Introduction

This tutorial introduces geospatial data visualization in R.

This tutorial is aimed at beginners and intermediate users of R with the aim of showcasing how to visualize geospatial data, i.e. how to generate maps in R, and how to prepare data for geospatial visualizations using R. The aim is not to provide a fully-fledged analysis but rather to show and exemplify selected useful methods associated with generating maps. Very recommendable and detailed resources for geospatial data visualization using R can be found here, here, or here. If you are interested in cartography, here is a cheat sheet for cartography with R.

The entire R Notebook for the tutorial can be downloaded here. If you want to render the R Notebook on your machine, i.e. knitting the document to html or a pdf, you need to make sure that you have R and RStudio installed and you also need to download the bibliography file and store it in the same folder where you store the Rmd file.


Preparation and session set up

This tutorial is based on R. If you have not installed R or are new to it, you will find an introduction to and more information how to use R here. For this tutorials, we need to install certain packages from an R library so that the scripts shown below are executed without errors. Before turning to the code below, please install the packages by running the code below this paragraph. If you have already installed the packages mentioned below, then you can skip ahead and ignore this section. To install the necessary packages, simply run the following code - it may take some time (between 1 and 5 minutes to install all of the libraries so you do not need to worry if it takes some time).

NOTE

The installation of the packages will be relatively data intensive due to some of the required packages containing shp-files (shape files) - which renders these packages to be larger big in comparison to other R packages. It is thus recommendable to be logged into an institutional network that has a decent connectivity and download rate (e.g., a university network).

install.packages("sf")
install.packages("raster")
install.packages("dplyr")
install.packages("spData")
install.packages("tmap")  
install.packages("leaflet")
install.packages("ggplot2")
install.packages("spDataLarge",
                 repos = "https://nowosad.github.io/drat/", type = "source")
install.packages("ggspatial")
install.packages("rnaturalearth")
install.packages("rnaturalearthdata")
install.packages("ggmap")
install.packages("leaflet")
install.packages("maptools")
install.packages("rgdal")
install.packages("scales")
install.packages("maps")
install.packages("here")
install.packages("rgeos")
# install klippy for copy-to-clipboard button in code chunks
install.packages("remotes")
remotes::install_github("rlesur/klippy")

Now that we have installed the packages, we activate them as shown below.

library(sf)
library(raster)
library(dplyr)
library(spData)
library(spDataLarge)
library(tmap)  
library(ggplot2)
library(ggspatial)
library(rnaturalearth)
library(ggmap)
library(leaflet)
library(maptools)
library(rgdal)
library(scales)
library(rgeos)
# activate klippy for copy-to-clipboard button
klippy::klippy()

Once you have installed R and RStudio and also initiated the session by executing the code shown above, you are good to go.

Creating Basic Maps

We will start by generating maps of the world using a in-build world data set that is part of the spData package and the plot function for generating the map.

# Load the world data package
data(world)
plot(world)

We see that the world data set contains information on various factors, such as information about regions (e.g., continent or subregion), country names (name_long), population size (pop) or life expectancy (lifeExp). We can use this information to show a specific map as shown below.

plot(world["lifeExp"])

We can use the world data set and filter for specific features, e.g., we can visualize only a single continent by filtering for the continent we are interested in. In addition, we define the x-axis and y-axis limits so that we zoom in on the region of interest.

# extract europe (exclude russia and iceland)
world_eur <- world %>%
  dplyr::filter(continent == "Europe", 
                name_long != "Russian Federation", 
                name_long != "Iceland") %>%
  dplyr::select(name_long, geom)
# plot
plot(world_eur,
     xlim = c(5, 10),
     ylim = c(30, 70),
     main = "")

We can also overlay information such as population size over continents and countries as shown below.

