harmonizing wsim lesson 1 and 2

wsim-gldas-vis.qmd

@@ -1,6 +1,6 @@
 ---
 title: "WSIM-GLDAS Dataset Exploration and Visualizations"
-author: "Joshua Brinks"
+author: "Joshua Brinks & Elaine Fatumi"
 date: "December, 6 2023"
 ---
 
@@ -24,65 +24,103 @@ date: "December, 6 2023"
 
 ## Setup
 
-```r
-library(stars) # raster manipulation
-library(sf) # vector manipulation
-library(ggplot2) # advanced plotting
-library(lubridate) # date/time manipulation
-library(exactextractr) # zonal statistics
-
-```
 
 ## Load Data
 
-Load in data from previous vignette.
-
-I think the previous vignette should end with a 2000-2014 12-month integration CONUS dataset.
+We'll start again with the WSIM-GLDAS 12 month integration anomaly file and quickly subset it to the continental United States.
+
+```{r warning=FALSE}
+#| code-fold: true
+wsim_gldas <- stars::read_stars("composite_anom_12mo.nc", proxy = FALSE)
+keeps<-seq(lubridate::ymd("2000-12-01"),
+           lubridate::ymd("2014-12-01"), 
+           by = "year")
+# filter using that vector
+wsim_gldas <- dplyr::filter(wsim_gldas, time %in% keeps)
+# you may want to clear your memory if your system is limited
+gc()
+wsim_deficit <- wsim_gldas['deficit']
+# generate a vector of dates for subsetting
+
+usa <- httr::GET("https://www.geoboundaries.org/api/current/gbOpen/USA/ADM1/")
+usa <- httr::content(usa)
+usa <- sf::st_read(usa$gjDownloadURL)
+drops<-
+  c("Alaska", "Hawaii", 
+    "American Samoa",
+    "Puerto Rico",
+    "Commonwealth of the Northern Mariana Islands", 
+    "Guam", 
+    "United States Virgin Islands")
+
+usa<-usa[!(usa$shapeName %in% drops),]
+wsim_deficit_usa<-wsim_deficit[usa]
+```
 
-```r
+Now we'll verify this with `print()` and `plot()`.
 
+```{r}
+print(wsim_deficit_usa)
+```
+The output shows that we've selected a single attribute ('deficit') and 15 time-steps in the 'time' dimension.
+
+```{r warning=FALSE, message=FALSE}
+wsim_deficit_usa |>
+  dplyr::slice(index = 15, along = "time") |>
+  plot(reset = FALSE, breaks = c(0,-5,-10,-20,-40,-50))
+
+plot(sf::st_geometry(usa),
+     add = TRUE,
+     lwd = 3,
+     fill = NA,
+     border = 'purple')
 ```
 
-Verify data structure with `print` or `summary.`
 
 ## Exploratory Histogram
 
 Create histogram of raster values for a single time step.
 
-Basic plotting method is OK, but check if it can be done with `ggplot`so we can use a uniform palette across all modules.
+Get the values out of the raster and create a histogram.
 
-```r
+```{r}
+# filter for the first time-step in the file
+usa1 <-
+  wsim_deficit_usa |> dplyr::slice(time, 1)
 
+# extract the values into a data.frame
+usa1<-as.data.frame(as.numeric(wsim_deficit_usa$deficit))
+# appropriately name the values (it was lost in the example)
+names(usa1)<-"Deficit"
 ```
 
-Extreme values or other items of note might require additional visualization or other data exploration.
-
-## Multi-Panel Time Series
 
-Create a multipanel time series of 12 month integration CONUSA; similar to what we used to identify our case studies. Each panel will represent 1 year.
-
-Load in a CONUSA geojson from geoBoundaries. Copy methods from previous vignette.
-
-```r
+Check the values
 
+```{r}
+ggplot2::ggplot(usa1, ggplot2::aes(Deficit))+
+  ggplot2::geom_histogram(na.rm = TRUE)
 ```
+There are some bad outliers, we can just zoom into the majority of values by setting x-axis limits.
 
-
-Start with the basic plotting commands--create the time series with `slice` or other method used in previous vignette.
-
-```r
-
+```{r}
+ggplot2::ggplot(usa1, ggplot2::aes(Deficit))+
+  ggplot2::geom_histogram(na.rm = TRUE)+
+  ggplot2::xlim(c(-10,0))
 ```
 
-The palette will not exist and be difficult to use.
+Extreme values or other items of note might require additional visualization or other data exploration.
 
-Try a built in palette for stars (not sure if possible?).
+## Multi-Panel Time Series
 
-Introduce the official WSIM palette. This may only be helpful within a `ggplot` function.
+Create a multipanel time series of 12 month integration CONUSA; similar to what we used to identify our case studies. Each panel will represent 1 year.**
 
-```r
-# numbers are return period values for a composite surplus (blues) and deficit (reds) dataset
-leg_colors<-c(
+Load in a CONUSA geojson from geoBoundaries. Copy methods from previous vignette.
+
+```{r warning = FALSE}
+wsim_deficit_usa |>
+  plot(reset = FALSE,
+       col = leg_colors<-c(
     '#9B0039',
     # -50 to -40
     '#D44135',
@@ -93,25 +131,17 @@ leg_colors<-c(
     # -10 to -5
     '#FFEDA3',
     # -5 to -3
-    '#FFFFFF',
-    # -3 to 3
-    '#CEFFAD',
-    # 3 to 5
-    '#00F3B5',
-    # 5 to 10
-    '#00CFCE',
-    # 10 to 20
-    '#009ADE',
-    # 20 to 40
-    '#0045B5')
+    '#FFFFFF'))
 ```
 
 Once hot spots are easily identified pick a region of interest to zoom in on using the 1 month integration dataset.
 
 Load in the 1 month integration dataset and subset/index the dataset to the region of interest (copy code from previous vignette). Use `dplyr::slice` or other method to pull out just the 12 months from the year of interest. Code demonstrating these techniques in previous vignette.
 
-```r
+```{r}
+wsim_gldas_1mo <- stars::read_stars("composite_anom_1mo.nc", proxy = FALSE)
 
+print(wsim_gldas_1mo)
 ```
 
 Create a multi-panel figure with each panel representing 1 month to identify the most intense months of drought or flooding. Starting with this one maybe use `ggplot` and a nice palette, legend, and panel headings. Will probably have to use some sort of faceting to make individual panels (might not be possible).
@@ -120,10 +150,6 @@ Create a multi-panel figure with each panel representing 1 month to identify the
 
 ```
 
-## Use Case Background
-
-Now that we've keyed in on a particular event, bring in the backup information we've collected to discuss what actually happened.
-
 ## Point Location Time Series
 
 Visualize an individual cell with particular extreme or maybe volatile values. Use Google Maps to identify the latitude/longitude of a place of interest. Maybe an urban center or other important location in the region that suffered from the extreme event.