From 2a052030538bc86a717111d9194baf81e8aa0695 Mon Sep 17 00:00:00 2001
From: Heather Savoy <47045484+HeatherSavoy-USDA@users.noreply.github.com>
Date: Mon, 11 Sep 2023 00:27:16 -0600
Subject: [PATCH 1/4] Add first visualization tutorial draft
---
Visualizations/GRWG_Maps_R.md | 340 +++++++++++++++++
Visualizations/Visualizations-LandingPage.md | 13 +
Visualizations/assets/R_background-1.png | Bin 0 -> 66260 bytes
Visualizations/assets/R_elev-1.png | Bin 0 -> 117337 bytes
Visualizations/assets/R_final-1.png | Bin 0 -> 88982 bytes
.../assets/R_forest_study_area-1.png | Bin 0 -> 61266 bytes
Visualizations/assets/R_forests-1.png | Bin 0 -> 42689 bytes
.../assets/R_forests_study_area_clear-1.png | Bin 0 -> 56552 bytes
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.../assets/R_raster_bad_transform-1.png | Bin 0 -> 114091 bytes
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.../assets/R_raster_manual_transform-1.png | Bin 0 -> 99768 bytes
.../assets/R_raster_with_vector-1.png | Bin 0 -> 69975 bytes
Visualizations/assets/R_study_area_4326-1.png | Bin 0 -> 39064 bytes
Visualizations/assets/R_study_area_5070-1.png | Bin 0 -> 47119 bytes
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...mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg | Bin 0 -> 1337590 bytes
assets/images/sources.md | 1 +
index.md | 6 +
tutorials/GRWG_Maps.Rmd | 351 ++++++++++++++++++
20 files changed, 711 insertions(+)
create mode 100644 Visualizations/GRWG_Maps_R.md
create mode 100644 Visualizations/Visualizations-LandingPage.md
create mode 100644 Visualizations/assets/R_background-1.png
create mode 100644 Visualizations/assets/R_elev-1.png
create mode 100644 Visualizations/assets/R_final-1.png
create mode 100644 Visualizations/assets/R_forest_study_area-1.png
create mode 100644 Visualizations/assets/R_forests-1.png
create mode 100644 Visualizations/assets/R_forests_study_area_clear-1.png
create mode 100644 Visualizations/assets/R_raster-1.png
create mode 100644 Visualizations/assets/R_raster_bad_transform-1.png
create mode 100644 Visualizations/assets/R_raster_binned-1.png
create mode 100644 Visualizations/assets/R_raster_manual_transform-1.png
create mode 100644 Visualizations/assets/R_raster_with_vector-1.png
create mode 100644 Visualizations/assets/R_study_area_4326-1.png
create mode 100644 Visualizations/assets/R_study_area_5070-1.png
create mode 100644 Visualizations/assets/R_study_area_sf_plot-1.png
create mode 100644 assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg
create mode 100644 tutorials/GRWG_Maps.Rmd
diff --git a/Visualizations/GRWG_Maps_R.md b/Visualizations/GRWG_Maps_R.md
new file mode 100644
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--- /dev/null
+++ b/Visualizations/GRWG_Maps_R.md
@@ -0,0 +1,340 @@
+---
+title: Making publication-quality maps in R
+layout: single
+author: Heather Savoy
+author_profile: true
+header:
+ overlay_color: "444444"
+ overlay_image: /assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg
+---
+
+**Last Update:** 11 September 2023
**Download RMarkdown**:
+[GRWG\_Maps.Rmd](https://geospatial.101workbook.org/tutorials/GRWG_Maps.Rmd)
+
+## Overview
+
+This tutorial covers creating maps in R using the `sf` and `ggplot2`
+packages.