# plot world map
plot(world["continent"], reset = FALSE)
# define size bases on population size
cex <- sqrt(world$pop) / 10000
# center world map
world_cents <- sf::st_centroid(world, of_largest = TRUE)
# plot
plot(sf::st_geometry(world_cents), 
     add = TRUE, 
     cex = cex)

Overlaying is interesting because it allows us to highlight certain regions or countries as shown below.

# extract map of europe
world_eur <- world %>%
  dplyr::filter(continent == "Europe")
# extract germany
ger <- world %>%
  dplyr::filter(name_long == "Germany")
# plot germany
plot(sf::st_geometry(ger), expandBB = c(.2, .2, .2, .2), col = "gray", lwd = 3)
# plot europe
plot(world_eur[0], add = TRUE)

We can also add information to the world data set and use the added information to generate customized plots.

# countries I have been to
countries <- c("United States", "Norway", "France", "United Arab Emirates", 
             "Qatar", "Sweden", "Poland", "Austria", "Hungary", "Romania", 
             "Germany", "Bulgaria", "Greece", "Turkey", "Croatia", 
             "Switzerland", "Belgium", "Netherlands", "Spain", "Ireland", 
             "Australia", "China", "Italy", "Denmark", "United Kingdom", 
             "Slovenia", "Finland", "Slovakia", "Czech Republic", "Japan", 
             "Saudi Arabia", "Serbia")
# data frame with countries I have visited
visited <- world %>%
  dplyr::filter(name_long %in% countries)
# plot world
plot(world[0], col = "lightgray")
# overlay countries I have visited in orange 
plot(sf::st_geometry(visited), add = TRUE, col = "orange")

Creating Maps with ggplot2

So far, we have used the base plot function to generate maps. However, it is also possible to use ggplot2 to generate maps and the easiest way is to use borders to draw a map.

# plot map
ggplot() +
  borders()

Another option is to add geom_sf to a ggplot2 object as shown below. A nice feature is that we can add perspective and projection.

# plot map
ggplot(data = world) +
  geom_sf(fill = "white") +
  coord_sf(crs = "+proj=laea +lat_1=-28 +lat_2=-36 +lat_0=-32 +lon_0=135 +x_0=1000000 +y_0=2000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")

Or have a map that shows the world in a bit of an unusual perspective due to the Labert projection.

# plot map
ggplot(data = world) +
  geom_sf(fill = "beige") +
  coord_sf(crs = "+proj=lcc +lat_1=-28 +lat_2=-36 +lat_0=-32 +lon_0=135 +x_0=1000000 +y_0=2000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs") +
  theme(panel.grid.major = element_line(color = "gray50", 
                                         size = 0.25),
        panel.background = element_rect(fill = "aliceblue"))

The nice thing about ggplot2 is, of course, that it is very easy to add layers and create very pretty visualizations.

# plot map
ggplot(data = world) +
  geom_sf() + 
  theme_bw() +
  # adding axes title
  labs(x = "Longitude", y = "Latitude") +
  # adding title and subtitle
  ggtitle("A map of Australia") +
  # defining coordinates
  coord_sf(xlim = c(100.00, 160.00), 
           ylim = c(-45.00, -10.00), 
           expand = T) +
  # add distance measure
  annotation_scale(location = "bl", width_hint = 0.5) +
  # add compass 
  annotation_north_arrow(location = "br")

Again, we can customize the map according to what we want. In addition, we load a map with a higher resolution using the ne_countries function from the rnaturalearth package.

