Problem in showing a graph on Shiny: combining clustering and linear regression - r

Question

I expanded the result from my last question with a new idea.
Error in Running R Shiny App: Operation not allowed without an active reactive context
This time in addition to clustered points in Iris data (see my previous question), I want to show the regression line (on the plot), slope & intercept (on the sidebar) for the selected points as in:
The regression code is available here (separate server.R and ui.R files):
library(shiny)
shinyServer(function(input, output) {
model <- reactive({
brushed_data <- brushedPoints(iris, input$brush1,
xvar = "Petal.Length", yvar = "Petal.Width")
if(nrow(brushed_data) < 2){
return(NULL)
}
lm(Petal.Width ~ Petal.Length, data = brushed_data)
})
output$slopeOut <- renderText({
if(is.null(model())){
"No Model Found"
} else {
model()[[1]][2]
}
})
output$intOut <- renderText({
if(is.null(model())){
"No Model Found"
} else {
model()[[1]][1]
}
})
output$plot1 <- renderPlot({
plot(iris$Petal.Length, iris$Petal.Width, xlab = "Petal.Length",
ylab = "Petal.Width", main = "Iris Dataset",
cex = 1.5, pch = 16, bty = "n")
if(!is.null(model())){
abline(model(), col = "blue", lwd = 2)
}
})
})
and
library(shiny)
shinyUI(fluidPage(
titlePanel("Visualize Many Models"),
sidebarLayout(
sidebarPanel(
h3("Slope"),
textOutput("slopeOut"),
h3("Intercept"),
textOutput("intOut")
),
mainPanel(
plotOutput("plot1", brush = brushOpts(
id = "brush1"
))
)
)
))
I used the following code. However, I have a problem with merging these two ideas and the plot is not shown:
Here is the main code for this question (server and ui in one file):
# Loading Libraries and data
library(shiny)
library(caret)
library(ggplot2)
data(iris)
ui <- pageWithSidebar(
# heading 1
headerPanel(h1("Clustering Iris Data")),
sidebarPanel(
sliderInput("k", "Number of clusters:",
min = 1, max = 5, value = 3),
sliderInput("prob", "Training percentage:",
min=0.5, max=0.9, value = 0.7),
# bold text
tags$b("Slope:"),
textOutput("slopeOut"),
# empty line
br(),
# bold text
tags$b("Intercept:"),
textOutput("intOut")
),
# Enabling the submit button disables the hovering feature
# submitButton("submit")),
mainPanel(
# img(src='iris_types.jpg', align = "center", height="50%", width="50%"),
plotOutput("plot1",
click = "plot_click",
brush = brushOpts(id = "brush1")
),
verbatimTextOutput("info")
)
)
#----------------------------------------------------------------------------
server <- function(input, output) {
# the clustering part
get_training_data <- reactive({
inTrain <- createDataPartition(y=iris$Species,
p=input$prob,
list=FALSE)
training <- iris[ inTrain,]
testing <- iris[-inTrain,]
kMeans1 <- kmeans(subset(training,
select=-c(Species)),
centers=input$k)
training$clusters <- as.factor(kMeans1$cluster)
training
})
#-------------------------
# the linear model part
model <- reactive({
brushed_data <- brushedPoints(iris, input$brush1,
xvar = "Petal.Length", yvar = "Petal.Width")
if(nrow(brushed_data) < 2){
return(NULL)
}
lm(Petal.Width ~ Petal.Length, data = brushed_data)
})
# reactive
output$slopeOut <- renderText({
if(is.null(model())){
"No Model Found"
} else {
model()[[1]][2]
}
})
# reactive
output$intOut <- renderText({
if(is.null(model())){
"No Model Found"
} else {
model()[[1]][1]
}
})
#------------------------------------------------
# if (x()<4) 1 else 0
output$plot1 <- reactive({
if(is.null(model())) {
# If no regression model exists, show the regular scatter plot
# with clustered points and hovering feature
renderPlot({
plot(Petal.Width,
Petal.Length,
colour = clusters,
data = get_training_data(),
xlab="Petal Width",
ylab="Petal Length")
})
output$info <- renderPrint({
# With ggplot2, no need to tell it what the x and y variables are.
# threshold: set max distance, in pixels
# maxpoints: maximum number of rows to return
# addDist: add column with distance, in pixels
nearPoints(iris, input$plot_click, threshold = 10, maxpoints = 1,
addDist = FALSE)
})
# closing if
}
else
# If there is a regression model, show the plot with the regression line for the brushed points
renderPlot({
plot(Petal.Width,
Petal.Length,
colour = clusters,
data = get_training_data(),
xlab = "Petal.Length",
ylab = "Petal.Width",
main = "Iris Dataset",
cex = 1.5, pch = 16, bty = "n")
if(!is.null(model())){
abline(model(), col = "blue", lwd = 2)
}
})
# closing reactive statement
})
# curly brace for server function
}
shinyApp(ui, server)

Answer 1

You were assigning the wrong data type to the output$plot1.
It expects something that was created by the function renderPlot(...) while you were giving it a result of reactive(...).
Restructure your code such that you immediately assign
output$plot1 <- renderPlot(...)
Since renderPlot opens a reactive environment, just as reactive does, you can just replace the function. But make sure that you remove the renderPlot calls from within the environment.
After changing that, you will run into some more errors you have in your code but I bet you can work it out from there.