Weston Testo 11/25/2021
In this walkthrough, I show an easy and reproducible way to make species occurrence maps in R. There are lots of ways to do this seemingly simple task, but I can speak from experience that it is one thing to make a map with dots, and a very different thing to make visually appealing maps consistently. We’ve all seen papers where maps are at different scales, legends overlap important parts of the map, or there are so many layers of data that finding the occurrence points is a little bit scrolling through a GeoCities website. I’m not trying to pick on anyone, either - I’d love to have re-do’s of a few papers I have published so I could have a new go at the maps!
Obviously, map design comes down in part to personal preference and specific needs, but I think there are some principles that are broadly applicable.
Here are a few things that are important to me:
- Consistent spatial scale across maps
- Non-cluttered layout (no ugly legends covering 1/3 of the map)
- Easily customizable (symbology, location, color scheme)
- Inclusion of physical features (rivers, topography, etc.)
- Publication quality (without post-R modification)
- Scaleability
- This code should work for taxa from anywhere on Earth, at most scales.
- Flexibility
- You can easily control the layout of the map, from symbols to legend placement.
- Get the right map the first time – no need to open Illustrator!
- Elevation, made easy
- Uses the elevatr package’s API functionality to provide elevation data.
- Allows you to use data just for the extent of your map, avoiding big rasters.
- Only care about alpine zones? No problem – adjust threshold easily.
- Consistency
- Found a new record? Workflow allows for maps to be updated easily.
In this walkthrough, we’ll plot occurrence data for the fern genus Stigmatopteris for Panamá that have been downloaded from the Pteridophyte Collections Consortium’s data portal.
We’ll start off by mapping all occurrences of a single species, Stigmatopteris longicaudata, in Panamá. Then, we’ll focus on the records of that species in the Ngäbe-Buglé comarca indígena. Finally, we’ll zoom back out to the entire country and map the occurrences of three different species.
For the purposes of keeping things clear and accessible to users with
varying familiarity with R, I have not wrapped anything into functions
for this walkthough. But, this can pretty easily be done if you want to
condense things a bit. As we’ll see, the structure of this code also
works nicely with loops or (preferably) the purrr package if you want
to automate this process for a group of taxa.
There are a total of three sections in this walkthrough, each of which will focus on making maps with different types of inputs. For the sake of clarity, I include all the steps in Part 1, but have focused on just the important differences in parts 2 and 3. If you want to be able to make these maps "out of the box", you can use the R scripts that are available in this repository. There are three of them, and they all have all of the steps that you need to produce a finished map. Each script filename starts with a prefix that corresponds to the parts of this walkthrough.
First, we’ll want to load the packages we’ll be using:
library(raster) #for processing some spatial data
library(rnaturalearth) #for downloading shapefiles
library(sf) #for processing shapefiles
library(elevatr) #for downloading elevation data
library(dplyr) #to keep things tidy
library(magrittr) #to keep things very tidy
library(ggspatial) #for scale bars and arrows
library(ggplot2) #for tidy plotting
library(ggpubr) #for easy selection of symbols
library(colourpicker) #for easy selection of colorsNote: I’ve noticed that some users of R version 4.x.x. run into
problems with rnaturalearth, specifically that some commands will
not work and errors related to the rnaturalearthhires package.
Unfortunately, as of the time of writing, installing
rnaturalearthhires on R version 4+ is problematic, and installing
either the CRAN or development versions by the usual means does not
work. If you are having problems with this, you can solve this by
doing the following: install.packages("remotes") followed by
remotes::install_github("ropenscilabs/rnaturalearthhires") and then
library("rnaturalearthhires").