# load data
world <- rnaturalearth::ne_countries(returnclass = "sf") 
# add to prevent errors
sf::sf_use_s2(FALSE)
# extract locations
world_points<- st_centroid(world)
# extract labels
world_points <- cbind(world, sf::st_coordinates(sf::st_centroid(world$geometry)))
# generate annotated world map
ggplot(data = world) +
  # land is gray
  geom_sf(fill= "gray90") +
  # axes labels
  labs(x = "Longitude", y = "Latitude") +
  # define zoom
  coord_sf(xlim = c(100.00, 180.00), 
           ylim = c(-45.00, -10.00), expand = T) +
  # add scale bar
  annotation_scale(location = "bl", width_hint = 0.5) +
  # add compass
  annotation_north_arrow(location = "br", which_north = "true", 
                         style = north_arrow_fancy_orienteering) +
  # define theme (add grid lines)
  theme(panel.grid.major = element_line(color = "gray60", 
                                         linetype = "dashed", 
                                         size = 0.25),
        # define background color
         panel.background = element_rect(fill = "aliceblue")) +
  # add text
  geom_text(data= world_points,aes(x=X, y=Y, label=name),
            color = "gray20", fontface = "italic", check_overlap = T, size = 3)

We can explore other designs and maps and show different regions of the world.

# load data
europe <- ne_countries(scale = "medium", continent='europe', returnclass = "sf") 
# plot map
ggplot(data = europe) +
  # add map and define filling
  geom_sf(mapping = aes(fill = ifelse(name_long == "Germany", "0", "1"))) +
  # simply black and white background
  theme_bw() +
  # adding axes title
  labs(x = "Longitude", y = "Latitude") +
  # adding title and subtitle
  ggtitle("A map of central Europe") +
  # defining coordinates
  coord_sf(xlim = c(-10, 30), 
           ylim = c(40, 60)) +
  # add distance measure
  annotation_scale(location = "bl", width_hint = 0.5) +
  # add compass 
  annotation_north_arrow(location = "br",
                         # make compass fancy
                         style = north_arrow_fancy_orienteering) +
  theme(legend.position = "none",
        # add background color
        panel.background = element_rect(fill = "lightblue"),
        # remove grid lines
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank()) +
  # define fill colors
  scale_fill_manual(name = "Country", values = c("darkgray", "beige")) +
  # add text
  geom_sf_text(aes(label = name_long), size=2.5, color = "gray20")

Adding External Information

While being very useful, displaying basic maps is usually less relevant because, typically, we want to add different layers to a map. In order to add layers to a map, we need to combine the existing data with some data that we would like to lay over the existing map.

We will now lad external information which contains the locations of Australian airports.

# load data
airports <- base::readRDS(url("https://slcladal.github.io/data/apd.rda", "rb")) %>%
  dplyr::mutate(ID = as.character(ID)) %>%
  dplyr::filter(Country == "Australia")
# inspect
head(airports)
##     ID                           Name          City   Country Latitude
## 1 3317   Brisbane Archerfield Airport      Brisbane Australia -27.5703
## 2 3318     Northern Peninsula Airport      Amberley Australia -10.9508
## 3 3319          Alice Springs Airport Alice Springs Australia -23.8067
## 4 3320 Brisbane International Airport      Brisbane Australia -27.3842
## 5 3321             Gold Coast Airport   Coolangatta Australia -28.1644
## 6 3322   Cairns International Airport        Cairns Australia -16.8858
##   Longitude
## 1   153.008
## 2   142.459
## 3   133.902
## 4   153.117
## 5   153.505
## 6   145.755

Next, we load an additional data set about the route volume of Australian airports (how many routes go in and out of these airports).

# read in routes data
routes <- base::readRDS(url("https://slcladal.github.io/data/ard.rda", "rb")) %>%
  dplyr::rename(ID = destinationAirportID) %>%
  dplyr::group_by(ID) %>%
  dplyr::summarise(flights = n())
# inspect
head(routes)
## # A tibble: 6 × 2
##   ID     flights
##   <chr>    <int>
## 1 "\\N"      221
## 2 "1"          5
## 3 "10"         2
## 4 "100"       44
## 5 "1001"       4
## 6 "1004"       7

We can now merge the airports and the route volume data sets to combine the information about the location with the information about the number of routes that end at each airport.

