Episode 7 updates for line sizes and other

episodes/07-vector-shapefile-attributes-in-r.Rmd

@@ -48,6 +48,7 @@ and other prerequisites you will need to work through the examples in this
 episode.
 
 
+
 ::::::::::::::::::::::::::::::::::::::::::::::::::
 
 This episode continues our discussion of vector layer attributes and covers how
@@ -58,7 +59,7 @@ attribute values.
 
 ## Load the Data
 
-We will continue using the `sf`, `terra` and `ggplot2` packages in this
+We will continue using the `sf`, `terra` `dplyr` and `ggplot2` packages in this
 episode. Make sure that you have these packages loaded. We will continue to
 work with the three ESRI `shapefiles` (vector layers) that we loaded in the
 [Open and Plot Vector Layers in R](06-vector-open-shapefile-in-r/) episode.
@@ -86,7 +87,7 @@ point_HARV
 
 We can use the `ncol` function to count the number of attributes associated
 with a spatial object too. Note that the geometry is just another column and
-counts towards the total.
+counts towards the total. Let's look at the roads file:
 
 ```{r shapefile-attributes}
 ncol(lines_HARV)
@@ -215,7 +216,7 @@ connecting line thickness to a data variable.
 
 ```{r plot-subset-shapefile-unique-colors, fig.cap="Map of the footpaths in the study area where each feature is colored differently."}
 ggplot() +
-  geom_sf(data = footpath_HARV, aes(color = factor(OBJECTID)), size = 1.5) +
+  geom_sf(data = footpath_HARV, aes(color = factor(OBJECTID)), linewidth = 1.5) +
   labs(color = 'Footpath ID') +
   ggtitle("NEON Harvard Forest Field Site", subtitle = "Footpaths") +
   coord_sf()
@@ -250,7 +251,7 @@ Now let's plot that data:
 
 ```{r harv-boardwalk-map, fig.cap="Map of the boardwalks in the study area."}
 ggplot() +
-  geom_sf(data = boardwalk_HARV, size = 1.5) +
+  geom_sf(data = boardwalk_HARV, linewidth = 1.5) +
   ggtitle("NEON Harvard Forest Field Site", subtitle = "Boardwalks") +
   coord_sf()
 ```
@@ -283,7 +284,7 @@ Now we can plot the data:
 
 ```{r harv-stone-wall-map, fig.cap="Map of the stone walls in the study area where each feature is colored differently."}
 ggplot() +
-  geom_sf(data = stoneWall_HARV, aes(color = factor(OBJECTID)), size = 1.5) +
+  geom_sf(data = stoneWall_HARV, aes(color = factor(OBJECTID)), linewidth = 1.5) +
   labs(color = 'Wall ID') +
   ggtitle("NEON Harvard Forest Field Site", subtitle = "Stonewalls") +
   coord_sf()
@@ -389,7 +390,7 @@ Now we can create our plot.
 
 ```{r harv-path-line-types, fig.cap="Roads and trails in the area with different line thickness for each type of paths."}
 ggplot() +
-  geom_sf(data = lines_HARV, aes(size = TYPE)) +
+  geom_sf(data = lines_HARV, aes(linewidth = TYPE)) +
   scale_size_manual(values = line_width) +
   ggtitle("NEON Harvard Forest Field Site",
           subtitle = "Roads & Trails - Line width varies") +
@@ -405,21 +406,17 @@ ggplot() +
 We can add a legend to our plot too. When we add a legend, we use the following
 elements to specify labels and colors:
 
-- `bottomright`: We specify the location of our legend by using a default
-  keyword. We could also use `top`, `topright`, etc.
-- `levels(objectName$attributeName)`: Label the legend elements using the
-  categories of levels in an attribute (e.g., levels(lines\_HARV$TYPE) means
-  use the levels boardwalk, footpath, etc).
-- `fill =`: apply unique colors to the boxes in our legend. `palette()` is the
-  default set of colors that R applies to all plots.
 
 Let's add a legend to our plot. We will use the `road_colors` object
 that we created above to color the legend. We can customize the
 appearance of our legend by manually setting different parameters.
 
