我正在研究一个原始的闪亮应用程序，它将映射来自Open Data for Scotland项目的一些数据。我制定了SPARQL查询，这些查询生成的数据框类似于下面提供的摘录
dz_label overall.quantiles
S010001 8
S010002 9
我有强化的shapefile，我正在尝试使用以下代码映射它们：
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)
代码返回错误
Error: 'x' and 'units' must have length > 0
如果我理解类似的讨论（1，2），则问题与价值类相关联。但是，我的理解是as.factor()应该解决这个问题，但显然事实并非如此。该应用程序的完整代码包含在下面。当然，我会感激任何帮助。
服务器
# 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.")
)
)
))
答案 0 :(得分：3)
fill=factor(overall.quantiles)
我遇到了类似的问题，并且能够以这种方式修复它。