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Revisions

  1. calpolystat revised this gist Jun 20, 2015. 2 changed files with 3 additions and 3 deletions.
    0 #Prov_View.txt → #Prob_View.txt
    View file
    File renamed without changes.
    6 changes: 3 additions & 3 deletions ui.R
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    Original file line number Diff line number Diff line change
    @@ -110,15 +110,15 @@ shinyUI(fluidPage(
    actionButton("go","Submit"),

    div("Shiny app by",
    a(href="facebook.com/irvinalcaraz",target="_blank",
    a(href="https://www.linkedin.com/in/irvinalcaraz",target="_blank",
    "Irvin Alcaraz"),align="right", style = "font-size: 8pt"),

    div("Base R code by",
    a(href="facebook.com/irvinalcaraz",target="_blank",
    a(href="https://www.linkedin.com/in/irvinalcaraz",target="_blank",
    "Irvin Alcaraz"),align="right", style = "font-size: 8pt"),

    div("Shiny source files:",
    a(href="https://gist.github.com/calpolystat/f4475cbfe4cc77cef168",
    a(href="https://gist.github.com/calpolystat/200f26d243f4f5bb7334",
    target="_blank","GitHub Gist"),align="right", style = "font-size: 8pt"),

    div(a(href="http://www.statistics.calpoly.edu/shiny",target="_blank",
  2. calpolystat created this gist Jun 20, 2015.
    7 changes: 7 additions & 0 deletions #Prov_View.txt
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    @@ -0,0 +1,7 @@
    Probability Distribution Viewer Shiny App

    Base R code created by Irvin Alcaraz
    Shiny app files created by Irvin Alcaraz

    Cal Poly Statistics Dept Shiny Series
    http://statistics.calpoly.edu/shiny
    7 changes: 7 additions & 0 deletions DESCRIPTION
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    Title: Probability Distribution Viewer
    Author: Irvin Alcaraz
    AuthorUrl: https://www.linkedin.com/in/irvinalcaraz
    License: MIT
    DisplayMode: Normal
    Tags: Probability Distribution
    Type: Shiny
    21 changes: 21 additions & 0 deletions LICENSE
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    The MIT License (MIT)

    Copyright (c) 2015 Irvin Alcaraz

    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:

    The above copyright notice and this permission notice shall be included in
    all copies or substantial portions of the Software.

    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
    THE SOFTWARE.
    330 changes: 330 additions & 0 deletions server.R
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    ##############################################
    # App Title: Probability Distribution Viewer #
    # Author: Irvin Alcaraz #
    ##############################################

    ###################
    # Misc Functions #
    ###################

    library(shinysky)
    library(shiny)

    ##Function to convert all values to xvalues so I can standardly
    ##use the density distribution functions
    xvalue = function(value,type,dist,params){

    switch(type,
    p = do.call(paste("q",dist,sep=""),c(value,params)),
    d = value
    )

    }

    ###################
    #Shiny Server Code#
    ###################
    shinyServer(function(input, output, session) {

    observe({
    if(input$dist == "beta"){
    if(input$p1.beta <= 0 || input$p2.beta <= 0 || is.na(input$p1.beta) || is.na(input$p2.beta)){
    showshinyalert(session,"shinyalert1",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return ()
    }else return ()
    })

