CerebralMastication

library(rJava)
.jinit()
.jaddClassPath(dir( "/home/jal/Documents/DSSAT/gsrad/gsrad_sample/lib/", full.names=TRUE ))
attach( javaImport( c("java.lang", "java.io")))
    
BristowCampbellStrategy <- function(b, ClearSkyTransmissivity, ExtraTerrestrialRadiation, SlopeAspectFactor, AirTemperatureDailyRange, AirTemperatureMonthlyRange ) {
	rd <-  .jnew( "cra/clima/gsrad/RadData" )
	algo <- .jnew( "cra/clima/gsrad/GSRBristowCampbellStrategy" )
	bcParams <- .jnew( "cra/clima/gsrad/BristowCampbellParameters" )

CerebralMastication

## make some non norm data
d1 <- rnorm(20, 5, 1)
d2 <- rnorm(5, 10, 2)
d <- c(d1,d2)
hist(d)

## no mean adjustment
myUniform <- ecdf( d )( d )
mean( myUniform )
## hey that mean's not .5!

CerebralMastication

 n <- 1e7
 myDf <- data.frame( a=rnorm(n), b=rnorm(n), c=rnorm(n) )
 myDf <- rbind(myDf, myDf)
  # memory use doubles every time the rbind() runs. 
  # see what happens if you keep running the this over and over
  # I have 4GB of RAM in my laptop running 64 bit Ubuntu. 
  # R gets killed after 2 rbind()

 myDf <- rbind(myDf, myDf)

CerebralMastication

estimatePi <- function(seed){
  set.seed(seed)
  numDraws <- 1e6
  r <- .5 #radius... in case the unit circle is too boring
  x <- runif(numDraws, min=-r, max=r)
  y <- runif(numDraws, min=-r, max=r)
  inCircle <- ifelse( (x^2 + y^2)^.5 < r , 1, 0)
  return(sum(inCircle) / length(inCircle) * 4)
}

CerebralMastication

library(rJava)
.jinit()

awsAccessKeyText <- "yourKeyHere"
awsSecretKeyText <- "yourSecretKeyHere"
pathToSdk <- "/home/jal/aws-java-sdk-1.1.0/"
testFile <- "/home/jal/aws-java-sdk-1.1.0/rJavaExamples/testFile.tst"

.jaddClassPath(paste(pathToSdk, "lib/aws-java-sdk-1.1.0.jar", sep=""))
.jaddClassPath(paste(pathToSdk, "third-party/commons-logging-1.1.1/commons-logging-1.1.1.jar", sep=""))

CerebralMastication

# Thanks to @greghirson who totally knocked this out of the park! 
# Based on his work, here's a simple script to pull monthly prices from the USDA NASS website.

library(RCurl)
library(XML)

x <- postForm(uri = "http://quickstats.nass.usda.gov/uuid/encode",
  source_desc = "SURVEY", sector_desc="CROPS", 
  group_desc="FIELD CROPS", commodity_desc="CORN",
  statisticcat_desc="PRICE RECEIVED", 

CerebralMastication

CerebralMastication

set.seed(2)
x <- 1:100

y <- 20 + 3 * x
e <- rnorm(100, 0, 60)
y <- 20 + 3 * x + e

plot(x,y)
yx.lm <- lm(y ~ x)
lines(x, predict(yx.lm), col="red")

CerebralMastication

# multivariate normal example using Cholesky decomposition

require(Matrix)

#make this reproducible
set.seed(2)

#how many draws in our starting data set?
n <- 1e4

CerebralMastication

# multivariate normal example using the MASS package


require(MASS)

#make this reproducible
set.seed(2)

#how many draws in our starting data set?
n <- 1e4