Now we’ll load the CSV with our occurrence data. The lat/long data are in decimal degrees (WGS84). The order of columns is important here: taxon, latitude, longitude.
points <- read.csv("stigmatopterisPM.csv")
head(points)## scientificName decimalLatitude decimalLongitude
## 1 Stigmatopteris alloeoptera 8.53166 -81.14583
## 2 Stigmatopteris contracta 8.81666 -82.41666
## 3 Stigmatopteris contracta 8.76666 -82.43333
## 4 Stigmatopteris contracta 9.06611 -82.73222
## 5 Stigmatopteris guianensis 9.25000 -78.83333
## 6 Stigmatopteris heterophlebia 8.75000 -82.30000
Now that we have our occurrence data loaded, let’s pick the taxon that we want generate the map for.
speciesList <- levels(points$scientificName)
print(speciesList)## [1] "Stigmatopteris alloeoptera" "Stigmatopteris contracta"
## [3] "Stigmatopteris guianensis" "Stigmatopteris heterophlebia"
## [5] "Stigmatopteris killipiana" "Stigmatopteris lechleri"
## [7] "Stigmatopteris longicaudata" "Stigmatopteris michaelis"
## [9] "Stigmatopteris sordida"
Cool - there are nine species of Stigmatopteris in Panamá, including the species that we want to make a map for: Stigmatopteris longicaudata. Let’s filter it out.
taxon <- "Stigmatopteris longicaudata" #specify taxon name here
pointsFiltered <- points %>% filter(scientificName == taxon) %>% droplevels()
unique(pointsFiltered$scientificName)## [1] Stigmatopteris longicaudata
## Levels: Stigmatopteris longicaudata
Great – we can see now that we only have 16 records and all of them are Stigmatopteris longicaudata.
Since we’ll be working with thesf package, we’ll convert our data to a
Simple Features object.
pointsFiltered <- st_as_sf(pointsFiltered,coords = c(3,2),
crs= '+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84+towgs84=0,0,0')Now, let’s download the shapefiles that we will be using as the basis of our map. You will notice that we load both country-level and state/province level shapefiles. This gives us more flexibility later when we want to select our focal areas for our map; if you don’t want state-level divisions to be drawn, you can simply comment out a line of code when it comes to plotting.
map <- ne_countries(scale = 10, returnclass = "sf")
states <- ne_states(returnclass = "sf")
ocean <- ne_download(scale = 10, type = 'ocean',
category = 'physical', returnclass = 'sf')
rivers <- ne_download(scale = 10, type = 'rivers_lake_centerlines',
category = 'physical', returnclass = 'sf')We can take a quick peek to make sure everything is there…
Pretty basic, but it’s a start.
We’ll want to add some information about topography to make things a bit more interesting and visually appealing, but accessing a global-scale raster file with elevation data is not ideal. So, our next step is to define the extent of our map. Once we have that done, we can access just the elevation data that we need to cover the map.
The approach we’ll use to define the extent of our map is to identify our area of interest. In this case, we’ll want to select Panamá.
focalArea <- map %>% filter(admin == "Panama")Note: If you wanted to frame your map around multiple countries, it
is easy but just requires slightly different grammar: map %>% filter(admin %in% c("Mexico", "Cuba")). Other countries within the
extent bounded by these countries will still appear, but you will have
the option to alter the appearance of the focal countries, as we will
see later. We can also use the same %in% operator and the c("a", "b") vector notation to select multiple state/provinces or species.
Another note: If you wanted to frame your map around the extent of
your occurrence points rather than a given political unit, you could
simply set your occurrence data to be focalArea: focalArea <- pointsFiltered.
We can now use st_buffer() and st_bbox() from the sf package to
define the extent of our map (we’ll call this limit) as well as the
extent of the elevation data that we will download (we’ll call this
clipLimit). limit will be defined by a 1° buffer around the
extent of Panamá, and clipLimit will be 2°, just to be safe. We’ll
also make a new SpatialPolygons object from clipLimit that will be
called limitExtent – we just need this to process the elevation
data.
Note: You may get a warning here about buffering latitude/longitude data; don’t worry about it.
limit <- st_buffer(focalArea, dist = 1) %>% st_bbox()
clipLimit <- st_buffer(focalArea, dist = 2) %>% st_bbox()
limitExtent <- as(extent(clipLimit), 'SpatialPolygons')
crs(limitExtent) <- "+proj=longlat +datum=WGS84 +no_defs"For the fun of it, we can take a look at how this all works. The extent
of the area where we will download elevational data is bounded by the
dashed line, the solid line shows the extent of our map, and we can see
Panamá in the center.
