Introduction

R is a statistical and computing language first developed in the early 1990s. Ever since then, it has grown in leaps and bounds in terms of development and usage. Apart from statistics, R can also be used for cartography and spatial analyses. This is where the RStudio comes in. RStudio is an Integrated Development Environment (IDE) for the R Language, in other words, the GUI for writing R code. You can consider RStudio as the more ‘beautiful’ site for writing the R language. Don’t take my word for it, just judge the book by its cover for yourself by counterchecking the interfaces of R versus RStudio yourself!

Did we mention you can do spatial analyses in R? Not only spatial analyses but also making webmaps out of them. Today, we will look at creating webmaps in R and also include a few GIS operations. When we mention R, take it as the R languages appearing in RStudio. Time to spin the decks with our RStudio.

The most famous tool used for creating webmaps in RStudio is the leaflet package. This package is powered by Javascript and is called into R using the library function. One can also create a webmap using the tmap package but that’s a story for another day.

Today, we will create a webmap of Kenyan wards showcasing dummy electoral results. To also demonstrate a few functionalities, a raster map will be loaded as well. For the political diehards, the numbers used herein are in no way representative or reflective of the true results to be counted on the voting day.

In R, packages are a collection of functions that run certain processes or produce certain outputs. Packages in R are stored in a directory called library. For those packages not installed in R by default, they are called into the R environment using library(). An example of this will be the packages which we need for spatial operations as shown below.

NB: If installing a package for the first time, use install.packages() and thereafter load it into R with library(package name).

library(sp)
library(sf)

## Linking to GEOS 3.9.1, GDAL 3.2.1, PROJ 7.2.1; sf_use_s2() is TRUE

library(rgeos)

## rgeos version: 0.5-9, (SVN revision 684)
##  GEOS runtime version: 3.9.1-CAPI-1.14.2 
##  Please note that rgeos will be retired by the end of 2023,
## plan transition to sf functions using GEOS at your earliest convenience.
##  GEOS using OverlayNG
##  Linking to sp version: 1.4-6 
##  Polygon checking: TRUE

library(rgdal)

## Please note that rgdal will be retired by the end of 2023,
## plan transition to sf/stars/terra functions using GDAL and PROJ
## at your earliest convenience.
## 
## rgdal: version: 1.5-28, (SVN revision 1158)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 3.2.1, released 2020/12/29
## Path to GDAL shared files: C:/Users/User/Documents/R/win-library/4.1/rgdal/gdal
## GDAL binary built with GEOS: TRUE 
## Loaded PROJ runtime: Rel. 7.2.1, January 1st, 2021, [PJ_VERSION: 721]
## Path to PROJ shared files: C:/Users/User/Documents/R/win-library/4.1/rgdal/proj
## PROJ CDN enabled: FALSE
## Linking to sp version:1.4-6
## To mute warnings of possible GDAL/OSR exportToProj4() degradation,
## use options("rgdal_show_exportToProj4_warnings"="none") before loading sp or rgdal.
## Overwritten PROJ_LIB was C:/Users/User/Documents/R/win-library/4.1/rgdal/proj

library(leaflet)

## Warning: package 'leaflet' was built under R version 4.1.3

This is just for starters. More packages will be added. In fact, more often than not, you will be using library to add more packages as you progress in handling more sophisticated tasks. In short, the more complex the task, the more packages that might be needed. There might arise a need where you may have to create one yourself!

For a description of each of the above packages, click on the hyperlinks below. 1. sp

Load the shapefile

Let’s load the shapefile we will use for making our webmap. The shapefile is available from here. Save and extract it in your directory.

kenya_wards <- readOGR(dsn = "D:/gis-articles-800/leaflet/kenya_wards.shp")

## OGR data source with driver: ESRI Shapefile 
## Source: "D:\gis-articles-800\leaflet\kenya_wards.shp", layer: "kenya_wards"
## with 1450 features
## It has 5 fields
## Integer64 fields read as strings:  gid

Leaflet

We earlier mentioned leaflet package is used to make webmaps. Let’s see how it looks without any data parsed into it.

# make a leaflet widget
leaflet()

A plain gray cold looking map.

leaflet, according to the help menu, is a function to create a Leaflet map widget. This is what you see above.