# combine tables
arrivals <- dplyr::left_join(airports, routes, by = "ID") %>%
  na.omit()
# inspect
head(arrivals)
##     ID                           Name          City   Country Latitude
## 2 3318     Northern Peninsula Airport      Amberley Australia -10.9508
## 3 3319          Alice Springs Airport Alice Springs Australia -23.8067
## 4 3320 Brisbane International Airport      Brisbane Australia -27.3842
## 5 3321             Gold Coast Airport   Coolangatta Australia -28.1644
## 6 3322   Cairns International Airport        Cairns Australia -16.8858
## 7 3323            Charleville Airport  Charlieville Australia -26.4133
##   Longitude flights
## 2   142.459       1
## 3   133.902      13
## 4   153.117     144
## 5   153.505      28
## 6   145.755      54
## 7   146.262       4

Now that we have that data (which contains geolocations (longitudes and latitudes), we can visualize the location of the airports on a map and add information about the route volume of the airports in the form of, e.g., points that we plot over the airport - the bigger the point, the higher the route volume. In addition, we add the locations as texts and also make these labels correspond to the route volume.

# create a layer of borders
ggplot(arrivals, aes(x=Longitude, y= Latitude)) +   
  borders("world", colour="gray20", fill="wheat1")  +
  geom_point(color="blue", alpha = .3, size = log(arrivals$flights)) +
  scale_x_continuous(name="Longitude", limits=c(110, 160)) +
  scale_y_continuous(name="Latitude", limits=c(-45, -10)) +
  theme(panel.background = element_rect(fill = "azure1", colour = "azure1")) +
  geom_text(aes(x=Longitude, y= Latitude, label=City),
            color = "gray20", check_overlap = T, size = log(arrivals$flights))

Creating Maps with ggmap

If you simply want to show specific locations on existing maps, then the ggmap package is an easy way to take on this talk.

# define box
sbbox <- ggmap::make_bbox(lon = c(152.8, 153.4), lat = c(-27.1, -27.7), f = .1)
# get map
brisbane = ggmap::get_map(location=sbbox, zoom=10, maptype="terrain")
# create map
brisbanemap = ggmap::ggmap(brisbane)
# display map
brisbanemap +
  geom_point(data = arrivals, mapping = aes(x = Longitude, y = Latitude), 
               color = "red", size = 2) +
  geom_text(data = arrivals, 
            mapping = aes(x = Longitude+0.1,
                          y = Latitude,
                          label = "Brisbane Airport"),
            size = 3, color = "gray20", 
            fontface = "bold", 
            check_overlap = T) +
  labs(x = "Longitude", y = "Latitude")

Interactive Maps with leaflet and maptools

The leaflet package offers very easy-to use options for generating interactive maps (here is the link to the leaflet cheat sheet provided by RStudio). The interactivity is achieved by the leaflet function from the leaflet package which creates a leaflet-map with html-widgets which can be used, e.g., in html rendered R Notebooks or Shiny applications. The advantage of using this function lies in the fact that it offers very detailed maps which enable zooming in on specific locations.

# generate basic leaflet map
m <- leaflet() %>% 
  leaflet::setView(lng = 153.05, lat = -27.45, zoom = 12)%>% 
  leaflet::addTiles()
# show map
m

Another option for interactive geospatial visualizations is provided by the maptools package which comes with a SpatialPolygonsDataFrame of the world and the population by country (in 2005). To make the visualization a bit more appealing, we will calculate the population density, add this variable to the data which underlies the visualization, and then display the information interactively. In this case, this means that you can use mouse-over or hoover effects so that you see the population density in each country if you put the cursor on that country (given the information is available for that country).

We start by loading the required package from the library, adding population density to the data, and removing data points without meaningful information (e.g. we set values like Inf to NA).

