EDS 223: week 1 lab

introduction

In this lab, we’ll explore the basics of map-making in R using the tmap package. The following materials are modified from Chapter 9 of Geocomputation with R by Rovin Lovelace

prerequisites

Let’s load all necessary packages.

#install.packages("sf")
#install.packages("terra")
#install.packages("spData")
#install.packages("spDataLarge", repos = "https://geocompr.r-universe.dev")
#remotes::install_github("r-tmap/tmap@v4")

library(sf)
library(terra)
library(dplyr)
library(spData)
library(spDataLarge)
library(tmap)    # for static and interactive maps

Let’s also read in some data from the spDataLarge package to work with later.

nz_elev = rast(system.file("raster/nz_elev.tif", package = "spDataLarge"))

map-making basics

Let’s start with a pre-loaded spatial object representing the states of New Zealand

nz

## Simple feature collection with 16 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1090144 ymin: 4748537 xmax: 2089533 ymax: 6191874
## Projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000
## First 10 features:
##                 Name Island Land_area Population Median_income Sex_ratio
## 1          Northland  North 12500.561     175500         23400 0.9424532
## 2           Auckland  North  4941.573    1657200         29600 0.9442858
## 3            Waikato  North 23900.036     460100         27900 0.9520500
## 4      Bay of Plenty  North 12071.145     299900         26200 0.9280391
## 5           Gisborne  North  8385.827      48500         24400 0.9349734
## 6        Hawke's Bay  North 14137.524     164000         26100 0.9238375
## 7           Taranaki  North  7254.480     118000         29100 0.9569363
## 8  Manawatu-Wanganui  North 22220.608     234500         25000 0.9387734
## 9         Wellington  North  8048.553     513900         32700 0.9335524
## 10        West Coast  South 23245.456      32400         26900 1.0139072
##                              geom
## 1  MULTIPOLYGON (((1745493 600...
## 2  MULTIPOLYGON (((1803822 590...
## 3  MULTIPOLYGON (((1860345 585...
## 4  MULTIPOLYGON (((2049387 583...
## 5  MULTIPOLYGON (((2024489 567...
## 6  MULTIPOLYGON (((2024489 567...
## 7  MULTIPOLYGON (((1740438 571...
## 8  MULTIPOLYGON (((1866732 566...
## 9  MULTIPOLYGON (((1881590 548...
## 10 MULTIPOLYGON (((1557042 531...

we’re going to start by using the tmap package to make some basic maps
tmap can work with spatial objects of a variety of clasess, meaning it’s highly versatile
this approach relies on a series of functions that typically start with “tm_”

the first element is always “tm_shape”

tm_shape(nz) +
  tm_fill()

now let’s plot just the boundaries

tm_shape(nz) +
  tm_borders()

and the shapes and boundaries together

tm_shape(nz) +
  tm_fill() +
  tm_borders()

map objects

tmap can store maps as objects
this means that we store a base map and add additional layers later

map_nz <- tm_shape(nz) +
  tm_polygons()
class(map_nz)

## [1] "tmap"

we can add new shapes on top of the base map
when we add a new shape, all subsequent aesthetic functions refer to it, until we add a new shape

In this case, we’re adding a layer with information on elevation and this layer to have 70% transparency.

map_nz1 <- map_nz +
  tm_shape(nz_elev) +
  tm_raster(col_alpha = 0.7)

map_nz1

## SpatRaster object downsampled to 1141 by 877 cells.

we can add points designating high points in the country

map_nz2 <- map_nz1 +
  tm_shape(nz_height) +
  tm_dots()

map_nz2

## SpatRaster object downsampled to 1141 by 877 cells.

aesthetic basics

up until now, we’ve been working with the default aesthetics
there are 2 types of aesthetics: fixed and those that change with the value of a variable
tmap works differently than ggplot2 and doesn’t use the “aes()” function

Let’s start by changing some fixed aesthetics…First, let’s change the color used to fill the NZ shapes.

tm_shape(nz) +
  tm_polygons(fill = "red")

now change the color of the boundaries

tm_shape(nz) + 
  tm_polygons(col = "blue")

and the width of the boundary lines

tm_shape(nz) + 
  tm_polygons(lwd = 3)

and the line type of the boundary lines

tm_shape(nz) + 
  tm_polygons(lty = 2)

all together now!

tm_shape(nz) + 
  tm_polygons(fill = "red", 
              fill_alpha = 0.3,
              col = "blue", 
              lwd = 3, 
              lty = 2)

