## Fuzzy Logix DB Lytix(TM)
## is a high-speed in-database analytics library
## written in C++, exposing ~700 functions through SQL.
## SQL as low-level language makes analyses
## consumable from all SQL-enabled clients.
## This demo shows how the
## AdapteR package of Fuzzy Logix is
## easing interaction with the DB Lytix(TM) in-database
## library.
##
## The demo highlights how to build an
## interactive stock returns correlation demo
## computed in database!
if(!exists("connection")) {
demo("connecting", package="AdapteR")
}
#############################################################
## For in-database analytics the matrix is in the warehouse
## to begin with.
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.")
###########################################################
## Correlation Matrix
## The SQL-through R way to compute a
## correlation matrix with DB Lytix:
##
# sqlQuery(connection, "
# SELECT a.TickerSymbol AS Ticker1,
# b.TickerSymbol AS Ticker2,
# FLCorrel(a.EquityReturn,
# b.EquityReturn) AS FLCorrel
# FROM FL_TRAIN.finEquityReturns a,
# FL_TRAIN.finEquityReturns b
# WHERE b.TxnDate = a.TxnDate
# AND a.TickerSymbol IN ('AAPL')
# AND b.TickerSymbol IN ('AAPL','HPQ','IBM',
# 'MSFT','ORCL')
# GROUP BY a.TickerSymbol,
# b.TickerSymbol
# ORDER BY 1, 2;")
vtemp <- readline("Above: The SQL-through R way to compute a correlation matrix with DB Lytix.")
## A remote matrix is easily created by specifying
## table, row id, column id and value columns
##
eqnRtn <- FLMatrix(table_name = getTestTableName("finEquityReturns"),
row_id_colname = "TxnDate",
col_id_colname = "TickerSymbol",
cell_val_colname = "EquityReturn",
sparse = FALSE)
## the equity return matrix is about 3k rows and cols
dim(eqnRtn)
vtemp <- readline("Above: a remote matrix is defined.")
## 1. select the desired colums from the full matrix
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("Next: the SQL syntax created for you")
## with this option each R command that uses DBLytix will log
## the SQL sent to Teradata.
## Such a dump can in many cases be used as a pure-sql script!
oldDebugSQL <- getOption("debugSQL")
options(debugSQL=TRUE)
## Note that no SQL is sent when defining data-sets
## 1. select the desired colums from the full matrix
sm <- eqnRtn[,c('AAPL','HPQ','IBM','MSFT','ORCL')]
## 2. use the default R 'cor' function
flCorr <- cor(sm)
vtemp <- readline("Note that SQL is not sent yet during definition")
flCorr
vtemp <- readline("Note that SQL is sent when data is printed or otherwise used")
options(debugSQL=oldDebugSQL)
## Casting methods fetch (selected) data from the warehouse into R memory
rEqnRtn <- as.matrix(eqnRtn[,c('AAPL','HPQ','IBM','MSFT','ORCL')])
rEqnRtn <- na.omit(rEqnRtn)
## the result is in the same format as the R results
rCorr <- cor(rEqnRtn, rEqnRtn)
round(rCorr,2)
round(flCorr,2)
vtemp <- readline("Note: The result is in the same format as the R results.")
########################################
## dimnames support
## sample 20 ticker columns
(randomstocks <- sample(colnames(eqnRtn), 20))
## indices of date rows in december 2016
(dec2006 <- grep("2006-12",rownames(eqnRtn)))
vtemp <- readline("Above: dimnames and index support")
vtemp <- readline("Above: Inspecting subsets of data in R is easy with matrix subsetting syntax")
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")
############################################################
## And of course you can now use
## thousands of R packages to operate on DB Lytix results,
if (!requireNamespace("gplots", quietly = TRUE)){
install.packages("gplots")
}
require(gplots)
## install.packages("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 (){
###########################################################
## Shiny Correlation Plot Demo
##
## metadata can be easily combined on the client
## download metadata from
## metadata contains sectors and industries
## that will be selectable in the shiny web ui
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()
## (When you want to end the interactive demo, shut the shiny web server by pressing <ctrl-c> twice here in the R terminal.)
## End of the scripted demo.
### Thank You ####
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