+
+
+
 ```{r add-legend-to-plot, fig.cap="Roads and trails in the study area using thicker lines than the previous figure."}
 ggplot() +
-  geom_sf(data = lines_HARV, aes(color = TYPE), size = 1.5) +
+  geom_sf(data = lines_HARV, aes(color = TYPE), linewidth = 1.5) +
   scale_color_manual(values = road_colors) +
   labs(color = 'Road Type') +
   ggtitle("NEON Harvard Forest Field Site",
@@ -435,7 +432,7 @@ parameters.
 
 ```{r modify-legend-plot, fig.cap="Map of the paths in the study area with large-font and border around the legend."}
 ggplot() +
-  geom_sf(data = lines_HARV, aes(color = TYPE), size = 1.5) +
+  geom_sf(data = lines_HARV, aes(color = TYPE), linewidth = 1.5) +
   scale_color_manual(values = road_colors) +
   labs(color = 'Road Type') +
   theme(legend.text = element_text(size = 20),
@@ -449,7 +446,7 @@ ggplot() +
 new_colors <- c("springgreen", "blue", "magenta", "orange")
 
 ggplot() +
-  geom_sf(data = lines_HARV, aes(color = TYPE), size = 1.5) +
+  geom_sf(data = lines_HARV, aes(color = TYPE), linewidth = 1.5) +
   scale_color_manual(values = new_colors) +
   labs(color = 'Road Type') +
   theme(legend.text = element_text(size = 20),
@@ -516,7 +513,7 @@ line width.
 ```{r harv-paths-bike-horses, fig.cap="Roads and trails in the area highlighting paths where horses and bikes are allowed."}
 ggplot() +
   geom_sf(data = lines_HARV) +
-  geom_sf(data = lines_showHarv, aes(color = BicyclesHo), size = 2) +
+  geom_sf(data = lines_showHarv, aes(color = BicyclesHo), linewidth = 2) +
   scale_color_manual(values = "magenta") +
   ggtitle("NEON Harvard Forest Field Site",
           subtitle = "Roads Where Bikes and Horses Are Allowed") +
@@ -562,7 +559,7 @@ Now we can create our plot:
 
 ```{r colored-state-boundaries, fig.cap="Map of the continental United States where the state lines are colored by region."}
 ggplot() +
-  geom_sf(data = state_boundary_US, aes(color = region), size = 1) +
+  geom_sf(data = state_boundary_US, aes(color = region), linewidth = 1) +
   scale_color_manual(values = colors) +
   ggtitle("Contiguous U.S. State Boundaries") +
   coord_sf()

index.md

@@ -1,19 +1,11 @@
 ---
-maintainers:
-- Leah Wasser
-- Jemma Stachelek
-- Tyson Swetnam
-- Lauren O'Brien
-- Janani Selvaraj
-- Lachlan Deer
-- Chris Prener
-- Juan Fung
+
 site: sandpaper::sandpaper_site
 ---
 
 **Lesson Authors:** Leah A. Wasser, Megan A. Jones, Zack Brym, Kristina Riemer, Jason Williams, Jeff Hollister,  Mike Smorul, Jemma Stachelek
 
-**Lesson Maintainers:** {{ page.maintainers | join: ', ' }}
+
 
 The episodes in this lesson cover how to open, work with, and plot
 vector and raster-format spatial data in R. Additional topics include
@@ -34,7 +26,7 @@
 
 This lesson assumes you have some knowledge of `R`. If you've never
 used `R` before, or need a refresher, start with our
 [Introduction to R for Geospatial Data](http://www.datacarpentry.org/r-intro-geospatial/)
 lesson.
 
 ### Geospatial Skill Level
@@ -42,20 +34,20 @@
 This lesson assumes you have some knowledge of geospatial data types
 and common file formats. If you have never worked with geospatial
 data before, or need a refresher, start with our
 [Introduction to Geospatial Concepts](http://www.datacarpentry.org/organization-geospatial/)
 lesson.
 
 ### Install Software and Download Data
 
 For installation instructions and to download the data used in this
 lesson, see the
 [Geospatial Workshop Overview](http://www.datacarpentry.org/geospatial-workshop/#setup).
 
 ### Setup RStudio Project
 
 Make sure you have set up a RStudio project for this lesson, as
 described in the
 [setup instructions](http://www.datacarpentry.org/geospatial-workshop/#setup)
 and that your working directory is correctly set.
 
 

r-raster-vector-geospatial.Rproj

@@ -0,0 +1,16 @@
+Version: 1.0
+ProjectId: 32618674-7875-479d-aff6-55bf7903a906
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX
+
+BuildType: Website