R: ggplot2 con geom_map devuelve el error "x y las unidades deben tener una longitud> 0" a pesar de los valores convertidos en factores

sparql shiny

Question

Estoy trabajando en una aplicación brillante primitiva que mapearía algunos datos del proyecto Open Data for Scotland . Resolví las consultas SPARQL que fabrican un marco de datos que se asemeja al extracto proporcionado a continuación

dz_label overall.quantiles
S010001 8
S010002 9

Tengo los shapefiles fortificados y estoy tratando de asignarlos con el uso del siguiente código:

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)

El código devuelve el error

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

Si entiendo discusiones similares ( 1 , 2 ), el problema está asociado con las clases de valores. Sin embargo, entiendo que el as.factor() debería abordar esto, pero evidentemente este no es el caso. El código completo de la aplicación se incluye a continuación. Naturalmente, estaré agradecido por cualquier ayuda.

Servidor

# 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)

Estaba enfrentando un problema similar y pude solucionarlo de esta manera.