Creating a ggplot of a map with categorised data overlayed

[zg17048]

Preformatted text

I am trying to create a map displaying the probability a species will occur in x location the data was in raster format and I converted it into a dataframe. It is on a global scale and I created a lovely ggplot with a gradient scale using the following code:

ggplot(data=df, aes(y=Latitude, x=Longitude)) +
geom_tile(aes(fill=MAP)) +
theme_bw() +
coord_equal() +
scale_fill_gradientn("Occurrence Probability", colours=c("light grey","red"))+
theme(axis.title.x = element_text(size=16),
axis.title.y = element_text(size=16, angle=90),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.position = "right",
legend.key = element_blank()
)

And this all worked fine!
I then wanted to categorise the probability and did so using the following code:

df$category[df$MAP < 0.05] <- "Absent"
df$category[df$MAP > 0.05 & df$MAP < 0.5] <- "Low probability of presence"
df$category[df$MAP > 0.5] <- "High probability of presence"

this also worked!
But trying to plot this new categorised data onto a map proved a lot more difficult, does anyone have any suggestions?
I couldn't work out which scale function to use from ggplot or how to even format one for discrete data!

[andresrcs]

Hi!

To help us help you, could you please prepare a reproducible example (reprex) illustrating your issue? Please have a look at this guide, to see how to create one:

FAQ: How to do a minimal reproducible example ( reprex ) for beginners

A minimal reproducible example consists of the following items: A minimal dataset, necessary to reproduce the issue The minimal runnable code necessary to reproduce the issue, which can be run on the given dataset, and including the necessary information on the used packages. Let's quickly go over each one of these with examples: Minimal Dataset (Sample Data) You need to provide a data frame that is small enough to be (reasonably) pasted on a post, but big enough to reproduce your issue. Let's say, as an example, that you are working with the iris data frame head(iris) #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> 1 5.1 3.5 1.4 0.…

[zg17048]

data.frame(
stringsAsFactors = FALSE,
Longitude = c(-35.0208333333333,
-34.9791666666667,-34.9375,-34.8958333333333,-34.8541666666667,
-34.8125,-34.7708333333333),
Latitude = c(83.6041666666667,
83.6041666666667,83.6041666666667,83.6041666666667,83.6041666666667,
83.6041666666667,83.6041666666667),
MAP = c(0.000245005998294801,
0.000245005998294801,0.000245005998294801,0.000245005998294801,
0.000245005998294801,0.000245005998294801,
0.000244783994276077),
category = c("Absent","Absent","Absent",
"Absent","Absent","Absent","Absent")
)

Is that correct?

[jlacko]

More as a comment than answer; {ggplot2} works fine with vector data, and the geom_sf() is joy to use.

But ggplot2 doesn't like raster data. Base plot via {raster} and / or tm_raster() from {tmap} are often easier choices for convincing raster data visualization.