Adding vlines to a density plot in Shiny - r

Question

I am trying to add vlines to a density plot in shiny for R. For reproducible purposes, I will use the iris data set. The data is clustered using kmeans from the cluster package. Outliers are located by measuring distances from the centers of clusters. Then I wish to create a density plot that has vlines and labels for each of the outliers.
The code works outside of shiny. Inside my shiny app, I get an error message: Error in [: invalid subscript type 'list'. I have tried unlist and making the outliers a variety of other forms and still get the list error. How do I add the vlines?
Global
library(tidyverse)
library(cluster)
library(shiny)
require(iris)
UI
ui <- fluidPage({
pageWithSidebar(
headerPanel('Iris k-means clustering'),
sidebarPanel(
numericInput('clusters', 'Cluster count', 3, min = 1, max = 9)
),
mainPanel(
plotOutput('plot1')
)
)
})
Server
server <- function(input, output){
ClusterData <- reactive({
iris[,1:4]
})
# need to keep row numbers for outlier labels
ClusterData2 <- reactive({
ClusterData2 <- data.frame(ClusterData())
row.names(ClusterData2) <- 1:nrow(ClusterData2)
return(ClusterData2)
})
# scale the iris data
ScaledData <- reactive({
scale(ClusterData2())
})
# kmeans clustering
final <- reactive({
kmeans(ScaledData(), 3, nstart = 25)
})
# find centers
states.centers <- reactive({
final()$centers[final()$cluster, ]
})
# find outliers
distances <- reactive({
sqrt(rowSums((ScaledData() - states.centers())^2))
})
# bind distances back to data
outliers <- reactive({
cbind(ClusterData(), Distance = distances())
})
# bind cluster number to data
clusterMember <- reactive({
cbind(outliers(), clusterNum = final()$cluster)
})
# turn into data frame
clusterMember2 <- reactive({
as.data.frame(clusterMember())
})
# find points that are their own cluster
dist0 <- reactive({
clusterMember() %>%
filter(distances() == 0)
})
# arrange distances largest to smallest
distArrange <- reactive({
clusterMember() %>%
arrange(desc(Distance))
})
# find top 5 outliers
filtTop5 <- reactive({
distArrange()[1:5,]
})
# bind outliers and single clusters together
AllOutliers <- reactive({
rbind(filtTop5(), dist0())
})
# for density plot: vlines and labels
lines_amt <- reactive({
data.frame(vlines = (clusterMember2()$Petal.Length[c(AllOutliers())]), labels = c(AllOutliers()))
})
########## output
output$plot1 <- renderPlot({
ClusterData() %>%
ggplot(aes(x = Petal.Length)) +
geom_density(fill = "blue", alpha = 0.4) #+
geom_vline(data = lines_amt(), aes(xintercept = get(vlines)))
})
}
Run App
shinyApp(ui, server)
Thank you.
The plot should look like the link.
PetalLengthDensityPlot

Answer 1

I think I have it all sorted out. Essentially I removed your lines_amt() reactive, and replaced geom_vline(), while also adding an annotate() for the labels:
library(tidyverse)
library(cluster)
library(shiny)
ui <- fluidPage({
pageWithSidebar(
headerPanel('Iris k-means clustering'),
sidebarPanel(
numericInput('clusters', 'Cluster count', 3, min = 1, max = 9)
),
mainPanel(
plotOutput('plot1')
)
)
})
server <- function(input, output){
ClusterData <- reactive({
iris[,1:4]
})
# need to keep row numbers for outlier labels
ClusterData2 <- reactive({
ClusterData2 <- data.frame(ClusterData())
row.names(ClusterData2) <- 1:nrow(ClusterData2)
return(ClusterData2)
})
# scale the iris data
ScaledData <- reactive({
scale(ClusterData2())
})
# kmeans clustering
final <- reactive({
kmeans(ScaledData(), 3, nstart = 25)
})
# find centers
states.centers <- reactive({
final()$centers[final()$cluster, ]
})
# find outliers
distances <- reactive({
sqrt(rowSums((ScaledData() - states.centers())^2))
})
# bind distances back to data
outliers <- reactive({
cbind(ClusterData(), Distance = distances())
})
# bind cluster number to data
clusterMember <- reactive({
cbind(outliers(), clusterNum = final()$cluster)
})
# turn into data frame
clusterMember2 <- reactive({
as.data.frame(clusterMember())
})
# find points that are their own cluster
dist0 <- reactive({
clusterMember() %>%
filter(distances() == 0)
})
# arrange distances largest to smallest
distArrange <- reactive({
clusterMember() %>%
arrange(desc(Distance))
})
# find top 5 outliers
filtTop5 <- reactive({
distArrange()[1:5,]
})
# bind outliers and single clusters together
AllOutliers <- reactive({
rbind(filtTop5(), dist0())
})
# for density plot: vlines and labels #Made the lines and labels without this
# lines_amt <- reactive({
# data.frame(vlines = (clusterMember2()$Petal.Length[c(AllOutliers()$Petal.Length)]), labels = c(AllOutliers()))
# })
########## output
output$plot1 <- renderPlot({
ClusterData() %>%
ggplot(aes(x = Petal.Length)) +
geom_density(fill = "blue", alpha = 0.4) +
# geom_vline(xintercept = lines_amt()$vlines)
geom_vline(xintercept = AllOutliers()$Petal.Length)+ #Used this in place of your current geom_vline()
annotate("text", x = AllOutliers()$Petal.Length, #Added this to add the text
y = 0,
label = rownames(AllOutliers()),
hjust = -1,
vjust = -1)
# geom_vline(data = lines_amt(), aes(xintercept = get(vlines)))
})
}
shinyApp(ui, server)
Hopefully this helps!