Now let’s change the colors based on a value. We noticed that the New Zealand dataset has a column with each state’s land area

nz

## Simple feature collection with 16 features and 6 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 1090144 ymin: 4748537 xmax: 2089533 ymax: 6191874
## Projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000
## First 10 features:
##                 Name Island Land_area Population Median_income Sex_ratio
## 1          Northland  North 12500.561     175500         23400 0.9424532
## 2           Auckland  North  4941.573    1657200         29600 0.9442858
## 3            Waikato  North 23900.036     460100         27900 0.9520500
## 4      Bay of Plenty  North 12071.145     299900         26200 0.9280391
## 5           Gisborne  North  8385.827      48500         24400 0.9349734
## 6        Hawke's Bay  North 14137.524     164000         26100 0.9238375
## 7           Taranaki  North  7254.480     118000         29100 0.9569363
## 8  Manawatu-Wanganui  North 22220.608     234500         25000 0.9387734
## 9         Wellington  North  8048.553     513900         32700 0.9335524
## 10        West Coast  South 23245.456      32400         26900 1.0139072
##                              geom
## 1  MULTIPOLYGON (((1745493 600...
## 2  MULTIPOLYGON (((1803822 590...
## 3  MULTIPOLYGON (((1860345 585...
## 4  MULTIPOLYGON (((2049387 583...
## 5  MULTIPOLYGON (((2024489 567...
## 6  MULTIPOLYGON (((2024489 567...
## 7  MULTIPOLYGON (((1740438 571...
## 8  MULTIPOLYGON (((1866732 566...
## 9  MULTIPOLYGON (((1881590 548...
## 10 MULTIPOLYGON (((1557042 531...

Let’s try to plot the Land_area column. We might think that the following works, but it doesn’t!

#tm_shape(nz) +
#  tm_fill(col = nz$Land_area)

Instead, tmap is expecting a character string naming the attribute associated with the geometry

tm_shape(nz) +
  tm_fill(fill = "Land_area")

We can also add an argument that updates the title of the legend

tm_shape(nz) +
  tm_fill(fill = "Land_area", title = "Area")

## Deprecated tmap v3 code detected. Code translated to v4

We can even make it more precise using the “expression” function

tm_shape(nz) +
  tm_fill(fill = "Land_area", title = expression("Area (km"^2*")")) +
  tm_borders()

## Deprecated tmap v3 code detected. Code translated to v4

color settings

how we set and define colors can radically change the interpretation of our maps
the style argument has several options for breaking data into bins
- style = “pretty” (default) rounds breaks into evenly spaced whole numbers, where possible
- style = “equal” divides input values into bins with equal range (best for uniform distributions)
- style = “quantile” puts the same number of observations into each bin
- style = “jenks” identifies groups of similar values and maximizes differences between bins

Notice how the following maps display the same data, but look quite different

tm_shape(nz) + 
  tm_polygons(fill = "Median_income", style = "pretty")

## Deprecated tmap v3 code detected. Code translated to v4

tm_shape(nz) + 
  tm_polygons(fill = "Median_income", style = "equal")

## Deprecated tmap v3 code detected. Code translated to v4

tm_shape(nz) + 
  tm_polygons(fill = "Median_income", style = "quantile")

## Deprecated tmap v3 code detected. Code translated to v4

tm_shape(nz) + 
  tm_polygons(fill = "Median_income", style = "jenks")

## Deprecated tmap v3 code detected. Code translated to v4

We can also define custom bins

breaks = c(0, 3, 4, 5) * 10000
tm_shape(nz) + 
  tm_polygons(fill = "Median_income", breaks = breaks)

## Deprecated tmap v3 code detected. Code translated to v4

in some cases we might not want to use bins
- style = “cont” displays colors as a continuous spectrum
- style = “cat” uses a unique vale for each categorical value

map_nz +
  tm_shape(nz_elev) +
  tm_raster(col_alpha = 0.7) +
  tm_scale_continuous()

## SpatRaster object downsampled to 1141 by 877 cells.

map_nz +
  tm_shape(nz) +
  tm_polygons(fill = "Island")

map layout

now that we have the basics, we can turn to all the other elements that make a cohesive map
tmap has lots of options, but we will explore just a few

To clearly give readers the context of our map, we can include a compass and scale bar

map_nz +
  tm_compass(type = "4star", position = c("left", "top")) +
  tm_scale_bar(breaks = c(0, 100, 200), text.size = 1)

## Warning: As of version 4.0, tm_scale_bar has been renamed to tm_scalebar and is
## therefore deprecated

Instead of using a compass and scale bar, we could add latitude/longitudes graticules

map_nz +
  tm_graticules()

We can also update the background color

map_nz +
  tm_graticules() +
  tm_layout(bg.color = "lightblue")

faceted and animated maps

we might have data that varies over time and we want to look at the how it changes
one approach is by using faceted plots, a series of plots

urb_1970_2030 <- urban_agglomerations %>% 
  filter(year %in% c(1970, 1990, 2010, 2030))

tm_shape(world) +
  tm_polygons() +
  tm_shape(urb_1970_2030) +
  tm_symbols(col = "black", border.col = "white", size = "population_millions") +
  tm_facets(by = "year", nrow = 2, free.coords = FALSE)

## Deprecated tmap v3 code detected. Code translated to v4

interactive maps

tmap is especially powerful because it allows us to make interactive maps using the same syntax
all we need to do is enter the interactive mode

tmap_mode("view")

## tmap mode set to 'view'

map_nz

To go back to regular plotting, we just need enter plotting mode

tmap_mode("plot")

## tmap mode set to 'plot'

EDS 223: week 1 lab

Ruth Oliver

2022-10-03

introduction

prerequisites

prerequisites

map-making basics

map objects

aesthetic basics

color settings

map layout

faceted and animated maps

interactive maps