Assignment 10: Data Visualization

This semester we’ve spent a lot of time processing spatial datasets for the Pacific Northwest. In the past few lectures, we’ve talked about visualizing data and making maps. This assignment is a chance to practice pulling online data into R, processing unfamiliar spatial data, and making maps that combine multiple data sources and use multiple aesthetics/scales.

By the end of this assignment you should be able to:

You’ll need to accept the link to access the questions.

Instructions

  1. After you’ve joined the assignment repository, you should have this file (named Readme.md) inside of a R project named assignment-10-xx where xx is your github username (or initials).

  2. Once you’ve verified that you’ve correctly cloned the assignment repository, create a new Quarto document. Name this file assignment-10-xxx.qmd and give it a title (like M Williamson Assignment 10). Make sure that you select the html output option (Quarto can do a lot of cool things, but the html format is the least-likely to cause you additional headaches). We’ll be using Quarto throughout the course so it’s worth checking out the other tutorials in the getting started section.

  3. Copy the questions below into your document and change the color of their text.

  4. Save the changes and make your first commit!

  5. Answer the questions making sure save and commit at least 3 more times (having 4 commits is part of the assignment).

  6. Render the document to html (you should now have at least 3 files in the repository: Readme.md, assignment-10-xx.qmd, and assignment-10-xx.html). Commit these changes and push them to the repository on GitHub. You should see the files there when you go to github.com.

The Assignment

  1. Get data to visualize from the following sources:
  1. Use the occ_search function from rgbif to pull at least 1000 observations of a species of your choice in a country of your choice: occurance.df <- occ_search(scientificName = "Any Species Name", country = "Any Country", hasCoordinate = TRUE, limit=1000)
  2. Use the geodata package to pull a raster of interest - some variable that you think is relevant to your species. Possible functions for different types of data can be seen here. Here’s an example: soils <- geodata::soil_world(var = "nitrogen", depth=5, path=tempfile()). Use path=tempfile() to download a version that will be deleted when the R server restarts, but if this give you trouble, use path=getwd(). If you do this, do not commit this data and push it to GitHub.
  3. Use the geodata package to pull in state/province/equivalent level boundaries for your country of choice using geodata::gadm(country = "Your Country", level=1, path=tempfile()). The same tempfile() instructions for part b apply here. This data will be downloaded as a SpatVector and can be transformed to an sf object with st_as_sf().

  1. Write pseudocode for how you will prepare your data for visualization, then execute your plan. Some possible objectives might be cropping your data to an area of interest and transforming the data to tidy format.

  2. Use ggplot2 to create a map of the raster data with the species presence points overlayed on top. Add state/province/equivalent level boundaries.

  3. Change the raster color scale, legend name, title, and theme from ggplot2 defaults. You can try any other ggplot customization you’d like now as well.

  4. Use tmap to recreate this plot with zooming functionality and any other interactive elements you’d like to add. Optionally, you can substitute the raster for tidycensus or other polygon data at this stage.

Note

Solutions are here