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Upload Example in Shiny App. Takes Pupil's term marks and does some analyses
# load required libraries
library(shiny)
library(plyr)
library(ggplot2)
library(googleVis)
library(reshape2)
####creation of example data on local directory for uploading####
# #load a list of common first names
# faveNames<- read.csv("http://dl.dropbox.com/u/25945599/faveNames.csv",stringsAsFactors=FALSE)
#
# set.seed(4359) # change if want new set
# # create a distribution of results
# # marks improve and reduce in variance over school year
# term1 <- floor(rnorm(25,mean=60,sd=10))
# term2 <- floor(rnorm(25,mean=65,sd=9))
# term3 <- floor(rnorm(25,mean=70,sd=8))
# # sample 25 names and combine with marks
# pupils <- faveNames[sample(nrow(faveNames), size=25, replace=FALSE), ]
# pupils <- arrange(pupils,Gender,Name)
#
# scores <- cbind(pupils,term1)
# scores <- cbind(scores,term2)
# scores <- cbind(scores,term3)
#
# # deleberately increase girls marks by 2 and reduce boys by 2
# scores[scores$Gender=="F",]$term1 <- scores[scores$Gender=="F",]$term1+2
# scores[scores$Gender=="F",]$term2 <- scores[scores$Gender=="F",]$term2+2
# scores[scores$Gender=="F",]$term3 <- scores[scores$Gender=="F",]$term3+2
#
# scores[scores$Gender=="M",]$term1 <- scores[scores$Gender=="M",]$term1-2
# scores[scores$Gender=="M",]$term2 <- scores[scores$Gender=="M",]$term2-2
# scores[scores$Gender=="M",]$term3 <- scores[scores$Gender=="M",]$term3-2
#
#
#
# write.csv(scores,"yourfilelocation/scores.csv", row.names=FALSE)
#
shinyServer(function(input, output) {
Data <- reactive({
# input$file1 will be NULL initially. After the user selects and uploads a
# file, it will be a data frame with 'name', 'size', 'type', and 'datapath'
# columns. The 'datapath' column will contain the local filenames where the
# data can be found.
inFile <- input$file1
if (is.null(inFile))
return(NULL)
df.raw <- read.csv(inFile$datapath, header=input$header, sep=input$sep, quote=input$quote)
# calculate term and pupil averages
t1Av <- colMeans(df.raw[3:5])[1]
t2Av <- colMeans(df.raw[3:5])[2]
t3Av <- colMeans(df.raw[3:5])[3]
df.raw$Av <- round(rowMeans(df.raw[3:5]),1)
# reshape th data.frame for further analysis
df.melt <- melt(df.raw, id.vars=c("Name","Gender"))
colnames(df.melt) <- c("Name","Gender","Term","Mark")
# get average boy and girl marks
girls <-round(tapply(X = df.melt$Mark, INDEX = list(df.melt$Gender), FUN = mean)["F"],1)
boys <-round(tapply(X = df.melt$Mark, INDEX = list(df.melt$Gender), FUN = mean)["M"],1)
# create a list of data for use in rendering
info <- list(df.raw=df.raw,df.melt=df.melt,t1Av=t1Av,t2Av=t2Av,t3Av=t3Av,girls=girls,boys=boys)
return(info)
})
# allows pageability and number of rows setting
myOptions <- reactive({
list(
page=ifelse(input$pageable==TRUE,'enable','disable'),
pageSize=input$pagesize
)
} )
output$raw <- renderGvis({
if (is.null(input$file1)) { return() }
gvisTable(Data()$df.raw,options=myOptions())
})
output$density <- renderPlot({
if (is.null(input$file1)) { return() }
print(ggplot(Data()$df.melt, aes(x=Mark, fill=Term)) + geom_density(alpha=.3))
})
output$genderDensity <- renderPlot({
if (is.null(input$file1)) { return() }
df.gender<- subset(Data()$df.melt,Term!="Av")
str(df.gender)
print(ggplot(df.gender, aes(x=Mark, fill=Gender)) + geom_density(alpha=.3))
})
output$sexDiff <- renderPrint({
if (is.null(input$file1)) { return() }
df.gender<- subset(Data()$df.melt,Term!="Av")
aov.by.gender <- aov(Mark ~ Gender, data=df.gender)
summary(aov.by.gender)
})
output$caption1 <- renderText( {
if (is.null(input$file1)) { return() }
"Ms Twizzle's Class - Science Results"
})
output$caption2 <- renderText( {
if (is.null(input$file1)) { return() }
paste0("Average Mark Term 1:",Data()$t1Av," Term 2:",Data()$t2Av," Term 3:",Data()$t3Av)
})
output$caption3 <- renderText( {
if (is.null(input$file1)) { return() }
paste0("Analysis of Variance by Gender - Boys Average Mark:",Data()$boys, " Girls Average Mark:",Data()$girls)
})
output$notes2 <- renderUI( {
if (is.null(input$file1)) { return() }
HTML("The above graph shows the variation in pupils' marks by term. The annual spread
will normally be greater as the example data is random and normally some pupils will
tend to be better than others over each term")
})
output$notes3 <- renderUI( {
if (is.null(input$file1)) { return() }
HTML("The Analysis of Variance indicates whether there is a statistically significant
difference between boys and girls in the class. With this 'fixed' data, there is a
significant difference at the 5% level")
})
})
shinyUI(pageWithSidebar(
headerPanel("Uploaded File Analysis"),
sidebarPanel(
helpText("This app is shows how a user can update a csv file from their own hard drive for instant analysis.
In the default case, it uses standard format school marks that could be used by many teachers
Any file can be uploaded but analysis is only available
if the data is in same format as the sample file, downloadable below
"),
a("Pupil Marks", href="http://dl.dropbox.com/u/25945599/scores.csv"),
tags$hr(),
fileInput('file1', 'Choose CSV File from local drive, adjusting parameters if necessary',
accept=c('text/csv', 'text/comma-separated-values,text/plain')),
checkboxInput('header', 'Header', TRUE),
radioButtons('sep', 'Separator',
c(Comma=',',
Semicolon=';',
Tab='\t'),
'Comma'),
radioButtons('quote', 'Quote',
c(None='',
'Double Quote'='"',
'Single Quote'="'"),
'Double Quote'),
tags$head(tags$style(type="text/css",
"label.radio { display: inline-block; margin:0 10 0 0; }",
".radio input[type=\"radio\"] { float: none; }"))
),
mainPanel(
tabsetPanel(
tabPanel("Pupil Marks",
h4(textOutput("caption1")),
checkboxInput(inputId = "pageable", label = "Pageable"),
conditionalPanel("input.pageable==true",
numericInput(inputId = "pagesize",
label = "Pupils per page",value=13,min=1,max=25)),
htmlOutput("raw"),
value = 1),
tabPanel("Term Details",
h4(textOutput("caption2")),
plotOutput("density"),
htmlOutput("notes2"),
value = 2),
tabPanel("Gender difference",
h4(textOutput("caption3")),
plotOutput("genderDensity", height="250px"),
verbatimTextOutput("sexDiff"),
htmlOutput("notes3"),
value = 3),
id="tabs1")
)
))
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