Add map tiles

Our map can’t be blank. We will spice things up by adding some basemaps. Basemaps are added using addTiles(), part of the functions within the leaflet package. addTiles() adds maptiles, which are collection of joined square boxes of the requested image or vector data.

leaflet() %>%
  addTiles() # you can see a global map appears and all seven continents shown twice in one view

You can see a basemap of the globe has been added. However, it has been replicated thrice! This is because no spatial object, which can reference to a place on the earth’s surface, has been added.

Add polygons

Let’s add a spatial object, our kenya_wards shapefile.

# add kenya wards map to leaflet
#leaflet() %>%
 # addTiles() %>%
  #addPolygons(data = kenya_wards) # you can see that the kenya wards shapefile was added to leaflet
# you also note it took some time loading

You may have noticed that it took some time for it to load. That is not good news for something that will go online at some point. Just how long was the delay?

# check loading time
#start_time <- Sys.time()
#leaflet() %>%
 
# addTiles() %>%
 # addPolygons(data = kenya_wards)
#end_time <- Sys.time()
#difference <- end_time - start_time
#difference

Twenty seconds? Enough to do a 100m sprint and back. Let’s reduce the size of this shapefile using the rmapshaper package. As a matter of fact, we will reduce it without using library() to call the package into R. How? A simple :: does the trick.

# reduce size of shapefile
kenya_wards2 <- rmapshaper::ms_simplify(input = kenya_wards, keep = 0.03, keep_shapes = T)

## Registered S3 method overwritten by 'geojsonlint':
##   method         from 
##   print.location dplyr

Let’s reload the leaflet map with the simplified kenya_wards2 spatial object. We will also check the time it takes to load for each and know who is faster now.

# compare with kenya_wards2 shapefile
#start_time <- Sys.time()
#leaflet() %>%
 # addTiles() %>%
  #addPolygons(data = kenya_wards2)
#end_time <- Sys.time()
#difference <- end_time - start_time
#difference

Three seconds?! The loading time just got reduced by more than a half.

# draw leaflet map with kenya_wards2
leaflet() %>%
 addTiles() %>%
  addPolygons(data = kenya_wards2)

Join spatial object with data frame

To view the attributes for a shapefile, the @data suffix is added after the shapefile name. This is unlike working with tables where simply calling their name displays R displays as many of the table’s attributes as it can handle.

head(kenya_wards2@data)  # show first six attribute rows

##    gid pop2009 county         subcounty                   ward
## 1  241   17431 ISIOLO Isiolo Sub County                 WABERA
## 2 1455   18755 Migori  Rongo Sub County   North Kamagambo Ward
## 3 1456   27756 Migori  Rongo Sub County Central Kamagambo Ward
## 4 1457   27179 Migori  Rongo Sub County   South Kamagambo Ward
## 5 1458   22874 Migori Awendo Sub County       North Sakwa Ward
## 6 1459   36200 Migori Awendo Sub County       South Sakwa Ward

Alright. We promised our webmap would show both administrative and electoral result attributes. Looking at it, only administrative attributes can be seen. Luckily, We have a curated table showing the dummy electoral results.

# load the table with voter data 
ward_data <- read.csv("D:/gis-articles-800/leaflet/wards_full.csv", header = T)
head(ward_data)

##   X  gid pop2009 county         subcounty                   ward         uid
## 1 1  241   17431 ISIOLO Isiolo Sub County                 WABERA rIdiIpv9fBt
## 2 2 1455   18755 Migori  Rongo Sub County   North Kamagambo Ward QC41mItjIzF
## 3 3 1456   27756 Migori  Rongo Sub County Central Kamagambo Ward M8rGveWTIMm
## 4 4 1457   27179 Migori  Rongo Sub County   South Kamagambo Ward DABObbHgPMX
## 5 5 1458   22874 Migori Awendo Sub County       North Sakwa Ward EmSsP2C6A3h
## 6 6 1459   36200 Migori Awendo Sub County       South Sakwa Ward OpbsijPbYuv
##         scuid        cuid voters david_waih george_waj raila_odin reuben_kig
## 1 I2LYLqKU6AW bzOfj0iwfDH   1779         39         66         52         79
## 2 fT37q3rXQ35 fVra3Pwta0Q   1682         69         52         17         94
## 3 fT37q3rXQ35 fVra3Pwta0Q   1232         18         94         11         30
## 4 fT37q3rXQ35 fVra3Pwta0Q   1991         21         25         25         59
## 5 ka9Uv3Ckcbd fVra3Pwta0Q   1065         33         33         67         29
## 6 ka9Uv3Ckcbd fVra3Pwta0Q   1986         74         39         84         93
##   william_ru
## 1         56
## 2         95
## 3         86
## 4         97
## 5         97
## 6         60

