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32.4. 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
32.4.1. 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
32.4.2. 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:
##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.
32.4.3. Syntax Summary for QGIS R scripts
A number of input and output parameter types are offered.
32.4.3.1. 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) |
lentelė |
Layer = table |
dataframe conversion from csv, default object of |
field |
Field = Field Layer |
name of the Field selected, e.g. |
raster |
Layer = raster |
RasterBrick object, default object of |
multiple raster |
Layer = multiple raster |
RasterBrick objects, default object of |
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: |
extent |
E = extent |
Extent object of the |
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
32.4.3.2. Output parameter types
Parameter |
Syntax example |
---|---|
vector |
Output = output vector |
raster |
Output = output raster |
lentelė |
Output = output table |
file |
Output = output file |
Pastaba
You can save plots as png
from the Processing Result Viewer, or you can choose to
save the plot directly from the algorithm interface.
32.4.3.3. 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)
32.4.4. Examples
32.4.4.1. 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 algorithmLayer
is the input vector layerSize
is a numerical parameter with a default value of 10Output
is the vector layer that will be created by the algorithmlibrary(sp)
loads the sp libraryspatpoly = as(Layer, "Spatial")
translate to an sp objectCall the
spsample
function of thesp
library and run it using the input defined above (Layer
andSize
)Create a SpatialPointsDataFrame object using the
SpatialPointsDataFrame
functionCreate 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.
32.4.4.2. 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.
32.4.4.3. 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.
32.4.4.4. 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
32.4.4.5. 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.