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18.34. R - Estrutura de rotinas de processamento

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

Escrever rotinas R no Processamento pode ser bastante complicado por causa da sintaxe que deve ser adotada.

Cada script começa com ** Input ** e ** Output ** precedido de `` ## ``.

18.34.1. Entradas

Antes de especificar as entradas você também pode definir o grupo de algoritmos em que o script será colocado. Se o grupo já existir, o algoritmo será adicionado ao outro, caso contrário um novo grupo será automaticamente criado:

  1. Criar Grupo, ##My Group=group

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

  1. vetor, ##Layer = vector

  2. Campo de Vetor, ##F = Field Layer (Local do nome da camada de entrada)

  3. raster, ##r = raster
  4. table, ##t = table

  5. número, ##Num = number

  6. Carácter, ##Str = string

  7. booleano , ##Bol = boolean

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

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

18.34.2. Saídas

Quanto às entradas, cada saída tem de ser definida no início do script:

  1. vetor, ##output= output vector

  2. raster, ##output= output raster
  3. table, ##output= output table
  4. plots, ##showplots
  5. R output in the Result Viewer, just put inside the script > before the output you want to display

18.34.3. 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)

18.34.3.1. 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:

  1. Point pattern analysis is the group of the algorithm
  2. Layer is the input vector layer
  3. Size is the numerical parameter with a default value of 10
  4. Output is the vector layer that will be created by the algorithm
  5. library(sp) loads the sp library (that should be already installed in your computer and that installation has to be made in R)
  6. call the spsample function of the sp library and pass it to all the input defined above
  7. 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.

18.34.3.2. 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.

18.34.3.3. 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.

18.34.3.4. 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:

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

18.34.3.5. 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.