Module 1: Mastering R for Spatial Data

The goal of this module is to build a rock-solid foundation in handling, manipulating, and visualizing spatial data within the R environment. We will focus on the “tidyverse” approach to spatial analysis, which treats spatial data as a special kind of data frame, making many operations intuitive and powerful.

By the end of this notebook, you will be able to:

Set up your R environment by loading the essential packages for geospatial analysis.
Understand, load, inspect, and manipulate vector data using the sf package.
Understand, load, and perform fundamental operations on raster data using the terra package.
Create beautiful and informative static maps using the tmap and ggplot2 packages.
Generate interactive maps for data exploration using tmap’s “view” mode.

1.1 Setup: Installing and Loading Core Packages

For our work, we will rely on a core set of packages that form the backbone of modern spatial analysis in R.

If you have not installed these packages before, you must do so first. You can run the install.packages() function in your R console for each one. You only need to do this once.

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# Run these lines in your R console if you haven't installed the packages yet
# install.packages("sf")
# install.packages("terra")
# install.packages("tmap")
# install.packages("ggplot2") # The premier package for data visualization
# install.packages("dplyr")
# install.packages("spData")
# install.packages("geodata")
# install.packages("spDataLarge", repos = "https://nowosad.github.io/drat/", type = "source")

Once installed, we need to load the packages into our current R session using the library() function. We do this at the start of every script.

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# --- Core Packages ---
library(sf)         # Handles vector data (Simple Features). The modern standard.
library(terra)      # Handles raster data. The modern, high-performance successor to the 'raster' package.
library(tmap)       # Used for creating beautiful thematic maps (like ggplot2 for maps).
library(ggplot2)    # A powerful and versatile grammar of graphics for visualization.
library(dplyr)      # A grammar of data manipulation, works seamlessly with 'sf'.

# --- Data Packages ---
library(spData)     # Contains example spatial datasets for practice.
library(geodata)    # For downloading common global spatial datasets like country boundaries or climate data.

1.2 Vector Data with `sf`

What is Vector Data?

Vector data represents geographical features using discrete geometric objects: Points, Lines, and Polygons.

The `sf` Object

The sf package provides a framework for working with vector data in R. An sf object is fundamentally an R data frame with a special list-column that stores the geometry. This structure is powerful because it allows you to use dplyr verbs directly on your spatial data.

Worked Example: Exploring World Countries

Let’s load the world dataset from the spData package and see what an sf object looks like.

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# Load the 'world' dataset
data(world)

# 1. Inspect the object's class
class(world)

[1] "sf"         "tbl_df"     "tbl"        "data.frame"

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# 2. Print the object
print(world)

Simple feature collection with 177 features and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -180 ymin: -89.9 xmax: 180 ymax: 83.64513
Geodetic CRS:  WGS 84
# A tibble: 177 × 11
   iso_a2 name_long continent region_un subregion type  area_km2     pop lifeExp
 * <chr>  <chr>     <chr>     <chr>     <chr>     <chr>    <dbl>   <dbl>   <dbl>
 1 FJ     Fiji      Oceania   Oceania   Melanesia Sove…   1.93e4  8.86e5    70.0
 2 TZ     Tanzania  Africa    Africa    Eastern … Sove…   9.33e5  5.22e7    64.2
 3 EH     Western … Africa    Africa    Northern… Inde…   9.63e4 NA         NA  
 4 CA     Canada    North Am… Americas  Northern… Sove…   1.00e7  3.55e7    82.0
 5 US     United S… North Am… Americas  Northern… Coun…   9.51e6  3.19e8    78.8
 6 KZ     Kazakhst… Asia      Asia      Central … Sove…   2.73e6  1.73e7    71.6
 7 UZ     Uzbekist… Asia      Asia      Central … Sove…   4.61e5  3.08e7    71.0
 8 PG     Papua Ne… Oceania   Oceania   Melanesia Sove…   4.65e5  7.76e6    65.2
 9 ID     Indonesia Asia      Asia      South-Ea… Sove…   1.82e6  2.55e8    68.9
10 AR     Argentina South Am… Americas  South Am… Sove…   2.78e6  4.30e7    76.3
# ℹ 167 more rows
# ℹ 2 more variables: gdpPercap <dbl>, geom <MULTIPOLYGON [°]>

