gemini://republic.circumlunar.space/users/johngodlee/posts/2022-09-10-shiny

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].

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:

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

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) )

Shiny app to explore climate space of SEOSAW region