Making up some data
Next up, we will make some fake spatial data to work with. This will
be data frame with 4 columns: lon, lat, species and beak length. We’ll
simulate location and trait data for 3 fake species in North America.
Each species will have a different spatial distribution and beak
length.
wp_lat <- rnorm(1000,40,4)
warbler_lon <- rnorm(500,-110,2)
fake_sp_data <- rbind(data.frame(lon=rnorm(1000,-100,6),
lat=wp_lat,
species = rep("Drab Woodpecker",1000),
beak_length = rnorm(1000,(5+(wp_lat-40)/4),.5)),
data.frame(lon=warbler_lon,
lat=rnorm(500,35,2),
species = rep("Plain Warbler",1000),
beak_length = rnorm(500,3+(warbler_lon+110)/10,.25)),
data.frame(lon=rnorm(100,-90,5),
lat=rnorm(100,45,2),
species = rep("Fire-breasted Trainbearer",1000),
beak_length = rnorm(100,1.5,.1)))
fake_sp_data %>% group_by(species) %>% summarise(mean_beak_length = mean(beak_length))
## # A tibble: 3 × 2
## species mean_beak_length
## <chr> <dbl>
## 1 Drab Woodpecker 4.99
## 2 Fire-breasted Trainbearer 1.51
## 3 Plain Warbler 2.99
OK, so now lets do some basic plots to see where these birds are. In
base R, we can see what it looks like if we just plot these birds
out.
plot(fake_sp_data$lon,fake_sp_data$lat)
Not super helpful! Lat and lon automatically scale, and it’s hard to
get a sense of where these birds are across the continent. So next,
let’s try to get the points plotted on a map. To do this, we will use
ggplot2 and the function “map_data”
#First import country data.
#Because this data is located in the western hemisphere, we will filter to countries with longitudes less than -50 degrees
countries <- map_data("world") %>% filter(long < -30)
#Now plot using ggplot
basic_map1 <- ggplot() +
#This line adds the countries in, and colors the interior and border
geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill="grey90", color="darkgrey")
plot(basic_map1)
So now we have a basic map, but it looks…. bad. So let’s change it to
a more attractive projection, narrow the spatial area and make the plot
have less gridlines and such.
basic_map2 <- ggplot() +
# This will limit the range to reasonable coordinates and reproject to a more attractive projection
coord_map("mollweide",xlim=c(-140,-55),ylim=c(10,70)) +
geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill="grey90", color="darkgrey") +
#All this stuff just makes things look neater
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),
axis.ticks.y = element_blank(),axis.text.y = element_blank(),
axis.title.x=element_blank(),axis.title.y=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "null"),
panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(panel.background = element_rect(fill = alpha("white",.5)))
print(basic_map2)
Wow! So much better. Now let’s plot our points
data_map1 <- ggplot() +
coord_map("mollweide",xlim=c(-140,-55),ylim=c(10,70)) +
geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill="grey90", color="darkgrey") +
#This line will add all of our points in. We will adapt it as we go along
geom_point(data=fake_sp_data,aes(x=lon,y=lat)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),
axis.ticks.y = element_blank(),axis.text.y = element_blank(),
axis.title.x=element_blank(),axis.title.y=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "null"),
panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(panel.background = element_rect(fill = alpha("white",.5)))
data_map1
Great! We’re on the right track. Now let’s try to make this a little
more appealing by shrinking the points and adding colors that represent
species.
#We have three groups so let's assign three colors to represent the species. (because the base colors are trash)
sp_colors <- c("tan4","skyblue2","orange2")
data_map2 <- ggplot() +
coord_map("mollweide",xlim=c(-140,-55),ylim=c(10,70)) +
geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill="grey90", color="darkgrey") +
#We add two more arguments now, color and size
geom_point(data=fake_sp_data,aes(x=lon,y=lat,color=species),size=.25) +
scale_color_manual(values = sp_colors) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),
axis.ticks.y = element_blank(),axis.text.y = element_blank(),
axis.title.x=element_blank(),axis.title.y=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "null"),
panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(panel.background = element_rect(fill = alpha("white",.5)))
data_map2
Note how we get a legend automatically too.
So now you are able to plot datapoints on a map with unique colors by
group. Let’s try something else, plotting colors representing beak
size.
You may have noticed I programmed in some variation to the fake data,
so that beak size tends to vary for two of the species across space.
