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clarifying language about vertical vs horizontal resolution and the m…
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README.md

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- [Jemma Stachelek][stachelek_jemma]
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- [Ivo Arrey][arreyves]
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- Drake Asberry
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- [Jon Jablonski][jonjab]
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[stachelek_jemma]: https://carpentries.org/instructors/#jsta
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[arreyves]: https://carpentries.org/instructors/#arreyves

episodes/01-raster-structure.Rmd

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This map shows the elevation of our study site in Harvard Forest. From the
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legend, we can see that the maximum elevation is ~400, but we can't tell whether
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this is 400 feet or 400 meters because the legend doesn't show us the units. We
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can look at the metadata of our object to see what the units are. Much of the
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metadata that we're interested in is part of the CRS. We introduced the
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this is 400 feet or 400 meters because the legend doesn't show us the units.
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Unfortunately, nothing in the technical metadata (that which is built into
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the file format) tells us what the _vertical_ resolution is. For now you will
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have to trust us that it is meters.
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However, we can look at the metadata of our object to see what the horizontal
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units are: in other words how many meters from the origin are the
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x and y points. That metadata, is part of the CRS. We introduced the
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concept of a CRS in [an earlier
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lesson](https://datacarpentry.org/organization-geospatial/03-crs).
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## Challenge
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What units are our data in?
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What units are our horizontal data in?
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::::::::::::::: solution
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episodes/08-vector-plot-shapefiles-custom-legend.Rmd

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---
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title: Plot Multiple Vector Layers
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teaching: 40
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exercises: 20
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teaching: 30
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exercises: 15
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source: Rmd
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---
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episodes/09-vector-when-data-dont-line-up-crs.Rmd

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title: Handling Spatial Projection & CRS
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teaching: 40
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teaching: 30
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exercises: 20
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source: Rmd
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---

instructors/instructor-notes.md

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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.md)
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#### [1 Intro to Raster Data in R](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.md)
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#### [2 Plot Raster Data in R](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.md)
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#### [3 Reproject Raster Data in R](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.md)
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#### [4 Raster Calculations in R](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.md)
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#### [5 Work With Multi-Band Rasters in R](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.md)
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#### [6 Open and Plot Shapefiles in R](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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- There is a known bug in the `geom_sf()` function that leads to an intermittent error on some platforms.
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If you see the following error message, try to re-run your plotting command and it should work.
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The `ggplot` development team is working on fixing this bug.
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## Error message
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* Error message *
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```error
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Error in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, :
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polygon edge not found
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```
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#### [Explore and Plot by Shapefile Attributes](07-vector-shapefile-attributes-in-r.md)
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#### [7 Explore and Plot by Shapefile Attributes](07-vector-shapefile-attributes-in-r.md)
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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.md)
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#### [8 Plot Multiple Vector Layers](08-vector-plot-shapefiles-custom-legend.md)
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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.md)
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#### [9 Handling Spatial Projection \& CRS in R](09-vector-when-data-dont-line-up-crs.md)
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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.md)
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#### [10 Convert from .csv to a Vector Layer](10-vector-csv-to-shapefile-in-r.md)
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#### [Manipulate Raster Data in R](11-vector-raster-integration.md)
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#### [11 Manipulate Raster Data](11-vector-raster-integration.md)
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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.md)
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#### [12 Raster Time Series Data](12-time-series-raster.md)
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#### [Create Publication-quality Graphics](13-plot-time-series-rasters-in-r.md)
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#### [13 Create Publication-quality Graphics](13-plot-time-series-rasters-in-r.md)
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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.md)
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#### [14 Derive Values from Raster Time Series](14-extract-ndvi-from-rasters-in-r.md)
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- This is the first time in the workshop that learners will have worked with date data.
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researchers, particularly those outside of environmental sciences), then move to raster data if there is extra time.
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- Skip Introduction to Geospatial Concepts. Spend at most 30 minutes reviewing things as this is currently not
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an interactive curriculum. Most of the concepts you can cover within the R for Raster and Vector Data curriculum.
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- Covering Episode 10 immediately after 3 can be helpful to solidify the concepts of projections
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## Common problems
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