There are lots of ways to include elevation information in maps like these: contours, hillshades, including shape and aspect, etc. Here, we’ll keep things simple, and just include a subtle grayscale gradient depicting mountainous areas - in this case, above 1500 m. I think it looks nice, and conveys important information without cluttering the map and obscuring the most important point: where the species occur.
Regardless of how you choose to visualize elevation on a map, the fundamental problem is dealing with the underlying data. Digital elevation models (DEM) are generally available as TIFFs, and as with other raster data, high-resolution DEM files take up a lot of space and are not particularly easy to process in R.
If you are just making maps from a single small region, it is relatively
straightforward to download a few DEM tiles, stitch them together, and
use that locally. Of course, this quickly becomes burdensome if you
start to make maps from different regions. Here, we’re going to get
around this challenge by using the get_elev_raster function from the
elevatr package, which downloads DEM files for a specified region.
It is fast and flexible, which makes it a great fit for this
application, and since we have already explicitly defined the extent of
our map, we can use it easily.
Here, we’ll download the DEM tile for our area of interest, convert the TIFF to a three-column data frame for ease of ggplot2 plotting, and delete all elevations below our specified threshold of 1500 m.
elev<-get_elev_raster(locations = limitExtent, z = 6, override_size_check = T)
elevDF<-as.data.frame(elev, xy=TRUE)
colnames(elevDF)<-c("x", "y", "elevation")
elevDF[, 3][elevDF[, 3] < 1500] <- NA
This looks right – we can see the Cordillera de Talamanca in the western part of the country, along with some other scattered peaks. The color scheme is clear but light enough so that things are visible.
Now that we have all of the layers of our map loaded, we are almost ready to visualize our map. Before we do that, we need to set a few of the important parameters: 1) the symbol and color used to mark occurrences, and 2) the position of our legend within the map.
Point shapes are specified in R by the pch (point character)
argument, which are coded numerically, with values from 1 to 25. If you
are like me, you probably can’t remember which number corresponds to
which shape – no worries, we can use the show_point_shapes() from the
ggpubr package to help us out:
ggpubr::show_point_shapes()
It is pretty clear that these are all just variations on the same few themes, and it might not be clear what the difference is between, say, pch = 15 and pch = 22. If you need clarification on this, check out this blog post. We’ll pick pch = 17 for our map.
While we’re at it, we can also specify the color that we want to use for
the symbol. I prefer to use hex codes for this purpose, as it provides a
bit more freedom. The one downside of using hex codes is that
#E61D19 is a little harder to remember than “red”. You can find
hex codes online, but an easier option is to use the colourpicker
package, which (amongst other things) provides an interactive color
picker widget.
When this widget appears, click on the box below “Select any colour”,
choose your color, and click “Done”. Your color choice will be
automatically populated. Here, we chose a red, which just happens to
appear on Panamá’s flag: #D21034.
pch <- 17
fillColor <- colourPicker(numCols = 1)Here's what the interface looks like -- pretty cool!
The last thing that we need to do is specify the position of our symbol legend, which will show the nice blue triangle we have specified, along with the name of the taxon shown in the map. I prefer to place this inside the map when possible, and I’ll draw a small box around it and provide a partially transparent background so it is visible and isn’t confused with an actual occurrence point. The latter details are specified in the plotting code block itself, but because we may want to adjust the position based on details of a given map, I’ll specify the position here.
Note: depending on the details of your RStudio interface, the dimensions of the map and the position of the legend may vary slightly between the ‘Plots’ pane and the saved plot. So, don’t necessarily trust the appearance in the ‘Plots’ pane – check out the exported version first. It is common to have to move the legend around a few times to get it right.
