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Shiny app to explore climate space of SEOSAW region

DATE: 2022-09-10

AUTHOR: John L. Godlee

I made an R Shiny web app to explore the climate space of the SEOSAW region. The app can be found here, on shinyapps.io[1].

1: https://johngodlee.shinyapps.io/climate_space/

A big part of getting the app to run smoothly was to pre-process the data sources so they could be loaded quickly from disk, subsetted quickly, and rendered quickly with ggplot(). I haven't styled the app much to make it look pretty, as it was more a learning experience on how code reactive objects in Shiny.

I loaded country outlines of Africa and the SEOSAW ecoregion from the {seosawr} R package, and simplified them using {rmapshaper}:

africa <- seosawr::africa
seosaw_region <- seosawr::seosaw_region
seosaw_bbox <- seosawr::seosaw_bbox

africa_simp <- ms_simplify(africa, 
  keep = 0.01, keep_shapes = FALSE) %>% 
  st_intersection(., seosaw_bbox)

seosaw_region_simp <- ms_simplify(seosaw_region, 
  keep = 0.01, keep_shapes = FALSE)

I used climate data from WorldClim[2], which I downloaded at 10 minute spatial resolution using the {raster} package:

2: https://www.worldclim.org/

bioclim <- getData("worldclim", var = "bio", res = 10)

which returns a raster stack object. Then I cropped and masked the climate data with the SEOSAW region polygon:

bioclim_crop <- mask(crop(bioclim, seosaw_region_simp), seosaw_region_simp)

and finally, extracted the values and coordinates of each raster cell for each raster layer, resulting in a large matrix, with cells for rows, and bioclim variables or coordinates as columns, which I saved as a .rds file.

bioclim_val <- cbind(coordinates(bioclim_crop), values(bioclim_crop))
bioclim_val_fil <- bioclim_val[
  !apply(bioclim_val, 1, function(x) { 
    all(is.na(x[!names(x) %in% c("x", "y")])) 
  }),
]
saveRDS(bioclim_val_fil, "app/data/bioclim_val_fil.rds")

The app allows you to draw a rectangle around the climate space of interest using two bioclim variables which you can select from a dropdown list. This process uses the brush operator in the Shiny plotOutput() function. I subsetted the raster matrix to the values returned by input$brush using reactive() in the Shiny app.

rasterMapInput <- reactive({
val_sel <- val[,c("x", "y", input$xvar, input$yvar)]
if (!is.null(input$brush)) {
  xmin <- input$brush$xmin
  xmax <- input$brush$xmax
  ymin <- input$brush$ymin
  ymax <- input$brush$ymax
  val_sel <- val_sel[
    val_sel[,3] > xmin &
      val_sel[,3] < xmax &
      val_sel[,4] > ymin & 
      val_sel[,4] < ymax, 
    c("x", "y")] 
}
as.data.frame(val_sel)
})

Then I simply used ggplot() with rasterMapInput() as the data input to geom_tile() to map the climate space selected on the map of southern Africa.

# Extract values from selected raster layers
valInput <- reactive({
as.data.frame(val[,c(input$xvar, input$yvar)])
})

output$plot1 <- renderPlot(
ggplot() + 
  geom_bin2d(data = valInput(), 
    mapping = aes_string(x = names(valInput())[1], y = names(valInput())[2]),
      colour = bg_col, fill = bg_col, bins = 100) + 
  theme_classic() + 
  theme(legend.position = "none")
)

output$plot2 <- renderPlot(
map_plot + 
  geom_tile(data = rasterMapInput(), 
    aes(x = x, y = y), 
    fill = bg_col) 
)

Screenshot of the app with a dry and wet region selected.