7. Mapping With ggplot2

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Affiliation

Published

January 1, 2021

“There’s no map to human behaviour.”

— Bjork

“Here be dragons.”

— Unknown

Yesterday you learned how to create ggplot2 figures, change their aesthetics, labels, colour palettes, and facet/arrange them. Now you are going to look at how to create maps.

Most of the work that you will perform as environmental/biological scientists involves going out to a location and sampling information there. Sometimes only once, and sometimes over a period of time. All of these different sampling methods lend themselves to different types of figures. One of those, collection of data at different points, is best shown with maps. As you will see over the course of Day 3, creating maps in ggplot2 is very straight forward and is extensively supported. For that reason you are going to have plenty of time to also learn how to do some more advanced things. Your goal in this chapter is to produce the figure below.

Using prepared data

Before you begin let’s go ahead and load the packages you will need, as well as the several dataframes required to make the final product.

# Load libraries
library(tidyverse)
library(ggpubr)

# Load data
load("../../data/south_africa_coast.Rdata")
load("../../data/sa_provinces.RData")
load("../../data/rast_annual.Rdata")
load("../../data/MUR.Rdata")
load("../../data/MUR_low_res.RData")

# Choose which SST product you would like to use
sst <- MUR_low_res
# OR
sst <- MUR

# The colour palette we will use for ocean temperature
cols11 <- c("#004dcd", "#0068db", "#007ddb", "#008dcf", "#009bbc",
            "#00a7a9", "#1bb298", "#6cba8f", "#9ac290", "#bec99a")

A new concept?

The idea of creating a map in R may be daunting to some, but remember that a basic map is nothing more than a simple figure with an x and y axis. We tend to think of maps as different from other scientific figures, whereas in reality they are created the exact same way. Let’s compare a dot plot of the chicken weight data against a dot plot of the coastline of South Africa.

Chicken dots:

ggplot(data = ChickWeight, aes(x = Time, y = weight)) +
  geom_point()

South African coast dots:

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_point()

Does that look familiar? Notice how the x and y axis tick labels look the same as any map you would see in an atlas. This is because they are. But this isn’t a great way to create a map. Rather it is better to represent the land mass with a polygon. With ggplot2 this is a simple task.

Land mask

Now that you have seen that a map is nothing more than a bunch of dots and shapes on specific points along the x and y axes you are going to look at the steps you would take to build a more complex map. Don’t worry if this seems daunting at first. You are going to take this step by step and ensure that each step is made clear along the way. The first step is to create a polygon. Note that you create an aesthetic argument inside of geom_polygon() and not ggplot() because some of the steps you will take later on will not accept the group aesthetic. Remember, whatever aesthetic arguments we put inside of ggplot() will be inserted into all of our other geom_...() lines of code.

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) # The land mask

Borders

The first thing you will add is the province borders as seen in Figure @ref(fig:map-goal). Notice how you only add one more line of code to do this.

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) +
  geom_path(data = sa_provinces, aes(group = group)) # The province borders

Force lon/lat extent

Unfortunately when you added our borders it increased the plotting area of our map past what you would like. To correct that you will need to explicitly state the borders you want.

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) +
  geom_path(data = sa_provinces, aes(group = group)) + 
  coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) # Force lon/lat extent

The map, but with the extra bits snipped off.

Ocean temperature

This is starting to look pretty fancy, but it would be nicer if there was some colour involved. So let’s add the ocean temperature. Again, this will only require one more line of code. Starting to see a pattern? But what is different this time and why?

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_raster(data = sst, aes(fill = bins)) + # The ocean temperatures
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) +
  geom_path(data = sa_provinces, aes(group = group)) +
  coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0)

Ocean temperature (°C) visualised as an ice cream spill.

That looks… odd. Why do the colours look like someone melted a big bucket of ice cream in the ocean? This is because the colours you see in this figure are the default colours for discrete values in ggplot2. If you want to change them we may do so easily by adding yet one more line of code.

