R/Stacked_Barplot.R
In corneliusfritz/intervisu: Interactive data visualisation with shiny
#' Interactive Stacked Barplot
#'
#' Takes any data and creates an stacked barplot with multiple interactive features.
#'
#' @param data A data.frame object that is to be analyzed
#' @param n A numeric value indicating the limit from what number of different values a variable is seen as categorical variable, all variables that have more than n different values are being treated as metric values
#' @param height A two-dimensional numeric value indicating the height of the barplot and boxplot
#' @param m A numeric value telling hiow many observations need to be in each cell to perform the approximative chi^2-Test, rather than the exact Fisher-Test(default at 5)
#' @author Cornelius Fritz <cornelius.fritz@campus.lmu.de>
#' @example demo/demo2.R
#' @details In the sidebar you can choose two categorical variables and plot a stacked Barplot, each bar conditioned on the first categorical variable.
#' If you click on one of the shown barplots, a boxplot conditioned onto that specifical level of the categorical variable will be shown underneath the barplot. You can also decide which
#' metric variable should be plotted in the boxplot.
#'
#' @details Eventually the staked barplot describes the frequency table of these two categorical variables. The independence of those variables can be testes ether with a approximate chi^2 test or a exact fisher-test.
#' Which test is used depends on the cell counts in the contigency table and the parameter \code{m}, only if at least one cell in the frequency table has less observations than \code{m} the exact fisher-test is performed.
#' The test can be interpreted as to weather the hypothesis of independence holds true or not. In case of a 2x2 frequency table the Odds Ratio value is given as well.
#' You can also interactively melt levels of the factorial variable together.
#' In order to do this you at first need to select one level of the factorial variable by clicking on it with a single-click. The chosen boxplot will be highlighted. The other level that you want to melt together with the first one
#' is identified by a double-click. Once you clicked on one level with a single-click and on one level with a double-click, those two levels are melted into one level. The
#' labels on the x-axis are changed accordingly. You can always return to the older version of the factorial variable (with one more level than the current) by pressing the return button.
#'
#' @export
#'
Stacked_Barplot = function(data, n=10,m=5,height=c(500,300)) {
data=data[complete.cases(data),]
position3= function(x){
return(ceiling((x)/1.2 -0.1))
}
table_transform= function(table,click){
table_new=matrix(ncol=dim(table)[2], nrow=dim(table)[1]*2)
colnames(table_new)=colnames(table)
rownames(table_new)=c(rownames(table), seq(100,101,length.out = dim(table)[1]))
for(i in 1: nrow(table)) {
for(j in 1: ncol(table)) {
if(j!=click) {
table_new[i,j]=table[i,j]
}
}
}
table_new[,click]=c(rep(0,dim(table)[1]),table[,click])
return(as.table( table_new))
}
data=faktor2(data,n)[[1]]
vec=faktor2(data,n)[[2]]
if(sum(vec)<=1) {
stop("Error: In order to use this function there are at lease two factorial variables needed")
}
choices = list()
for (i in 1: length(names(data)[vec])) {
choices[[i]]=i
}
names(choices)=names(data)[vec]
choices1 = list()
for (i in 1: length(names(data)[!vec])) {
choices1[[i]]=i
}
names(choices1)=names(data)[!vec]
ui <- fluidPage(
tags$div( HTML(
"<h2><center>Stacked Barplots</center></h2>")
),
sidebarLayout(
sidebarPanel(
selectInput("check2", label = h3("1. Categorical Variable"),
choices = choices,
selected = 1),
selectInput("check1", label = h3("2. Categorical Variable"),
choices = choices,
selected = 2),
selectInput("select", label = h3("Metric Variable"),
choices = choices1,
selected = 1),
actionButton("back", label = "Backwards")
),
mainPanel(
fluidRow(
column(12,splitLayout(cellWidths = c("70%", "30%"),plotOutput("Plot1",height = height[1],click = "click1", dblclick = "click2"),plotOutput("Plot2", height = height[1])))
),
fluidRow(
column(7.6,splitLayout(cellWidths = c("65%", "35%"),plotOutput("Plot3",height = height[2]),htmlOutput("test")))
)
)
)
)
server <- function(input, output) {
prop <- reactiveValues(
prop=prop.table(table(data[,names(choices)[as.numeric(1)]],data[,names(choices)[as.numeric(2)]]),margin = 2),
abs=table(table(data[,names(choices)[as.numeric(1)]],data[,names(choices)[as.numeric(2)]]))
