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Merge pull request #28 from ayanamartins/master
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[WIP] Tutorial tab [2]
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@andrechalom
andrechalom committed Jul 12, 2015
2 parents fdc2476 + 5dc677a commit c3c4b85
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3 changes: 2 additions & 1 deletion help.html
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<p>The idea behind this package is to help students to understand the logic behind these tests,
with lots of flexibility on what is the statistic that will be calculated.</p>
<p>Rsampling and and this web interface were written by <a href="mailto:[email protected]">Andre Chalom</a>,
<a href="mailto:[email protected]">Alexandre Adalardo</a> and <a href="mailto:[email protected]">Paulo Prado</a>.</p>
<a href="mailto:[email protected]">Alexandre Adalardo</a>, <a href="mailto:[email protected]">Ayana Martins</a>
and <a href="mailto:[email protected]">Paulo Prado</a>.</p>
<p>Please visit our <a href="https://github.com/andrechalom/Rsampling-shiny">project webpage</a>
and the <a href="https://github.com/lageIBUSP/Rsampling">Rsampling library</a> page
on <a href="https://github.com">github</a>,
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224 changes: 223 additions & 1 deletion server.R
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library(shiny)
library(gridExtra)
library(Rsampling)
shinyServer(function(input, output, session) {
###########################################
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})
session$onSessionEnded(function() {
run_iter$suspend()
})
})
###########################################
####### FUNCTIONS USED IN TUTORIAL #######
###########################################
###########################################################################
values <- reactiveValues()
values$saveDist <- list()
observeEvent(input$clear1, {
isolate(values$saveDist <- list())
})
observeEvent(input$clear2, {
isolate(values$saveDist <- list())
})
observeEvent(input$clear3, {
isolate(values$saveDist <- list())
})
observeEvent(input$tabEvent, {
isolate(values$saveDist <- list())
})
#################################################################
###Mangrove trees
mangStat <- mean(rhyzophora[ which(rhyzophora$soil.instability=='high'),]$root)-
mean(rhyzophora[ which(rhyzophora$soil.instability=='medium'),]$root)
### Specific plots for this dataset
output$mangPlot <- renderPlot({
par(mar = c(4,4,2,2))
boxplot(root ~ soil.instability, data=rhyzophora, type="n",
main="Original data",
xlab="Soil instability", ylab="area covered by aerial root (m2)")
text(2,33,paste("mean difference = \n",round(mangStat,4)),col="red")
})
output$mangPlotRandom <- renderPlot({
thisSet <- randomizedMang()
thisDf <- data.frame(matrix(unlist(thisSet), nrow=24))
names(thisDf) <- c("si","canopy.trunk","root","n.roots")
thisStat <- mean(thisDf[ which(thisDf$si==1),]$root)-
mean(thisDf[ which(thisDf$si==2),]$root)
isolate(values$saveDist <- rbind(values$saveDist, thisStat))
par(mar = c(4,4,2,2))
boxplot(root ~ si, data=thisDf, type="n",
main="Randomized data", col="#428bca",
xlab="Soil instability", ylab="area covered by aerial root (m2)")
text(2,33,paste("mean difference = \n",round(thisStat,4)))
})
### Display data set as a plot
output$mangTable <- renderPlot({
data <- rhyzophora[,c(1,3)]
data$root <- round(data$root,3)
tbl <- tableGrob(data,
cols = c("soil.instability",
"roots"),
show.rownames =FALSE,
gpar.corefill = gpar(fill = "grey80", col = "white"))
grid.draw(tbl)
},height=500)
output$mangTableRandom <- renderPlot({
thisSet <- randomizedMang()
thisDf <- data.frame(matrix(unlist(thisSet), nrow=24))
names(thisDf) <- c("si","canopy.trunk","root","n.roots")
thisDf$si[thisDf$si == 1] <- "high"
thisDf$si[thisDf$si == 2] <- "medium"
data <- thisDf[,c(1,3)]
data$root <- round(data$root,3)
tbl <- tableGrob(data,
cols = c("soil.instability",
"roots"),
show.rownames =FALSE,
gpar.corefill = gpar(fill = "#428bca", col = "white"))
grid.draw(tbl)
},height=500)
#Randomization output
randomizedMang <- reactive({
input$mangRand # triggers the calculations when the "..." is pressed
Rsampling(type = "normal_rand", dataframe = rhyzophora,
statistics = function(dataframe) return(dataframe), cols = 3,
ntrials = 1)
})
### main plot of the program: generates a histogram of distribution()
output$distPlotMang <- renderPlot({
par(mar = c(4,3,2,0))
Rsampling::dplot(dist = as.numeric(values$saveDist), svalue = mangStat,
extreme = FALSE, vline = TRUE, rejection = FALSE,
breaks = seq(-15,15,2), xlim=c(-15,15))
})
######################################################################
###More mangrove trees
rhyzSlope <- coef(lm(n.roots ~ canopy.trunk, data=rhyzophora))[2]