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_raster(data = sst, aes(fill = bins)) +
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) +
  geom_path(data = sa_provinces, aes(group = group)) +
  scale_fill_manual("Temp. (°C)", values = cols11) + # Set the colour palette
  coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0)

Ocean temperatures (°C) around South Africa.

There’s a colour palette that would make Jacques Cousteau swoon. When you set the colour palette for a figure in ggplot2 you must use that colour palette for all other instances of those types of values, too. What this means is that any other discrete values that will be filled in, like the ocean colour above, must use the same colour palette (there are some technical exceptions to this rule that you will not cover in this course). You normally want ggplot2 to use consistent colour palettes anyway, but it is important to note that this constraint exists. Let’s see what I mean. Next you will add the coastal pixels to our figure with one more line of code. You won’t change anything else. Note how ggplot2 changes the colour of the coastal pixels to match the ocean colour automatically.

ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_raster(data = sst, aes(fill = bins)) +
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) +
  geom_path(data = sa_provinces, aes(group = group)) +
  geom_tile(data = rast_annual, aes(x = lon, y = lat, fill = bins), 
            colour = "white", size = 0.1) + # The coastal temperature values
  scale_fill_manual("Temp. (°C)", values = cols11) +
  coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0)

Map of South Africa showing *in situ* temeperatures (°C) as pixels along the coast.

Final touches

You used geom_tile() instead of geom_rast() to add the coastal pixels above so that you could add those little white boxes around them. This figure is looking pretty great now. And it only took a few rows of code to put it all together! The last step is to add several more lines of code that will control for all of the little things you want to change about the appearance of the figure. Each little thing that is changed below is annotated for your convenience.

final_map <- ggplot(data = south_africa_coast, aes(x = lon, y = lat)) +
  geom_raster(data = sst, aes(fill = bins)) +
  geom_polygon(colour = "black", fill = "grey70", aes(group = group)) +
  geom_path(data = sa_provinces, aes(group = group)) +
  geom_tile(data = rast_annual, aes(x = lon, y = lat, fill = bins), 
            colour = "white", size = 0.1) +
  scale_fill_manual("Temp. (°C)", values = cols11) +
  coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) +
  scale_x_continuous(position = "top") + # Put x axis labels on top of figure
  theme(axis.title = element_blank(), # Remove the axis labels
        legend.text = element_text(size = 7), # Change text size in legend
        legend.title = element_text(size = 7), # Change legend title text size
        legend.key.height = unit(0.3, "cm"), # Change size of legend
        legend.background = element_rect(colour = "white"), # Add legend background
        legend.justification = c(1, 0), # Change position of legend
        legend.position = c(0.55, 0.4) # Fine tune position of legend
        )
final_map

The cleaned up map of South Africa. Resplendent with coastal and ocean temperatures (°C).

That is a very clean looking map so go ahead and save it on your local disk.

ggsave(plot = final_map, "figures/map_complete.pdf", height = 6, width = 9)

Session info

installed.packages()[names(sessionInfo()$otherPkgs), "Version"]

R>    ggpubr lubridate   forcats   stringr     dplyr     purrr     readr     tidyr 
R>   "0.6.0"   "1.9.3"   "1.0.0"   "1.5.1"   "1.1.4"   "1.0.2"   "2.1.5"   "1.3.1" 
R>    tibble   ggplot2 tidyverse 
R>   "3.2.1"   "3.5.1"   "2.0.0"

Reuse

CC BY-NC-SA 4.0

Citation

BibTeX citation:

@online{j._smit2021,
  author = {J. Smit, Albertus},
  title = {7. {Mapping} {With} Ggplot2},
  date = {2021-01-01},
  url = {http://tangledbank.netlify.app/BCB744/intro_r/07-mapping.html},
  langid = {en}
}

For attribution, please cite this work as:

