A step beyond creating kml files of your digital soil information is the creation of customized interactive mapping products that can be visualized within your web browser. Some notes about how to do this using R are given in our book and with example here. I wanted to teach myself how i might be able to embed these interactive maps into a website, so i created an example to help me. In doing this i hope others who have not come across interactive mapping before are able to get some basic insights about it.

Interactive mapping makes sharing your data with colleagues simpler, and importantly improves the visualization experience via customization features that are difficult to achieve via the Google Earth software platform. The interactive mapping is made possible via the Leaflet R package. Leaflet is one of the most popular open-source JavaScript libraries for interactive maps. The Leaflet R package makes it easy to integrate and control Leaflet maps in R. More detailed information about Leaflet can be found here, and information specifically about the R package is here.

There is a common workflow for creating Leaflet maps in R. First is the creation of a map widget (calling ); followed by the adding of layers or features to the map by using layer functions (e.g. addTiles, addMarkers, addPolygons) to modify the map widget. The map can then be printed and visualized in the R image window or saved to HTML file for visualization within a web browser or even embed it into a website like the one you are reading. The following R script is a quick taste of creating an interactive Leaflet map. It is assumed that the leaflet and magrittr packages are installed.

First lets get our workflow initialised by loading up our R packages.

    # Required R Packages

Point Data

We will be working with a small data set of soil information that was collected from the Hunter Valley, NSW in 2010 called . This data set is contained in the package. So first load it in:


    ## 'data.frame':    100 obs. of  6 variables:
    ##  $ site: Factor w/ 100 levels "a1","a10","a11",..: 1 2 3 4 5 6 7 8 9 10 ...
    ##  $ x   : num  337860 344060 347035 338235 341760 ...
    ##  $ y   : num  6372416 6376716 6372741 6368766 6366016 ...
    ##  $ OC  : num  2.03 2.6 3.42 4.1 3.04 4.07 2.95 3.1 4.59 1.77 ...
    ##  $ EC  : num  0.129 0.085 0.036 0.081 0.104 0.138 0.07 0.097 0.114 0.031 ...
    ##  $ pH  : num  6.9 5.1 5.9 6.3 6.1 6.4 5.9 5.5 5.7 6 ...

Using the coordinates function from the sp package we can define which columns in the data frame refer to actual spatial coordinates— here the coordinates are listed in columns x and y.

    coordinates(HV100) <- ~x + y

    ## Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots
    ##   ..@ data       :'data.frame':  100 obs. of  4 variables:
    ##   .. ..$ site: Factor w/ 100 levels "a1","a10","a11",..: 1 2 3 4 5 6 7 8 9 10 ...
    ##   .. ..$ OC  : num [1:100] 2.03 2.6 3.42 4.1 3.04 4.07 2.95 3.1 4.59 1.77 ...
    ##   .. ..$ EC  : num [1:100] 0.129 0.085 0.036 0.081 0.104 0.138 0.07 0.097 0.114 0.031 ...
    ##   .. ..$ pH  : num [1:100] 6.9 5.1 5.9 6.3 6.1 6.4 5.9 5.5 5.7 6 ...
    ##   ..@ coords.nrs : int [1:2] 2 3
    ##   ..@ coords     : num [1:100, 1:2] 337860 344060 347035 338235 341760 ...
    ##   .. ..- attr(*, "dimnames")=List of 2
    ##   .. .. ..$ : chr [1:100] "1" "2" "3" "4" ...
    ##   .. .. ..$ : chr [1:2] "x" "y"
    ##   ..@ bbox       : num [1:2, 1:2] 335160 6365091 350960 6382816
    ##   .. ..- attr(*, "dimnames")=List of 2
    ##   .. .. ..$ : chr [1:2] "x" "y"
    ##   .. .. ..$ : chr [1:2] "min" "max"
    ##   ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot
    ##   .. .. ..@ projargs: chr NA

Note now that by using the str function, the class of HV100 has now changed from a dataframe to a SpatialPointsDataFrame. The SpatialPointsDataFrame structure is essentially the same data frame, except that additional spatial elements have been added or partitioned into slots. Some important ones being the bounding box (sort of like the spatial extent of the data), and the coordinate reference system (proj4string), which we need to define for our data set. To define the CRS, we have to know some things about where our data are from, and what was the corresponding CRS used when recording the spatial information in the field. For this data set the CRS used was WGS1984 UTM Zone 56. To explicitly tell R this information we define the CRS as a character string which describes a reference system in a way understood by the PROJ.4 projection library. An interface to the PROJ.4 library is available in thergdalpackage. Alternative to using Proj4 character strings, we can use the corresponding yet simpler EPSG code (European Petroleum Survey Group).rgdal also recognizes these codes. If you are unsure of the Proj4 or EPSG code for the spatial data that you have, but know the CRS, you should consult Spatial Reference for assistance. The EPSG code for WGS1984 UTM Zone 56 is: 32556. So lets define to CRS for this data.

    proj4string(HV100) <- CRS("+init=epsg:32756")

    ## CRS arguments:
    ##  +init=epsg:32756 +proj=utm +zone=56 +south +datum=WGS84 +units=m
    ## +no_defs +ellps=WGS84 +towgs84=0,0,0

To look at the locations of the data in Google Earth, we first need to make sure the data is in the WGS84 geographic CRS. If the data is not in this CRS (which is not the case for this data), then we need to perform a coordinate transformation. This is facilitated by using the spTransform function in sp. The EPSG code for WGS84 geographic is:

  1. We can then export out our transformed HV100 data set to a KML file and visualize it in Google Earth. We will use it later for our interactive web mapping.
    HV100.ll <- spTransform(HV100, CRS("+init=epsg:4326"))
    #Export KML
    #writeOGR(HV100.ll, "HV100.kml", "ID", "KML")


Most of the functions needed for handling raster data are contained in the raster package. There are functions for reading and writing raster files from and to different raster formats. In DSM we work quite a deal with data in table format and then rasterise this data so that we can make a map. To do this in R, lets bring in a data frame. This could be either from a text-file, but as for the previous occasions the data is imported from the ithir package. This data is a digital elevation model with 100m grid resolution, from the Hunter Valley, NSW, Australia.


