R: ggplot2 con geom_map restituisce l'errore "xe le unità devono avere lunghezza> 0" nonostante i valori convertiti in fattori

Question

Sto lavorando a un'applicazione primitiva lucida che mapperebbe alcuni dati dal progetto Open Data for Scotland. Ho lavorato fuori le query SPARQL che producono un frame di dati che assomiglia l'estratto di seguito

dz_label overall.quantiles 
S010001 8 
S010002 9 

ho gli shapefile fortificate e sto cercando di mappare loro con l'uso del seguente codice:

ggplot() + 
     geom_map(data = dta.simd, aes(map_id = dz_label, 
            fill = as.factor(dta.simd$overall.quantiles)), 
       map = shps.dzs2001) + 
     geom_path(data = shps.dzs2001, aes(x=long, y=lat, group=group), 
       colour="black", size=0.25) 

il codice restituisce l'errore

Error: 'x' and 'units' must have length > 0 

Se ho capito discussioni simili (1, 2), il problema è associato con le classi di valore. Tuttavia, la mia comprensione è che il as.factor() dovrebbe affrontare questo ma, evidentemente questo non è il caso. Il codice completo per l'app è incluso di seguito. Naturalmente, sarò grato per qualsiasi aiuto.

Server

# Libs 
require(shiny); require(SPARQL); require(ggplot2); require(rgeos); require(maptools); 
require(RCurl) 

# Server function 
shinyServer(function(input, output) { 

    # Source the data 

    ## Source the SPARQL data 
    ### Define endpoint URL. 
    endpoint <- "http://data.opendatascotland.org/sparql.csv" 

    ### Create Query and download table for the SIMD rank 
    query.simd <- "PREFIX stats: <http://statistics.data.gov.uk/id/statistical-geography/> 
     PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> 
     PREFIX simd: <http://data.opendatascotland.org/def/simd/> 
     PREFIX cube: <http://purl.org/linked-data/cube#> 
     PREFIX stats_dim: <http://data.opendatascotland.org/def/statistical-dimensions/> 
     PREFIX year: <http://reference.data.gov.uk/id/year/> 

     SELECT DISTINCT 
     ?dz_label 
     ?overall_rank 
     ?income_deprivation_rank 
     ?employment_deprivation_rank 
     ?health_deprivation_rank 
     ?education_deprivation_rank 
     ?access_deprivation_rank 
     ?housing_deprivation_rank 
     ?crime_deprivation_rank 

     WHERE { 

     GRAPH <http://data.opendatascotland.org/graph/simd/rank> { 
     ?overall_rank_observation stats_dim:refArea ?dz . 
     ?overall_rank_observation stats_dim:refPeriod year:2012 . 
     ?overall_rank_observation simd:rank ?overall_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/income-rank> { 
     ?income_rank_observation stats_dim:refArea ?dz . 
     ?income_rank_observation stats_dim:refPeriod year:2012 . 
     ?income_rank_observation simd:incomeRank ?income_deprivation_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/employment-rank> { 
     ?employment_rank_observation stats_dim:refArea ?dz . 
     ?employment_rank_observation stats_dim:refPeriod year:2012 . 
     ?employment_rank_observation simd:employmentRank ?employment_deprivation_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/health-rank> { 
     ?health_rank_observation stats_dim:refArea ?dz . 
     ?health_rank_observation stats_dim:refPeriod year:2012 . 
     ?health_rank_observation simd:healthRank ?health_deprivation_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/education-rank> { 
     ?education_rank_observation stats_dim:refArea ?dz . 
     ?education_rank_observation stats_dim:refPeriod year:2012 . 
     ?education_rank_observation simd:educationRank ?education_deprivation_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/geographic-access-rank> { 
     ?access_rank_observation stats_dim:refArea ?dz . 
     ?access_rank_observation stats_dim:refPeriod year:2012 . 
     ?access_rank_observation simd:geographicAccessRank ?access_deprivation_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/housing-rank> { 
     ?housing_rank_observation stats_dim:refArea ?dz . 
     ?housing_rank_observation stats_dim:refPeriod year:2012 . 
     ?housing_rank_observation simd:housingRank ?housing_deprivation_rank . 
     } 