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# 3. Manipulate data with dplyr
world_africa <- world %>%
  filter(continent == "Africa") %>%
  select(name_long, pop, gdpPercap, lifeExp, geom)

# Let's look at our new, smaller sf object
print(world_africa)

Simple feature collection with 51 features and 4 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -17.62504 ymin: -34.81917 xmax: 51.13387 ymax: 37.34999
Geodetic CRS:  WGS 84
# A tibble: 51 × 5
   name_long                     pop gdpPercap lifeExp                      geom
   <chr>                       <dbl>     <dbl>   <dbl>        <MULTIPOLYGON [°]>
 1 Tanzania                   5.22e7     2402.    64.2 (((33.90371 -0.95, 31.86…
 2 Western Sahara            NA            NA     NA   (((-8.66559 27.65643, -8…
 3 Democratic Republic of t…  7.37e7      785.    58.8 (((29.34 -4.499983, 29.2…
 4 Somalia                    1.35e7       NA     55.5 (((41.58513 -1.68325, 41…
 5 Kenya                      4.60e7     2753.    66.2 (((39.20222 -4.67677, 39…
 6 Sudan                      3.77e7     4188.    64.0 (((23.88711 8.619775, 24…
 7 Chad                       1.36e7     2077.    52.2 (((23.83766 19.58047, 19…
 8 South Africa               5.45e7    12390.    61.0 (((16.34498 -28.57671, 1…
 9 Lesotho                    2.15e6     2677.    53.3 (((28.97826 -28.9556, 28…
10 Zimbabwe                   1.54e7     1925.    59.4 (((31.19141 -22.25151, 3…
# ℹ 41 more rows

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# 4. Check the Coordinate Reference System (CRS)
st_crs(world_africa)

Coordinate Reference System:
  User input: EPSG:4326 
  wkt:
GEOGCRS["WGS 84",
    DATUM["World Geodetic System 1984",
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["geodetic latitude (Lat)",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["geodetic longitude (Lon)",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    USAGE[
        SCOPE["Horizontal component of 3D system."],
        AREA["World."],
        BBOX[-90,-180,90,180]],
    ID["EPSG",4326]]

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# 5. Basic Plotting
plot(st_geometry(world_africa), main = "Map of Africa (Geometries Only)")

Exercise 1: Vector Data Manipulation

Now it’s your turn. Use the us_states dataset, which is also included in the spData package.

Load the us_states data and inspect its Coordinate Reference System (CRS).

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# Your code here

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data(us_states)
st_crs(us_states)

Coordinate Reference System:
  User input: EPSG:4269 
  wkt:
GEOGCS["NAD83",
    DATUM["North_American_Datum_1983",
        SPHEROID["GRS 1980",6378137,298.257222101,
            AUTHORITY["EPSG","7019"]],
        TOWGS84[0,0,0,0,0,0,0],
        AUTHORITY["EPSG","6269"]],
    PRIMEM["Greenwich",0,
        AUTHORITY["EPSG","8901"]],
    UNIT["degree",0.0174532925199433,
        AUTHORITY["EPSG","9122"]],
    AUTHORITY["EPSG","4269"]]

Use dplyr::filter() to create a new sf object containing only the states in the “West” region.

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# Your code here

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western_states <- us_states %>%
  filter(REGION == "West")

# Optional: Print the names to check your work
print(western_states$NAME)

 [1] "Arizona"    "Colorado"   "Idaho"      "Montana"    "Nevada"    
 [6] "California" "New Mexico" "Oregon"     "Utah"       "Washington"
[11] "Wyoming"

Calculate the area of each western state using st_area(). The result will be in square meters. Create a new column in your western_states object called area_sqkm that contains the area in square kilometers (Hint: 1 \(km^2\) = 1,000,000 \(m^2\) ).