    ## 'data.frame':    21518 obs. of  3 variables:
    ##  $ X        : num  340210 340310 340110 340210 340310 ...
    ##  $ Y        : num  6362641 6362641 6362741 6362741 6362741 ...
    ##  $ elevation: num  177 175 178 172 173 ...

As the data is already a raster (such that the row observation indicate locations on a regular spaced grid), but in a table format, we can just use the rasterFromXYZ function from raster. Also we can define the CRS just like we did with the HV100 point data we worked with before.

    r.DEM <- rasterFromXYZ(HV_dem)
    proj4string(r.DEM) <- CRS("+init=epsg:32756")

For web mapping and associated Google Earth mapping, remember that we need to reproject our data because it is in the UTM system, and need to get it to WGS84 geographic. The raster re-projection is performed using the projectRaster function. Look at the help file for this function. Probably the most important parameters are crs, which takes the CRS string of the projection you want to convert the existing raster to, assuming it already has a defined CRS. The other is method which controls the interpolation method. For continuous data, bilinear would be suitable, but for categorical,ngb, (which is nearest neighbor interpolation) is probably better suited.

    p.r.DEM <- projectRaster(r.DEM, crs = "+init=epsg:4326", method = "bilinear")

Interactive Mapping

Now we have our data. Lets first provide an example of what is meant by an interactive map. Explaination is to follow.

    leaflet() %>% 
      addMarkers(lng = 151.210558, lat = -33.852543, 
        popup = "The view from here is amazing!") %>% 

Interactive features of this map include markers with text, plus ability to zoom and map panning. More will be discussed about the layer functions of the leaflet map further on. What has not been encountered yet is the forward pipe operator %>%. This operator will forward a value, or the result of an expression, into the next function call or expression. To use this operator the magrittr package is required. The example script below shows the same example using and not using the forward pipe operator.

    #Draw 100 random uniformly distributed numbers between 0 and 1
    x <- runif(100)


    ## [1] 1.005229

    ##..is equivalent to (using forward pipe operator)
    x %>% range %>% sum %>% sqrt

    ## [1] 1.005229

Sometimes what we want to do in R can get lost within a jumble of brackets, whereas using the forward pipe operator the process of operations is a lot clearer. So lets begin to construct some Leaflet mapping using our prepared data from a little earlier regarding the point (HV100.ll) and raster data (p.r.DEM). Firstly, lets create a basic map — example of not using and then using the forward pipe operator.

    # Basic map
    #without piping operator
    addMarkers(addTiles(leaflet()), data = HV100.ll)

    #with forward pipe operator
    leaflet() %>%  
      addTiles() %>% 
      addMarkers(data = HV100.ll)

With the above, we are calling upon a pre-existing base map via the addTiles() function. Leaflet supports base maps using map tiles, popularized by Google Maps and now used by nearly all interactive web maps. By default, OpenStreetMap tiles are used. Alternatively, many popular free third-party base maps can be added using the addProviderTiles() function, which is implemented using the leaflet-providers plugin. For example, previously we used the Esri.WorldImagery base mapping. The full set of possible base maps can be found here. Note that an internet connection is required for access to the base maps and map tiling. The last function used above the the addMarkers function, we we simply call up the point data we used previously, which are those soil point observations and measurements from the Hunter Valley, NSW. A basic map will have been created with your plot window. For the next step, lets populate the markers we have created with some of the data that was measured, then add the Esri.WorldImagery base mapping.

    # Populate pop-ups
    my_pops <- paste0(
      "<strong>Site: </strong>", 
      <strong> Organic Carbon (%): </strong>', 
      <strong> soil pH: </strong>', 

    # Create interactive map
    leaflet() %>% 
      addProviderTiles("Esri.WorldImagery") %>% 
      addMarkers(data = HV100.ll, popup = my_pops)

Further, we can colour the markers and add a map legend. Here we will get the quantiles of the measured SOC percentage and color the markers accordingly. Note that you will need the colour ramp package RColorBrewer installed.

    # Colour ramp
    pal1 <- colorQuantile("YlOrBr", domain = HV100.ll$OC)

    # Create interactive map
    leaflet() %>% 
      addProviderTiles("Esri.WorldImagery") %>% 
      addCircleMarkers(data = HV100.ll, color = ~pal1(OC), popup = my_pops) %>% 
      addLegend("bottomright", pal = pal1, values = HV100.ll$OC,
                title = "Soil Organic Carbon (%) quantiles",
                opacity = 0.8

It is very worth consulting the help files associated with the leaflet R package for further tips on creating further customized maps. The website dedicated to that package, which was mentioned above is also a very helpful resource too.

Raster maps can also be featured in our interactive mapping too, as illustrated in the following script.

    #Colour ramp
    pal2 <- colorNumeric(
      brewer.pal(n = 9, name = "YlOrBr"), 
      domain = values(p.r.DEM), 
      na.color = "transparent"

    #interactive map
    leaflet() %>% 
      addProviderTiles("Esri.WorldImagery") %>% 
      addRasterImage(p.r.DEM, colors = pal2, opacity = 0.7) %>%
      addLegend("topright", opacity=0.8, pal = pal2, values = values(p.r.DEM), 
                title = "Elevation")