We’ll be specifying the position of the legend within the map using
ggplot2’s legend.position() argument. This relies on an xy
coordinate system, so keep in mind:
- c(0, 0) = bottom-left
- c(0, 1) = top-left
- c(1, 0) = bottom-right
- c(1, 1) = top-right
Given what we’ve seen so far from the layout of the map, I will choose a position near the bottom towards the middle, which should place the legend in the Pacific Ocean, far away from any Stigmatopteris. I avoid picking the bottom-left here, as we will place our directional arrow and scale bar in the bottom-left corner.
position <- c(0.55, 0.085)Now that we have specified the most important parameters for the map, let’s put it all together!
occMap <-
ggplot() +
geom_tile(data = elevDF, aes(x=x, y=y, fill=elevation), alpha =0.4)+
scale_fill_gradient(low="#a3a0a0", high= "#000000",
na.value="transparent")+
geom_sf(data = ocean, color = "black", size = 0.05, fill = "#add8e6")+
geom_sf(data = rivers, color = "#d7f2fa", size = 0.5)+
geom_sf(data = states, color = "black", size = 0.05, fill = "#f5f5f5", alpha = 0.5)+
geom_sf(data = focalArea, color = "black", size = 0.15,
linetype = "dashed", fill = "#acbdac", alpha = 0.2)+
geom_sf(data = pointsFiltered, aes(geometry = geometry,
shape = scientificName,
color = scientificName), size = 2)+
scale_shape_manual(values = pch)+
scale_color_manual(values = fillColor)+
coord_sf(
xlim = c(limit["xmin"], limit["xmax"]),
ylim = c(limit["ymin"], limit["ymax"])) +
labs(x="Longitude", y="Latitude", color = taxon)+
annotation_scale(location = "bl", width_hint = 0.3) +
annotation_north_arrow(location = "bl", which_north = "true",
pad_x = unit(0.5, "in"), pad_y = unit(0.3, "in"),
style = north_arrow_fancy_orienteering) +
guides(colour = guide_legend(override.aes = list(size = 3)))+
theme(legend.position = position,
legend.key = element_blank(),
legend.title = element_blank(),
legend.background=element_blank(),
legend.text = element_text(face = "italic"),
legend.box.margin = margin(0, 0, 0, 0),
legend.box.background = element_rect(color = "black", size = 0.1,
linetype = "longdash",
fill = alpha("white", 0.15)))+
theme(panel.grid.major = element_line(colour = "#c9c9c9",
linetype = "dashed",
size = 0.3),
panel.background = element_rect(fill = "#f0f8ff"),
panel.border = element_rect(fill = NA))+
guides(fill="none")+
guides(shape="none")+
guides(color=guide_legend(override.aes = list(shape = pch,
fill=fillColor, size =3)))
occMap
Great – everything looks like it has come out in the right spot.
Note: if you want to edit something in the map other than the items we have already set, you can modify some of the following:
-If you want to change the color of the ocean
-Modify the fill
argument on the line that starts geom_sf(data = ocean
-If you want to change the color of Panamá
-Modify the fill
argument on the line that starts geom_sf(data = focalArea
-If you want to change the position or size of the scale bar
-Adjust
the arguments for the annotation_scale function
-If you want to change the position or type of the arrow
-Adjust the
arguments for the annotation_north_arrow function
-If you want to change the color/shading of the mountainous areas
-Adjust the arguments for the scale_fill_gradient function
-If you want to change the details (color, bounding box) of the
legend
-Modify the arguments for the legend.box.background function
-If you want to use a symbol with a border -This is a little bit more complicated:
First, pick a pch value between 21 and 25.
Then, take this bit of code in the plotting block
geom_sf(data = pointsFiltered, aes(geometry = geometry, shape = scientificName, color = scientificName), size = 2)+
and modify it so it looks like this (basically change "color" to "fill" and move out of aes:
geom_sf(data = pointsFiltered, aes(geometry = geometry, shape = scientificName, color = scientificName), fill = fillColor, size = 2)+
and then change this line
scale_color_manual(values = fillColor)+
to this:
scale_color_manual(values = "black")+
Of course, you can pick a different color for the border, but the
basic idea is that for pch values of 21-25, color now refers to
the border and fill refers to the inside color of the symbol.
