• Please update to QGIS version >= 2.18.2 (preferably by using our install guide) if you want to use RQGIS in combination with the developer version of QGIS. This version contains a major bug fix which RQGIS relies on. However, we strongly recommend to use the QGIS long-term release, currently 2.14.14!

• If you encounter segfault errors using SAGA 2.2.2 and 2.2.3 on macOS with QGIS installed via homebrew -> please re-install saga-gis-lts (v2.3.1) to fix the issue.

#### General

Resource: CRAN Travis CI Travis CI Appveyor
Platforms: Multiple Linux macOS Windows
R CMD check

Test coverage

#### Github

RQGIS establishes an interface between R and QGIS, i.e. it allows the user to access QGIS functionalities from within R. It achieves this by establishing a tunnel to the Python QGIS API via the reticulate-package. This provides the user with an extensive suite of GIS functions, since QGIS allows you to call native as well as third-party algorithms via its processing framework (see also https://docs.qgis.org/2.14/en/docs/user_manual/processing/index.html). Third-party providers include among others GDAL, GRASS GIS, SAGA GIS, the Orfeo Toolbox, TauDEM and tools for LiDAR data. RQGIS brings you this incredibly powerful geoprocessing environment to the R console.

The main advantages of RQGIS are:

1. It provides access to QGIS functionalities. Thereby, it calls Python QGIS API but R users can stay in their programming environment of choice without having to touch Python.
2. It offers a broad suite of geoalgorithms making it possible to solve most GIS problems.
3. R users can use just one package (RQGIS) instead of using RSAGA and rgrass7 to access SAGA and GRASS functions. This, however, does not mean that RSAGA and rgrass7 are obsolete since both packages offer various other advantages. For instance, RSAGA provides many user-friendly and ready-to-use GIS functions such as rsaga.slope.asp.curv and multi.focal.function.

# Installation

## Package installation

In order to run RQGIS properly, you need to download various third-party software packages. Our vignette should help you with the download and installation procedures on various platforms (Windows, Linux, Mac OSX). To access it, use vignette("install_guide", package = "RQGIS"). Overall, we recommend to use the current LTR of QGIS (2.14) with RQGIS.

You can install:

• the latest released version from CRAN with:
install.packages("RQGIS")
• the latest RQGIS development version from Github with:
devtools::install_github("jannes-m/RQGIS")

# RQGIS usage

Subsequently, we will show you a typical workflow of how to use RQGIS. Basically, we will follow the steps also described in the QGIS documentation. In our first and very simple example we simply would like to retrieve the centroid coordinates of a spatial polygon object. First off, we will download the administrative areas of Germany using the raster package.

# attach packages
library("raster")
library("rgdal")

ger <- getData(name = "GADM", country = "DEU", level = 1)
# ger is of class "SpatialPolygonsDataFrame"

Now that we have a spatial object, we can move on to using RQGIS. First of all, we need to specify all the paths necessary to run the QGIS-API. Fortunately, set_env does this for us (assuming that QGIS and all necessary dependencies were installed correctly). The only thing we need to do is: specify the root path to the QGIS-installation. If you do not specify a path, set_env tries to find the OSGeo4W-installation first in the ‘C:/OSGeo4W’-folders. If this is unsuccessful, it will search your C: drive though this might take a while. If you are running RQGIS under Linux or on a Mac, set_env assumes that your root path is “/usr” and “/applications/QGIS.app/Contents”, respectively. Please note, that most of the RQGIS functions, you are likely to work with (such as find_algorithms, get_args_man and run_qgis), require the output list (as returned by set_env) containing the paths to the various installations necessary to run QGIS from within R. This is why, set_env caches its result in a temporary folder, and loads it back into R when called again (to overwrite an existing cache, set parameter new to TRUE).

