A comparison of terra and raster packages

The terra package looks designed to replace an old favourite raster. It is made by a similar team. The terra documentation states “can do more, is simpler to use, and it is faster.”

So should you make the switch to terra? I’ll answer that here.

TLDR: terra is simpler and faster than raster and will be easy for existing raster users to learn. Compatibility with other packages can be an issue, but conversion back to raster objects is easy. Verdict: make the switch.

There are a few important considerations when changing packages:

1. How long will it take me to learn the new syntax?

2. How much help is available online?

3. Is it faster than what I used to use?

4. Will it be compatible with other packages I use?

I will test each in turn.

The data

We’ll use this data from one of my courses.

1 How long will it take to learn terra’s syntax?

First, let’s take a look at some basic syntax and compare it with raster

You can read in data much the same way, with the command rast():

library(terra)
r <- rast("data-for-course/spatial-data/MeanAVHRRSST.grd")
plot(r)

ext(r)

## SpatExtent : 82.5, 181.25, -72.25, -9.75 (xmin, xmax, ymin, ymax)

Now let’s crop and reproject it:

#create an extent object
ext2 <- ext(r)

#constrain it in x direction
ext2[1] <- 120
ext2[2] <- 170

r2 <- crop(r, ext2)

r3 <- project(r2, "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0")

plot(r3)

So much of the syntax is familiar (or identical), if slightly different. It took me about 10 minutes to translate what I know from raster to terra syntax.

Note there are some important caveats with terra when it comes to cluster computations and saving data see ?terra for more information.

2 How much help is available online?

It’s early days yet. But the terra package documentation is outstanding, as good as it was for raster. This was one reason raster became so popular.

?terra provides a very helpful description, a menu of functions and at the very end a translation of function names from raster to terra (many are the same)

So users will be once again grateful to Robert Hijmans and the authorship team for the effort tney put into package documentation

There are a few courses/ blogs online if you google it and some limited posts on stackexchange sites.

No vignette with the package as yet.

So the verdict is that the documentation of the package and functions is excellent. Currently, there is limited existing documentation of troubleshooting errors and bugs online. So you might have to ask yourself. But online content will grow as the package becomes more popular.

3 Is terra faster than raster?

I take the author’s word that its faster, but let’s see how much faster:

library(microbenchmark)

r_raster <- raster::raster("data-for-course/spatial-data/MeanAVHRRSST.grd")
robin_proj <- "+proj=robin +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0"

tout <- microbenchmark(
project(r, robin_proj),
raster::projectRaster(r_raster, crs = robin_proj),
times = 10
)
tout

## Unit: milliseconds
##                                               expr     median    
##                             project(r, robin_proj)      76.3  
##  raster::projectRaster(r_raster, crs = robin_proj)     529.6 

So something like 7 times faster for the computationally demanding task of reprojecting a raster.

4 Will terra be compatible with other packages I use?

The answer here obviously depends on what packages you want to use. A key one for me is tmap for mapping. This doesn’t work with terra unfortunately. So the next question, how onerous is it to convert a terra raster to a raster raster?

library(tmap)

r_raster <- raster::raster(r)

tm_shape(r_raster) +
  tm_raster()

The multi-tool function raster() does the job, so I’ll take that for now.

Summary

terra looks set to replace raster. It is faster and just as easy to use as raster. Making the switch to terra isn’t as hard as it may seem, its use will seem very familiar to raster users.

There are probably common errors and bugs with particular data types for the R community to find and there isn’t help online for thoes yet. There will be challenges in compatibility with other packages. But conversion back to raster objects is easy.

There are also new features in terra, to handle vector data and manage very large datasets. So plenty more to explore.