## ----knitr_opts, include=FALSE, message=FALSE, warning=FALSE------------------
library(xtable)

## ----install-pkg, eval=FALSE--------------------------------------------------
#  if (!requireNamespace("BiocManager", quietly = TRUE))
#      install.packages("BiocManager")
#  BiocManager::install("MetaGxOvarian")

## ----loadlib, message=FALSE, warning=FALSE------------------------------------
library(MetaGxOvarian)
esets <- MetaGxOvarian::loadOvarianEsets()[[1]]

## ----sample_number_summary----------------------------------------------------
numSamples <- vapply(seq_along(esets), FUN=function(i, esets) {
    length(sampleNames(esets[[i]]))
    }, numeric(1), esets=esets)


SampleNumberSummaryAll <- data.frame(NumberOfSamples = numSamples,
                                     row.names = names(esets))
total <- sum(SampleNumberSummaryAll[,"NumberOfSamples"])
SampleNumberSummaryAll <- rbind(SampleNumberSummaryAll, total)
rownames(SampleNumberSummaryAll)[nrow(SampleNumberSummaryAll)] <- "Total"

xtable(SampleNumberSummaryAll, digits = 2)

## ----sample_number_summaries_pdata--------------------------------------------
pDataID <- c("sample_type", "histological_type", "primarysite", "summarygrade",
             "summarystage", "tumorstage", "grade",
             "age_at_initial_pathologic_diagnosis", "pltx", "tax",
             "neo", "days_to_tumor_recurrence", "recurrence_status",
             "days_to_death", "vital_status")


pDataPercentSummaryTable <- NULL
pDataSummaryNumbersTable <- NULL

pDataSummaryNumbersList = lapply(esets, function(x)
  vapply(pDataID, function(y) sum(!is.na(pData(x)[,y])), numeric(1)))

pDataPercentSummaryList = lapply(esets, function(x)
  vapply(pDataID, function(y)
    sum(!is.na(pData(x)[,y]))/nrow(pData(x)), numeric(1))*100)

pDataSummaryNumbersTable = sapply(pDataSummaryNumbersList, function(x) x)
pDataPercentSummaryTable = sapply(pDataPercentSummaryList, function(x) x)

rownames(pDataSummaryNumbersTable) <- pDataID
rownames(pDataPercentSummaryTable) <- pDataID
colnames(pDataSummaryNumbersTable) <- names(esets)
colnames(pDataPercentSummaryTable) <- names(esets)

pDataSummaryNumbersTable <- rbind(pDataSummaryNumbersTable, total)
rownames(pDataSummaryNumbersTable)[nrow(pDataSummaryNumbersTable)] <- "Total"


# Generate a heatmap representation of the pData
pDataPercentSummaryTable<-t(pDataPercentSummaryTable)
pDataPercentSummaryTable<-cbind(Name=(rownames(pDataPercentSummaryTable))
                                ,pDataPercentSummaryTable)

nba<-pDataPercentSummaryTable
gradient_colors = c("#ffffff","#ffffd9","#edf8b1","#c7e9b4","#7fcdbb",
                    "#41b6c4","#1d91c0","#225ea8","#253494","#081d58")

library(lattice)
nbamat<-as.matrix(nba)
rownames(nbamat)<-nbamat[,1]
nbamat<-nbamat[,-1]
Interval<-as.numeric(c(10,20,30,40,50,60,70,80,90,100))

levelplot(nbamat,col.regions=gradient_colors,
          main="Available Clinical Annotation",
          scales=list(x=list(rot=90, cex=0.5),
                      y= list(cex=0.5),key=list(cex=0.2)),
          at=seq(from=0,to=100,length=10),
          cex=0.2, ylab="", xlab="", lattice.options=list(),
          colorkey=list(at=as.numeric(factor(c(seq(from=0, to=100, by=10)))),
                  labels=as.character(c( "0","10%","20%","30%", "40%","50%",
                                         "60%", "70%", "80%","90%", "100%"),
                                      cex=0.2,font=1,col="brown",height=1,
                                      width=1.4), col=(gradient_colors)))

## ----sessinInfo, echo=FALSE, eval=TRUE----------------------------------------
sessionInfo()