Question is, just how do we take the columns from voters to william_ru and join them to our spatial kenya_wards2? In Qgis, we could do it using joins tool. R also has its own tricks up its sleeve for the same purpose.

Actually, the original kenya_wards shapefile contains all the electoral data when viewed in Qgis. It was created in Qgis using the joins tool alluded to earlier. For some reason, however, this electoral data columns are not visible when loaded in R. Nevertheless, we will take the bull by its horns and merge these electoral attributes with our shapefile so that eventually our shapefile is in top-notch shape.

The sp package has the merge() function which is used to merge a spatial object with a data frame –which in our case is the table above.

kenya_wards3 <- merge(x = kenya_wards2, 
                      y = ward_data, by.x = "gid", 
                      by.y = "gid", # show which column to join by
                      suffixes = c(".x", ".y")) # add suffixes to differentiate by source
head(kenya_wards3@data)

##      gid pop2009.x county.x       subcounty.x                 ward.x X
## 962  241     17431   ISIOLO Isiolo Sub County                 WABERA 1
## 47  1455     18755   Migori  Rongo Sub County   North Kamagambo Ward 2
## 48  1456     27756   Migori  Rongo Sub County Central Kamagambo Ward 3
## 49  1457     27179   Migori  Rongo Sub County   South Kamagambo Ward 4
## 50  1458     22874   Migori Awendo Sub County       North Sakwa Ward 5
## 51  1459     36200   Migori Awendo Sub County       South Sakwa Ward 6
##     pop2009.y county.y       subcounty.y                 ward.y         uid
## 962     17431   ISIOLO Isiolo Sub County                 WABERA rIdiIpv9fBt
## 47      18755   Migori  Rongo Sub County   North Kamagambo Ward QC41mItjIzF
## 48      27756   Migori  Rongo Sub County Central Kamagambo Ward M8rGveWTIMm
## 49      27179   Migori  Rongo Sub County   South Kamagambo Ward DABObbHgPMX
## 50      22874   Migori Awendo Sub County       North Sakwa Ward EmSsP2C6A3h
## 51      36200   Migori Awendo Sub County       South Sakwa Ward OpbsijPbYuv
##           scuid        cuid voters david_waih george_waj raila_odin reuben_kig
## 962 I2LYLqKU6AW bzOfj0iwfDH   1779         39         66         52         79
## 47  fT37q3rXQ35 fVra3Pwta0Q   1682         69         52         17         94
## 48  fT37q3rXQ35 fVra3Pwta0Q   1232         18         94         11         30
## 49  fT37q3rXQ35 fVra3Pwta0Q   1991         21         25         25         59
## 50  ka9Uv3Ckcbd fVra3Pwta0Q   1065         33         33         67         29
## 51  ka9Uv3Ckcbd fVra3Pwta0Q   1986         74         39         84         93
##     william_ru
## 962         56
## 47          95
## 48          86
## 49          97
## 50          97
## 51          60

You will notice that there are several duplicate columns. We will remove those that we don’t need. These will be the columns denoted with the suffix .y since our shapefile already had them even before the merge operation. They are denoted by the suffix .y in our table. We will remove them and remain with the voter data.

kenya_wards4 <- kenya_wards3[ , -c(7:13)] # remove duplicate columns
head(kenya_wards4@data)

##      gid pop2009.x county.x       subcounty.x                 ward.x X voters
## 962  241     17431   ISIOLO Isiolo Sub County                 WABERA 1   1779
## 47  1455     18755   Migori  Rongo Sub County   North Kamagambo Ward 2   1682
## 48  1456     27756   Migori  Rongo Sub County Central Kamagambo Ward 3   1232
## 49  1457     27179   Migori  Rongo Sub County   South Kamagambo Ward 4   1991
## 50  1458     22874   Migori Awendo Sub County       North Sakwa Ward 5   1065
## 51  1459     36200   Migori Awendo Sub County       South Sakwa Ward 6   1986
##     david_waih george_waj raila_odin reuben_kig william_ru
## 962         39         66         52         79         56
## 47          69         52         17         94         95
## 48          18         94         11         30         86
## 49          21         25         25         59         97
## 50          33         33         67         29         97
## 51          74         39         84         93         60

Now we have our electoral data: the voters and results for each of our named aspirants.