+
+To download the Rmarkdown file for this tutorial to your local machine
+(or any SCINet cluster within OoD), you can use the following lines:
+
+ library(httr)
+ tutorial_name <- 'GRWG_Maps.Rmd'
+ GET(paste0('https://geospatial.101workbook.org/tutorials/',tutorial_name),
+ write_disk(tutorial_name))
+
+*Language:* `R`
+
+*Primary Libraries/Packages:*
+
+| Name | Description | Link |
+|:--|:--|:--|
+| sf | Simple Features for R | https://r-spatial.github.io/sf/ |
+| ggplot2 | Create Elegant Data Visualizations Using the Grammar of Graphics | https://ggplot2.tidyverse.org/ |
+
+## Nomenclature
+
+- *Raster:* Data defined on a grid of geospatial coordinates
+- *CRS:* Coordinate Reference System, also known as a spatial
+ reference system. A system for defining geospatial coordinates.
+
+## Data Details
+
+- Data:
+- Link: []()
+- Other Details:
+
+## Steps
+
+- Acquire data:
+ - Access informational boundaries.
+ - Define study area and points.
+ - Download raster to fill study area.
+- Incrementally plot:
+ - Map background
+ - Raster
+ - Study area
+ - Points
+ - Annotations
+- Format legends, axes, and map scale
+
+## Step 0: Import Libraries/Packages
+
+ library(httr) # Access data from online resources
+ library(sf) # Handle vector data
+ library(terra) # Handle raster data
+ library(USAboundaries) # US boundaries vector data
+ library(dplyr) # General data manipulation
+ library(ggplot2) # Visualizations
+ library(ggspatial) # Extra geospatial annotations for maps
+
+## Step 1: Acquire data
+
+We can use the `us_states()` function from the `USABoundaries` packages
+to easily define a study area based on states. This function returns a
+`sf` object. For `sf` objects, there is a default plotting method that
+can be used.
+
+ # Pull the boundaries for some select states for our study area
+ my_area <- us_states(states = c('LA','MS','AL','GA','FL'))
+
+ # Save the bounding box for later use
+ my_bbox <- my_area %>%
+ st_bbox()
+
+ # Plot using the default plotting method for `sf` objects
+ plot(my_area['name'])
+
+![](../Visualizations/assets/R_study_area_sf_plot-1.png)
+
+But we will use the `ggplot2` functions to build more complicated maps.
+With both the `ggplot2` and `sf` packages, we can take advantage of the
+`geom_sf()` function to quickly plot `sf` objects in the `ggplot2`
+framework. Unlike most `ggplot()` uses, you don’t have to specify the
+`x` and `y` mapping aesthetic for `geom_sf()` since that will be assumed
+from the geometry column.
+
+ ggplot() +
+ geom_sf(data=my_area)
+
+![](../Visualizations/assets/R_study_area_4326-1.png)
+
+Say you want to use a certain CRS in the project for which you are
+making your map. This can affect the plotted map.
+
+ # Specify the target CRS
+ target_crs <- 5070
+ target_crs_str <- paste0('EPSG:',target_crs)
+
+ # Transform area to that CRS and save the bounding box in the CRS for later use
+ my_area_tcrs <- my_area %>%
+ st_transform(target_crs)
+ my_bbox_tcrs <- my_area_tcrs %>%
+ st_bbox()
+
+ # Plot the transformed area
+ ggplot() +
+ geom_sf(data=my_area_tcrs)
+
+![](../Visualizations/assets/R_study_area_5070-1.png)
+
+The plot
+coordinate system depends on the order of the `geom_sf()` calls, or
+what’s specifically stated in `coord_sf()`.
+
+Let’s now consider another vector dataset we’d like to plot within our
+study area. We can use the GeoPlatform API to download a subset of the
+*FS National Forests Dataset (US Forest Service Proclaimed Forests)*
+dataset from the US Forest Service.
+
+
+ # GeoPlatform API url
+ api_url <- 'https://geoapi.geoplatform.gov'
+
+ # Read collections catalog
+ collections_url <- paste0(api_url,'/collections')
+ collections_content <- GET(collections_url) %>%
+ content()
+ collections_md <- collections_content$collections
+
+ # Get collection ID for collection with 'forest' in the title
+ search_term <- 'forest'
+ collection_titles <- sapply(collections_md, function(col) tolower(col$title))
+ collection_id <- collections_md[[grep(search_term,collection_titles)]]$id
+
+ # Download data within bounding box from collection
+ bbox_str <- paste0(my_bbox,collapse = ',')
+ data_url <- paste0(collections_url, "/" ,collection_id,
+ "/items?limit=10000&bbox=", bbox_str)
+ tmp_file <- 'temp.zip'
+ GET(data_url, write_disk(tmp_file,overwrite=TRUE))
+
+ collection_sf <- st_read(tmp_file)
+
+ # View shape
+ ggplot() +
+ geom_sf(data=collection_sf)
+
+![](../Visualizations/assets/R_forests-1.png)
+
+Now let’s plot both this
+forest dataset and our study area.