Let’s focus on one species, and plot variation in beak size across
space.
woodpecker_data <- fake_sp_data %>% filter(species == "Drab Woodpecker")
data_map3 <- ggplot() +
coord_map("mollweide",xlim=c(-140,-55),ylim=c(10,70)) +
geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill="grey90", color="darkgrey") +
#Here we will change the color argument to beak_length, a continuous variable
geom_point(data=woodpecker_data,aes(x=lon,y=lat,color=beak_length),size=.25) +
#Also note I removed the manual color scale here, and replaced it with a flexible
#function to add a custom color scale. We can use this to set colors on a gradient,
#customizing the colors at the low, mid and high points. We also want to set the midpoint
#to the mean beak length for clarity.
scale_color_gradient2(low="tan",mid="grey",high="forestgreen",midpoint = mean(woodpecker_data$beak_length)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),
axis.ticks.y = element_blank(),axis.text.y = element_blank(),
axis.title.x=element_blank(),axis.title.y=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "null"),
panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(panel.background = element_rect(fill = alpha("white",.5)))
data_map3
That look’s ok, but let’s try something else to make things look
really good!
Enter dggridR. This package will let us summarise spatial data on an
equal-area hexagonal grid. This is helpful for a number of reasons.
First off, the hexagons look cool. Also, it’s problematic to group
spatial data by un-equal spatial units. Think 1 by 1 lat long
grid cells. They are radically different in size depending on where you
are on the Earth’s surface. So don’t do it!
We are going to group our woodpecker data into hexagonal grids and
see if there are differences across space in the average beak
length.
#This code creates grids according to a certain resolution
hexgrid6 <- dgconstruct(res = 6)
# Now let's take our woodpecker lat longs and figure out which cell they each belong to
# Just ignore how annoying and unintuitive this function is... Just save the code somewhere
woodpecker_data$cell <- dggridR::dgGEO_to_SEQNUM(hexgrid6,
in_lon_deg = woodpecker_data$lon,
in_lat_deg = woodpecker_data$lat)[[1]]
#Now let's find the average beak length in each cell
wp_by_cell <- woodpecker_data %>%
group_by(cell) %>%
summarise(mean_bl = mean(beak_length))
#We're now going to take our average beak length data and merge it with the hexagon grids
# to get it in the right format for plotting.
grid <- dgcellstogrid(hexgrid6,wp_by_cell$cell,frame=TRUE,wrapcells=TRUE)
grid <- merge(grid,wp_by_cell,by.x="cell",by.y="cell")
#This format encodes the location of each point of the hexagon, so you'll see cells each have 6 rows
head(grid)
## cell long lat order hole piece group mean_bl
## 1 3671 -84.63027 29.04292 1 FALSE 1 3671.1 2.777139
## 2 3671 -85.51548 27.85954 2 FALSE 1 3671.1 2.777139
## 3 3671 -87.06069 28.20538 3 FALSE 1 3671.1 2.777139
## 4 3671 -87.77260 29.71763 4 FALSE 1 3671.1 2.777139
## 5 3671 -86.91910 30.91640 5 FALSE 1 3671.1 2.777139
## 6 3671 -85.31770 30.58166 6 FALSE 1 3671.1 2.777139
#Now let's take that data and plot it
data_map4 <- ggplot() +
coord_map("mollweide",xlim=c(-140,-55),ylim=c(10,70)) +
geom_polygon(data=countries, aes(x=long, y=lat, group=group), fill="grey90", color="darkgrey") +
geom_polygon(data=grid, aes(x=long, y=lat, group=group, fill=mean_bl), alpha=0.9) +
geom_path (data=grid, aes(x=long, y=lat, group=group), alpha=0.4, color="white") +
#Not the subtle change here to "scale_fill_gradient2"
scale_fill_gradient2(low="tan",mid="grey",high="forestgreen",midpoint = mean(grid$mean_bl)) +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.ticks.x = element_blank(),axis.text.x = element_blank(),
axis.ticks.y = element_blank(),axis.text.y = element_blank(),
axis.title.x=element_blank(),axis.title.y=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "null"),
panel.spacing = unit(c(0, 0, 0, 0), "null")) +
theme(panel.background = element_rect(fill = alpha("white",.5)))
data_map4
Look at that gradient. We can clearly see that the beak length varies
by latitude.
So that’s it. Hopefully, the above code can serve as a template for
your next mapping-related project. If there is anything else you would
like to see added, please let me know and I’ll update this document when
I have the time!
