17.32. R Syntax in Processing scripts¶

Module contributed by Matteo Ghetta - funded by Scuola Superiore Sant’Anna

Writing R scripts in Processing could be quite tricky because of the syntax that has to be adopted.

Each script starts with the Input and Output preceded with ##.

17.32.1. Inputs¶

Before you specify teh inputs you can also set the algorithm group in which your script will be put. If the group already exists, the algorithm will be added to the other, else a new group will be automatically created:

group creation, ##My Group=group

Then you have to specify all the input types and eventually the additional parameters. You can have different inputs:

vector, ##Layer = vector
vector Field, ##F = Field Layer (where Layer is the name of the input Layer)
table, ##Layer = raster
number, ##Num = number
string, ##Str = string
boolean, ##Bol = boolean

you can also have a dropdown menu with all the parameters you want; the items must be separated with semi columns ;:

##type=selection point;lines;point+lines

17.32.2. Outputs¶

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, ##showplots
R output in the Result Viewer, just put inside the script > before the output you want to display

17.32.3. Script body¶

The script body follows an R style syntax and the Log panel can help you if something went wrong with your script.

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

library(sp)

17.32.3.1. 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
##Size=number 10
##Output= output vector
library(sp)
pts=spsample(Layer,Size,type="random")
Output=SpatialPointsDataFrame(pts, as.data.frame(pts))

and get through the lines:

Point pattern analysis is the group of the algorithm
Layer is the input vector layer
Size is the 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 (that should be already installed in your computer and that installation has to be made in R)
call the spsample function of the sp library and pass it to all the input defined above
create the output vector with the SpatialPointsDataFrame function

That’s it! Just run the algorithm with a vector layer you have in the QGIS Legend, choose a number of the random point and you will get them in the QGIS Map Canvas.

17.32.3.2. Example with raster output¶

The following script will perform a basic ordinary kriging and will create a raster map of the interpolated values:

##Basic statistics=group
##Layer=vector
##Field=Field Layer
##Output=output raster
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

from a vector and its field in input the algorithm will use the autoKrige function of the automap R package and 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.

17.32.3.3. 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.

17.32.3.4. Example with console output¶

We can take 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 of above with just 2 edits:

no more output specified (the fourth line has been removed)
the last line begins with > that tells Processing to print the object in the result viewer

17.32.3.5. Example with plot¶

Creating plots is very simple. You have to use the ##showplots parameter as the following script shows:

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

the script takes a field of the vector layer in input and creates a QQ Plot to test the normality of the distribution.

The plot is automatically added to the Result Viewer of Processing.

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