The link2GI package provides a small linking tool to simplify the use of GRASS GIS, SAGA GIS, Orfeo Toolbox (OTB) and GDAL binaries for R users, with the focus on making this software accessible to non-operating system specialists or highly experienced GIS geeks. In fact, it is a direct result of numerous graduate courses with R(-GIS) novices in the hostile world of university computer pools running on extremely limited Windows systems.
This vignette:
link2GI according to specific system requirementsR has a lot of classes for storing and manipulating spatial data. For vector data, the sp and currently the great sf packages are well known, and the raster data world is largely covered by the terra and recently the stars packages. For more specific links, such as those needed for manipulating atmospheric models, packages like ncdf4 are very helpful.
The spatial analysis itself is often supported by wrapper packages that integrate external libraries, command line tools, or a mixture of both in an R-like syntax geosphere, Distance, igraph, or spatstat.
A comprehensive introduction to the spatial R-biotope and its backgrounds is excellently treated in Geocomputation with R, which is not only highly recommended as a reference book, but is also an indispensable basis for working and analyzing spatial data with R/Python.
Despite all these spatial analysis and data handling capabilities in the R world, it can be said (at least from a non-R point of view) that there is still a huge gap between R and the mature open source Geographic Information System (GIS) and even more so the Remote Sensing (RS) software community. QGIS, GRASS GIS, and SAGA GIS provide an extensive, growing, and mature collection of sophisticated algorithms. The algorithms provided are fast, stable and most of them are well proven. Probably most R users who are somehow connected to the GI community know that there are great wrapper packages to bridge this gap. For GRASS GIS ⅞ it is rgrass and for SAGA GIS it is the RSAGA and Rsagacmd packages.
Since there is also no wrapper for the Orfeo Toolbox, which is indispensable in exploration, it is also very helpful to provide a lightweight wrapper for the use of OTB modules from R..
Unfortunately you will run into a lot of technical problems depending on the chosen operating system (OS) or library dependencies or GIS software versions. In the case of RSAGA for example, the main problem was that the SAGA GIS developers not only change the syntax and strategy of the command line interface (CLI), but also within the same release the calls differ from OS to OS. So the maintenance of RSAGA is at least tedious (but thumbs up again). Another example is GRASS GIS, which is known for a sophisticated setup of the environment and the spatial properties of the database. If you “only” want to use a specific GRASS algorithm from R, you will probably get lost in setting up all the OS dependencies that are necessary to set up a correct temporary or permanent GRASS environment from “outside”. This is not only due to the strict space and projection requirements of GRASS, but much more due to the demanding OS environments, especially Windows.
To cut a long story short, it is a bit cumbersome to deal with all this stuff when you just want to start GRASS from the R command line, e.g. for a powerful random walk cost analysis (r.walk) call as provided by GRASS.
Linking simply means to provide all the necessary environment settings to satisfy the existing wrapper packages as well as full access to the command line APIs of the mentioned software tools. link2GI tries to analyze which software is installed in order to create a temporary environment that satisfies the above mentioned requirements.
GRASS GIS has the most demanding requirements. It needs a lot of environment and path variables as and a correct setup of the geographic data parameters. The linkGRASS function tries to find all installations and lets you (optionally) choose the one you want to use and generate the necessary variables. As a result you can use both the rgrass package and the command line API of GRASS.
SAGA GIS is much easier to set up. Again, the linkSAGA function will try to find all SAGA installations, let you (optionally) choose one, and generate the necessary variables. You can also use RSAGA, but you have to pass the result of linkSAGA like RSAGA::rsaga.env(path = saga$sagaPath). For easy use you can just use the R system() call to interface R with the saga_cmd API.
The Orfeo Toolbox (OTB) is a very powerful remote sensing toolbox. It is widely used for classification, filtering and machine learning applications. You will find some of the implemented algorithms in various R packages, but always much slower or only running on small data chunks. link2GI searches and links all OTB installations of a given search path and returns the result as a concise list. Due to a missing wrapper package, a list-based OTB module and function parser is also available, which can be piped into the runOTB function for a convenient function call.
Although GDAL is perfectly integrated into R, in some cases it is advantageous to use system calls and grab the binaries directly. In particular, the evolution to GDAL 3.x and optionally different boxed versions of GDAL binaries working with different Python and proj4/proj6 libs makes it sometimes difficult to grab the correct version of GDAL. link2GIgenerates a list of all paths and commands of allGDAL` installations in the given search path. With this list you can easily use all available API calls of each installation.