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UPDATE: This workshop is no long being developed here. Please use the Projections Workshop instead.

Workshop: Coordinate Reference Sytems

UC Davis DataLab
Spring 2021
Instructor: Michele Tobias, PhD <mmtobias@ucdavis.edu>

Description

In this workshop, participants will learn about projected coordinate reference systems (CRS, commonly called “projections”) and how to apply them in R to spatial data. We will discuss the components of a CRS, how to apply them, how to translate your data into a different CRS, and how to choose a CRS.

Learning Objectives

By the end of this workshop, participants will have a better understanding of what a projected coordinate system is, why you would choose one over another, and how to apply them correctly to geospatial data in R.

Prerequisites

Participants should have a basic understanding of R (for example, be able to create variables and load common data formats like a CSV) and a basic understanding of GIS data formats (e.g., raster and vector data). Participants should also install R and RStudio.

Contributing

The course reader is a live webpage, hosted through GitHub, where you can enter curriculum content and post it to a public-facing site for learners.

To make alterations to the reader:

  1. Run git pull, or if it's your first time contributing, see Setup.

  2. Edit an existing chapter file or create a new one. Chapter files are R Markdown files (.Rmd) at the top level of the repo. Enter your text, code, and other information directly into the file. Make sure your file:

    • Follows the naming scheme ##_topic-of-chapter.Rmd (the only exception is index.Rmd, which contains the reader's front page).
    • Begins with a first-level header (like # This). This will be the title of your chapter. Subsequent section headers should be second-level headers (like ## This) or below.
    • Uses caching for resource-intensive code (see Caching).

    Put any supporting resources in data/ or img/. For large files, see Large Files. You do not need to add resources generated by your R code (such as plots). The knit step saves these in docs/ automatically.

  3. Run knit.R to regenerate the HTML files in the docs/. You can do this in the shell with ./knit.R or in R with source("knit.R").

  4. Run renv::snapshot() in an R session at the top level of the repo to automatically add any packages your code uses to the project package library.

  5. When you're finished, git add:

    • Any files you added or edited directly, including in data/ and img/
    • docs/ (all of it)
    • _bookdown_files/ (contains the knitr cache)
    • renv.lock (contains the renv package list)
    • .gitattributes (contains the Git LFS file list)

    Then git commit and git push. The live web page will update automatically after 1-10 minutes.

Caching

If one of your code chunks takes a lot of time or memory to run, consider caching the result, so the chunk won't run every time someone knits the reader. To cache a code chunk, add cache=TRUE in the chunk header. It's best practice to label cached chunks, like so:

```{r YOUR_CHUNK_NAME, cache=TRUE}
# Your code...
```

Cached files are stored in the _bookdown_files/ directory. If you ever want to clear the cache, you can delete this directory (or its subdirectories). The cache will be rebuilt the next time you knit the reader.

Beware that caching doesn't work with some packages, especially packages that use external libraries. Because of this, it's best to leave caching off for code chunks that are not resource-intensive.

Large Files

If you want to include a large file (say over 1 MB), you should use git LFS. You can register a large file with git LFS with the shell command:

git lfs track YOUR_FILE

This command updates the .gitattributes file at the top level of the repo. To make sure the change is saved, you also need to run:

git add .gitattributes

Now that your large is registered with git LFS, you can add, commit, and push the file with git the same way you would any other file, and git LFS will automatically intercede as needed.

GitHub provides 1 GB of storage and 1 GB of monthly bandwidth free per repo for large files. If your large file is more than 50 MB, check with the other contributors before adding it.

Setup

Git LFS

This repo uses Git Large File Storage (git LFS) for large files. If you don't have git LFS installed, download it and run the installer. Then in the shell (in any directory), run:

git lfs install

Then your one-time setup of git LFS is done. Next, clone this repo with git clone. The large files will be downloaded automatically with the rest of the repo.

R Packages

This repo uses renv for package management. Install renv according to the installation instructions on their website.

Then open an R session at the top level of the repo and run:

renv::restore()

This will download and install the correct versions of all the required packages to renv's package library. This is separate from your global R package library and will not interfere with other versions of packages you have installed.

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