# attach RQGIS
library("RQGIS")

# set the environment, i.e. specify all the paths necessary to run QGIS from
# within R
set_env()
# under Windows set_env would be much faster if you specify the root path:
# set_env("C:/OSGeo4W~1")

## $root ## [1] "C:\\OSGeo4W64" ## ##$qgis_prefix_path
## [1] "C:\\OSGeo4W64\\apps\\qgis-ltr"
##
## $python_plugins ## [1] "C:\\OSGeo4W64\\apps\\qgis-ltr\\python\\plugins" Secondly, we would like to find out how the function in QGIS is called which gives us the centroids of a polygon shapefile. To do so, we use find_algorithms. Here, we look for a geoalgorithm that contains the words “polygon” and “centroid”. Note that you can use regular expressions. library("RQGIS") find_algorithms(search_term = "([Pp]olygon)(centroid)") ## [1] "Polygon centroids------------------------------------>qgis:polygoncentroids" ## [2] "Polygon centroids------------------------------------>saga:polygoncentroids" This gives us two functions we could use. Here, we’ll choose the QGIS function named qgis:polygoncentroids. Subsequently, we would like to know how we can use it, i.e. which function parameters we need to specify. get_usage(alg = "qgis:polygoncentroids") ## ALGORITHM: Polygon centroids ## INPUT_LAYER <ParameterVector> ## OUTPUT_LAYER <OutputVector> Consequently qgis:polygoncentroids only expects a parameter called INPUT_LAYER, i.e. the path to a polygon shapefile whose centroid coordinates we wish to extract, and a parameter called OUTPUT_LAYER, i.e. the path to the output shapefile. Since it would be tedious to specify manually each and every function argument, especially if a function has more than two or three arguments, we have written a convenience function, named get_args_man, that retrieves all function arguments and respective default values for a given GIS function. It returns these values in the form of a list. If a function argument lets you choose between several options (drop-down menu in a GUI), setting get_arg_man’s options-argument to TRUE makes sure that the first option will be selected (QGIS GUI behavior). For example, qgis:addfieldtoattributestable has three options for the FIELD_TYPE-parameter, namely integer, float and string. Setting options to TRUE means that the field type of your new column will be of type integer. params <- get_args_man(alg = "qgis:polygoncentroids") params ##$INPUT_LAYER
## [1] "None"
##
## $OUTPUT_LAYER ## [1] "None" In our case, qgis:polygoncentroids has only two function arguments and no default values. Naturally, we need to specify manually our input and output layer. We can do so in two ways. Either we use directly our parameter-argument list… params$INPUT_LAYER  <- ger
params$OUTPUT_LAYER <- file.path(tempdir(), "ger_coords.shp") out <- run_qgis(alg = "qgis:polygoncentroids", params = params, load_output = TRUE) ##$OUTPUT_LAYER
## [1] "C:\\Users\\pi37pat\\AppData\\Local\\Temp\\Rtmpeil4bS/ger_coords.shp"

… or we can use R named arguments in run_qgis

out <- run_qgis(alg = "qgis:polygoncentroids",
INPUT_LAYER = ger,
OUTPUT_LAYER = file.path(tempdir(), "ger_coords.shp"),
## $OUTPUT_LAYER ## [1] "C:\\Users\\pi37pat\\AppData\\Local\\Temp\\Rtmpeil4bS/ger_coords.shp" Please note that our INPUT_LAYER is a spatial object residing in R’s global environment. Of course, you can also use a path to specify INPUT_LAYER (e.g. “ger.shp”) which is the better option if your data is somewhere stored on your hard drive. Finally, run_qgis calls the QGIS API to run the specified geoalgorithm with the corresponding function arguments. Since we set load_output to TRUE, run_qgis automatically loads the QGIS output back into R (an sf-object in the case of vector data and a raster-object in the case of raster data). Naturally, we would like to check if the result meets our expectations. # first, plot the federal states of Germany plot(ger) # next plot the centroids created by QGIS plot(out$geometry, pch = 21, add = TRUE, bg = "lightblue", col = "black")

Of course, this is a very simple example. We could have achieved the same using sp::coordinates(). To harness the real power of integrating R with a GIS, we will present a second, more complex example. Yet to come in the form of a paper…