demo/matrix.correlation.shiny.R

In Fuzzy-Logix/AdapteR: Converts R syntax to SQL to provide fast analytics using in-database C++ library DB Lytix.

## Fuzzy Logix DB Lytix(TM)
## Stock correlation Demo AdapteR
if(!exists("connection")) {
  stop("no connection variable found. Run demo(connecting) \n ")
}
options(debugSQL=FALSE)
#############################################################
sqlQuery(connection,
           limitRowsSQL(pSelect=paste0("select * from ",getTestTableName("finEquityReturns")),
                        pRows=10))

vtemp <- readline("Above: The table has equity returns stored as triples (what was the equity return of which ticker on what date).\nThese triples define a matrix in deep format.")

eqnRtn <- FLMatrix(table_name        = getTestTableName("finEquityReturns"),
                   row_id_colname    = "TxnDate",
                   col_id_colname    = "TickerSymbol",
                   cell_val_colname  = "EquityReturn",
                   sparse = FALSE)


dim(eqnRtn)

vtemp <- readline("Above: a remote matrix is defined.")

sm <- eqnRtn[,c('AAPL','HPQ','IBM','MSFT','ORCL')]

vtemp <- readline("Next: the R/AdapteR way to compute a correlation matrix -- transparently in-database")

## 2. use the default R 'cor' function
flCorr <- cor(sm)
flCorr

vtemp <- readline("Note that SQL is sent when data is printed or otherwise used")


rEqnRtn <- as.matrix(sm)
rEqnRtn <- na.omit(rEqnRtn)

rCorr <- cor(rEqnRtn, rEqnRtn)
round(rCorr,2)
round(flCorr,2)

vtemp <- readline("Note: The result is in the same format as the R results.")


########################################

(randomstocks <- sample(colnames(eqnRtn), 20))

(dec2006 <- grep("2006-12",rownames(eqnRtn)))

vtemp <- readline("Above: dimnames and index support")

E <- eqnRtn[dec2006, randomstocks]
vtemp <- readline("NO SQL is sent during the definition of subsetting")

if(!is.Hadoop())
print(E)
vtemp <- readline("Data is fetched on demand only, e.g. when printing")



############################################################
if (!requireNamespace("gplots", quietly = TRUE)){
    install.packages("gplots")
}
require(gplots)

metaInfo <- tryCatch({
    read.csv("http://raw.githubusercontent.com/aaronpk/Foursquare-NASDAQ/master/companylist.csv")
}, error=function(e) NULL)

if(is.null(metaInfo))
    metaInfo <- tryCatch({
        read.csv("https://raw.githubusercontent.com/aaronpk/Foursquare-NASDAQ/master/companylist.csv")
    }, error=function(e) NULL)


M <- cor(eqnRtn[,intersect(
    metaInfo$Symbol[metaInfo$Sector %in% c("Basic Industries")],
    colnames(eqnRtn))])

heatmap.2(as.matrix(M),
          symm=TRUE,
          distfun=function(c) as.dist(1 - c),
          trace="none",
          col=redgreen(100),
          cexCol = 1,
          cexRow = 1)

vtemp <- readline("You can use FL results in other R packages, e.g. plotting -- or shiny (next)")

run.FLCorrelationShiny <- function (){
###########################################################
    table(metaInfo$industry)
    table(metaInfo$Sector)
    stockCorrelPlot <- function(input){
        ## get selected and available ticker symbols
        metastocks <- as.character(
            metaInfo$Symbol[
                metaInfo$industry %in% input$industries |
                metaInfo$Sector %in% input$sectors])
        stocks <- intersect(
            unique(c(input$stocks,
                     metastocks)),
            colnames(eqnRtn))
        if(length(stocks)==0)
            return(NULL)
        ## compute correlation matrix
        flCorr <- as.matrix(cor(eqnRtn[,stocks]))

        ## plot with company names and stocks
        rownames(flCorr) <- metaInfo$Name[
            match(rownames(flCorr),
                  metaInfo$Symbol)]
        heatmap.2(flCorr, symm=TRUE,
                  distfun=function(c) as.dist(1 - c),
                  trace="none",
                  col=redgreen(100),
                  cexCol = 1, srtCol=90,
                  cexRow = 1)
    }
    if (!requireNamespace("R.utils", quietly = TRUE)){
        install.packages("R.utils")
    }
    require(R.utils)
    if (!requireNamespace("shiny", quietly = TRUE)){
        install.packages("shiny")
    }
    require(shiny)
    shinyApp(
        ui = fluidPage(
            fluidRow(
                column(3,
                       selectInput(
                           "sectors", "Sectors:",
                           choices = levels(metaInfo$Sector),
                           selected = "Energy",
                           multiple = TRUE)),
                column(3,
                       selectInput(
                           "industries", "Industries:",
                           choices = levels(metaInfo$industry),
                           selected = "Commercial Banks",
                           multiple = TRUE)),
                column(6,
                       selectInput(
                           "stocks", "Stocks:",
                           choices = colnames(eqnRtn),
                           selected = c(),
                           multiple = TRUE))),
            fluidRow(plotOutput("correlations"))
        ),
        server = function(input, output) {
        output$correlations <- renderPlot(
            stockCorrelPlot(input), height=800)
    }
    )
}

assign("metaInfo",metaInfo,envir=environment(run.FLCorrelationShiny))

## To explore correlations interactively, we defined a function above.
## Simply execute now
## run.FLCorrelationShiny()

### Thank You ####