### Specific plots for this dataset
output$rhyzPlot <- renderPlot({
par(mar = c(4,4,2,2))
plot(n.roots ~ canopy.trunk, data=rhyzophora, pch=19,
main="Original data",
xlab="canopy area / trunk area", ylab="number of roots")
abline(lm(n.roots ~ canopy.trunk, data=rhyzophora))
text(3000,150,paste("slope =",round(rhyzSlope,4)),col="red")
})
output$rhyzPlotRandom <- renderPlot({
thisSet <- randomizedRhyz()
thisDf <- data.frame(matrix(unlist(thisSet), nrow=24))
names(thisDf) <- c("si","canopy.trunk","root","n.roots")
thisSlope <- coef(lm(n.roots ~ canopy.trunk, data=thisDf))[2]
isolate(values$saveDist <- rbind(values$saveDist, thisSlope))
par(mar = c(4,4,2,2))
plot(n.roots ~ canopy.trunk, data=thisDf, pch=19,
main="Randomized data", col="#428bca",
xlab="canopy area / trunk area", ylab="number of roots")
abline(lm(n.roots ~ canopy.trunk, data=thisDf))
text(3000,150,paste("slope =",round(thisSlope,4)))
})
output$rhyzTable <- renderPlot({
data <- rhyzophora[,c(2,4)]
data$canopy.trunk <- round(data$canopy.trunk,2)
tbl <- tableGrob(data,
cols = c("canopy.trunk",
"n.roots"),
show.rownames =FALSE,
gpar.corefill = gpar(fill = "grey80", col = "white"))
grid.draw(tbl)
},height=500)
output$rhyzTableRandom <- renderPlot({
thisSet <- randomizedRhyz()
thisDf <- data.frame(matrix(unlist(thisSet), nrow=24))
names(thisDf) <- c("si","canopy.trunk","root","n.roots")
data <- thisDf[,c(2,4)]
data$canopy.trunk <- round(data$canopy.trunk,2)
tbl <- tableGrob(data,
cols = c("canopy.trunk",
"n.roots"),
show.rownames =FALSE,
gpar.corefill = gpar(fill = "#428bca", col = "white"))
grid.draw(tbl)
},height=500)
#Randomization output
randomizedRhyz <- reactive({
input$rhyzRand # triggers the calculations when the "..." is pressed
Rsampling(type = "normal_rand", dataframe = rhyzophora,
statistics = function(dataframe) return(dataframe), cols = 4,
ntrials = 1)
})
### main plot of the program: generates a histogram of distribution()
output$distPlotRhyz <- renderPlot({
par(mar = c(4,3,2,0))
Rsampling::dplot(dist = as.numeric(values$saveDist), svalue = rhyzSlope,
extreme = FALSE, vline = TRUE, rejection = FALSE,
breaks = seq(-0.04,0.04,0.005), xlim=c(-0.04,0.04))
})
###########################################################################
###Azteca
### Specific plots for this dataset
aztStat <- mean(azteca$extract.new)-mean(azteca$extract.old)
output$aztPlot <- renderPlot({
par(mar = c(4,4,2,2))
plot(c(azteca$extract.new[1], azteca$extract.old[1]), type="o",
main="Original data", ylim = c(0,60), xlim=c(0.9,2.1), pch=19,
xlab="Treatment", ylab="Number of recruited ants", xaxt='n')
for(i in 2:dim(azteca)[1])
{
points(c(azteca$extract.new[i], azteca$extract.old[i]), pch=19,type="o")
}
mtext("Extract of \n new leaves",1, at=1, line=1.5)
mtext("Extract of \n old leaves",1, at=2, line=1.5)
text(1.8,52,paste("mean difference = \n",
round(aztStat,4)),col="red")
})
output$aztPlotRandom <- renderPlot({
thisSet <- randomizedAzt()
thisDf <- data.frame(matrix(unlist(thisSet), nrow=21))
names(thisDf) <- c("plant","extract.new","extract.old")
thisStat <- mean(thisDf$extract.new)-mean(thisDf$extract.old)
isolate(values$saveDist <- rbind(values$saveDist, thisStat))
par(mar = c(4,4,2,2))
plot(c(thisDf$extract.new[1], thisDf$extract.old[1]), type="o",
main="Randomized data", ylim = c(0,60), xlim=c(0.9,2.1), pch=19,
col="#428bca",
xlab="Treatment", ylab="Number of recruited ants", xaxt='n')
for(i in 2:dim(azteca)[1])
{
points(c(thisDf$extract.new[i], thisDf$extract.old[i]), pch=19,type="o",
col="#428bca")
}
mtext("Extract of \n new leaves",1, at=1, line=1.5)
mtext("Extract of \n old leaves",1, at=2, line=1.5)
text(1.8,52,paste("mean difference = \n",
round(thisStat,4)))
})
### Display data set as a plot
output$aztTable <- renderPlot({
data <- azteca
tbl <- tableGrob(data,
cols = c("plant",
"extract.new",
"extract.old"),
show.rownames =FALSE,
gpar.corefill = gpar(fill = "grey80", col = "white"))
grid.draw(tbl)
},height=500)
output$aztTableRandom <- renderPlot({
thisSet <- randomizedAzt()
thisDf <- data.frame(matrix(unlist(thisSet), nrow=21))
data <- thisDf
tbl <- tableGrob(data,
cols = c("plant",
"extract.new",
"extract.old"),
show.rownames =FALSE,
gpar.corefill = gpar(fill = "#428bca", col = "white"))
grid.draw(tbl)
},height=500)
#Randomization output
randomizedAzt <- reactive({
input$aztRand # triggers the calculations when the "Do it again!" button is pressed
Rsampling(type = "within_rows", dataframe = azteca,
statistics = function(dataframe) return(dataframe), cols = c(2,3),
ntrials = 1)
})
### main plot of the program: generates a histogram of distribution()
output$distPlotAzt <- renderPlot({
par(mar = c(4,3,2,0))
Rsampling::dplot(dist = as.numeric(values$saveDist), svalue = aztStat,
extreme = FALSE, vline = TRUE, rejection = FALSE,
breaks = seq(-8,8,1), xlim=c(-8,8))
})
})
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