Now that we have a nice looking plot, let’s save it. To keep things in order, we’ll create a subdirectory within our working directory named plots, and then create a directory within that with the name of the taxon in our map. We’ll also use the taxon name as the title for the plots that we’ll save. We’ll save a version as a PDF and as a TIFF, and we can adjust dimensions based on guidelines of the journal we are going to publish in.
The nice part of this approach to saving the plots is that new directories and filenames will be created automatically the next time you make a map for a different taxon. This will allow you to keep track of everything and avoid confusing naming conventions.
dir.create("plots")
nameSave<-get("taxon") %>% sub("\\s","_",.)
saveDirectory<-paste("plots/",nameSave,sep="")
dir.create(saveDirectory)
fileNameTiff<-paste(nameSave,".tiff",sep="")
fileNamePDF<-paste(nameSave,".PDF",sep="")Now we can go ahead and save the plots. Note that the ggsave function
will save the last item open in the Plots panel, so it is important
that we run dev.off() when we are done to clean the slate.
ggsave(filename = fileNameTiff, path = saveDirectory, width = 15, height = 15,
units = "cm", device = 'tiff', dpi=300)
ggsave(filename = fileNamePDF, path = saveDirectory, width = 15, height = 15,
units = "cm", device = 'pdf', dpi=300)
dev.off()## null device
## 1
That’s it! You can go check your newly saved plots and marvel at the distribution of Stigmatopteris longicaudata!
Now let’s move onto some new mapping scenarios.
Now that we have made a map of occurrences of Stigmatopteris longicaudata from Panamá, let’s take the opportunity to zoom in a bit and look at collections from just a single part of the country. We’ll look at records from the Ngäbe-Buglé comarca indígena, in the northwestern part of the country.
If you want to generate a map of records from a single state-level region and focus the map on that region (rather than the whole country), we’ll need to adjust a few steps in the workflow that we used before. For the sake of space, we’ll just focus on the differences here, but it is best practice to run the code from the top, in order to avoid specifying something incorrectly.
-Change the focalArea from Panamá to Ngäbe-Buglé
-Filter the occurrence records to only retain those in Ngäbe-Buglé
-Change the extent of our map
Unsurprisingly, this works much like we did before, but we’ll be using the states object rather than map. To make sure we get the spelling right, let’s quickly check the names of all the state-level administrative units in Panamá:
stateList <- states %>% filter(admin == "Panama")
print(stateList$name)## [1] "Bocas del Toro" "Chiriquí" "Kuna Yala" "Emberá"
## [5] "Darién" "Panama" "Coclé" "Veraguas"
## [9] "Colón" "Ngöbe Buglé" "Los Santos" "Herrera"
Good thing we did that – they use an “ö” rather than an “ä” in the shapefile metadata. Now we can filter out the right focal area:
focalState <- states %>% filter(name == "Ngöbe Buglé")Now that we have this shapefile, we can filter our pointsFiltered
object to only retain those in Ngöbe Buglé using st_join() from the
sf package.
pointsFilteredState <- st_join(pointsFiltered, focalState,
join = st_intersects) %>%
filter(!is.na(name)) %>%
select(scientificName, geometry)We can see we are now down to a total of seven records, which looks right.
Now that we have redefined our focal area, we can use it to set the
extent of our map. As above, I’m changing object names here to keep
track of things. If I were starting from scratch, I would need to use
limitExtentState to define the DEM data to download with elevatr,
but since we already have those data, I’ll skip that step here.
limitState <- st_buffer(focalState, dist = 1) %>% st_bbox()
clipLimitState <- st_buffer(focalState, dist = 2) %>% st_bbox()
limitExtentState <- as(extent(clipLimitState), 'SpatialPolygons')
crs(limitExtentState) <- "+proj=longlat +datum=WGS84 +no_defs"With these new inputs, we are ready to plot our new map, with only slightly modified ggplot2 code. We’ll change the position of the legend a bit for this one, as we are zoomed in a bit more.