    observe({
    if(input$dist == "cauchy"){
    if(input$p2.cauchy <= 0 || is.na(input$p2.cauchy)){
    showshinyalert(session,"shinyalert2",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "chisq"){
    if(input$p1.chisq <= 0 || is.na(input$p1.chisq)){
    showshinyalert(session,"shinyalert3",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "exp"){
    if(input$p1.exp <= 0 || is.na(input$p1.exp)){
    showshinyalert(session,"shinyalert4",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "f"){
    if(input$p1.f <= 0 || input$p2.f <= 0 || is.na(input$p1.f) || is.na(input$p2.f)){
    showshinyalert(session,"shinyalert5",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "gamma"){
    if(input$p1.gamma <= 0 || input$p2.gamma <= 0 || is.na(input$p1.gamma) || is.na(input$p2.gamma)){
    showshinyalert(session,"shinyalert6",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "logis"){
    if(input$p2.logis <= 0 || is.na(input$p2.logis)){
    showshinyalert(session,"shinyalert7",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "lnorm"){
    if(input$p1.lnorm <= 0 || input$p2.lnorm <= 0 || is.na(input$p1.lnorm) || is.na(input$p2.lnorm)){
    showshinyalert(session,"shinyalert8",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    input$go
    isolate({
    if(input$dist == "t"){
    if(input$p1.t <= 0 || is.na(input$p1.t)){
    showshinyalert(session,"shinyalert9",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })
    })

    observe({
    if(input$dist == "weibull"){
    if(input$p1.weibull <= 0 || input$p2.weibull <= 0 || is.na(input$p1.weibull) || is.na(input$p2.weibull)){
    showshinyalert(session,"shinyalert10",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    observe({
    if(input$dist == "norm"){
    if(input$p2.norm <= 0 || is.na(input$p1.norm) || is.na(input$p2.norm)){
    showshinyalert(session,"shinyalert11",
    "Looks like something is wrong in your parameters, please check them and then hit submit again",
    "danger")
    }else return()
    }else return()
    })

    output$distPlot <- renderPlot({
    input$go

    isolate({

    ##Each distribution has a different number of parameters
    ##so I create lists that contain them based on the distribution.
    ##I omitted the ability to have a threshold and NCP for all distributions.
    ##I originally had very general code for this but it ended up causing to many
    ##problems so this method works.
    params = switch(input$dist,
    "beta"= list(input$p1.beta,input$p2.beta),
    "cauchy" = list(input$p1.cauchy,input$p2.cauchy),
    "chisq"=list(input$p1.chisq),
    "exp" = list(input$p1.exp),
    "f"=list(input$p1.f,input$p2.f),
    "gamma"= list(input$p1.gamma,input$p2.gamma),
    "logis" = list(input$p1.logis,input$p2.logis),
    "lnorm" = list(input$p1.lnorm,input$p2.lnorm),
    "norm" = list(input$p1.norm,input$p2.norm),
    "t" = list(input$p1.t),
    "unif" = list(input$p1.unif,input$p2.unif),
    "weibull" = list(input$p1.weibull,input$p2.weibull)
    )

    ##Distributions I still can implement
    ##Continuous
    ##tukey

    ##Discrete
    ##binom geom hypergeometric nbinom pois

    ##Nonparametric
    ##Wilcoxon

    ##These if statements take the value to be shaded and converts it
    ##to xvalues that will later be used by the polygon function
    if (!is.null("input$shadeval2")){
    xvalue1 = xvalue(input$shadeval1,input$type,input$dist,params)
    xvalue2 = xvalue(input$shadeval2,input$type,input$dist,params)
    }else{
    xvalue1 = xvalue(input$shadeval1,input$type,input$dist,params)
    }


    ##These if statements draw the graphs based on the different distributions
    ##Some Distributions were easier to graph through a more specific method so
    ##they are separated from the others
    if (input$dist == 'beta'
    || input$dist == 'logis' || input$dist == 'norm'
    || input$dist == 't' || input$dist == 'unif'){

    minx = do.call(paste("q",input$dist,sep=""),c(.0001,params))
    maxx = do.call(paste("q",input$dist,sep=""),c(.9999,params))
    x = seq(from=minx,to=maxx,length=1000)
    hx = x

    for(k in 1:1000){
    hx[k] = do.call(paste("d",input$dist,sep=""),c(x[k],params))
    }

    miny = 0
    miny = 0
    if (is.infinite(max(hx)) || max(hx)>1)
    {
    maxy = 1
    }else{
    maxy = round(max(hx),digits=2)
    }

    plot(x,hx,type="n",xlab="X",ylab="Density",
    main="Probability Density",axes=FALSE,ylim=c(miny,maxy))
    lines(x,hx)
    # axis(1,pos=0,col.axis="grey",col.ticks="grey",col="grey")
    axis(1,pos=0)
    axis(2,at=round(seq(from=miny,to=maxy,length=5),digits=3),pos=minx)