# load data
data(wrld_simpl)
# calculate population density and add it to the data 
wrld_simpl@data$PopulationDensity <- round(wrld_simpl@data$POP2005/wrld_simpl@data$AREA,2)
wrld_simpl@data$PopulationDensity <- ifelse(wrld_simpl@data$PopulationDensity == "Inf", NA, wrld_simpl@data$PopulationDensity)
wrld_simpl@data$PopulationDensity <- ifelse(wrld_simpl@data$PopulationDensity == "NaN", NA, wrld_simpl@data$PopulationDensity)
# inspect
head(wrld_simpl@data, 10)
##     FIPS ISO2 ISO3 UN                NAME   AREA  POP2005 REGION SUBREGION
## ATG   AC   AG  ATG 28 Antigua and Barbuda     44    83039     19        29
## DZA   AG   DZ  DZA 12             Algeria 238174 32854159      2        15
## AZE   AJ   AZ  AZE 31          Azerbaijan   8260  8352021    142       145
## ALB   AL   AL  ALB  8             Albania   2740  3153731    150        39
## ARM   AM   AM  ARM 51             Armenia   2820  3017661    142       145
## AGO   AO   AO  AGO 24              Angola 124670 16095214      2        17
## ASM   AQ   AS  ASM 16      American Samoa     20    64051      9        61
## ARG   AR   AR  ARG 32           Argentina 273669 38747148     19         5
## AUS   AS   AU  AUS 36           Australia 768230 20310208      9        53
## BHR   BA   BH  BHR 48             Bahrain     71   724788    142       145
##          LON     LAT PopulationDensity
## ATG  -61.783  17.078           1887.25
## DZA    2.632  28.163            137.94
## AZE   47.395  40.430           1011.14
## ALB   20.068  41.143           1151.00
## ARM   44.563  40.534           1070.09
## AGO   17.544 -12.296            129.10
## ASM -170.730 -14.318           3202.55
## ARG  -65.167 -35.377            141.58
## AUS  136.189 -24.973             26.44
## BHR   50.562  26.019          10208.28

We can now display the data and use color coding to indicate the different population densities.

# define colors
qpal <- colorQuantile(rev(viridis::viridis(10)),
                      wrld_simpl$PopulationDensity, n=10)
# generate visualization
l <- leaflet(wrld_simpl, options =
               leafletOptions(attributionControl = FALSE, minzoom=1.5)) %>%
  addPolygons(
    label=~stringr::str_c(
      NAME, ' ',
      formatC(PopulationDensity, big.mark = ',', format='d')),
    labelOptions= labelOptions(direction = 'auto'),
    weight=1, color='#333333', opacity=1,
    fillColor = ~qpal(PopulationDensity), fillOpacity = 1,
    highlightOptions = highlightOptions(
      color='#000000', weight = 2,
      bringToFront = TRUE, sendToBack = TRUE)
    ) %>%
  addLegend(
    "topright", pal = qpal, values = ~PopulationDensity,
    title = htmltools::HTML("Population density <br> (2005)"),
    opacity = 1 )
# display visualization
l

Adding Shapes to Maps

The following code will download a shape file and associated metadata to a temporary directory. Note that just downloading the .shp is not enough, the associated .shx and .dbf files are also needed.

# Download shapefile to a temporary location so we can read it in
tmpdir <- tempdir()
urls <- list(
  'https://slcladal.github.io/data/shapes/AshmoreAndCartierIslands.shp',
  'https://slcladal.github.io/data/shapes/AshmoreAndCartierIslands.shx',
  'https://slcladal.github.io/data/shapes/AshmoreAndCartierIslands.dbf'
)

for (url in urls) {
  dest <- paste(tmpdir, "/", basename(url), sep="")
  download.file(url, dest)
}

australia <- rgdal::readOGR(tmpdir, layer='AshmoreAndCartierIslands', stringsAsFactors = F)
## OGR data source with driver: ESRI Shapefile 
## Source: "/tmp/Rtmpz0IsGU", layer: "AshmoreAndCartierIslands"
## with 15 features
## It has 14 fields

We can now generate a first map based on the shape file we downloaded.

# plot australia based on shp file
ggplot() + 
  geom_polygon(data = australia, 
               aes(x = long, y = lat, group = group),
               colour = "black", fill = "gray90") +
  theme_void()

Next, we convert the data set into a tidy format.

