Tree-based Models

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Basic statistics


                
Scatter plot matrices

Decision tree


                

Plotting the decision tree



Random forest


                

Variable importance



R session info

              
Note

This web application is developed with Shiny.


List of Packages Used
library(shiny)
library(shinyAce)
library(psych)
library(rpart)
library(partykit)
library(randomForest)

Code

Source code for this application is based on "The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012).

The code for this web application is available at GitHub.

If you want to run this code on your computer (in a local R session), run the code below:
library(shiny)
runGitHub("trees","mizumot")


Citation in Publications

Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp


Article

Mizumoto, A., & Plonsky, L. (2015). R as a lingua franca: Advantages of using R for quantitative research in applied linguistics. Applied Linguistics, Advance online publication. doi:10.1093/applin/amv025


Recommended

To learn more about R, I suggest this excellent and free e-book (pdf), A Guide to Doing Statistics in Second Language Research Using R, written by Dr. Jenifer Larson-Hall.

Also, if you are a cool Mac user and want to use R with GUI, MacR is defenitely the way to go!


Author

Atsushi MIZUMOTO, Ph.D.
Associate Professor of Applied Linguistics
Faculty of Foreign Language Studies /
Graduate School of Foreign Language Education and Research,
Kansai University, Osaka, Japan



Code for “Tree-based Models“
by Atsushi Mizumoto

show with app
library(shiny)
library(shinyAce)
library(psych)
library(rpart)
library(partykit)
library(randomForest)



shinyServer(function(input, output) {


    bs <- reactive({
        x <- read.csv(text=input$text, sep="\t")
        describe(x)[2:13]
    })

    output$textarea.out <- renderPrint({
        bs()
    })




    makecorPlot <- function(){
        x <- read.csv(text=input$text, sep="\t")
        pairs.panels(x)
    }
    
    output$corPlot <- renderPlot({
        print(makecorPlot())
    })
    



    output$varselect1 <- renderUI({
        
            dat <- read.csv(text=input$text, sep="\t", na.strings=c("","NA","."))
            cols <- names(dat)
            selectInput("vars1", "Select one criterion (outcome) variable:", choices=cols, selected=NULL, multiple=FALSE)

    })


    output$varselect2 <- renderUI({
        
            dat <- read.csv(text=input$text, sep="\t", na.strings=c("","NA","."))
            cols <- names(dat)
            selectInput("vars2", "Click the box below and select the explanatory variables:", choices=cols, multiple=T)
        
    })
    
    
    
    
    decisiontree <-  reactive({
        
        if (input$explvars == "all") {
            
            dat <- read.csv(text=input$text, sep="\t", na.strings=c("","NA","."))
            #outcome <- dat[,input$vars1]
            #dat[,input$vars1] <- NULL
            #res <- rpart(outcome ~., data=dat)
            res <- rpart(as.formula(paste(input$vars1, "~.")),data=dat)
            print(res)
            
        } else { # select
            
            if (is.null(input$vars2) == TRUE){
             res <- c("You need to select the explanatory variables.")
             print(res)

            } else {
            dat <- read.csv(text=input$text, sep="\t", na.strings=c("","NA","."))
            res <- rpart(as.formula(paste(input$vars1," ~ ",paste(input$vars2,collapse="+"))),data=dat)
            print(res)

            }
        }
        
        list(res = res)

    })
    
    output$dtree <- renderPrint({
        decisiontree()
    })
    



    dtreePlot <- function(){
        
        if (input$explvars == "all") {

            res <- decisiontree()$res
            plot(as.party(res))
        
        } else {
            
            if (is.null(input$vars2) == TRUE){
            
                NULL
            
            } else {
            
                res <- decisiontree()$res
                plot(as.party(res))
            }
        }
    }
    
    output$dtreePlot <- renderPlot({
        print(dtreePlot())
    })




   randforest <-  reactive({
        
        if (input$explvars == "all") {
            
            dat <- read.csv(text=input$text, sep="\t", na.strings=c("","NA","."))
            forest <- randomForest(as.formula(paste(input$vars1,"~.")),data=dat)
            impt <- forest$importance
            
        } else { # select
            
            if (is.null(input$vars2) == TRUE){
             forest <- c("You need to select the explanatory variables.")
             impt <- c("You need to select the explanatory variables.")
            } else {
            dat <- read.csv(text=input$text, sep="\t", na.strings=c("","NA","."))
            forest <- randomForest(as.formula(paste(input$vars1," ~ ",paste(input$vars2,collapse="+"))),data=dat)
            impt <- forest$importance
            }
        }
        
        list(result = forest, importance = impt)

    })
    
    output$randforest <- renderPrint({
        randforest()
    })




    errorPlot <- function(){
        
        if (input$explvars == "all") {

            forest <- randforest()$result
            plot(forest, main="Error rate")
        
        } else {
            
            if (is.null(input$vars2) == TRUE){
            
                NULL
            
            } else {
            
                forest <- randforest()$result
                plot(forest, main="Error rate")
            }
        }
    }
    
    output$errorPlot <- renderPlot({
        print(errorPlot())
    })




    varimPlot <- function(){
        
        if (input$explvars == "all") {

            forest <- randforest()$result
            varImpPlot(forest, main="")
        
