Interactive plotting with R raster: values on mouseover

Question

I'd like to do a small program in R for interactive visualization and modification of some raster datasets, seen as colored images. The user should open a file (from the terminal it's OK), plot it, select the points to edit with mouse clicks, and insert the new values.

So far I achieved that easily. I use the plot() function from the raster package to visualize the plot, then click() to select the points and edit their value via the terminal.

I'd like to add the ability to show the values on mouse over. I've searched for ways on how to do this, but this doesn't seem to be possible with the standard R packages. Is this correct?

In this case, I may be forced to use external packages, such as gGobi, iPlots, Shiny or Plotly. However, I'd greatly prefer to KISS and use only "standard" graphics tools, such as the raster plot() function or maybe trellis graphics objects (e.g. from rasterVis).

I understand a Shiny app would probably be best, but it takes lots of time to learn and perfect.

Answer 1

With leaflet, mapview, and leafem you can achieve something like this:

library(raster) library(mapview) library(leaflet) library(leafem)  f <- system.file("external/test.grd", package="raster") r <- raster(f)  leaflet() %>%    addRasterImage(r, layerId = "values") %>%    addMouseCoordinates() %>%   addImageQuery(r, type="mousemove", layerId = "values") 

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Putting that in a shiny app you get:

library(raster) library(mapview) library(leaflet) library(shiny)  f <- system.file("external/test.grd", package="raster") r <- raster(f)  ui <- fluidPage(   leafletOutput("map") )  server <- function(input, output){   output$map <- renderLeaflet({     leaflet() %>%        addRasterImage(r, layerId = "values") %>%        addMouseCoordinates() %>%       addImageQuery(r, type="mousemove", layerId = "values")   }) }  shinyApp(ui, server) 

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The following example illustrates the idea of converting the raster to Simple Features / Shapefiles. Its not realy useable for big Files, but the labels can be designed individually, the data is editable and can easily be shown in a Table.

library(raster) library(leaflet) library(shiny) library(sf) library(DT) library(dplyr)  ## DATA f <- system.file("external/test.grd", package="raster") r <- raster(f) r1 = aggregate(r, 30)  sp = st_as_sf(rasterToPolygons(r1)) cn = st_coordinates(st_transform(st_centroid(sp),4326)) sp = st_transform(sp, 4326) sp = cbind(sp, cn) sp$id <- 1:nrow(sp) colnames(sp)[1] <- "value"   ## UI ui <- fluidPage(   leafletOutput("map"),   uiOutput("newValueUI"),   textInput("newVal", label = "Enter new value"),   actionButton("enter", "Enter new value"),   hr(),   dataTableOutput("table") )   ## SERVER server <- function(input, output){    ## Reactive Shapefile   sp_react <- reactiveValues(sp = sp)      ## Leaflet Map   output$map <- renderLeaflet({     pal= colorNumeric(topo.colors(25), sp_react$sp$value)     leaflet() %>%        addPolygons(data = sp_react$sp, label= paste(         "Lng: ", as.character(round(sp_react$sp$X,4)),         "Lat: ", as.character(round(sp_react$sp$Y,4)),         "Val: ", as.character(round(sp_react$sp$value,4))),         color = ~pal(sp_react$sp$value),          layerId = sp_react$sp$id       )   })      ## Observe Map Clicks   observeEvent(input$map_shape_click, {          click_id = input$map_shape_click$id          click_grid <- sp_react$sp[sp_react$sp$id == click_id,]    })      ## Observe Action Button   observeEvent(input$enter, {     click_id <- input$map_shape_click$id     sp_react$sp[sp_react$sp$id == click_id,]$value <- as.numeric(input$newVal)   })    ## Data Table   output$table <- DT::renderDataTable({     sp_react$sp %>% st_set_geometry(NULL) %>%        dplyr::select(id,X,Y,value)   })   proxy = dataTableProxy('table')      ## Table Proxy   observeEvent(input$map_shape_click$id, {     req(input$map_shape_click$id)     proxy %>% selectRows(as.numeric(input$map_shape_click$id))   }) }  shinyApp(ui, server) 

Answer 2

I give you a simple example of how to do it in R without external Java libraries, if you want Javan's features you can adapt it, but each java graphics library is different and I have never done anything similar.

set.seed(123) mydata <- data.frame(x = runif(10), y = runif(10))  edit_plot <- function(data) {   plot(data)    sel <- locator(n = 1)   if(is.null(sel)) return(TRUE)   dd <- (data$x - sel$x)^2 + (data$y - sel$y)^2    data[which.min(dd),] <- edit(data[which.min(dd),])   r <- edit_plot(data)   if(r) return(TRUE) } edit_plot(mydata) 

To exit press Esc when locator is active.