Add instructor notes (#252)

Author: ErinBecker

_extras/guide.md

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+## Instructor notes
+
+## Lesson motivation and learning objectives
+
+This lesson is designed to introduce learners to the fundamental principles and skills for working with
+raster and vector geospatial data in R. It begins by introducing the structure of and simple plotting of 
+raster data. It then covers re-projection of raster data, performing raster math, and working with multi-band
+raster data. After introducing raster data, the lesson moves into working with vector data. Line, point, and
+polygon shapefiles are included in the data. Learners will plot multiple raster and/or vector layers
+in a single plot, and learn how to customize plot elements such as legends and titles. They will 
+also learn how to read data in from a csv formatted file and re-format it to a shapefile. Lastly, learners
+will work with multi-layered raster data set representing time series data and extract summary statistics
+from this data. 
+
+## Lesson design
+
+#### Overall comments
+
+* As of initial release of this lesson (August 2018), the timing is set to be the same for each episode. This
+is very likely incorrect and will need to be updated as these lessons are taught. If you teach this lesson, 
+please put in an issue or PR to suggest an updating timing scheme!!
+
+* The code examples presented in each episode assume that the learners still have all of the data and packages
+loaded from all previous episodes in this lesson. If learners close out of their R session during the breaks or
+at the end of the first day, they will need to either save the workspace or reload the data and packages. 
+Because of this, it is essential that learners save their code to a script throughout the lesson.
+
+#### [Intro to Raster Data in R](../01-raster-structure/)
+
+* Be sure to introduce the datasets that will be used in this lesson. There are many data files. It may
+be helpful to draw a diagram on the board showing the types of data that will be plotted and analyzed 
+throughout the lesson. 
+* If the [Introduction to Geospatial Concepts](https://datacarpentry.org/organization-geospatial/) lesson was
+included in your workshop, learners will have been introduced to the GDAL library. It will be useful to make 
+the connection back to that lesson explicitly.
+* If the [Introduction to R for Geospatial Data](https://datacarpentry.org/r-intro-geospatial/) lesson was included
+in your workshop, learners will be familiar with the idea of packages and with most of the functions used
+in this lesson.
+* The Dealing with Missing Data and Bad Data Values in Rasters sections have several plots showing alternative ways of displaying missing
+data. The code for generating these plots is **not** shared with the learners, as it relies on many functions
+they have not yet learned. For these and other plots with hidden demonstration code, show the images in the 
+lesson page while discussing those examples. 
+* Be sure to draw a distinction between the DTM and the DSM files, as these two datasets will be used
+throughout the lesson.
+
+#### [Plot Raster Data in R](../02-raster-plot/)
+
+* `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. 
+* `dplyr` syntax should be familiar to your learners from the [Introduction to R for Geospatial Data](https://datacarpentry.org/r-intro-geospatial/) lesson. 
+* This may be the first time learners are exposed to hex colors, so be sure to explain that concept.
+* Starting in this episode and continuing throughout the lesson, the `ggplot` calls can be very long. Be sure
+to explicitly describe each step of the function call and what it is doing for the overall plot. 
+
+#### [Reproject Raster Data in R](../03-raster-reproject-in-r/)
+
+* No notes yet. Please add your tips and comments!
+
+#### [Raster Calculations in R](../04-raster-calculations-in-r/)
+
+* The `overlay()` function syntax is fairly complex compared to other function calls the learners have seen. 
+Be sure to explain it in detail.
+
+#### [Work With Multi-Band Rasters in R](../05-raster-multi-band-in-r/)
+
+* No notes yet. Please add your tips and comments!
+
+#### [Open and Plot Shapefiles in R](../06-vector-open-shapefile-in-r/)
+
+* Learners may have heard of the `sp` package. If it comes up, explain that `sf` is a
+more modern update of `sp`. 
+* There is a known bug in the `geom_sf()` function that leads to an intermittent error on some platforms. 
+If you see the following error message, try to re-run your plotting command and it should work. 
+The `ggplot` development team is working on fixing this bug.
+
+## Error message
+> ~~~
+> Error in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y,  : 
+  polygon edge not found
+> ~~~
+> {: .error}
+
+#### [Explore and Plot by Shapefile Attributes](../07-vector-shapefile-attributes-in-r/)
+
+* No notes yet. Please add your tips and comments!
+
+#### [Plot Multiple Shapefiles in R](../08-vector-plot-shapefiles-custom-legend/)
+
+* No notes yet. Please add your tips and comments!
+
+#### [Handling Spatial Projection & CRS in R](../09-vector-when-data-dont-line-up-crs/)
+
+* Note that, although `ggplot` automatically reprojects vector data when plotting multiple shapefiles with
+different projections together, it is still important to be aware of the CRSs of your data and to keep track
+of how they are being transformed. 
+
+
+#### [Convert from .csv to a Shapefile in R](../10-vector-csv-to-shapefile-in-r/)
+
+* No notes yet. Please add your tips and comments!
+
+#### [Manipulate Raster Data in R](../11-vector-raster-integration/)
+
+* Learners have not yet been exposed to the `melt()` function in this workshop. They will need to have 
+the syntax explained. 
+* This is the first instance of a faceted plot in this workshop. 
+
+#### [Raster Time Series Data in R](../12-time-series-raster/)
+
+* No notes yet. Please add your tips and comments! 
+
+#### [Create Publication-quality Graphics](../13-plot-time-series-rasters-in-r/)
+
+* Be sure to show learners the before and after plots to motivate the complexity of the 
+`ggplot` calls that will be used in this episode. 
+
+#### [Derive Values from Raster Time Series](../14-extract-ndvi-from-rasters-in-r/)
+
+* This is the first time in the workshop that learners will have worked with date data.
+
+#### Concluding remarks
+
+* No notes yet. Please add your tips and comments! 
+
+## 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).
+
+* 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.
 
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