Instructor Notes

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Instructor notes

Technical tips and tricks

Leave about 30 minutes at the start of each workshop and another 15 mins at the start of each session for technical difficulties like WiFi and installing things (even if you asked students to install in advance, longer if not).
Be sure to actually go through examples of an R help page: help files can be intimidating at first, but knowing how to read them is tremendously useful.
Don’t worry about being correct or knowing the material back-to-front. Use mistakes as teaching moments: the most vital skill you can impart is how to debug and recover from unexpected errors.

Common problems

TBA - Instructors please add situations you encounter here.

Introduction to R and RStudio

Instructor Note

Leave about 30 minutes at the start of each workshop and another 15 mins at the start of each session for technical difficulties like WiFi and installing things (even if you asked students to install in advance, longer if not).
the teaching
Be sure to actually go through examples of an R help page: help files can be intimidating at first, but knowing how to read them is tremendously useful.
Don’t worry about being correct or knowing the material back-to-front. Use mistakes as teaching moments: the most vital skill you can impart is how to debug and recover from unexpected errors.

Instructor Note

This lesson is designed to introduce learners to the core concepts of R that they will need in order to complete the other lessons in this workshop.
It is intended for learners who have no prior experience with R. If your workshop learners have all completed another Software or Data Carpentry R workshop, or have taken courses in R, you can skip this lesson and move straight into the [Introduction to Geospatial Raster and Vector Data with R] (https://datacarpentry.org/r-raster-vector-geospatial/) lesson.
This lesson is a trimmed-down version of the R for Reproducible Scientific Analysis SWC lesson. It does not cover visualization in detail, as the later lesson in this workshop covers visualization in the context of geospatial data.

Instructor Note

Have your learners open RStudio and follow along as you explain each panel. Make sure that your RStudio environment is the default so learners can follow along.
Be sure to explain how to execute code from the script window, whether you’re using the Run button or the keyboard shortcut.
Learners will be using several packages in the next lesson, so be sure to introduce what a package is and how it is installed.

Project Management With RStudio

Instructor Note

Make sure learners download the data files in Challenge 1 and move those files to their data/ directory.

When learners load an RStudio project, their R session’s working directory should automatically be set to the same folder as the .RProj file. We’ll be using relative paths throughout the lesson to refer to files, so it’s important to make sure that learners have loaded the right project and are in the right directory! You may also want to introduce other ways to make file paths, such as the here package, after creating the project.

Data Structures

Instructor Note

Learners will work with factors in the following lesson. Be sure to cover this concept.

Subsetting Data

Instructor Note

The episode after this one covers the dplyr package, which has an alternate subsetting mechanism. Learners do still need to learn the base R subsetting covered here, as dplyr won’t work in all situations. However, the examples in the rest of the workshop focus on dplyr syntax.

Data frame Manipulation with dplyr

Instructor Note

Introduce the dplyr package as a simpler, more intuitive way of doing subsetting.
Unlike other SWC and DC R lessons, this lesson does not include data reshaping with tidyr as it isn’t used in the rest of the workshop.

Introduction to Visualization

Instructor Note

This episode introduces geom_col and geom_histogram. These geoms are used in the rest of the workshop, along with geoms specifically for geospatial data.
Emphasize that we will go much deeper into visualization and creating publication-quality graphics later in the workshop.

Now that learners know the fundamentals of R, the rest of the workshop will apply these concepts to working with geospatial data in R.
Packages and functions specific for working with geospatial data will be the focus of the rest of the workshop.
They will have lots of challenges to practice applying and expanding these skills in the next lesson.