     GRAPH <http://data.opendatascotland.org/graph/simd/crime-rank> { 
     ?crime_rank_observation stats_dim:refArea ?dz . 
     ?crime_rank_observation stats_dim:refPeriod year:2012 . 
     ?crime_rank_observation simd:crimeRank ?crime_deprivation_rank . 
     } 

    { 
     SELECT ?dz ?dz_label WHERE 
    { 
     ?dz a <http://data.opendatascotland.org/def/geography/DataZone> . 
     ?dz rdfs:label ?dz_label . 
    } 
    } 
    }" 

    ## Make the data table for SIMD 
    dta.simd <- SPARQL(url = endpoint, query = query.simd, format = "csv")$results 
    ### Clean the data frame 
    dta.simd$dz_label <- gsub("Data Zone","", dta.simd$dz_label) 

    # Readings shapefiles (pass only file that ends with shp) 
    shps.dzs2001 <- readShapeSpatial("data/SG_DataZone_Bdry_2001.shp") 

    ## Read shapefiles 
    shps.dzs2001 <- fortify(shps.dzs2001, region= "DZ_CODE") 

    # Switch on the gclibpermit 
    gpclibPermit() 

    # Make the plot 
    output$distPlot <- renderPlot({ 

    ### Get the column index fumber 
    col.ind <- match(input$firstvar, colnames(dta.simd)) 


    #### Calculate the breaks 
    ##### Take tha quantilies value from the UI 
    ##### Obtain the breaks 
    dta.simd$column <- as.numeric(as.character(dta.simd[,col.ind])) 
    dta.simd$overall.quantiles <- ceiling(sapply(dta.simd$column,function(x) 
     sum(x-dta.simd$column>=0))/(length(dta.simd$column)/input$quannum)) 

    #### Graph the chart 
    ggplot() + 
     geom_map(data = dta.simd, aes(map_id = dz_label, 
            fill = as.factor(dta.simd$overall.quantiles)), 
       map = shps.dzs2001) + 
     geom_path(data = shps.dzs2001, aes(x=long, y=lat, group=group), 
       colour="black", size=0.25) 

    ### Re-calculate summary stats 
    summary.data <- head(dta.simd) 

    }) 
    ### Do the summary tables 
    output$summary.table <- renderDataTable({summary.data}) 
}) 

UI

require(shiny) 

# Define list of options 
choices.list <- list("Overall Rank" = "overall_rank", 
        "Income" = "income_deprivation_rank", 
        "Employment" = "employment_deprivation_rank", 
        "Health" = "health_deprivation_rank", 
        "Education" = "education_deprivation_rank", 
        "Access to Services" = "access_deprivation_rank", 
        "Housing" = "housing_deprivation_rank", 
        "Crime" = "crime_deprivation_rank" 
        ) 

# Define UI for application that draws a histogram 
shinyUI(fluidPage(

    # Application title 
    titlePanel("Scottish Index of Multiple Deprivation 2012"), 

    # Sidebar with a slider input for the number of bins 
    sidebarLayout(
    sidebarPanel(
     selectInput(inputId = "firstvar", 
        label = "Index domain to map", 
        choices = choices.list, 
        selected = "income_deprivation_rank"), 
     sliderInput("quannum", label = "Number of quantiles", 
        min = 0, 
        max = 100, 
        value = 10)), 

    # Show a plot of the generated distribution 
    mainPanel(
     plotOutput("distPlot", height = "700px"), 
     dataTableOutput("summary.data"), 
     h5("Notes"), 
     p("The data is sourced live from the Open Data for Scotland Project. The shapefiles are sourced from the Scottish Neighbourhood Statistics.") 
    ) 
) 
)) 

Answer 1

fill=factor(overall.quantiles) 

stavo affrontando un problema simile e sono riuscito a risolvere il problema in questo modo.