K-means clustering example using iris data from CSV file
823 views
Skip to first unread message
Guilherme Moraes Ferraudo
Jul 27, 2016, 8:50:42 PM7/27/16
to Shiny - Web Framework for R
Hello,
Please, how could be implemented the K-means clustering example (original is available on link below) using iris data from CSV file uploaded by user instead of iris data available on R?
Iris k-means clustering
Thanks in advance.
Guilherme
Tareef Kawaf
Jul 28, 2016, 7:29:22 AM7/28/16
to Shiny - Web Framework for R
Guilherme,
You may find this example on the shiny.rstudio.com website helpful on how to handle uploads from end users.
Best,
Tareef
Guilherme Moraes Ferraudo
Jul 28, 2016, 7:44:21 AM7/28/16
to Tareef Kawaf, Shiny - Web Framework for R
Hi Tareef,
Thanks for reply.
I've read this example and implemented it with some differences. But when I run my app the following error message shows up on my screen:
ERROR: object 'dataframe' not found
What is wrong?
Below my current code:
server.r file
palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
"#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))
shinyServer(function(input, output, session) {
# The selected file, if any
userFile <- reactive({
# If no file is selected, don't do anything
validate(need(input$file, message = FALSE))
input$file
})
# The user's data, parsed into a data frame
dataframe <- reactive({
read.csv(userFile()$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
})
output$contents = renderTable({dataframe()})
# Combine the selected variables into a new data frame
selectedData <- reactive({
filedata()[, c(input$xcol, input$ycol)]
})
clusters <- reactive({
kmeans(selectedData(), input$clusters)
})
output$kmeansPlot <- renderPlot({
par(mar = c(5.1, 4.1, 0, 1))
plot(selectedData(),
col = clusters()$cluster,
pch = 20, cex = 3)
points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
})
return(dataframe)
})
ui.r file
shinyUI(
navbarPage("", inverse = TRUE,
tabPanel("Prof. Antonio Sergio Ferraudo - Multivariate analysis"),
tabPanel("Cluster",
mainPanel(tabsetPanel(type = "tabs",
#Page showing data importing table
tabPanel("Data table",
sidebarPanel(width = 2,
fileInput('file', 'Choose CSV File',
accept=c('text/csv',
'text/comma-separated-values,text/plain',
'.csv')),
tags$hr(),
checkboxInput('header', 'Header', TRUE),
radioButtons('sep', 'Separator',
c(Comma=',',
Semicolon=';',
Tab='\t'),
','),
radioButtons('quote', 'Quote',
c(None='',
'Double Quote'='"',
'Single Quote'="'"),
'"')
),
tableOutput('contents')),
tabPanel("K-means clustering",
headerPanel('k-means clustering'),
sidebarPanel(width =2,
selectInput('xcol', 'X Variable', choices = colnames(dataframe)),
selectInput('ycol', 'Y Variable', choices = colnames(dataframe), selected = colnames(dataframe)[2]),
numericInput('clusters', 'Cluster count', 3, min = 1, max = 9)
),
plotOutput('kmeansPlot'))
) #end of tabsetPanel
) #end of mainPanel
), #end of tabPanel,
tabPanel("Principal component"),
navbarMenu("Coming up",
tabPanel("Correspondence"),
tabPanel("Discriminant"),
tabPanel("Factors")
)
)
)
Thanks again.
--
You received this message because you are subscribed to a topic in the Google Groups "Shiny - Web Framework for R" group.
To unsubscribe from this topic, visit https://groups.google.com/d/topic/shiny-discuss/_Ftr0j-d-Lc/unsubscribe.
To unsubscribe from this group and all its topics, send an email to shiny-discus...@googlegroups.com.
To view this discussion on the web visit https://groups.google.com/d/msgid/shiny-discuss/f7be029f-e294-4e6e-9ffb-d104e43a6219%40googlegroups.com.
Tareef Kawaf
Jul 28, 2016, 7:47:39 AM7/28/16
to Guilherme Moraes Ferraudo, Shiny - Web Framework for R
I am not as facile with shiny as others on this list, but from looking at the error, it looks like your return(dataframe) might want to be return(dataframe()) since dataframe is defined as a reactive.
Guilherme Moraes Ferraudo
Jul 28, 2016, 7:49:57 AM7/28/16
to Tareef Kawaf, Shiny - Web Framework for R
Hi Tareef,
I've implemented your suggestion and take a look at the new error message:
ERROR: object 'dataframe' not found
Thanks!
Tareef Kawaf
Jul 28, 2016, 8:47:37 AM7/28/16
to Guilherme Moraes Ferraudo, Shiny - Web Framework for R
Hi Guilherme,
I didn't pay as much attention in the first look at your code, but shiny is complaining because you are referencing dataframe and dataframe hasn't been loaded yet. dataframe will get loaded when an upload occurs, so you wouldn't be able to have the UI show the column names of a data frame that doesn't exist yet.
I am not sure what the best pattern to follow would be here, but if I had to guess I would look at this article on building dynamic UIs and have that part of the UI show up once the user has selected the csv file to upload.
HTH
Guilherme Moraes Ferraudo
Jul 28, 2016, 5:36:24 PM7/28/16
to Tareef Kawaf, Shiny - Web Framework for R
Tareef,
I did your recommendation following the article: http://shiny.rstudio.com/articles/dynamic-ui.html
But I could not achieve my goal. Please, follows below my script:
server.r file
palette(c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3",
"#FF7F00", "#FFFF33", "#A65628", "#F781BF", "#999999"))
shinyServer(function(input, output, session) {
# The selected file, if any
userFile <- reactive({
# If no file is selected, don't do anything
validate(need(input$file, message = FALSE))
input$file
})
# The user's data, parsed into a data frame
dataframe <- reactive({
read.csv(userFile()$datapath,
header = input$header,
sep = input$sep,
quote = input$quote)
})
output$contents = renderTable({dataframe()})
# Partial example
output$colNames <- renderUI({
colnamesDf = colnames(dataframe())
checkboxGroupInput(input$xcol, 'X Variable', choices = colnamesDf)
numericInput('clusters', 'Cluster count', 3, min = 1, max = 9)
})
# Combine the selected variables into a new data frame
selectedData <- reactive({
dataframe()[, c(input$xcol, input$ycol)]
})
clusters <- reactive({
kmeans(selectedData(), input$clusters)
})
output$kmeansPlot <- renderPlot({
par(mar = c(5.1, 4.1, 0, 1))
plot(selectedData(),
col = clusters()$cluster,
pch = 20, cex = 3)
points(clusters()$centers, pch = 4, cex = 4, lwd = 4)
})
})
ui.r file
#uiOutput('colNames'),
#selectInput('xcol', 'X Variable', choices = list()),
#selectInput('ycol', 'Y Variable', choices = list(),),
uiOutput('colNames')
),
plotOutput('kmeansPlot'))
) #end of tabsetPanel
) #end of mainPanel
), #end of tabPanel,
tabPanel("Principal component"),
navbarMenu("Coming up",
tabPanel("Correspondence"),
tabPanel("Discriminant"),
tabPanel("Factors")
)
)
)
Thanks in advance
Best
Reply all
Reply to author
Forward
0 new messages