Merge pull request #3 from cforgaci/3-raster-cf

I am closing the review on this part and merging it into the main branch. Thanks all for the input!

config.yaml

@@ -59,7 +59,15 @@ contact: '[email protected]'
 
 # Order of episodes in your lesson
 episodes:
-- introduction.Rmd
+- 09-open-and-plot-vector-layers.Rmd
+- 10-explore-and-plot-by-vector-layer-attributes.Rmd
+- 11-plot-multiple-shape-files.Rmd
+- 12-handling-spatial-projection-and-crs.Rmd
+- 13-intro-to-raster-data.Rmd
+- 14-plot-raster-data.Rmd
+- 15-reproject-raster-data.Rmd
+- 16-raster-calculations.Rmd
+- 17-work-with-multi-band-rasters.Rmd
 
 # Information for Learners
 learners:

episodes/09-open-and-plot-vector-layers.Rmd

@@ -0,0 +1,189 @@
+---
+title: "Open and Plot Vector Layers"
+teaching: 25
+exercises: 5
+---
+
+:::::::::::::::::::::::::::::::::::::: questions 
+
+- How can I read, examine and visualize point, line and polygon vector data in R?
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- Know the difference between point, line, and polygon vector data.
+- Load vector data into R.
+- Access the attributes of a vector object in R.
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+:::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: instructor
+
+Make sure that the `sf` package and its dependencies are installed before the 
+workshop. The installation can take quite some time, so allocate enough extra
+time before the workshop for solving installation problems. We recommend one
+or two installation 'walk-in' hours on a day before the workshop and 15-30 
+minutes at the beginning of the first workshop day should be enough to tackle 
+installation issues.
+
+::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
+
+::: prereq
+
+If you have not installed the `sf` package yet, run `install.packages("sf")` first. Note that the `sf` package has some external dependencies, namely GEOS, PROJ.4, GDAL and UDUNITS, which need to be installed beforehand. Follow the workshop [setup instructions]() for the installation of `sf` and its dependencies.
+
+:::
+
+First we need to load the packages we will use in this lesson. We will use the packages `tidyverse` and `here` with which you are already familiar from the previous lesson. In addition, we need to load the [`sf`](https://r-spatial.github.io/sf/) package for working with spatial vector data. 
+
+```{r lib, message=FALSE}
+library(tidyverse)  # tools for wrangling, reshaping and visualizing data
+library(here)       # managing paths
+library(sf)         # work with spatial vector data
+```
+
+::: callout
+
+# The `sf` package
+
+`sf` stands for Simple Features which is a standard defined by the Open Geospatial Consortium for storing and accessing geospatial vector data. PostGIS uses the same standard; so those of you who used PostGIS, might find some resemblances with the functions used by the `sf` package. 
+
+:::
+
+## Import shapefiles
+
+Let's start by opening a shapefile. Shapefiles a common file format to store spatial vector data used in GIS software. We will read a shapefile with the administrative boundary of Delft with the function `st_read()` from the `sf` package. 
+
+```{r results='hide'}
+boundary_Delft <- st_read("data/delft-boundary.shp")
+```
+
+::: callout
+
+# All `sf` functions start with `st_`
+
+Note that all functions from the `sf` package start with the standard prefix `st_` which stands for Spatial Type. This is helpful in at least two ways: (1) it  makes the interaction with or translation to/from software using the simple features standard like PostGIS easy, and (2) it allows for easy autocompletion of function names in RStudio.
+
+:::
+
+## Spatial Metadata
+
+The `st_read()` function gave us a message with a summary of metadata about the file that was read in. To examine the metadata in more detail, we can use other, more specialised, functions from the `sf` package. The `st_geometry_type()` function, for instance, gives us information about the geometry type, which in this case is `POLYGON`.
+
+```{r}
+st_geometry_type(boundary_Delft)
+```
+
+The `st_crs()` function returns the coordinate reference system (CRS) used by the shapefile, which in this case is `WGS84` and has the unique reference code `EPSG: 4326`. 
+
+```{r}
+st_crs(boundary_Delft)
+```
+
+::: callout
+
+# Examining the output of `st_crs()`
+
+As the output of `st_crs()` can be long, you can use `$Name` and `$epsg` after the `crs()` call to extract the projection name and EPSG code respectively.
+
+:::
+
+The `st_bbox()` function shows the extent of the layer. As `WGS84` is a **geographic CRS**, the extent of the shapefile is displayed in degrees.
+
+```{r}
+st_bbox(boundary_Delft)
+```
+
+We need a **projected CRS**, which in the case of the Netherlands is typically the Amersfort / RD New projection. To reproject our shapefile, we will use the `st_transform()` function. For the `crs` argument we can use the EPSG code of the CRS we want to use, which is `28992` for the `Amersfort / RD New` projection. 
+
+```{r}
+boundary_Delft <- st_transform(boundary_Delft, 28992)
+st_crs(boundary_Delft)
+```
+
+Notice that the bounding box is measured in meters after the transformation.
+
+```{r}
+st_bbox(boundary_Delft)
+```
+
+We confirm the transformation by examining the reprojected shapefile.
+
+```{r}
+boundary_Delft
+```
+
+::: callout
+
+More about CRS in [Handling Spatial Projection & CRS]().
+
+:::
+
+
+
+## Plot a vector layer
+
+Now, let's plot this shapefile. You are already familiar with the `ggplot2` package from [Introduction to Visualisation](). `ggplot2` has special `geom_` functions for spatial data. We will use the `geom_sf()` function for `sf` data.
+
+```{r}
+
+ggplot(data = boundary_Delft) +
+  geom_sf(size = 3, color = "black", fill = "cyan1") +
+  labs(title = "Delft Administrative Boundary") +
+  coord_sf(datum = st_crs(28992))  # this is needed to display the axes in meters
+
+```
+
+::::::::::::::::::::::::::::::::::::: challenge 
+
+### Challenge 1: Import line and point vector layers
+
+Read in `delft-streets.shp` and `delft-leisure.shp` and assign them to `lines_Delft` and `point_Delft` respectively. Answer the following questions:
+
+1. What type of R spatial object is created when you import each layer?
+2. What is the CRS and extent for each object?
+3. Do the files contain points, lines, or polygons?
+4. How many features are in each file?
+
+:::::::::::::::::::::::: solution 
+
+```{r results='hide'}
+lines_Delft <- st_read("data/delft-streets.shp")
+point_Delft <- st_read("data/delft-leisure.shp")
+```
+
+We can check the type of data with the `class()` function from base R. Both `lines_Delft` and `point_Delft` are objects of class `"sf"`, which extends the `"data.frame"` class. 
+
+```{r}
+class(lines_Delft)
+class(point_Delft)
+```
+
+`lines_Delft` and `point_Delft` are in the correct CRS.
+
+```{r}
+st_crs(lines_Delft)$epsg
+st_crs(point_Delft)$epsg
+```
+
+When looking at the bounding boxes with the `st_bbox()` function, we see the spatial extent of the two objects in a projected CRS using meters as units. `lines_Delft()` and `point_Delft` have similar extents.
+
+```{r}
+st_bbox(lines_Delft)
+st_bbox(point_Delft)
+```
+
+:::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+
+
+::::::::::::::::::::::::::::::::::::: keypoints 
+
+- Metadata for vector layers include geometry type, CRS, and extent.
+- Load spatial objects into R with the `st_read()` function.
+- Spatial objects can be plotted directly with `ggplot` using the `geom_sf()` function. No need to convert to a data frame.
+
+::::::::::::::::::::::::::::::::::::::::::::::::