+
+ ggplot() +
+ geom_sf(data=my_area_tcrs) +
+ geom_sf(data=collection_sf)
+
+![](../Visualizations/assets/R_forest_study_area-1.png)
+
+First, it is
+hard to distinguish the two vector datasets with the default coloring.
+Second, we requested forests within a bounding box of our study area. So
+let’s find the intersection of our forests and study area, and color the
+forests polygons.
+
+
+ my_forests_tcrs <- collection_sf %>%
+ st_transform(target_crs) %>%
+ st_buffer(1) %>%
+ st_intersection(my_area_tcrs)
+ #> Warning: attribute variables are assumed to be spatially constant throughout all geometries
+
+
+ ggplot() +
+ geom_sf(data=my_area_tcrs) +
+ geom_sf(data=my_forests_tcrs,
+ fill = 'forestgreen')
+
+![](../Visualizations/assets/R_forests_study_area_clear-1.png)
+
+And let’s also get a raster dataset to fill in our study area. We will
+grab some elevation from The National Map. Again, we will use our study
+area bounding box to define our subset.
+
+ tnm_url <- 'https://elevation.nationalmap.gov/arcgis/rest/services/'
+ my_raster <- 'elev.tiff'
+ GET(paste0(tnm_url,'3DEPElevation/ImageServer/exportImage?bbox=',bbox_str,
+ '&bboxSR=4326&format=tiff&f=image'),
+ write_disk(my_raster,overwrite=TRUE))
+
+ elev <- rast(my_raster)
+
+ # Visualize with default terra plotting function for rasters
+ plot(elev)
+
+![](../Visualizations/assets/R_elev-1.png)
+
+We will reproject this raster
+into our target CRS and apply a mask defined by our study area. Then, we
+will also convert this `terra` object into a `data.frame` to help with
+our `ggplot`ting.
+
+ elev_masked <- elev %>%
+ project(target_crs_str) %>%
+ mask(my_area_tcrs)
+
+ elev_df <- elev_masked %>%
+ as.data.frame(xy=TRUE)
+
+ elev_df %>%
+ ggplot(aes(x,y,fill=elev)) +
+ geom_raster()
+
+![](../Visualizations/assets/R_raster-1.png)
+
+ elev_df %>%
+ ggplot(aes(x,y,fill=elev)) +
+ geom_raster() +
+ scale_fill_continuous(trans = 'sqrt')
+ #> Warning in self$trans$transform(x): NaNs produced
+ #> Warning: Transformation introduced infinite values in discrete y-axis
+
+![](../Visualizations/assets/R_raster_bad_transform-1.png)
+
+ elev_df %>%
+ ggplot(aes(x,y,fill=sqrt(elev+50))) +
+ geom_raster()
+
+![](../Visualizations/assets/R_raster_manual_transform-1.png)
+
+ ggplot() +
+ geom_raster(aes(x,y,fill=elev),
+ data = elev_df) +
+ scale_fill_fermenter(palette = 'BrBG',
+ name = 'Elevation',
+ breaks = c(-50,0,50,250,500,750))
+
+![](../Visualizations/assets/R_raster_binned-1.png)
+
+ ggplot() +
+ geom_raster(aes(x,y,fill=elev),
+ data = elev_df) +
+ geom_sf(data=my_area_tcrs,
+ fill = NA) +
+ geom_sf(data=my_forests_tcrs,
+ fill = 'red') +
+ scale_fill_fermenter(palette = 'BrBG',
+ name = 'Elevation',
+ breaks = c(-50,0,50,250,500,750))
+
+![](../Visualizations/assets/R_raster_with_vector-1.png)
+
+Now, we can add some nice map background layers and annotations. First,
+let’s build a background to show the context of neighboring states and
+water.