position <- c(0.70, 0.08)
occStateMap <-
ggplot() +
geom_tile(data = elevDF, aes(x=x, y=y, fill=elevation), alpha =0.4)+
scale_fill_gradient(low="#a3a0a0", high= "#000000",
na.value="transparent")+
geom_sf(data = ocean, color = "black", size = 0.05, fill = "#add8e6")+
geom_sf(data = rivers, color = "#d7f2fa", size = 0.5)+
geom_sf(data = states, color = "black", size = 0.05, fill = "#f5f5f5", alpha = 0.5)+
geom_sf(data = focalState, color = "black", size = 0.15,
linetype = "dashed", fill = "#acbdac", alpha = 0.2)+
geom_sf(data = pointsFilteredState, aes(geometry = geometry,
shape = scientificName,
color = scientificName), size = 2)+
scale_shape_manual(values = pch)+
scale_color_manual(values = fillColor)+
coord_sf(
xlim = c(limitState["xmin"], limitState["xmax"]),
ylim = c(limitState["ymin"], limitState["ymax"])) +
labs(x="Longitude", y="Latitude", color = taxon)+
annotation_scale(location = "bl", width_hint = 0.3) +
annotation_north_arrow(location = "bl", which_north = "true",
pad_x = unit(0.5, "in"), pad_y = unit(0.3, "in"),
style = north_arrow_fancy_orienteering) +
guides(colour = guide_legend(override.aes = list(size = 3)))+
theme(legend.position = position,
legend.key = element_blank(),
legend.title = element_blank(),
legend.background=element_blank(),
legend.text = element_text(face = "italic"),
legend.box.margin = margin(0, 0, 0, 0),
legend.box.background = element_rect(color = "black", size = 0.1,
linetype = "longdash",
fill = alpha("white", 0.9)))+
theme(panel.grid.major = element_line(colour = "#c9c9c9",
linetype = "dashed",
size = 0.3),
panel.background = element_rect(fill = "#f0f8ff"),
panel.border = element_rect(fill = NA))+
guides(fill="none")+
guides(shape="none")+
guides(color=guide_legend(override.aes = list(shape = pch,
fill=fillColor, size =3)))
occStateMap
Again, remember that the Plots panel is a bit misleading, but if you save the output, it should look like the above image. Now let’s try the last type of map we’ll cover here: a multi-species occurrence map.
Now that we have made a map of occurrences of Stigmatopteris longicaudata from Ngäbe-Buglé, let’s zoom back out and make another plot of the whole country. But this time, we’ll include information from not one, but three species of Stigmatopteris. As we’ll see, this involves a few other modifications to the workflow.
In this case, we will be able to use the exact same approach to defining the focal area and the extent of the map as in Part 1. What we’ll have to change is simply how we filter the occurrence data, along with changing how we specify symbols for plotting. We’ll also have to make some minor adjustments to how we format file and directory names for saving the plots at the end.
-Filter multiple species records, instead of one
-Specify multiple symbols and multiple colors
-Modify our saving preferences
Previously, we were just working with occurrence records of a single
species, Stigmatopteris longicaudata. Now, we are going to map records
of three species: Stigmatopteris longicaudata, Stigmatopteris
sordida, and Stigmatopteris heterophlebia. As I pointed out before,
the == notation we used previously won’t work to filter multiple
records. Instead, we need to do the following:
taxa<-c("Stigmatopteris longicaudata", "Stigmatopteris sordida", "Stigmatopteris heterophlebia")
pointsFilteredMulti<-points %>% filter(scientificName %in% taxa) %>% droplevels()
unique(pointsFilteredMulti$scientificName)## [1] Stigmatopteris heterophlebia Stigmatopteris longicaudata
## [3] Stigmatopteris sordida
## 3 Levels: Stigmatopteris heterophlebia ... Stigmatopteris sordida
We now have a total of 43 records of these three species – great! Now let’s pick shapes and colors for the symbols.
