Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Update 02-spatial-data.Rmd #689

Merged
merged 2 commits into from
Dec 14, 2021
Merged

Update 02-spatial-data.Rmd #689

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
0e98ed5
Update 02-spatial-data.Rmd
Robinlovelace Dec 13, 2021
e8c6a0a
Update 02-spatial-data.Rmd
Robinlovelace Dec 13, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 6 additions & 4 deletions 02-spatial-data.Rmd
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -694,11 +694,13 @@ Consequently, the discrete borders of these features become blurred, and dependi
source("code/02-raster-intro-plot2.R", print.eval = TRUE)
```

### R packages for raster data handling
### R packages for working with raster data

R has several packages able to read and process spatial raster data; see \Wref(the-history-of-r-spatial) for more context.
However, currently, two main packages with this purpose exist -- **terra** and **stars**.^[We are not mentioning the **raster** package here as it is now being replaced with **terra**.]
We are focusing on the **terra** package in this book; however, it may be worth knowing the basic similarities and differences between the packages before deciding which one to use.
Over the last two decades, several packages packages for reading and processing raster datasets have been developed.
As outlined in Section \@ref(the-history-of-r-spatial), chief among them was **raster**, which led to a step change in R's raster capabilities when it was launched in 2010 and the premier package in the space until the development of **terra** and **stars**.
Both more recently developed package provide powerful and performant functions for working with raster datasets and there is substantial overlap between their possibly use cases.
In this book we focus on **terra**, which replaces the older and (in most cases) slower **raster**.
Before learning about the how **terra**'s class system works, this section describes similarities and differences between **terra** and **raster**; this knowledge will help decide which is most appropriate in different situations.

First, **terra** focuses on the most common raster data model (regular grids), while **stars** also allows storing less popular models (including regular, rotated, sheared, rectilinear, and curvilinear grids).
While **terra** usually handle one or multi-layered rasters^[It also has an additional class `SpatRasterDataset` for storing many collections of datasets.], the **stars** package provides ways to store raster data cubes -- a raster object with many layers (e.g., bands), for many moments in time (e.g., months), and many attributes (e.g., sensor type A and sensor type B).
Expand Down