28.4. Appendix D: QGIS R script syntax

Contributed by Matteo Ghetta - funded by Scuola Superiore Sant’Anna

Writing R scripts in Processing is a bit tricky because of the special syntax.

A Processing R script starts with defining its Inputs and Outputs, each preceded with double hash characters (##).

Before the inputs, the group to place the algoritm in can be specified. If the group already exists, the algorithm will be added to it, if not, the group will be created. In the example below, the name of the group is My group:

##My Group=group

28.4.1. Inputs

All input data and parameters have to be specified. There are several types of inputs:

vector: ##Layer = vector
vector field: ##F = Field Layer (where Layer is the name of an input vector layer the field belongs to)
raster: ##r = raster
table: ##t = table
number: ##Num = number
string: ##Str = string
boolean: ##Bol = boolean
elements in a dropdown menu. The items must be separated with semicolons ;: ##type=selection point;lines;point+lines

28.4.2. Resultados

As for the inputs, each output has to be defined at the beginning of the script:

vector: ##output= output vector
raster: ##output= output raster
table: ##output= output table
plots: ##output_plots_to_html (##showplots in earlier versions)
To show R output in the Result Viewer, put > in front of the command whose output you would like to show.

28.4.3. Syntax Summary for QGIS R scripts

A number of input and output parameter types are offered.

28.4.3.1. Input parameter types

Parameter	Syntax example	Returning objects
vector	Layer = vector	sf object (or SpatialDataFrame object, if ##load_vector_using_rgdal is specified)
vector point	Layer = vector point	sf object (or SpatialDataFrame object, if ##load_vector_using_rgdal is specified)
vector line	Layer = vector line	sf object (or SpatialDataFrame object, if ##load_vector_using_rgdal is specified)
vector polygon	Layer = vector polygon	sf object (or SpatialPolygonsDataFrame object, if ##load_vector_using_rgdal is used)
multiple vector	Layer = multiple vector	sf object (or SpatialDataFrame objects if ##load_vector_using_rgdal is specified)
tabela	Layer = table	dataframe conversion from csv, default object of `read.csv` function
field	Field = Field Layer	name of the Field selected, e.g. `"Area"`
raster	Layer = raster	RasterBrick object, default object of `raster` package
multiple raster	Layer = multiple raster	RasterBrick objects, default object of `raster` package
number	N = number	integer or floating number chosen
string	S = string	string added in the box
longstring	LS = longstring	string added in the box, could be longer then the normal string
selection	S = selection first;second;third	string of the selected item chosen in the dropdown menu
crs	C = crs	string of the resulting CRS chosen, in the format: `"EPSG:4326"`
extent	E = extent	Extent object of the `raster` package, you can extract values as `E@xmin`
point	P = point	when clicked on the map, you have the coordinates of the point
file	F = file	path of the file chosen, e.g. «/home/matteo/file.txt»
folder	F = folder	path of the folder chosen, e.g. «/home/matteo/Downloads»

A parameter can be OPTIONAL, meaning that it can be ignored.

In order to set an input as optional, you add the string optional before the input, e.g:

##Layer = vector
##Field1 = Field Layer
##Field2 = optional Field Layer

28.4.3.2. Output parameter types

Parameter	Syntax example
vector	Output = output vector
raster	Output = output raster
tabela	Output = output table
file	Output = output file

Nota

You can save plots as png from the Processing Result Viewer, or you can choose to save the plot directly from the algorithm interface.

28.4.3.3. Script body

The script body follows R syntax and the Log panel can help you if there is something wrong with your script.