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# Your code here

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# First, create the 'western_states' object from the previous task
western_states <- us_states %>% 
  filter(REGION == "West")

# Now, add the new area column
western_states_with_area <- western_states %>%
  mutate(area_sqkm = st_area(.) / 1000000)

# View the NAME and the new area column
print(western_states_with_area[, c("NAME", "area_sqkm")])

Simple feature collection with 11 features and 2 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -124.7042 ymin: 31.33224 xmax: -102.0422 ymax: 49.00236
Geodetic CRS:  NAD83
First 10 features:
         NAME      area_sqkm                       geometry
1     Arizona 295348.8 [m^2] MULTIPOLYGON (((-114.7196 3...
2    Colorado 269350.7 [m^2] MULTIPOLYGON (((-109.0501 4...
3       Idaho 216065.1 [m^2] MULTIPOLYGON (((-116.916 45...
4     Montana 379804.1 [m^2] MULTIPOLYGON (((-116.0492 4...
5      Nevada 286105.0 [m^2] MULTIPOLYGON (((-119.9992 4...
6  California 409575.9 [m^2] MULTIPOLYGON (((-118.6034 3...
7  New Mexico 314949.2 [m^2] MULTIPOLYGON (((-109.0452 3...
8      Oregon 250851.5 [m^2] MULTIPOLYGON (((-123.5477 4...
9        Utah 219662.3 [m^2] MULTIPOLYGON (((-114.0417 4...
10 Washington 174949.3 [m^2] MULTIPOLYGON (((-122.7699 4...

Create a simple plot of your western_states object using plot(). Customize it by changing the color of the polygons (col) and their borders (border).

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# Your code here

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# First, create the 'western_states' object from task 2
western_states <- us_states %>% 
  filter(REGION == "West")

plot(st_geometry(western_states), 
     main = "Western US States", 
     col = "khaki", 
     border = "darkgreen")

1.3 Raster Data with `terra`

What is Raster Data?

While vector data uses discrete shapes, raster data represents the world as a continuous grid of cells, or pixels. Each pixel in the grid has a value representing some measured phenomenon. Think of it like a digital photograph.

Epidemiological examples: Satellite imagery, elevation models, or surfaces showing environmental variables like land surface temperature, precipitation, or vegetation density (NDVI).

The `terra` Package

The terra package is the modern, high-performance engine for raster data in R. It is designed to be easier to use and much faster than its predecessor, the raster package, especially with the very large files common in remote sensing.

Worked Example: Exploring Elevation in Uganda

Let’s download elevation data for Uganda and perform two fundamental raster operations: cropping and masking.

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# We will use the sf object for Uganda we can get from the 'world' dataset
uganda_sf <- world %>% filter(iso_a2 == "UG")

# 1. Download Data
# The geodata package can download various global datasets.
# We will get elevation data at 30-arcsecond resolution.
temp_path <- tempdir()
elevation_uganda <- geodata::elevation_30s(country = "UGA", path = temp_path)

# 2. Inspect the SpatRaster Object
print(elevation_uganda)

class       : SpatRaster 
size        : 720, 696, 1  (nrow, ncol, nlyr)
resolution  : 0.008333333, 0.008333333  (x, y)
extent      : 29.4, 35.2, -1.6, 4.4  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source      : UGA_elv_msk.tif 
name        : UGA_elv_msk 
min value   :         580 
max value   :        4656

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# 3. Basic Plotting
plot(elevation_uganda, main = "Elevation of Uganda (Rectangular Extent)")
plot(st_geometry(uganda_sf), add = TRUE, border = "red", lwd = 2)

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# 4. Crop and Mask
elevation_cropped <- crop(elevation_uganda, uganda_sf)
elevation_masked <- mask(elevation_cropped, uganda_sf)

# 5. Plot the Final Result
plot(elevation_masked, main = "Masked Elevation of Uganda (m)")
plot(st_geometry(uganda_sf), add = TRUE, border = "black", lwd = 1)

Exercise 2: Raster Data Manipulation

Let’s practice these skills by creating a map of the mean annual temperature for Brazil.

Download the global average maximum temperature data from WorldClim at 10-minute resolution. This will be a SpatRaster with 12 layers (one for each month). Calculate the mean across all 12 layers to get a single-layer raster of the annual average. Remember: The data is in °C * 10, so you’ll need to divide the final result by 10.

