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    R-some: R and RDF

    22 Jul 2014

    In our database project, the guy (student) who has to configure the database backend asked me, if I could program (or name) a tool that converts tables into RDF data triplets. At first I thought of writing a perl script, but then had a look around. And guess which was the easiest option: R, the Swiss Army Knife of data-processing and analysis. There is a handy package called rrdf. Here is an example how easy one can generate valid turtle from a data.frame:

    
require(rrdf)

## generate some data for a fictional mathcourse with students that get a grade
mathcourse <- data.frame(subjects = paste0("s:Student#",1:20),pred0 = "http://example.org/attend",objects = "course:MathCourse",grade=sample(1:6,20,replace = T))

## create an empty RDF store
newstore <- new.rdf()

##define some prefixes
add.prefix(newstore, prefix="s", "http://example.org/Student/")
add.prefix(newstore, prefix="course", "http://example.org/Course/")

## assign the subject MathCourse an object called MathCourseGrade 
add.triple(newstore,"course:MathCourse","http://example.org/hasGrade","http://example.org/MathCourseGrade")

## add Students and their grades
apply(mathcourse,1,function(x) add.triple(newstore,x[1],x[2],x[3]))
apply(mathcourse,1,function(x) add.data.triple(newstore,x[1],"http://example.org/MathCourseGrade",data=as.character(x[4]),type="integer"))

    Now serialize it in turtle

    cat(asString.rdf(newstore))

@prefix course: <http://example.org/Course/> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix s:     <http://example.org/Student/> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

<course:MathCourse>  <http://example.org/hasGrade>
                <http://example.org/MathCourseGrade> .

<s:Student#1>  <http://example.org/MathCourseGrade>
                3 ;
        <http://example.org/attend>  <course:MathCourse> .

<s:Student#7>  <http://example.org/MathCourseGrade>
                4 ;
        <http://example.org/attend>  <course:MathCourse> .

<s:Student#12>  <http://example.org/MathCourseGrade>
                6 ;
        <http://example.org/attend>  <course:MathCourse> .
.
.
.

    But we can also query online SPARQL endpoints. Let’s have a look at restaurants 100m around my office:

    ##look for food
food <- as.data.frame(sparql.remote("http://linkedgeodata.org/sparql",
'Prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#>
Prefix ogc: <http://www.opengis.net/ont/geosparql#>
Prefix geom: <http://geovocab.org/geometry#>
Prefix lgdo: <http://linkedgeodata.org/ontology/>
Prefix geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>

Select *
From <http://linkedgeodata.org> {
  ?s
    a lgdo:Amenity ;
    rdfs:label ?l ;
    geo:lat ?lat;
    geo:long ?long;
    lgdo:cuisine ?food;
    geom:geometry [
      ogc:asWKT ?g
    ] .
     Filter(<bif:st_intersects> (?g, <bif:st_point> (7.847607, 48.000874), 0.1)) .
 
}',jena = F),stringsAsFactors = F)

    This gives us:

     > food$food
[1] "kebab"
> food$label
[1] "Ezo"

    Our oriental Pizza baker/Kebap shop around the corner. Awesome. Now let’s show this on a map (using the package RgoogleMaps):

    require(RgoogleMaps)
lat <- as.numeric(food$lat[1])
long <- as.numeric(food$long[1])

center <- c(lat,long)
zoom <- 15
MyMap <- GetMap(center=center, zoom=zoom,markers = paste0("&markers=color:red|label:E|", center[1],",",center[2]),destfile = "EZO.png");

PlotOnStaticMap(MyMap,center[1],center[2],destfile = "EZO.png")

    TADA: alt text

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