# convert the data into tidy format
australia_tidy <- broom::tidy(australia, region = "name")
# inspect data
head(australia_tidy)
## # A tibble: 6 × 7
##    long   lat order hole  piece group                         id                
##   <dbl> <dbl> <int> <lgl> <fct> <fct>                         <chr>             
## 1  123. -12.2     1 FALSE 1     Ashmore and Cartier Islands.1 Ashmore and Carti…
## 2  123. -12.2     2 FALSE 1     Ashmore and Cartier Islands.1 Ashmore and Carti…
## 3  123. -12.2     3 FALSE 1     Ashmore and Cartier Islands.1 Ashmore and Carti…
## 4  123. -12.2     4 FALSE 1     Ashmore and Cartier Islands.1 Ashmore and Carti…
## 5  123. -12.2     5 FALSE 1     Ashmore and Cartier Islands.1 Ashmore and Carti…
## 6  123. -12.2     6 FALSE 1     Ashmore and Cartier Islands.1 Ashmore and Carti…

Now, we extract the names of the states and territories as well as the longitudes and latitudes where we want to display the labels. Then, we display the information on the map.

# extract names of states and their long and lat
australia_states <- australia_tidy %>%
  dplyr::group_by(id) %>%
  dplyr::summarise(long = mean(long),
                   lat = mean(lat))

We can now generate a map based on the shp-file. In addition, we define colors for the states and territories and customize the map.

# define colors
clrs <- viridis_pal()(15)
# plot map
p <- ggplot() +
  # plot map
  geom_polygon(data = australia_tidy, 
               aes(x = long, y = lat, group = group, fill = id, alpha = .75), 
               asp = 1, colour = "gray50") +
  # add text
  geom_text(data = australia_states, aes(x = long, y = lat, label = id), 
            size = 3, color = "gray20", fontface = "bold", 
            check_overlap = T) +
  geom_text(data= world_points,aes(x=X, y=Y, label=name),
            color = "gray20", fontface = "bold", check_overlap = T, size = 5) +
  # color states
  scale_fill_manual(values = clrs) +
  # define theme and axes
  theme_void() +
  scale_x_continuous(name = "Longitude", limits = c(110, 160)) +
  scale_y_continuous(name = "Latitude", limits = c(-45, -10)) +
  theme(panel.grid.major = element_line(color = "gray60", 
                                         linetype = "dashed", 
                                         size = 0.25),
        # define background color
        panel.background = element_rect(fill = "aliceblue"),
        legend.position = "none")+ 
  # add compass
  annotation_north_arrow(location = "tl", which_north = "true", 
                         style = north_arrow_fancy_orienteering)
# show plot
p

You can create customized polygons by defining longitudes and latitudes. In fact, you can generate very complex polygons like this. However, in this example, we only create a very basic one as a poof-of-concept.

# create data frame with longitude and latitude values
lat <- c(-25, -27.5, -25, -30, -30, -35, -25)
long <- c(150, 140, 130, 135, 140, 147.5, 150)
mypolygon <- as.data.frame(cbind(long, lat))
# inspect data
mypolygon
##    long   lat
## 1 150.0 -25.0
## 2 140.0 -27.5
## 3 130.0 -25.0
## 4 135.0 -30.0
## 5 140.0 -30.0
## 6 147.5 -35.0
## 7 150.0 -25.0

We can now plot out polygon over the map produced above.

p + 
  geom_polygon(data=mypolygon,
               aes(x = long, y = lat),
               alpha = 0.2, 
               colour = "gray20", 
               fill = "red") +
  ggplot2::annotate("text", 
                    x = mean(long),
                    y = mean(lat),
                    label = "My Polygon Area", 
                    colour="white", 
                    size=3)

We will end this introduction here but if you want to want to learn more, check out the detailed resources for geospatial data visualization using R can be found here or here.