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# Your code here

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temp_path <- tempdir() # Define temp_path if not already defined
global_tmax <- geodata::worldclim_global(var = "tmax", res = 10, path = temp_path)
mean_annual_tmax <- mean(global_tmax)
mean_annual_tmax_c <- mean_annual_tmax / 10

# Optional: print the final raster object to check it
print(mean_annual_tmax_c)

class       : SpatRaster 
size        : 1080, 2160, 1  (nrow, ncol, nlyr)
resolution  : 0.1666667, 0.1666667  (x, y)
extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source(s)   : memory
name        :      mean 
min value   : -4.970683 
max value   :  3.851019

Create an sf object for Brazil from the world dataset.

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# Your code here

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brazil_sf <- world %>%
  filter(name_long == "Brazil")
  
print(brazil_sf)

Simple feature collection with 1 feature and 10 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -73.98724 ymin: -33.76838 xmax: -34.72999 ymax: 5.244486
Geodetic CRS:  WGS 84
# A tibble: 1 × 11
  iso_a2 name_long continent   region_un subregion type  area_km2    pop lifeExp
* <chr>  <chr>     <chr>       <chr>     <chr>     <chr>    <dbl>  <dbl>   <dbl>
1 BR     Brazil    South Amer… Americas  South Am… Sove… 8508557. 2.04e8    75.0
# ℹ 2 more variables: gdpPercap <dbl>, geom <MULTIPOLYGON [°]>

Crop and then mask the global temperature raster (mean_annual_tmax_c) to the exact shape of Brazil.

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# Your code here

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# Re-create objects from previous tasks to make this chunk self-contained
temp_path <- tempdir()
global_tmax <- geodata::worldclim_global(var = "tmax", res = 10, path = temp_path)
mean_annual_tmax_c <- mean(global_tmax) / 10
brazil_sf <- world %>% filter(name_long == "Brazil")

# Now perform the crop and mask
tmax_brazil_cropped <- crop(mean_annual_tmax_c, brazil_sf)
tmax_brazil_masked <- mask(tmax_brazil_cropped, brazil_sf)

print(tmax_brazil_masked)

class       : SpatRaster 
size        : 234, 236, 1  (nrow, ncol, nlyr)
resolution  : 0.1666667, 0.1666667  (x, y)
extent      : -74, -34.66667, -33.83333, 5.166667  (xmin, xmax, ymin, ymax)
coord. ref. : lon/lat WGS 84 (EPSG:4326) 
source(s)   : memory
name        :     mean 
min value   : 1.781475 
max value   : 3.414175

Plot your final masked temperature raster for Brazil. Add the country’s border to the plot for context.

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# Your code here

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# Re-create the necessary objects from previous tasks
temp_path <- tempdir()
global_tmax <- geodata::worldclim_global(var = "tmax", res = 10, path = temp_path)
mean_annual_tmax_c <- mean(global_tmax) / 10
brazil_sf <- world %>% filter(name_long == "Brazil")
tmax_brazil_masked <- mask(crop(mean_annual_tmax_c, brazil_sf), brazil_sf)

# Now create the plot
plot(tmax_brazil_masked, main = "Mean Annual Max Temperature for Brazil (°C)")
plot(st_geometry(brazil_sf), add = TRUE, border = "black")

1.4 Creating Maps with `tmap`

While plot() is great for quick checks, the tmap package provides a powerful and flexible system for creating publication-quality thematic maps. It uses a “grammar of graphics” approach, similar to ggplot2, where you build a map layer by layer.

The main components are: * tm_shape(): Specifies the sf or SpatRaster object to be mapped. * tm_polygons(), tm_lines(), tm_dots(), tm_raster(): Specify how to draw the shape. * tm_layout(), tm_compass(), tm_scale_bar(): Functions to customize the map’s appearance and add map furniture.

A key feature of tmap is its dual-mode functionality. You can create: 1. Static Maps (tmap_mode("plot")): High-quality images suitable for publications, reports, and presentations. 2. Interactive Maps (tmap_mode("view")): Dynamic maps that you can pan, zoom, and query in a web browser or the RStudio Viewer. This is incredibly useful for data exploration.

Worked Example: Static Maps

Let’s create a high-quality map showing life expectancy in Africa, highlighting Uganda, and another map showing Uganda’s elevation.