)
click <- reactiveValues(
click1=NULL, click2=NULL
)
factor <- reactiveValues(
factor=list()
)
count <- reactiveValues(
count=1
)
col <- reactiveValues(
col=NA
)
observeEvent(input$click1, {
click$click1=input$click1
if(length(factor$factor)<=1) {
factor$factor[[count$count]]=data[,names(choices)[as.numeric(input$check2)]]
}
col$col=c(heat.colors(length(rownames(prop$prop))),heat.colors(length(rownames(prop$prop)),alpha = 0.7))
})
observeEvent(input$back, {
if(count$count >1) {
count$count=count$count-1
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],factor$factor[[count$count]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
}
})
observeEvent(input$click2, {
click$click2=input$click2
if(!is.na(click$click1$x)) {
factor$factor[[count$count+1]]=factor_melt(factor_leveln1=position3(x=click$click1$x),
factor_leveln2 =position3(x=click$click2$x),
factor1 = factor$factor[[count$count]])
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],factor$factor[[count$count+1]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
count$count=count$count+1
col$col=heat.colors(length(rownames(prop$prop)))
}
})
observeEvent(input$check1, {
if(length(factor$factor)<1) {
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],data[,names(choices)[as.numeric(input$check2)]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
col$col=heat.colors(length(rownames(prop$prop)))
} else {
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],factor$factor[[count$count]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
col$col=heat.colors(length(rownames(prop$prop)))
}
})
observeEvent(input$check2, {
factor$factor=list()
col$col=heat.colors(length(rownames(prop$prop)))
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],data[,names(choices)[as.numeric(input$check2)]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
})
output$Plot1 <- renderPlot(height = height[1],{
if(is.null(click$click1)){
barplot(prop$prop, ylim=c(0,1),col=heat.colors(length(rownames(prop$prop))),xlab=names(choices)[as.numeric(input$check2)])
} else {
if(position3(click$click1$x)> ncol(prop$prop)){
prop1=table_transform(table = prop$prop, click = position3(click$click1$x)-1)
} else {
prop1=table_transform(table = prop$prop, click = position3(click$click1$x))
}
barplot(prop1, ylim=c(0,1),col=col$col,xlab=names(choices)[as.numeric(input$check2)])
}
})
output$Plot2 <- renderPlot(height = height[1],{
plot.new()
legend(x=0,y=0.8, fill=heat.colors(length(rownames(prop$prop))), legend=rownames(prop$prop),title=names(choices)[as.numeric(input$check1)],cex=1.5)
})
output$Plot3 <- renderPlot(height = height[2],{
if(!is.null(click$click1)){
data=faktor2(data,n)[[1]]
if(length(factor$factor)==0) {
plot(data[,names(choices)[as.numeric(input$check1)]],
data[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)]))
} else if(length(levels(factor$factor[[count$count]]))<position3(click$click1$x)) {
data3=data
data3[,names(choices)[as.numeric(input$check2)]]=factor$factor[[count$count]]
data1=data3[(as.character(data3[,names(choices)[as.numeric(input$check2)]]))==(levels(factor$factor[[count$count]])[position3(click$click1$x)-1]),]
plot(data1[,names(choices)[as.numeric(input$check1)]],
data1[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)],"=",levels(factor$factor[[count$count]])[position3(click$click1$x)-1]))
} else if(is.null(levels(factor$factor[[count$count]])[position3(click$click1$x)-1])) {
data1=data
plot(data1[,names(choices)[as.numeric(input$check1)]],
data1[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]))
} else if(length(levels(factor$factor[[count$count]]))==1){
plot(data[,names(choices)[as.numeric(input$check1)]],
data[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)],"=",levels(factor$factor[[count$count]])))
} else {
data3=data
data3[,names(choices)[as.numeric(input$check2)]]=factor$factor[[count$count]]
data1=data3[(as.character(data3[,names(choices)[as.numeric(input$check2)]]))==(levels(factor$factor[[count$count]])[position3(click$click1$x)]),]
plot(data1[,names(choices)[as.numeric(input$check1)]],
data1[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)],"=",levels(factor$factor[[count$count]])[position3(click$click1$x)]))
}
}
})
output$test = renderUI({
if((ncol(prop$abs)>1)&(nrow(prop$abs)>1)) {
if(is.element(T,as.logical(prop$abs<5))) {
warning("Some cells include less than ", m, " observations. Because of that an exact Fisher's test is computed.")