Citation & Session Info

Schweinberger, Martin. 2022. Introduction to Geospatial Data Visualization with R. Brisbane: The University of Queensland. url: https://slcladal.github.io/gviz.html (Version 2022.09.13).

@manual{schweinberger2022gviz,
  author = {Schweinberger, Martin},
  title = {Introduction to Geospatial Data Visualization with R},
  note = {https://slcladal.github.io/gviz.html},
  year = {2022},
  organization = "The University of Queensland, Australia. School of Languages and Cultures},
  address = {Brisbane},
  edition = {2022.09.13}
}
sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.1 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
##  [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
##  [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices datasets  utils     methods   base     
## 
## other attached packages:
##  [1] rgeos_0.5-9         scales_1.2.0        rgdal_1.5-32       
##  [4] maptools_1.1-4      leaflet_2.1.1       ggmap_3.0.0        
##  [7] rnaturalearth_0.1.0 ggspatial_1.1.5     ggplot2_3.3.6      
## [10] tmap_3.3-3          spDataLarge_2.0.6   spData_2.0.1       
## [13] dplyr_1.0.9         raster_3.5-21       sp_1.5-0           
## [16] sf_1.0-7           
## 
## loaded via a namespace (and not attached):
##  [1] bitops_1.0-7            RColorBrewer_1.1-3      httr_1.4.3             
##  [4] backports_1.4.1         tools_4.2.1             bslib_0.3.1            
##  [7] utf8_1.2.2              R6_2.5.1                KernSmooth_2.23-20     
## [10] DBI_1.1.3               colorspace_2.0-3        withr_2.5.0            
## [13] gridExtra_2.3           rnaturalearthdata_0.1.0 tidyselect_1.1.2       
## [16] curl_4.3.2              compiler_4.2.1          leafem_0.2.0           
## [19] cli_3.3.0               labeling_0.4.2          sass_0.4.1             
## [22] classInt_0.4-7          proxy_0.4-27            stringr_1.4.0          
## [25] digest_0.6.29           foreign_0.8-82          rmarkdown_2.14         
## [28] base64enc_0.1-3         dichromat_2.0-0.1       jpeg_0.1-9             
## [31] pkgconfig_2.0.3         htmltools_0.5.2         maps_3.4.0             
## [34] fastmap_1.1.0           highr_0.9               htmlwidgets_1.5.4      
## [37] rlang_1.0.4             farver_2.1.1            jquerylib_0.1.4        
## [40] generics_0.1.3          jsonlite_1.8.0          crosstalk_1.2.0        
## [43] magrittr_2.0.3          s2_1.0.7                Rcpp_1.0.8.3           
## [46] munsell_0.5.0           fansi_1.0.3             viridis_0.6.2          
## [49] abind_1.4-5             lifecycle_1.0.1         terra_1.5-34           
## [52] stringi_1.7.8           leafsync_0.1.0          yaml_2.3.5             
## [55] tmaptools_3.1-1         plyr_1.8.7              grid_4.2.1             
## [58] parallel_4.2.1          crayon_1.5.1            lattice_0.20-45        
## [61] stars_0.5-5             klippy_0.0.0.9500       knitr_1.39             
## [64] pillar_1.7.0            rjson_0.2.21            codetools_0.2-18       
## [67] wk_0.6.0                XML_3.99-0.10           glue_1.6.2             
## [70] evaluate_0.15           leaflet.providers_1.9.0 renv_0.15.4            
## [73] png_0.1-7               vctrs_0.4.1             RgoogleMaps_1.4.5.3    
## [76] gtable_0.3.0            purrr_0.3.4             tidyr_1.2.0            
## [79] assertthat_0.2.1        xfun_0.31               broom_1.0.0            
## [82] lwgeom_0.2-8            e1071_1.7-11            class_7.3-20           
## [85] viridisLite_0.4.0       tibble_3.1.7            units_0.8-0            
## [88] ellipsis_0.3.2

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