Remember that you have to load all additional libraries in the script:

library(sp)

28.4.4. Examples

28.4.4.1. Example with vector output

Let’s take an algorithm from the online collection that creates random points from the extent of an input layer:

##Point pattern analysis=group
##Layer=vector polygon
##Size=number 10
##Output=output vector
library(sp)
spatpoly = as(Layer, "Spatial")
pts=spsample(spatpoly,Size,type="random")
spdf=SpatialPointsDataFrame(pts, as.data.frame(pts))
Output=st_as_sf(spdf)

Explanation (per line in the script):

Point pattern analysis is the group of the algorithm
Layer is the input vector layer
Size is a numerical parameter with a default value of 10
Output is the vector layer that will be created by the algorithm
library(sp) loads the sp library
spatpoly = as(Layer, "Spatial") translate to an sp object
Call the spsample function of the sp library and run it using the input defined above (Layer and Size)
Create a SpatialPointsDataFrame object using the SpatialPointsDataFrame function
Create the output vector layer using the st_as_sf function

That’s it! Just run the algorithm with a vector layer you have in the QGIS Legend, choose the number of random point. The resulting layer will be added to your map.

28.4.4.2. Example with raster output

The following script will perform basic ordinary kriging to create a raster map of interpolated values from a specified field of the input point vector layer by using the autoKrige function of the automap R package. It will first calculate the kriging model and then create a raster. The raster is created with the raster function of the raster R package:

##Basic statistics=group
##Layer=vector point
##Field=Field Layer
##Output=output raster
##load_vector_using_rgdal
require("automap")
require("sp")
require("raster")
table=as.data.frame(Layer)
coordinates(table)= ~coords.x1+coords.x2
c = Layer[[Field]]
kriging_result = autoKrige(c~1, table)
prediction = raster(kriging_result$krige_output)
Output<-prediction

By using ##load_vector_using_rgdal, the input vector layer will be made available as a SpatialDataFrame objects, so we avoid having to translate it from an sf object.

28.4.4.3. Example with table output

Let’s edit the Summary Statistics algorithm so that the output is a table file (csv).

The script body is the following:

##Basic statistics=group
##Layer=vector
##Field=Field Layer
##Stat=Output table
Summary_statistics<-data.frame(rbind(
    sum(Layer[[Field]]),
    length(Layer[[Field]]),
    length(unique(Layer[[Field]])),
    min(Layer[[Field]]),
    max(Layer[[Field]]),
    max(Layer[[Field]])-min(Layer[[Field]]),
    mean(Layer[[Field]]),
    median(Layer[[Field]]),
    sd(Layer[[Field]])),
  row.names=c("Sum:","Count:","Unique values:","Minimum value:","Maximum value:","Range:","Mean value:","Median value:","Standard deviation:"))
colnames(Summary_statistics)<-c(Field)
Stat<-Summary_statistics

The third line specifies the Vector Field in input and the fourth line tells the algorithm that the output should be a table.

The last line will take the Stat object created in the script and convert it into a csv table.

28.4.4.4. Example with console output

We can use the previous example and instead of creating a table, print the result in the Result Viewer:

##Basic statistics=group
##Layer=vector
##Field=Field Layer
Summary_statistics<-data.frame(rbind(
sum(Layer[[Field]]),
length(Layer[[Field]]),
length(unique(Layer[[Field]])),
min(Layer[[Field]]),
max(Layer[[Field]]),
max(Layer[[Field]])-min(Layer[[Field]]),
mean(Layer[[Field]]),
median(Layer[[Field]]),
sd(Layer[[Field]])),row.names=c("Sum:","Count:","Unique values:","Minimum value:","Maximum value:","Range:","Mean value:","Median value:","Standard deviation:"))
colnames(Summary_statistics)<-c(Field)
>Summary_statistics

The script is exactly the same as the one above except for two edits:

no output specified (the fourth line has been removed)
the last line begins with >, telling Processing to make the object available through the result viewer

28.4.4.5. Example with plot

To create plots, you have to use the ##output_plots_to_html parameter as in the following script:

##Basic statistics=group
##Layer=vector
##Field=Field Layer
##output_plots_to_html
####output_plots_to_html
qqnorm(Layer[[Field]])
qqline(Layer[[Field]])

The script uses a field (Field) of a vector layer (Layer) as input, and creates a QQ Plot (to test the normality of the distribution).

The plot is automatically added to the Processing Result Viewer.