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_extras/guide.md

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title: "Instructor Notes"
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---
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{% include base_path.html %}
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## Instructor notes
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## Lesson motivation and learning objectives
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at the end of the first day, they will need to either save the workspace or reload the data and packages.
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Because of this, it is essential that learners save their code to a script throughout the lesson.
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#### [Intro to Raster Data in R](../01-raster-structure/)
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#### [Intro to Raster Data in R]({{ relative_root_path }}/{% link _episodes/01-raster-structure.md %})
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* Be sure to introduce the datasets that will be used in this lesson. There are many data files. It may
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be helpful to draw a diagram on the board showing the types of data that will be plotted and analyzed
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* Be sure to draw a distinction between the DTM and the DSM files, as these two datasets will be used
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throughout the lesson.
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#### [Plot Raster Data in R](../02-raster-plot/)
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#### [Plot Raster Data in R]({{ relative_root_path }}/{% link _episodes/02-raster-plot.md %})
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* `geom_bar()` is a new geom for the learners. They were introduced to `geom_col()` in the [Introduction to R for Geospatial Data](https://datacarpentry.org/r-intro-geospatial/) lesson.
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* `dplyr` syntax should be familiar to your learners from the [Introduction to R for Geospatial Data](https://datacarpentry.org/r-intro-geospatial/) lesson.
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* This may be the first time learners are exposed to hex colors, so be sure to explain that concept.
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* Starting in this episode and continuing throughout the lesson, the `ggplot` calls can be very long. Be sure
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to explicitly describe each step of the function call and what it is doing for the overall plot.
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#### [Reproject Raster Data in R](../03-raster-reproject-in-r/)
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#### [Reproject Raster Data in R]({{ relative_root_path }}/{% link _episodes/03-raster-reproject-in-r.md %})
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* No notes yet. Please add your tips and comments!
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#### [Raster Calculations in R](../04-raster-calculations-in-r/)
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#### [Raster Calculations in R]({{ relative_root_path }}/{% link _episodes/04-raster-calculations-in-r.md %})
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* The `overlay()` function syntax is fairly complex compared to other function calls the learners have seen.
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Be sure to explain it in detail.
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#### [Work With Multi-Band Rasters in R](../05-raster-multi-band-in-r/)
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#### [Work With Multi-Band Rasters in R]({{ relative_root_path }}/{% link _episodes/05-raster-multi-band-in-r.md %})
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* No notes yet. Please add your tips and comments!
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#### [Open and Plot Shapefiles in R](../06-vector-open-shapefile-in-r/)
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#### [Open and Plot Shapefiles in R]({{ relative_root_path }}/{% link _episodes/06-vector-open-shapefile-in-r.md %})
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* Learners may have heard of the `sp` package. If it comes up, explain that `sf` is a
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more modern update of `sp`.
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> ~~~
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> {: .error}
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#### [Explore and Plot by Shapefile Attributes](../07-vector-shapefile-attributes-in-r/)
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#### [Explore and Plot by Shapefile Attributes]({{ relative_root_path }}/07-vector-shapefile-attributes-in-r/)
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* No notes yet. Please add your tips and comments!
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#### [Plot Multiple Shapefiles in R](../08-vector-plot-shapefiles-custom-legend/)
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#### [Plot Multiple Shapefiles in R]({{ relative_root_path }}/08-vector-plot-shapefiles-custom-legend/)
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* No notes yet. Please add your tips and comments!
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#### [Handling Spatial Projection & CRS in R](../09-vector-when-data-dont-line-up-crs/)
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#### [Handling Spatial Projection & CRS in R]({{ relative_root_path }}/09-vector-when-data-dont-line-up-crs/)
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* Note that, although `ggplot` automatically reprojects vector data when plotting multiple shapefiles with
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different projections together, it is still important to be aware of the CRSs of your data and to keep track
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of how they are being transformed.
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#### [Convert from .csv to a Shapefile in R](../10-vector-csv-to-shapefile-in-r/)
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#### [Convert from .csv to a Shapefile in R]({{ relative_root_path }}/10-vector-csv-to-shapefile-in-r/)
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* No notes yet. Please add your tips and comments!
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#### [Manipulate Raster Data in R](../11-vector-raster-integration/)
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#### [Manipulate Raster Data in R]({{ relative_root_path }}/11-vector-raster-integration/)
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* Learners have not yet been exposed to the `melt()` function in this workshop. They will need to have
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the syntax explained.
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* This is the first instance of a faceted plot in this workshop.
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#### [Raster Time Series Data in R](../12-time-series-raster/)
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#### [Raster Time Series Data in R]({{ relative_root_path }}/12-time-series-raster/)
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* No notes yet. Please add your tips and comments!
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#### [Create Publication-quality Graphics](../13-plot-time-series-rasters-in-r/)
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#### [Create Publication-quality Graphics]({{ relative_root_path }}/13-plot-time-series-rasters-in-r/)
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* Be sure to show learners the before and after plots to motivate the complexity of the
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`ggplot` calls that will be used in this episode.
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#### [Derive Values from Raster Time Series](../14-extract-ndvi-from-rasters-in-r/)
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#### [Derive Values from Raster Time Series]({{ relative_root_path }}/14-extract-ndvi-from-rasters-in-r/)
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* This is the first time in the workshop that learners will have worked with date data.
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