test=fisher.test(prop$abs)
if((ncol(prop$abs)==2)&(nrow(prop$abs)==2)){
Ergebnis=paste0("<p><b>Fisher's Exact Test for Count Data </b></p><br> p-palue= ",round(test$p.value,digits = 8)," <br> alternative hypothesis: true odds ratio <br> is not equal to 1 <br> 95 percent confidence interval:",
"[",round(test$conf.int[1],digits = 3),", ",round(test$conf.int[2],digits = 3),"] <br> OR=", round(test$estimate,digits = 8))
} else {
Ergebnis=paste0("<p><b>Fisher's Exact Test for Count Data </b></p><br> p-palue= ",round(test$p.value,digits = 8)," <br> alternative hypothesis: two.sided")
}
} else {
test=chisq.test(prop$abs)
test$p.value
if((ncol(prop$abs)==2)&(nrow(prop$abs)==2)){
Ergebnis=paste0("<p><b>Pearson's Chi-squared test <br> with Yates' continuity correction </b></p><br> X-squared = ",round(test$statistic,digits = 8),
",df=",round(test$parameter,digits = 1),"<br>p-value= ",round(test$p.value,digits = 8),",OR=", round(getOddsRatio(prop$abs),digits = 5))
} else {
Ergebnis=paste0("<p><b>Pearson's Chi-squared test </b></p><br> X-squared = ",round(test$statistic,digits = 8),
",df=",round(test$parameter,digits = 1),"<br>p-value= ",round(test$p.value,digits = 8),",OR=", round(getOddsRatio(prop$abs),digits = 5))
}
}
HTML(Ergebnis)
}
})
}
shinyApp(ui = ui, server = server)
}
corneliusfritz/intervisu documentation built on May 13, 2019, 10:51 p.m.
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#' Interactive Stacked Barplot
#'
#' Takes any data and creates an stacked barplot with multiple interactive features.
#'
#' @param data A data.frame object that is to be analyzed
#' @param n A numeric value indicating the limit from what number of different values a variable is seen as categorical variable, all variables that have more than n different values are being treated as metric values
#' @param height A two-dimensional numeric value indicating the height of the barplot and boxplot
#' @param m A numeric value telling hiow many observations need to be in each cell to perform the approximative chi^2-Test, rather than the exact Fisher-Test(default at 5)
#' @author Cornelius Fritz <cornelius.fritz@campus.lmu.de>
#' @example demo/demo2.R
#' @details In the sidebar you can choose two categorical variables and plot a stacked Barplot, each bar conditioned on the first categorical variable.
#' If you click on one of the shown barplots, a boxplot conditioned onto that specifical level of the categorical variable will be shown underneath the barplot. You can also decide which
#' metric variable should be plotted in the boxplot.
#'
#' @details Eventually the staked barplot describes the frequency table of these two categorical variables. The independence of those variables can be testes ether with a approximate chi^2 test or a exact fisher-test.
#' Which test is used depends on the cell counts in the contigency table and the parameter \code{m}, only if at least one cell in the frequency table has less observations than \code{m} the exact fisher-test is performed.
#' The test can be interpreted as to weather the hypothesis of independence holds true or not. In case of a 2x2 frequency table the Odds Ratio value is given as well.
#' You can also interactively melt levels of the factorial variable together.
#' In order to do this you at first need to select one level of the factorial variable by clicking on it with a single-click. The chosen boxplot will be highlighted. The other level that you want to melt together with the first one
#' is identified by a double-click. Once you clicked on one level with a single-click and on one level with a double-click, those two levels are melted into one level. The
#' labels on the x-axis are changed accordingly. You can always return to the older version of the factorial variable (with one more level than the current) by pressing the return button.