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# Set tmap mode to "plot" for static maps.
tmap_mode("plot")

# Map 1: A Choropleth Map of Life Expectancy
# A choropleth map is one where polygons are colored according to a variable's value.
map1 <- tm_shape(world_africa) +
  tm_polygons(
    col = "lifeExp",                      # The column to map
    palette = "plasma",                   # Color palette
    style = "quantile",                   # How to break data into color bins
    title = "Life Expectancy (Years)"     # Legend title
    ) +
  tm_shape(uganda_sf) +                   # Add a new shape layer for Uganda
  tm_borders(col = "black", lwd = 2.5) +  # Draw its borders
  tm_layout(
    main.title = "Life Expectancy in African Nations",
    main.title.position = "center",
    legend.position = c("left", "bottom"),
    frame = FALSE, 
    inner.margins = c(0.05, 0.1, 0.05, 0.05)
    ) +
  tm_compass(type = "8star", position = c("right", "top"), size = 3) +
  tm_scale_bar(position = c("right", "bottom"), breaks = c(0, 1000, 2000))

# Map 2: Combining Raster and Vector Data
# Let's map the masked Uganda elevation raster we created earlier.
map2 <- tm_shape(elevation_masked) +
  tm_raster(
    title = "Elevation (m)"
    ) +
  tm_shape(uganda_sf) +
  tm_borders(lwd = 1.5, col = "black") +
  tm_layout(main.title = "Elevation of Uganda")  +
  tm_compass(type = "arrow", position = c("left", "top")) +
  tm_scale_bar()

# Print the maps
map1
map2

The Power of Interactive Mode

The real magic of tmap for data exploration comes from its interactive mode. By simply running tmap_mode("view") once, all subsequent tmap plots become interactive Leaflet maps. You can click on features to see their data, zoom in on areas of interest, and switch between different basemaps. This is invaluable for sanity-checking your data and discovering patterns.

Let’s take our first map and view it interactively.

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# Switch to interactive view mode
tmap_mode("view")

# Re-run the same code for our first map
# No other changes are needed!
tm_shape(world_africa) +
  tm_polygons(
    fill = "lifeExp",
    # When hovering, show the country name and its life expectancy
    popup.vars = c("Country" = "name_long", "Life Expectancy" = "lifeExp")
    ) +
  tm_shape(uganda_sf) +
  tm_borders(col = "black", lwd = 2.5) +
  tm_layout(main.title = "Interactive Map of Life Expectancy in Africa")

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# --- IMPORTANT ---
# Switch back to plot mode for the rest of the document
tmap_mode("plot")

Exercise 3: Thematic Mapping with `tmap`

Now, create your own high-quality map of the United States.

Using the us_states dataset, create a choropleth map showing the total population in 2015 (total_pop_15). Add state borders with tm_borders().

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# Your code here

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# Ensure us_states data is loaded
data(us_states)

tm_shape(us_states) +
  tm_polygons(col = "total_pop_15", title = "Population (2015)") +
  tm_borders(col = "gray70")

Improve the map from Task 1. * Add a main title. * Change the color palette (try "YlGnBu" or "Reds"). * Add a compass and a scale bar. * Make the state borders white and thin (lwd = 0.5).

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# Your code here

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# Ensure us_states data is loaded
data(us_states)

tm_shape(us_states) +
  tm_polygons(
    col = "total_pop_15", 
    title = "Total Population (2015)",
    palette = "YlGnBu",
    style = "jenks" # Jenks style is good for skewed data
    ) +
  tm_borders(col = "white", lwd = 0.5) +
  tm_layout(
    main.title = "Population of the United States, 2015",
    frame = FALSE,
    legend.outside = TRUE
    ) +
  tm_compass(type = "arrow", position = c("right", "top")) +
  tm_scale_bar(position = c("left", "bottom"))

1.5 Creating Maps with `ggplot2`

For those already familiar with the ggplot2 package, its “grammar of graphics” can be extended to create highly customized, professional maps. The sf package integrates directly with ggplot2 through a special geometric layer: geom_sf().

The core idea is the same as any other ggplot: you initialize a plot with ggplot(), specify your data, and then add layers. For spatial data, geom_sf() is the primary layer.