Again, we’ll want to convert this to an sf object.
pointsFilteredMulti <- st_as_sf(pointsFilteredMulti,coords = c(3,2),
crs= '+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84+towgs84=0,0,0')This works just as before, except we are specifying three symbols and colors here. For the colourPicker widget, you’ll need to click on each of the three numbered boxes that appear and select a color for each. Obviously, you’ll want to change these according to the number of the taxa you want to map.
Note: Please keep in mind that many people have a difficult time visualizing various color combinations. This tends to matter more for other visualization situations (e.g., color ramps), and the choice of different symbols helps a lot, but it is important to keep this in mind. For more information, take a look at Fabio Crameri’s website and Thomas Lin Pedersen’s R package that uses his color ramps.
pchs <- c(17,18,19)
fillColors<-colourPicker(numCols = 3)Now that we have these, we can plot the occurrences of these species,
using basically the same code that we used in Part 1. We’ll change the
position of the legend some more, and decrease its transparency by
increasing the alpha value for the legend.box.background() argument.
position <- c(0.8, 0.175)
occMapMulti <-
ggplot() +
geom_tile(data = elevDF, aes(x=x, y=y, fill=elevation), alpha =0.4)+
scale_fill_gradient(low="#a3a0a0", high= "#000000",
na.value="transparent")+
geom_sf(data = ocean, color = "black", size = 0.05, fill = "#add8e6")+
geom_sf(data = rivers, color = "#d7f2fa", size = 0.5)+
geom_sf(data = states, color = "black", size = 0.05, fill = "#f5f5f5", alpha = 0.5)+
geom_sf(data = focalArea, color = "black", size = 0.15,
linetype = "dashed", fill = "#acbdac", alpha = 0.2)+
geom_sf(data = pointsFilteredMulti, aes(geometry = geometry,
shape = scientificName,
color = scientificName), size = 2)+
scale_shape_manual(values = pchs)+
scale_color_manual(values = fillColors)+
coord_sf(
xlim = c(limit["xmin"], limit["xmax"]),
ylim = c(limit["ymin"], limit["ymax"])) +
labs(x="Longitude", y="Latitude", color = taxon)+
annotation_scale(location = "bl", width_hint = 0.3) +
annotation_north_arrow(location = "bl", which_north = "true",
pad_x = unit(0.5, "in"), pad_y = unit(0.3, "in"),
style = north_arrow_fancy_orienteering) +
guides(colour = guide_legend(override.aes = list(size = 3)))+
theme(legend.position = position,
legend.key = element_blank(),
legend.title = element_blank(),
legend.background=element_blank(),
legend.text = element_text(face = "italic"),
legend.box.margin = margin(0, 0, 0, 0),
legend.box.background = element_rect(color = "black", size = 0.1,
linetype = "longdash",
fill = alpha("white", 0.9)))+
theme(panel.grid.major = element_line(colour = "#c9c9c9",
linetype = "dashed",
size = 0.3),
panel.background = element_rect(fill = "#f0f8ff"),
panel.border = element_rect(fill = NA))+
guides(fill="none")+
guides(shape="none")+
guides(color=guide_legend(override.aes = list(shape = pchs,
fill=fillColors, size =3)))
occMapMulti
Now that we have the plot, we can save it as before, but we just need to change a few things to be able to save them correctly.
nameSave<-get("taxon") %>% sub("\\s","_",.)
nameSave<-paste(nameSave[1],nameSave[2],nameSave[3],sep="&") ##
saveDirectory<-paste("plots/",nameSave,sep="")
dir.create(saveDirectory)
fileNameTiff<-paste(nameSave,".tiff",sep="")
fileNamePDF<-paste(nameSave,".PDF",sep="")
ggsave(filename = fileNameTiff, path = saveDirectory, width = 15, height = 15, units = "cm",
device = 'tiff', dpi=300)
ggsave(filename = fileNamePDF, path = saveDirectory, width = 15, height = 15, units = "cm",
device = 'pdf', dpi=300)
dev.off()That’s it! I hope this will help you make visually appealing, informative, and consistent species occurrence maps – if you have any comments or questions, let me know!