Instructor Notes

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Lesson motivation and learning objectives

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 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.

Lesson design

Introduction to R and RStudio

If your workshop includes the Introduction to Geospatial Concepts lesson, learners will have just been introduced to RStudio in the context of the overall Geospatial software landscape.
Have your learners open RStudio and follow along as you explain each pane. 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 libraries in the next lesson, so be sure to introduce what a library is and how it is installed.

Project Management With RStudio

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

Data Structures

Learners will work with factors in the following lesson. Be sure to cover this concept.
If needed for time reasons, you can skip the section on lists. The learners don’t use lists in the rest of the workshop.

Exploring Data Frames

Pay attention to and explain the errors and warnings generated from the examples in this episode.

Subsetting Data

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.

Dataframe Manipulation with dplyr

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

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.

Writing Data

Learners will need to have created the directory structure described in Project Management With RStudio in order for the code in this episode to work.

Concluding remarks

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 changes to practice applying and expanding these skills in the next lesson.

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.

[workshop-repo]: [yaml]: http://yaml.org/

Introduction to R for Geospatial Data: Instructor Notes