#'
#' @export
#'
Stacked_Barplot = function(data, n=10,m=5,height=c(500,300)) {
data=data[complete.cases(data),]
position3= function(x){
return(ceiling((x)/1.2 -0.1))
}
table_transform= function(table,click){
table_new=matrix(ncol=dim(table)[2], nrow=dim(table)[1]*2)
colnames(table_new)=colnames(table)
rownames(table_new)=c(rownames(table), seq(100,101,length.out = dim(table)[1]))
for(i in 1: nrow(table)) {
for(j in 1: ncol(table)) {
if(j!=click) {
table_new[i,j]=table[i,j]
}
}
}
table_new[,click]=c(rep(0,dim(table)[1]),table[,click])
return(as.table( table_new))
}
data=faktor2(data,n)[[1]]
vec=faktor2(data,n)[[2]]
if(sum(vec)<=1) {
stop("Error: In order to use this function there are at lease two factorial variables needed")
}
choices = list()
for (i in 1: length(names(data)[vec])) {
choices[[i]]=i
}
names(choices)=names(data)[vec]
choices1 = list()
for (i in 1: length(names(data)[!vec])) {
choices1[[i]]=i
}
names(choices1)=names(data)[!vec]
ui <- fluidPage(
tags$div( HTML(
"<h2><center>Stacked Barplots</center></h2>")
),
sidebarLayout(
sidebarPanel(
selectInput("check2", label = h3("1. Categorical Variable"),
choices = choices,
selected = 1),
selectInput("check1", label = h3("2. Categorical Variable"),
choices = choices,
selected = 2),
selectInput("select", label = h3("Metric Variable"),
choices = choices1,
selected = 1),
actionButton("back", label = "Backwards")
),
mainPanel(
fluidRow(
column(12,splitLayout(cellWidths = c("70%", "30%"),plotOutput("Plot1",height = height[1],click = "click1", dblclick = "click2"),plotOutput("Plot2", height = height[1])))
),
fluidRow(
column(7.6,splitLayout(cellWidths = c("65%", "35%"),plotOutput("Plot3",height = height[2]),htmlOutput("test")))
)
)
)
)
server <- function(input, output) {
prop <- reactiveValues(
prop=prop.table(table(data[,names(choices)[as.numeric(1)]],data[,names(choices)[as.numeric(2)]]),margin = 2),
abs=table(table(data[,names(choices)[as.numeric(1)]],data[,names(choices)[as.numeric(2)]]))
)
click <- reactiveValues(
click1=NULL, click2=NULL
)
factor <- reactiveValues(
factor=list()
)
count <- reactiveValues(
count=1
)
col <- reactiveValues(
col=NA
)
observeEvent(input$click1, {
click$click1=input$click1
if(length(factor$factor)<=1) {
factor$factor[[count$count]]=data[,names(choices)[as.numeric(input$check2)]]
}
col$col=c(heat.colors(length(rownames(prop$prop))),heat.colors(length(rownames(prop$prop)),alpha = 0.7))
})
observeEvent(input$back, {
if(count$count >1) {
count$count=count$count-1
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],factor$factor[[count$count]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
}
})
observeEvent(input$click2, {
click$click2=input$click2
if(!is.na(click$click1$x)) {
factor$factor[[count$count+1]]=factor_melt(factor_leveln1=position3(x=click$click1$x),
factor_leveln2 =position3(x=click$click2$x),
factor1 = factor$factor[[count$count]])
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],factor$factor[[count$count+1]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
count$count=count$count+1
col$col=heat.colors(length(rownames(prop$prop)))
}
})
observeEvent(input$check1, {
if(length(factor$factor)<1) {
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],data[,names(choices)[as.numeric(input$check2)]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
col$col=heat.colors(length(rownames(prop$prop)))
} else {
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],factor$factor[[count$count]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
col$col=heat.colors(length(rownames(prop$prop)))
}
})
observeEvent(input$check2, {
factor$factor=list()
col$col=heat.colors(length(rownames(prop$prop)))
prop$prop=table(data[,names(choices)[as.numeric(input$check1)]],data[,names(choices)[as.numeric(input$check2)]])
prop$abs=prop$prop
prop$prop=prop.table(prop$prop,margin = 2)
})
output$Plot1 <- renderPlot(height = height[1],{
if(is.null(click$click1)){
barplot(prop$prop, ylim=c(0,1),col=heat.colors(length(rownames(prop$prop))),xlab=names(choices)[as.numeric(input$check2)])