Make the webmap interactive

Now, to what we have been waiting for. Let’s load these improved kenya_wards2 data to our leaflet map. In addition, we will make the wards clickable. That is, some data will show up on screen when a particular ward is clicked by the user. The popup argument within the addPolygons() will enable this.

# make the polygons clickable and showing some data of each ward
leaflet() %>%
  addTiles() %>%
  addPolygons(
    data = kenya_wards4, 
    popup = paste0("County: ", kenya_wards4@data$county, "<br>",
                   "Ward: ", kenya_wards4@data$ward, "<br>",
                   "Voters: ", kenya_wards4@data$voters, "<br>",
                   "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
                   "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
                   "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
                   "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
                   "William Ruto: ", kenya_wards4@data$william_ru
                   )
    )

# upon clicking you see ward electoral data

Click on any ward and you will see a popup showing some information about that ward–both political (County name and ward name) and electoral (voters, David Waihiga, George Wajackoyah etc).

Furthermore, we can also change the aesthetics. We want to color code the wards according to the counties they fall under. This will make it easy to distinguish one county from another, thus also easening the navigation experience. Sounds easy, but it is not. To create the different color codes, we have to create a color function using colorFactor(). Once the function is created, it can be parsed to leaflet for display.

# create a color function for coloring map 
color_wards <- colorFactor(palette = topo.colors(47), domain = kenya_wards4@data$county.x)

# create a colored map with wards categorized to their counties by color
leaflet() %>%
  addTiles() %>%
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0("County: ", kenya_wards4@data$county, "<br>",
                   "Ward: ", kenya_wards4@data$ward, "<br>",
                   "Voters: ", kenya_wards4@data$voters, "<br>",
                   "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
                   "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
                   "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
                   "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
                   "William Ruto: ", kenya_wards4@data$william_ru
                   )
    )

How about making the wards highlight upon mouse hover? This can be done using the highlightOptions() function. As you can see, most of the function names under the leaflet package also hint to their purpose(s).

leaflet() %>%
  addTiles() %>%
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0("County: ", kenya_wards4@data$county, "<br>",
                   "Ward: ", kenya_wards4@data$ward, "<br>",
                   "Voters: ", kenya_wards4@data$voters, "<br>",
                   "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
                   "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
                   "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
                   "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
                   "William Ruto: ", kenya_wards4@data$william_ru
                   ),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # made the wards highlightable on hover
    )

The addMarker() would have been a nice complimenter to the highlightsOptions function in that it would show a label–such as the name of the ward–upon mouse hover. However, the addMarker() function needs longitude and latitude information, not included in our data and thus we prefer to explore it in another day.

Add basemaps

Currently, our map contains only one basemap layer, the default Open StreetMap (OSM) basemap that comes with the leaflet package. However, we can add more basemaps using the addProviderTiles() function.

#
leaflet() %>%
  addTiles(
    group = "OSM"
    ) %>% # the default web map layer is OSM, so we put into group name 'OSM'.
  addProviderTiles(
    providers$CartoDB.Positron, group = "Carto"
    ) %>% # added an extra webmap layer and called group - CartoDB
  addProviderTiles(
    providers$OpenStreetMap.France, 
    group = "OSM France"
    ) %>% #added an extra webmap layer called group - OSM France
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0("County: ", kenya_wards4@data$county, "<br>",
                   "Ward: ", kenya_wards4@data$ward, "<br>",
                   "Voters: ", kenya_wards4@data$voters, "<br>",
                   "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
                   "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
                   "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
                   "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
                   "William Ruto: ", kenya_wards4@data$william_ru),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # made the wards highlightable on hover
    )

Add layers control

There is one problem though. We can only see one basemap layer. Actually, the basemap for ‘OpenStreetMap.France’. For the other two, the user is at a loss from where to retrieve them, and that will be bad client experience. This problem is solved by the group argument included in our script below. The group argument does what it says - group our layers to a particular unit named by the user.

