Chapter 9: Making maps with R

Key Learnings from, and Solutions to the exercises in Chapter 8 of the book Geocomputation with R by Robin Lovelace, Jakub Nowosad and Jannes Muenchow.

In this chapter, I use {ggplot2} (Wickham 2016) to produce equivalent maps, as produced by {tmap} (Tennekes 2018) in the textbook. In addition, I use {cols4all} (Tennekes and Puts 2023) palettes for colour and fill scales.

library(sf)        # Simple Features in R
library(terra)     # Handling rasters in R
library(tidyterra) # For plotting rasters in ggplot2
library(magrittr)  # Using pipes with raster objects
library(tidyverse) # All things tidy; Data Wrangling
library(spData)    # Spatial Datasets
library(patchwork) # Composing plots
library(gt)        # Display GT tables with R

9.1 Introduction

• Cartography is a crucial aspect of geographic research, blending communication, detail, and creativity.
• Static maps in R can be created using the plot() function, but advanced cartography benefits from dedicated packages.
• The chapter focuses in-depth on the tmap package rather than multiple tools superficially.

# install.packages("colorblindcheck")
# install.packages("cols4all")

# A nice way to pick colour palettes for maps etc.
# cols4all::c4a_gui()
cols4all::c4a_palettes()

9.2 Static maps

• Most common type of geo-computation output, stored as .png (raster) and .pdf (vector).
• Base R’s plot() is the fastest way to create static maps from sf or terra, ideal for quick visual checks.
• tmap package offers:
  • Simple, ggplot2-like syntax.
  • Static and interactive maps with tmap_mode().
  • Support for multiple spatial classes (sf, terra).

9.2.1 tmap basics

• Grammar of graphics: Like ggplot2, tmap follows a structured approach, separating input data from aesthetics (visual properties).
• Basic structure: Uses tm_shape() to define the input dataset (vector or raster), followed by layer elements like tm_fill() and tm_borders().
• Layering approach:
  • tm_fill(): Fills (multi)polygon areas.
  • tm_borders(): Adds border outlines to (multi)polygons.
  • tm_polygons(): Combines fill and border.
  • tm_lines(): Draws lines for (multi)linestrings.
  • tm_symbols(): Adds symbols for points, lines, and polygons.
  • tm_raster(): Displays raster data.
  • tm_rgb(): Handles multi-layer rasters.
  • tm_text(): Adds text labels.
• Layering operator: The + operator is used to add multiple layers.
• Quick maps: qtm() provides a fast way to generate thematic maps (qtm(nz) ≈ tm_shape(nz) + tm_fill() + tm_borders()).
  • Limitations of qtm(): Less control over aesthetics, so not covered in detail in this chapter.

9.2.2 Map objects

• {tmap} allows storing maps as objects, enabling modifications and layer additions.
• Use tm_polygons() to create a map object, combining tm_fill() and tm_borders().
• Stored maps can be plotted later by simply calling the object.
• Additional layers are added using + tm_shape(new_obj), where new_obj represents a new spatial object.
• Aesthetic functions apply to the most recently added shape until another is introduced.
• Spatial objects can be manipulated with sf, e.g., st_union(), st_buffer(), and st_cast().
• Multiple layers can be added, such as:
  • Raster elevation (tm_raster())
  • Territorial waters (tm_lines())
  • High points (tm_symbols())
• tmap_arrange() combines multiple tmap objects into a single visualization.
• The + operator adds layers, but aesthetics are controlled within layer functions.

9.2.3 Visual variables

• Default aesthetics in tmap:
  • tm_fill() and tm_symbols() use gray shades.
  • tm_lines() uses a continuous black line.
  • Defaults can be overridden for customization.
• Types of map aesthetics:
  • Variable-dependent aesthetics (change with data).
  • Fixed aesthetics (constant values).
• Key aesthetic arguments in tmap:
  • fill: Polygon fill color.
  • col: Border, line, point, or raster color.
  • lwd: Line width.
  • lty: Line type.
  • size: Symbol size.
  • shape: Symbol shape.
  • fill_alpha, col_alpha: Transparency for fill and border.
• Applying aesthetics:
  • Use a column name to map a variable. Pass a character string referring to a column name.
  • Use a fixed value for constant aesthetics.
• Additional arguments for visual variables:
  • .scale: Controls representation on the map and legend.
  • .legend: Customizes legend settings.
  • .free: Defines whether each facet uses the same or different scales.

9.2.4 Scales

• Scales define how values are visually represented in maps and legends, depending on the selected visual variable (e.g., fill.scale, col.scale, size.scale).
• Default scale is tm_scale(), which auto-selects settings based on input data type (factor, numeric, integer).
• Colour settings impact spatial variability; customization options include:
  • breaks: manually set classification thresholds.
  • n: define the number of bins.
  • values: assign colour schemes (e.g., "BuGn").
• Family of scale functions in tmap:
  • tm_scale_intervals(): splits values into intervals using predefined styles ("pretty", "equal", "quantile", "jenks", "log10_pretty").
  • tm_scale_continuous(): creates continuous colour fields, suitable for rasters.
  • tm_scale_categorical(): assigns unique colours to categorical values.
• Colour palettes are key for readability and should be carefully chosen:
  • cols4all::c4a_gui() helps find suitable palettes.
  • Default values for visual variables can be checked with tmap_options().
• Three main colour palette types:
  • Categorical: distinct colours for unordered categories (e.g., land cover classes).
  • Sequential: gradient from light to dark, for continuous numeric variables.
  • Diverging: two sequential palettes meeting at a neutral reference point (e.g., temperature anomalies).
• Key considerations for colour choices:
  • Perceptibility: colours should match common associations (e.g., blue for water, green for vegetation).
  • Accessibility: use colour-blind-friendly palettes where possible.

References

Tennekes, Martijn. 2018. “Tmap: Thematic Maps in r” 84. https://doi.org/10.18637/jss.v084.i06.

Tennekes, Martijn, and Marco J. H. Puts. 2023. “Cols4all: A Color Palette Analysis Tool.” EuroVis (Short Papers). https://doi.org/10.2312/evs.20231040.

Wickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data Analysis.” https://ggplot2.tidyverse.org.