+
+ all_states <- us_states() %>%
+ st_transform(target_crs_str)
+
+ ggplot() +
+ geom_sf(data = all_states, #base color for all land
+ fill = "#e3f7dc",
+ col = "gray75",
+ linewidth = 0.15) +
+ geom_sf(data=my_area_tcrs, #our study area states
+ fill = NA,
+ linewidth = 0.5) +
+ scale_x_continuous(name = 'Longitude', # limit axes to study area bounding box
+ limits = range(my_bbox_tcrs[c(1,3)])) +
+ scale_y_continuous(name = 'Latitude',
+ limits = range(my_bbox_tcrs[c(2,4)])) +
+ theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)), # water!
+ panel.grid.major = element_line(color = 'white', linewidth = 0.3))
+
+![](../Visualizations/assets/R_background-1.png)
+
+Everything together, plus some annotations!
+
+
+ # Define points between which to draw an arrow
+ arrow_pts <- data.frame(x = c(-82,-89.25),
+ y = c(29.5, 32.5)) %>%
+ st_as_sf(coords = c('x','y'),
+ crs = 4326) %>%
+ st_transform(target_crs) %>%
+ st_coordinates() %>%
+ as_tibble()
+
+ # Draw the complete map
+ ggplot() +
+ geom_sf(data = all_states, #base color for all land
+ fill = "#e3f7dc",
+ col = "gray75",
+ linewidth = 0.15) +
+ geom_raster(aes(x,y,fill=elev), # elevation raster
+ data = elev_df) +
+ geom_sf(data=my_area_tcrs, # our study area states
+ fill = NA,
+ linewidth = 0.5) +
+ geom_sf(data=my_area_tcrs %>% # highlight boundary of the study area
+ st_union(),
+ fill = NA,
+ color = 'black',
+ linewidth = 0.5) +
+ geom_sf(data=my_forests_tcrs, # forest polygons
+ fill = 'red',
+ alpha = 0.5,
+ color = 'firebrick',
+ linewidth = 0.25) +
+ geom_curve(aes(x=arrow_pts$X[1], y = arrow_pts$Y[1], # curved arrow
+ xend = arrow_pts$X[2], yend = arrow_pts$Y[2]),
+ curvature = 0.25,
+ arrow = arrow(length = unit(2,'mm'),
+ type = 'closed'),
+ color = 'red') +
+ scale_fill_fermenter(palette = 'BrBG',
+ name = 'Elevation',
+ breaks = c(-50,0,50,250,500,750)) +
+ scale_x_continuous(name = 'Longitude',
+ limits = range(my_bbox_tcrs[c(1,3)])) +
+ scale_y_continuous(name = 'Latitude',
+ limits = range(my_bbox_tcrs[c(2,4)])) +
+ annotation_scale(location = "tl") +
+ annotation_north_arrow(height = unit(1, "cm"),
+ width = unit(1, "cm")) +
+ theme(panel.background = element_rect(fill = alpha('#afdbed', 0.5)),
+ panel.grid.major = element_line(color = 'white', linewidth = 0.3))
+ #> Warning: Removed 23 rows containing missing values (`geom_raster()`).
+
+![](../Visualizations/assets/R_final-1.png)
diff --git a/Visualizations/Visualizations-LandingPage.md b/Visualizations/Visualizations-LandingPage.md
new file mode 100644
index 0000000..92a62f3
--- /dev/null
+++ b/Visualizations/Visualizations-LandingPage.md
@@ -0,0 +1,13 @@
+---
+title: "Geospatial Visualizations"
+layout: single
+header:
+ overlay_color: "444444"
+ overlay_image: assets/images/elena-mozhvilo-znhEe1cbbQE-unsplash-1200_800.jpg
+---
+
+## Index
+
+| Tutorial | Python | R |
+|:--|:--|:--|
+| Creating publication-quality maps | Coming soon | [view](GRWG_Maps_R) [download](../tutorials/GRWG_Maps.Rmd) |
diff --git a/Visualizations/assets/R_background-1.png b/Visualizations/assets/R_background-1.png
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