J. Smit A (2021) 7. Mapping With ggplot2. http://tangledbank.netlify.app/BCB744/intro_r/07-mapping.html.

--- date: "2021-01-01" title: "7. Mapping With ggplot2" --- ![](../../images/CleanShot 2023-12-27 at 08.24.21.png){width=75%} > "*There's no map to human behaviour.*" > > --- Bjork > "*Here be dragons.*" > > --- Unknown ```{r map-opts, echo=FALSE} knitr::opts_chunk$set( comment = "R>", warning = FALSE, message = FALSE ) ``` Yesterday you learned how to create **ggplot2** figures, change their aesthetics, labels, colour palettes, and facet/arrange them. Now you are going to look at how to create maps. Most of the work that you will perform as environmental/biological scientists involves going out to a location and sampling information there. Sometimes only once, and sometimes over a period of time. All of these different sampling methods lend themselves to different types of figures. One of those, collection of data at different points, is best shown with maps. As you will see over the course of Day 3, creating maps in **ggplot2** is very straight forward and is extensively supported. For that reason you are going to have plenty of time to also learn how to do some more advanced things. Your goal in this chapter is to produce the figure below. ![Today's goal.](../../images/map_complete.png) # Using prepared data Before you begin let's go ahead and load the packages you will need, as well as the several dataframes required to make the final product. ```{r map-load} # Load libraries library(tidyverse) library(ggpubr) # Load data load("../../data/south_africa_coast.Rdata") load("../../data/sa_provinces.RData") load("../../data/rast_annual.Rdata") load("../../data/MUR.Rdata") load("../../data/MUR_low_res.RData") # Choose which SST product you would like to use sst <- MUR_low_res # OR sst <- MUR # The colour palette we will use for ocean temperature cols11 <- c("#004dcd", "#0068db", "#007ddb", "#008dcf", "#009bbc", "#00a7a9", "#1bb298", "#6cba8f", "#9ac290", "#bec99a") ``` # A new concept? The idea of creating a map in R may be daunting to some, but remember that a basic map is nothing more than a simple figure with an *x* and *y* axis. We tend to think of maps as different from other scientific figures, whereas in reality they are created the exact same way. Let's compare a dot plot of the chicken weight data against a dot plot of the coastline of South Africa. Chicken dots: ```{r map-point-1, fig.cap="Dot plot of chicken weight data."} ggplot(data = ChickWeight, aes(x = Time, y = weight)) + geom_point() ``` South African coast dots: ```{r map-point-2, fig.cap="Dot plot off South African coast."} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_point() ``` Does that look familiar? Notice how the *x* and *y* axis tick labels look the same as any map you would see in an atlas. This is because they are. But this isn't a great way to create a map. Rather it is better to represent the land mass with a polygon. With **ggplot2** this is a simple task. # Land mask Now that you have seen that a map is nothing more than a bunch of dots and shapes on specific points along the *x* and *y* axes you are going to look at the steps you would take to build a more complex map. Don't worry if this seems daunting at first. You are going to take this step by step and ensure that each step is made clear along the way. The first step is to create a polygon. Note that you create an aesthetic argument inside of `geom_polygon()` and not `ggplot()` because some of the steps you will take later on will not accept the `group` aesthetic. Remember, whatever aesthetic arguments we put inside of `ggplot()` will be inserted into all of our other `geom_...()` lines of code. ```{r map-polygon, fig.cap="The map of South Africa. Now with province borders!"} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_polygon(colour = "black", fill = "grey70", aes(group = group)) # The land mask ``` # Borders The first thing you will add is the province borders as seen in Figure \@ref(fig:map-goal). Notice how you only add one more line of code to do this. ```{r map-province, fig.cap="The map of South Africa. Now with province borders!"} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_polygon(colour = "black", fill = "grey70", aes(group = group)) + geom_path(data = sa_provinces, aes(group = group)) # The province borders ``` # Force lon/lat extent Unfortunately when you added our borders it increased the plotting area of our map past what you would like. To correct that you will need to explicitly state the borders you want. ```{r map-expand, fig.cap="The map, but with the extra bits snipped off."} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_polygon(colour = "black", fill = "grey70", aes(group = group)) + geom_path(data = sa_provinces, aes(group = group)) + coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) # Force lon/lat extent ``` # Ocean temperature This is starting to look pretty fancy, but it would be nicer if there was some colour involved. So let's add the ocean temperature. Again, this will only require one more line of code. Starting to see a pattern? But what is different this time and why? ```{r map-MUR, fig.cap="Ocean temperature (°C) visualised as an ice cream spill."} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_raster(data = sst, aes(fill = bins)) + # The ocean temperatures geom_polygon(colour = "black", fill = "grey70", aes(group = group)) + geom_path(data = sa_provinces, aes(group = group)) + coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) ``` That looks... odd. Why do the colours look like someone melted a big bucket of ice cream in the ocean? This is because the colours you see in this figure are the default colours for discrete values in **ggplot2**. If you want to change them we may do so easily by adding yet one more line of code. ```{r map-colour, fig.cap="Ocean temperatures (°C) around South Africa."} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_raster(data = sst, aes(fill = bins)) + geom_polygon(colour = "black", fill = "grey70", aes(group = group)) + geom_path(data = sa_provinces, aes(group = group)) + scale_fill_manual("Temp. (°C)", values = cols11) + # Set the colour palette coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) ``` There's a colour palette that would make Jacques Cousteau swoon. When you set the colour palette for a figure in **ggplot2** you must use that colour palette for all other instances of those types of values, too. What this means is that any other discrete values that will be filled in, like the ocean colour above, must use the same colour palette (there are some technical exceptions to this rule that you will not cover in this course). You normally want **ggplot2** to use consistent colour palettes anyway, but it is important to note that this constraint exists. Let's see what I mean. Next you will add the coastal pixels to our figure with one more line of code. You won't change anything else. Note how **ggplot2** changes the colour of the coastal pixels to match the ocean colour automatically. ```{r map-raster, fig.cap="Map of South Africa showing *in situ* temeperatures (°C) as pixels along the coast."} ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_raster(data = sst, aes(fill = bins)) + geom_polygon(colour = "black", fill = "grey70", aes(group = group)) + geom_path(data = sa_provinces, aes(group = group)) + geom_tile(data = rast_annual, aes(x = lon, y = lat, fill = bins), colour = "white", size = 0.1) + # The coastal temperature values scale_fill_manual("Temp. (°C)", values = cols11) + coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) ``` # Final touches You used `geom_tile()` instead of `geom_rast()` to add the coastal pixels above so that you could add those little white boxes around them. This figure is looking pretty great now. And it only took a few rows of code to put it all together! The last step is to add several more lines of code that will control for all of the little things you want to change about the appearance of the figure. Each little thing that is changed below is annotated for your convenience. ```{r map-final, fig.cap="The cleaned up map of South Africa. Resplendent with coastal and ocean temperatures (°C)."} final_map <- ggplot(data = south_africa_coast, aes(x = lon, y = lat)) + geom_raster(data = sst, aes(fill = bins)) + geom_polygon(colour = "black", fill = "grey70", aes(group = group)) + geom_path(data = sa_provinces, aes(group = group)) + geom_tile(data = rast_annual, aes(x = lon, y = lat, fill = bins), colour = "white", size = 0.1) + scale_fill_manual("Temp. (°C)", values = cols11) + coord_equal(xlim = c(15, 34), ylim = c(-36, -26), expand = 0) + scale_x_continuous(position = "top") + # Put x axis labels on top of figure theme(axis.title = element_blank(), # Remove the axis labels legend.text = element_text(size = 7), # Change text size in legend legend.title = element_text(size = 7), # Change legend title text size legend.key.height = unit(0.3, "cm"), # Change size of legend legend.background = element_rect(colour = "white"), # Add legend background legend.justification = c(1, 0), # Change position of legend legend.position = c(0.55, 0.4) # Fine tune position of legend ) final_map ``` That is a very clean looking map so go ahead and save it on your local disk. ```{r map-save, eval=FALSE} ggsave(plot = final_map, "figures/map_complete.pdf", height = 6, width = 9) ``` # Session info ```{r, echo=TRUE, include=TRUE} installed.packages()[names(sessionInfo()$otherPkgs), "Version"] ```