. Sketch on paper ideas for a web mapping app that could be used to make transport or land-use policies more evidence based: - In the city you live, for a couple of users per day - In the country you live, for dozens of users per day - Worldwide for hundreds of users per day and large data serving requirements

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E9. Update the code in coffeeApp/app.R so that instead of centering on Brazil the user can select which country to focus on: - Using textInput() - Using selectInput()

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E10. Reproduce Figure ?? and the 1st and 6th panel of Figure ?? as closely as possible using the ggplot2 package.

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E11. Join us_states and us_states_df together and calculate a poverty rate for each state using the new dataset. Next, construct a continuous area cartogram based on total population. Finally, create and compare two maps of the poverty rate: (1) a standard choropleth map and (2) a map using the created cartogram boundaries. What is the information provided by the first and the second map? How do they differ from each other?

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E12. Visualize population growth in Africa. Next, compare it with the maps of a hexagonal and regular grid created using the geogrid package.

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