    } else if (input$dist == 'chisq' || input$dist == 'exp'
    || input$dist == 'f' || input$dist == 'gamma'
    || input$dist == 'lnorm' || input$dist == 'weibull'){

    minx = 0
    maxx = do.call(paste("q",input$dist,sep=""),c(.999,params))
    x = NULL
    x = seq(from=minx,to=maxx,length=1000)
    hx = x

    for(k in 1:1000){
    hx[k] = do.call(paste("d",input$dist,sep=""),c(x[k],params))
    }

    miny = 0
    if (is.infinite(max(hx)) || max(hx)>1)
    {
    maxy = 1
    }else{
    maxy = round(max(hx),digits=2)
    }

    plot(x,hx,type="n",xlab="X",ylab="Density",
    main="Probability Density",axes=FALSE,ylim=c(miny,maxy))
    lines(x,hx)
    axis(1,pos=0)
    axis(2,at=round(seq(from=miny,to=maxy,length=5),digits=3),pos=minx)

    } else if (input$dist == 'cauchy'){

    minx = do.call(paste("q",input$dist,sep=""),c(.04,params))
    maxx = do.call(paste("q",input$dist,sep=""),c(.96,params))
    x = NULL
    x = seq(from=minx,to=maxx,length=1000)
    hx = x

    for(k in 1:1000){
    hx[k] = do.call(paste("d",input$dist,sep=""),c(x[k],params))
    }

    miny = 0
    if (is.infinite(max(hx)) || max(hx)>1)
    {
    maxy = 1
    }else{
    maxy = round(max(hx),digits=2)
    }

    plot(x,hx,type="n",xlab="X",ylab="Density",
    main="Probability Density",axes=FALSE,ylim=c(miny,maxy))
    lines(x,hx)
    axis(1,pos=0)
    axis(2,at=round(seq(from=miny,to=maxy,length=5),digits=3),pos=minx)

    }

    # # Debug
    # plot.new()
    # title(paste(maxx))
    if (input$type != 'none'){

    # These if statements shade the graphs correctly
    if(input$shade=="left"){

    i = x<=xvalue1
    polygon(c(minx,x[i],xvalue1),c(0,hx[i],0),col="deepskyblue3")
    area = do.call(paste("p",input$dist,sep=""),c(xvalue1,params))
    result = paste("P(X<",signif(xvalue1,digits=4),")=",signif(area,digits=3))
    mtext(result,3)
    axis(1,at=xvalue1,pos=0,col.ticks="red",col.axis="red",lwd.ticks = 2,cex.axis=2)

    }else if(input$shade=="right"){

    i = x>=xvalue1
    polygon(c(xvalue1,x[i],maxx),c(0,hx[i],0),col="deepskyblue3")
    area = 1-do.call(paste("p",input$dist,sep=""),c(xvalue1,params))
    result = paste("P(X>",signif(xvalue1,digits=4),")=",signif(area,digits=3))
    mtext(result,3)
    axis(1,at=xvalue1,pos=0,col.ticks="red",col.axis="red",lwd.ticks = 2,cex.axis=2)

    }else if(input$shade=="middle"){

    i = (x>=xvalue1)&(x<=xvalue2)
    polygon(c(xvalue1,x[i],xvalue2),c(0,hx[i],0),col="deepskyblue3")
    area = do.call(paste("p",input$dist,sep=""),c(xvalue2,params))-
    do.call(paste("p",input$dist,sep=""),c(xvalue1,params))
    result = paste("P(",signif(xvalue1,digits=4),"< X <",signif(xvalue2,digits=4),
    ")=",signif(area,digits=3))
    mtext(result,3)
    axis(1,at=c(xvalue1,xvalue2),pos=0,col.ticks="red",col.axis="red",lwd.ticks = 2,,cex.axis=2)