        } else {
            
            if (is.null(input$vars2) == TRUE){
            
                NULL
            
            } else {
            
                forest <- randforest()$result
                varImpPlot(forest, main="")
            }
        }
    }
    
    output$varimPlot <- renderPlot({
        print(varimPlot())
    })








    info <- reactive({
        info1 <- paste("This analysis was conducted with ", strsplit(R.version$version.string, " \\(")[[1]][1], ".", sep = "")
        info2 <- paste("It was executed on ", date(), ".", sep = "")
        cat(sprintf(info1), "\n")
        cat(sprintf(info2), "\n")
    })
    
    output$info.out <- renderPrint({
        info()
    })


})
library(shiny)
library(shinyAce)



shinyUI(bootstrapPage(

    headerPanel("Tree-based Models"),


########## Adding loading message #########

tags$head(tags$style(type="text/css", "
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")),

conditionalPanel(condition="$('html').hasClass('shiny-busy')",
tags$div("Loading...",id="loadmessage")),

#-----------------------------------------#

    mainPanel(
        tabsetPanel(

        tabPanel("Main",

            p(HTML("<b><div style='background-color:#FADDF2;border:1px solid black;'>The editor below only accepts tab-separated values copied and pasted from Excel/Numbers. </div></b>")),

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                mode="r", theme="cobalt"),

            br(),

            h3("Basic statistics"),
            verbatimTextOutput("textarea.out"),

            br(),

            strong("Scatter plot matrices"),
            br(),

            plotOutput("corPlot", width="120%"),

            br(),

            h3("Decision tree"),
            uiOutput("varselect1"),

            radioButtons("explvars", "Choose explanatory variables:",
                list("All" = "all", "Select" = "select"), selected = "all"),

                # Display this only if "expl.vars" is "select"
                conditionalPanel(condition = "input.explvars == 'select'",
                    uiOutput("varselect2")
                ),

            verbatimTextOutput("dtree"),

            br(),

            h3("Plotting the decision tree"),
            br(),

            plotOutput("dtreePlot", width="120%"),

            br(),

            h3("Random forest"),
            verbatimTextOutput("randforest"),

            plotOutput("errorPlot", width="80%"),

            br(),

            strong("Variable importance"),

            plotOutput("varimPlot"),

            br(),

            br(),
            br(),

            strong('R session info'),
            verbatimTextOutput("info.out")

            ),


        tabPanel("About",

            strong('Note'),
            p('This web application is developed with',
            a("Shiny.", href="http://www.rstudio.com/shiny/", target="_blank"),
            ''),

            br(),

            strong('List of Packages Used'), br(),
            code('library(shiny)'),br(),
            code('library(shinyAce)'),br(),
            code('library(psych)'),br(),
            code('library(rpart)'),br(),
            code('library(partykit)'),br(),
            code('library(randomForest)'),br(),

            br(),

            strong('Code'),
            p('Source code for this application is based on',
            a('"The handbook of Research in Foreign Language Learning and Teaching" (Takeuchi & Mizumoto, 2012).', href='http://mizumot.com/handbook/', target="_blank")),

            p('The code for this web application is available at',
            a('GitHub.', href='https://github.com/mizumot/trees', target="_blank")),

            p('If you want to run this code on your computer (in a local R session), run the code below:',
            br(),
            code('library(shiny)'),br(),
            code('runGitHub("trees","mizumot")')
            ),

            br(),

            strong('Citation in Publications'),
            p('Mizumoto, A. (2015). Langtest (Version 1.0) [Web application]. Retrieved from http://langtest.jp'),

            br(),

            strong('Article'),
            p('Mizumoto, A., & Plonsky, L. (2015).', a("R as a lingua franca: Advantages of using R for quantitative research in applied linguistics.", href='http://applij.oxfordjournals.org/content/early/2015/06/24/applin.amv025.abstract', target="_blank"), em('Applied Linguistics,'), 'Advance online publication. doi:10.1093/applin/amv025'),

            br(),

            strong('Recommended'),
            p('To learn more about R, I suggest this excellent and free e-book (pdf),',
            a("A Guide to Doing Statistics in Second Language Research Using R,", href="http://cw.routledge.com/textbooks/9780805861853/guide-to-R.asp", target="_blank"),
            'written by Dr. Jenifer Larson-Hall.'),

            p('Also, if you are a cool Mac user and want to use R with GUI,',
            a("MacR", href="https://sites.google.com/site/casualmacr/", target="_blank"),
            'is defenitely the way to go!'),

            br(),

            strong('Author'),
            p(a("Atsushi MIZUMOTO,", href="http://mizumot.com", target="_blank"),' Ph.D.',br(),
            'Associate Professor of Applied Linguistics',br(),
            'Faculty of Foreign Language Studies /',br(),
            'Graduate School of Foreign Language Education and Research,',br(),
            'Kansai University, Osaka, Japan'),

            br(),

            a(img(src="http://i.creativecommons.org/p/mark/1.0/80x15.png"), target="_blank", href="http://creativecommons.org/publicdomain/mark/1.0/"),

            p(br())

            )

))
))
Code license: GPL-3