Worked Example: Mapping US Population with `ggplot2`

Let’s use ggplot2 and geom_sf to create a map of population density for the us_states dataset.

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# Load the data if not already in the environment
data(us_states)

# We can add a new column for population density right in our pipe
us_states_plot <- us_states %>%
  mutate(pop_density = as.numeric(total_pop_15 / AREA))

# Create the map with ggplot2
ggplot(data = us_states_plot) +
  # The main geometry layer for sf objects
  geom_sf(aes(fill = pop_density)) + 
  
  # Customize the color scale
  scale_fill_viridis_c(
    trans = "log10", # Use a log scale for better visualization of skewed data
    name = "Population Density\n(people / sq. mile, log scale)"
    ) +
  
  # Add titles and a clean theme
  labs(
    title = "US Population Density by State, 2015",
    subtitle = "Data from the spData package",
    caption = "Map created with ggplot2"
  ) +
  theme_void() + # A minimal theme, good for maps
  theme(legend.position = "bottom")

Exercise 4: Thematic Mapping with `ggplot2`

Let’s use ggplot2 to visualize the economic data in the world_africa dataset we created earlier.

Using the world_africa object, create a simple map of the African continent using ggplot() and geom_sf().

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# Your code here

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# Assuming 'world_africa' was created in section 1.2
ggplot(data = world_africa) +
  geom_sf() +
  ggtitle("Map of Africa")

Map the gdpPercap (GDP per capita) variable to the fill aesthetic of the polygons.

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# Your code here

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ggplot(data = world_africa) +
  geom_sf(aes(fill = gdpPercap))

Improve your map. * Use scale_fill_viridis_c() for a better color scale and add a title to the legend. * Add a main title and a subtitle to the map using labs(). * Apply theme_bw() for a cleaner look. * Add a black border layer for Uganda (uganda_sf) on top of the other countries. (Hint: Use a second geom_sf() call, but set fill = NA so it doesn’t cover the colors).

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# Your code here

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# Assuming 'world_africa' and 'uganda_sf' are loaded
ggplot() +
  # Layer 1: African countries colored by GDP
  geom_sf(data = world_africa, aes(fill = gdpPercap)) +
  
  # Layer 2: Uganda outline
  geom_sf(data = uganda_sf, fill = NA, color = "red", linewidth = 0.8) +
  
  # Customize scales and labels
  scale_fill_viridis_c(name = "GDP per Capita (USD)") +
  labs(
    title = "GDP Per Capita in Africa",
    subtitle = "Highlighting Uganda",
    caption = "Source: spData package"
  ) +
  theme_bw()

Module 1 Finish Line

Congratulations on completing the first module!

You have acquired the fundamental skills for working with spatial data in R. You have learned how to use sf and dplyr to manage and query vector data, how to use terra to perform essential operations on raster data, and how to turn that data into informative and visually appealing maps.

Specifically, you now have two powerful mapping tools at your disposal: * tmap, which provides a straightforward grammar for creating both static, publication-quality maps and incredibly useful interactive maps for data exploration. * ggplot2, which extends its famous grammar of graphics to spatial data with geom_sf, allowing for deep customization and integration into a tidyverse workflow.

1.1 Setup: Installing and Loading Core Packages

1.2 Vector Data with sf

What is Vector Data?

The sf Object

Worked Example: Exploring World Countries

Exercise 1: Vector Data Manipulation

1.3 Raster Data with terra

What is Raster Data?

The terra Package

Worked Example: Exploring Elevation in Uganda

Exercise 2: Raster Data Manipulation

1.4 Creating Maps with tmap

Worked Example: Static Maps

The Power of Interactive Mode

Exercise 3: Thematic Mapping with tmap

1.5 Creating Maps with ggplot2

Worked Example: Mapping US Population with ggplot2

Exercise 4: Thematic Mapping with ggplot2

Module 1 Finish Line

1.2 Vector Data with `sf`

The `sf` Object

1.3 Raster Data with `terra`

The `terra` Package

1.4 Creating Maps with `tmap`

Exercise 3: Thematic Mapping with `tmap`

1.5 Creating Maps with `ggplot2`

Worked Example: Mapping US Population with `ggplot2`

Exercise 4: Thematic Mapping with `ggplot2`