    }else if(input$shade=="both"){

    i = (x<=xvalue1)
    j = (x>=xvalue2)
    polygon(c(minx,x[i],xvalue1),c(0,hx[i],0),col="deepskyblue3")
    polygon(c(xvalue2,x[j],maxx),c(0,hx[j],0),col="deepskyblue3")

    area = (1-do.call(paste("p",input$dist,sep=""),c(xvalue2,params)))+
    do.call(paste("p",input$dist,sep=""),c(xvalue1,params))
    result = paste("P(X < ",signif(xvalue1,digits=4)," or X > ",signif(xvalue2,digits=4),
    ")=",signif(area,digits=3))
    mtext(result,3)
    axis(1,at=c(xvalue1,xvalue2),pos=0,col.ticks="red",col.axis="red",lwd.ticks = 2,cex.axis=2)
    }


    }
    })
    })

    })
    139 changes: 139 additions & 0 deletions ui.R
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    ##############################################
    # App Title: Probability Distribution Viewer #
    # Author: Irvin Alcaraz #
    ##############################################


    if (!require("devtools"))
    install.packages("devtools")


    if (!require("shinysky")) devtools::install_github("ShinySky","AnalytixWare")
    library(shinysky)
    library(shiny)

    shinyUI(fluidPage(

    tags$head(tags$link(rel = "icon", type = "image/x-icon",
    href = "https://webresource.its.calpoly.edu/cpwebtemplate/5.0.1/common/images_html/favicon.ico")),

    titlePanel("Probability Viewer"),

    sidebarLayout(

    sidebarPanel(
    selectInput("dist",label=h4("Distribution"),
    choices=c("Beta" = "beta", "Cauchy" = "cauchy", "Chi-Squared" = "chisq",
    "Exponential" = "exp","F" = "f", "Gamma" = "gamma",
    "Logistic" = "logis","Log Normal" = "lnorm",
    "Normal"="norm","Student t" = "t","Uniform" = "unif",
    "Weibull" = "weibull"),selected="norm"),

    shinyalert("shinyalert1", TRUE, auto.close.after=5),
    shinyalert("shinyalert2", TRUE, auto.close.after=5),
    shinyalert("shinyalert3", TRUE, auto.close.after=5),
    shinyalert("shinyalert4", TRUE, auto.close.after=5),
    shinyalert("shinyalert5", TRUE, auto.close.after=5),
    shinyalert("shinyalert6", TRUE, auto.close.after=5),
    shinyalert("shinyalert7", TRUE, auto.close.after=5),
    shinyalert("shinyalert8", TRUE, auto.close.after=5),
    shinyalert("shinyalert9", TRUE, auto.close.after=5),
    shinyalert("shinyalert10", TRUE, auto.close.after=5),
    shinyalert("shinyalert11", TRUE, auto.close.after=5),