This still doesn’t answer our question of being able to retrieve any of our other two basemaps. For this, we will add a control widget that allows toggling one layer on or off. The addLayersControl() function comes into fore.

# we need to add a toggle on or off widget to choose which map layers we want to be visible

leaflet() %>%
  addTiles(
    group = "OSM"
    ) %>% # the default web map layer is OSM, so we put into group name 'OSM'.
  addProviderTiles(
    providers$CartoDB.Positron, 
    group = "Carto"
    ) %>% # added an extra webmap layer and called group - CartoDB
  addProviderTiles(
    providers$OpenStreetMap.France, 
    group = "OSM France"
    ) %>% #added an extra webmap layer called group - OSM France
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, 
    weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0(
      "County: ", kenya_wards4@data$county, "<br>",
      "Ward: ", kenya_wards4@data$ward, "<br>",
      "Voters: ", kenya_wards4@data$voters, "<br>",
      "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
      "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
      "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
      "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
      "William Ruto: ", kenya_wards4@data$william_ru
      ),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # made the wards highlightable on hover
    group = "administrative"
    ) %>% # put our polygons into the 'administrative' group
  addLayersControl(
    baseGroups = c(
      "OSM", "Carto", "OSM France"
      ), # group names of our OSM layers
    overlayGroups = c(
      "administrative"
      )
    ) # group name for our polygons

Add raster data

So far, we have worked with vector and raster data. How about raster data? To work with raster data in R, one has to use the raster and terra packages.

# load the terra and raster packages
library(terra)

## terra 1.5.21

## 
## Attaching package: 'terra'

## The following object is masked from 'package:rgdal':
## 
##     project

library(raster)

## Warning: package 'raster' was built under R version 4.1.3

We will use the rast function from the terra package to load the raster data into R Studio.

# load raster into R
landcover <- rast(x = "D:/gis-articles-800/leaflet/kenya_landcover_2017/kenya_landcover_2017.tif")
#landcover  #check attributes

However, this raster is too large to work with. It will not only take much time to load into leaflet, but it will cause errors to appear when forced into this lightweight (yet powerful) R package tool. Believe me, when it comes to working with leaflet and any other web based application, size matters. In R, the aggregate function reduces the resolution of a raster and subsequently also reducing the size of the file.

#reduce size of this raster
landcover2 <- terra::aggregate(landcover, fact=100, fun = "mean", overwrite = T)
#landcover2

In RStudio, our landcover2 is registered as a SpatRaster file. This will cause issues upon loading to leaflet() which strictly requires a RasterLayer object. We are not a geniuses to know this beforehand, rather, we discovered this by experience when we were parsing the landcover2 to leaflet and R splashed the following error into our faces, as if to say: “No, thank you”.

Error in addRasterImage(., landcover2, colors = "Spectral", opacity = 0.8,  : 
  inherits(x, "RasterLayer") is not TRUE

The raster() function will convert our landcover2 into a RasterLayer object.

# convert to raster layer object
landcover3 <- raster::raster(landcover2)

Time to add the raster to our leaflet map.

###
leaflet() %>%
  addTiles(
    group = "OSM"
    ) %>% # the default web map layer is OSM, so we put into group name 'OSM'.
  addProviderTiles(
    providers$CartoDB.Positron, 
    group = "Carto"
    ) %>% # added an extra webmap layer and called group - CartoDB
  addProviderTiles(
    providers$OpenStreetMap.France, 
    group = "OSM France"
    ) %>% #added an extra webmap layer called group - OSM France
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, 
    weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0(
      "County: ", kenya_wards4@data$county, "<br>",
      "Ward: ", kenya_wards4@data$ward, "<br>",
      "Voters: ", kenya_wards4@data$voters, "<br>",
      "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
      "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
      "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
      "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
      "William Ruto: ", kenya_wards4@data$william_ru
      ),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # make wards highlightable 
    group = "administrative",  # put polygons into a group called 'administrative'
  ) %>%
  addRasterImage(
    landcover3, 
    colors = rainbow(6), 
    opacity = .8, 
    group = "landcover", 
    layerId = "land_cover"
    ) %>%
  addLayersControl(
    baseGroups = c(
      "OSM", "Carto", "OSM France"
      ), # the group names of our OSM layers
    overlayGroups = c(
      "administrative"
      )
    ) # group name for our polygons