} else {
if(position3(click$click1$x)> ncol(prop$prop)){
prop1=table_transform(table = prop$prop, click = position3(click$click1$x)-1)
} else {
prop1=table_transform(table = prop$prop, click = position3(click$click1$x))
}
barplot(prop1, ylim=c(0,1),col=col$col,xlab=names(choices)[as.numeric(input$check2)])
}
})
output$Plot2 <- renderPlot(height = height[1],{
plot.new()
legend(x=0,y=0.8, fill=heat.colors(length(rownames(prop$prop))), legend=rownames(prop$prop),title=names(choices)[as.numeric(input$check1)],cex=1.5)
})
output$Plot3 <- renderPlot(height = height[2],{
if(!is.null(click$click1)){
data=faktor2(data,n)[[1]]
if(length(factor$factor)==0) {
plot(data[,names(choices)[as.numeric(input$check1)]],
data[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)]))
} else if(length(levels(factor$factor[[count$count]]))<position3(click$click1$x)) {
data3=data
data3[,names(choices)[as.numeric(input$check2)]]=factor$factor[[count$count]]
data1=data3[(as.character(data3[,names(choices)[as.numeric(input$check2)]]))==(levels(factor$factor[[count$count]])[position3(click$click1$x)-1]),]
plot(data1[,names(choices)[as.numeric(input$check1)]],
data1[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)],"=",levels(factor$factor[[count$count]])[position3(click$click1$x)-1]))
} else if(is.null(levels(factor$factor[[count$count]])[position3(click$click1$x)-1])) {
data1=data
plot(data1[,names(choices)[as.numeric(input$check1)]],
data1[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]))
} else if(length(levels(factor$factor[[count$count]]))==1){
plot(data[,names(choices)[as.numeric(input$check1)]],
data[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)],"=",levels(factor$factor[[count$count]])))
} else {
data3=data
data3[,names(choices)[as.numeric(input$check2)]]=factor$factor[[count$count]]
data1=data3[(as.character(data3[,names(choices)[as.numeric(input$check2)]]))==(levels(factor$factor[[count$count]])[position3(click$click1$x)]),]
plot(data1[,names(choices)[as.numeric(input$check1)]],
data1[,names(choices1)[as.numeric(input$select)]], col=heat.colors(length(rownames(prop$prop))),
xlab=paste(names(choices)[as.numeric(input$check1)]),
ylab=paste(names(choices1)[as.numeric(input$select)]),
main=paste(names(choices)[as.numeric(input$check2)],"=",levels(factor$factor[[count$count]])[position3(click$click1$x)]))
}
}
})
output$test = renderUI({
if((ncol(prop$abs)>1)&(nrow(prop$abs)>1)) {
if(is.element(T,as.logical(prop$abs<5))) {
warning("Some cells include less than ", m, " observations. Because of that an exact Fisher's test is computed.")
test=fisher.test(prop$abs)
if((ncol(prop$abs)==2)&(nrow(prop$abs)==2)){
Ergebnis=paste0("<p><b>Fisher's Exact Test for Count Data </b></p><br> p-palue= ",round(test$p.value,digits = 8)," <br> alternative hypothesis: true odds ratio <br> is not equal to 1 <br> 95 percent confidence interval:",
"[",round(test$conf.int[1],digits = 3),", ",round(test$conf.int[2],digits = 3),"] <br> OR=", round(test$estimate,digits = 8))
} else {
Ergebnis=paste0("<p><b>Fisher's Exact Test for Count Data </b></p><br> p-palue= ",round(test$p.value,digits = 8)," <br> alternative hypothesis: two.sided")
}
} else {
test=chisq.test(prop$abs)
test$p.value
if((ncol(prop$abs)==2)&(nrow(prop$abs)==2)){
Ergebnis=paste0("<p><b>Pearson's Chi-squared test <br> with Yates' continuity correction </b></p><br> X-squared = ",round(test$statistic,digits = 8),
",df=",round(test$parameter,digits = 1),"<br>p-value= ",round(test$p.value,digits = 8),",OR=", round(getOddsRatio(prop$abs),digits = 5))
} else {
Ergebnis=paste0("<p><b>Pearson's Chi-squared test </b></p><br> X-squared = ",round(test$statistic,digits = 8),
",df=",round(test$parameter,digits = 1),"<br>p-value= ",round(test$p.value,digits = 8),",OR=", round(getOddsRatio(prop$abs),digits = 5))
}
}
HTML(Ergebnis)
}
})
}
shinyApp(ui = ui, server = server)
}
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