    conditionalPanel(condition = "input.dist=='beta'",
    numericInput("p1.beta","First Shape",2,min=1)),
    conditionalPanel(condition = "input.dist=='beta'",
    numericInput("p2.beta","Second Shape",2,min=1)),
    conditionalPanel(condition = "input.dist=='cauchy'",
    numericInput("p1.cauchy","Location",0)),
    conditionalPanel(condition = "input.dist=='cauchy'",
    numericInput("p2.cauchy","Scale",2,min=1)),
    conditionalPanel(condition = "input.dist=='chisq'",
    numericInput("p1.chisq","DF",5,min=1)),
    conditionalPanel(condition = "input.dist=='exp'",
    numericInput("p1.exp","Rate",1,min=0)),
    conditionalPanel(condition = "input.dist=='f'",
    numericInput("p1.f","Num DF",20,min=1)),
    conditionalPanel(condition = "input.dist=='f'",
    numericInput("p2.f","Denom DF",20,min=1)),
    conditionalPanel(condition = "input.dist=='gamma'",
    numericInput("p1.gamma","Shape",1,min=0)),
    conditionalPanel(condition = "input.dist=='gamma'",
    numericInput("p2.gamma","Scale",1,min=0)),
    conditionalPanel(condition = "input.dist=='logis'",
    numericInput("p1.logis","Location",0)),
    conditionalPanel(condition = "input.dist=='logis'",
    numericInput("p2.logis","Scale",1,min=0)),
    conditionalPanel(condition = "input.dist=='lnorm'",
    numericInput("p1.lnorm","Log Mean",0,min=0)),
    conditionalPanel(condition = "input.dist=='lnorm'",
    numericInput("p2.lnorm","Log Standard Deviation",1,min=0)),
    conditionalPanel(condition = "input.dist=='norm'",
    numericInput("p1.norm","Mean",0)),
    conditionalPanel(condition = "input.dist=='norm'",
    numericInput("p2.norm","Standard Deviation",1,min = 0)),
    conditionalPanel(condition = "input.dist=='t'",
    numericInput("p1.t","DF",5,min=1)),
    conditionalPanel(condition = "input.dist=='unif'",
    numericInput("p1.unif","Minimum",0)),
    conditionalPanel(condition = "input.dist=='unif'",
    numericInput("p2.unif","Maximum",1)),
    conditionalPanel(condition = "input.dist=='weibull'",
    numericInput("p1.weibull","Shape",1,min=0)),
    conditionalPanel(condition = "input.dist=='weibull'",
    numericInput("p2.weibull","Scale",1,min=0)),

    HTML("<hr style='height: 2px; color: #F3F3F3; background-color: #F3F3F3; border: none;'>"),

    radioButtons("type",label=h4("Define Shaded Area By"),
    choices=c("Input percentile and calculate probability"="d",
    "Input probability and calculate percentile"="p", "Nothing"="none"),selected="none"),

    HTML("<hr style='height: 2px; color: #F3F3F3; background-color: #F3F3F3; border: none;'>"),

    conditionalPanel(condition = "input.type != 'none'",
    selectInput("shade",label=h4("Area to shade"),
    choices=c("Left Tail"="left","Right Tail"="right",
    "Both Tails"="both","Middle"="middle"))),

    # conditionalPanel(condition = "input.shade == 'left' || input.shade == 'right' ||
    # input.shade == 'both' || input.shade == 'middle' ",
    conditionalPanel(condition = "input.type != 'none'",
    numericInput("shadeval1",label=" ",0)),

    conditionalPanel(condition = "(input.shade == 'both' || input.shade=='middle') &&
    input.type != 'none'",
    numericInput("shadeval2",label=" ",value=0)),

    #HTML("<hr style='height: 2px; color: #F3F3F3; background-color: #F3F3F3; border: none;'>"),
    actionButton("go","Submit"),

    div("Shiny app by",
    a(href="facebook.com/irvinalcaraz",target="_blank",
    "Irvin Alcaraz"),align="right", style = "font-size: 8pt"),

    div("Base R code by",
    a(href="facebook.com/irvinalcaraz",target="_blank",
    "Irvin Alcaraz"),align="right", style = "font-size: 8pt"),

    div("Shiny source files:",
    a(href="https://gist.github.com/calpolystat/f4475cbfe4cc77cef168",
    target="_blank","GitHub Gist"),align="right", style = "font-size: 8pt"),

    div(a(href="http://www.statistics.calpoly.edu/shiny",target="_blank",
    "Cal Poly Statistics Dept Shiny Series"),align="right", style = "font-size: 8pt")

    ),

    mainPanel(
    plotOutput("distPlot"),

    tags$style(type="text/css",
    ".shiny-output-error { visibility: hidden; }",
    ".shiny-output-error:before { visibility: hidden; }"
    )

    )
    )
    ))