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded ellps WGS 84 in Proj4 definition: +proj=merc +a=6378137
## +b=6378137 +lat_ts=0 +lon_0=0 +x_0=0 +y_0=0 +k=1 +units=m +nadgrids=@null
## +wktext +no_defs +type=crs

## Warning in showSRID(uprojargs, format = "PROJ", multiline = "NO", prefer_proj =
## prefer_proj): Discarded datum World Geodetic System 1984 in Proj4 definition

Note that we have updated the addRasterImage() with group and layerId arguments. addRasterImage adds a raster into leaflet. As you saw earlier, group enables the control widget know which layer to toggle on/off. The layerId is for parsing into an opacity slider, which we shall work with shortly.

Our raster contains color codes that signify the landcovers of that particular region. The color codes are labelled using numbers. Even though we would prefer realworld names like forestcover, water et cetera to numerical codes, we will use them as they are for today. Numbers don’t lie anyway!

Add legend

A legend is a map element that elaborates on some map features. In leaflet, it is added using the addLegend() function.

leaflet() %>%
  addTiles(
    group = "OSM"
    ) %>% # the default web map layer is OSM, so we put into group name 'OSM'.
  addProviderTiles(
    providers$CartoDB.Positron, 
    group = "Carto"
    ) %>% # added an extra webmap layer and called group - CartoDB
  addProviderTiles(
    providers$OpenStreetMap.France, 
    group = "OSM France"
    ) %>% #added an extra webmap layer called group - OSM France
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, 
    weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0(
      "County: ", kenya_wards4@data$county, "<br>",
      "Ward: ", kenya_wards4@data$ward, "<br>",
      "Voters: ", kenya_wards4@data$voters, "<br>",
      "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
      "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
      "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
      "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
      "William Ruto: ", kenya_wards4@data$william_ru
      ),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # make wards highlightable 
    group = "administrative",  # put polygons into a group called 'administrative'
  ) %>%
  addRasterImage(
    landcover3, 
    colors = rainbow(6), 
    opacity = .8, 
    group = "landcover", 
    layerId = "land_cover"
    ) %>%   # add raster layer
  addLegend(
    data = landcover3, 
    position = "bottomleft", 
    colors = rainbow(6), 
    opacity = 0.5, 
    labels = c(
      '1', '2', '3', '4', '5', '6'
      ), 
    title = "Land classes"
    ) %>% # add legend
  addLayersControl(
    baseGroups = c(
      "OSM", "Carto", "OSM France"
      ), #group names of our OSM layers
    overlayGroups = c(
      "administrative"
      )
    )

In the addLegend function, the number of label names should match that of the color codes otherwise R will throw an error to your face!

Add an opacity slider

Despite the excitement of dealing with a raster in a webmap, our new kid on the block conceals all the underlying layers. One way to solve this is parsing the raster to the addLayerControl widget from where it can be toggled on/off like our OSM layers. A second and preferable option is to create an opacity slider whereby the transparency or opaqueness of the raster will be controlled. The opacity slider is not part of the leaflet package and thus has to be installed separately.

We shall proceed with this second option as it makes our webmap more versatile.

# load the opacity slider package
library(leaflet.opacity)

## Warning: package 'leaflet.opacity' was built under R version 4.1.3

Let’s throw the opacity slider into our leaflet map script. Remember the layerId we specified for our raster? It is now hereby put into action.

###
leaflet() %>%
  addTiles(
    group = "OSM"
    ) %>% # the default web map layer is OSM, so we put into group name 'OSM'.
  addProviderTiles(
    providers$CartoDB.Positron, 
    group = "Carto"
    ) %>% # added an extra webmap layer and called group - CartoDB
  addProviderTiles(
    providers$OpenStreetMap.France, 
    group = "OSM France"
    ) %>% #added an extra webmap layer called group - OSM France
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, 
    weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0(
      "County: ", kenya_wards4@data$county, "<br>",
      "Ward: ", kenya_wards4@data$ward, "<br>",
      "Voters: ", kenya_wards4@data$voters, "<br>",
      "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
      "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
      "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
      "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
      "William Ruto: ", kenya_wards4@data$william_ru
      ),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # make wards highlightable 
    group = "administrative",  # put polygons into a group called 'administrative'
  ) %>%
  addRasterImage(
    landcover3, 
    colors = rainbow(6), 
    opacity = .8, 
    group = "landcover", 
    layerId = "land_cover"
    ) %>%   # add raster layer
  addLegend(
    data = landcover3, 
    position = "bottomleft", 
    colors = rainbow(6), 
    opacity = 0.5, 
    labels = c(
      '1', '2', '3', '4', '5', '6'
      ), 
    title = "Land classes"
    ) %>% # add legend
  addLayersControl(
    baseGroups = c(
      "OSM", "Carto", "OSM France"
      ), # group names of our OSM layers
    overlayGroups = c(
      "administrative"
      )
    ) %>% # group name for our polygons
  addOpacitySlider(
    layerId = "land_cover"
    )

Get adventorous and play around with the opacity slider.

Parse the webmap to an object name

Finally, as the icing on the cake, or to cap this long exploration in leaflet, lets give an object name to our leaflet map script.

leaflet_wards <- leaflet() %>%
  addTiles(
    group = "OSM"
    ) %>% # the default web map layer is OSM, so we put into group name 'OSM'.
  addProviderTiles(
    providers$CartoDB.Positron, 
    group = "Carto"
    ) %>% # added an extra webmap layer and called group - CartoDB
  addProviderTiles(
    providers$OpenStreetMap.France, 
    group = "OSM France"
    ) %>% #added an extra webmap layer called group - OSM France
  addPolygons(
    data = kenya_wards4, 
    color = ~color_wards(kenya_wards4@data$county.x), # added the color component
    opacity = 0.3, 
    weight = 2,  # to make the colors less opaque and not shouting
    popup = paste0(
      "County: ", kenya_wards4@data$county, "<br>",
      "Ward: ", kenya_wards4@data$ward, "<br>",
      "Voters: ", kenya_wards4@data$voters, "<br>",
      "David Waihiga: ", kenya_wards4@data$david_waih, "<br>",
      "George Wajackoyah: ", kenya_wards4@data$george_waj, "<br>",
      "Raila Odinga: ", kenya_wards4@data$raila_odin, "<br>",
      "Reuben Kigame: ", kenya_wards4@data$reuben_kig, "<br>",
      "William Ruto: ", kenya_wards4@data$william_ru
      ),
    highlightOptions = highlightOptions(
      stroke = T, 
      color = "white",
      weight = 5,
      opacity = 0.5,
      bringToFront = T
      ), # made the wards highlightable on hover
    group = "administrative",  # put polygons into a group called 'administrative'
  ) %>%
  addRasterImage(
    landcover3, 
    colors = rainbow(6), 
    opacity = .8, 
    group = "landcover", 
    layerId = "land_cover"
    ) %>%   # add raster layer
  addLegend(
    data = landcover3, 
    position = "bottomleft", 
    colors = rainbow(6), 
    opacity = 0.5, 
    labels = c(
      '1', '2', '3', '4', '5', '6'
      ), 
    title = "Land classes"
    ) %>% # add legend
  addLayersControl(
    baseGroups = c(
      "OSM", "Carto", "OSM France"
      ), # group names of our OSM layers
    overlayGroups = c(
      "administrative", "landcover"
      )
    ) %>%# group name for our polygons + landcover raster
  addOpacitySlider(
    layerId = "land_cover"
    )

leaflet_wards # all our leaflet map functionalities are encompassed in this name

Like a small bomb that releases its contents to a wide area, so does our object name leaflet_wards work like so. Imagine all those scripts and functions we added are encapsulated in that one name! It’s for a good reason though. When you want to parse this leaflet map to an app, say like in shiny this one name is does all the magic needed for the map to appear in your app.

test_leaflet

samuel gachuhi

2022-08-09