Example 3-16 Randomly Partitioning Data

This example creates a data.frame object with the symbol myData in the local R session and adds a column to it that contains a randomly generated set of values. It pushes the data set to database memory as the object MYDATA. The example calls the Embedded R Execution function ore.groupApply, which partitions the data based on the partition column and then applies the lm function to each partition.

N <- 200
k <- 5
myData <- data.frame(a=1:N,b=round(runif(N),2))
myData$partition <- sample(rep(1:k, each = N/k,
                               length.out = N), replace = TRUE)
MYDATA <- ore.push(myData)
head(MYDATA)
results <- ore.groupApply(MYDATA, MYDATA$partition,
                          function(y) {lm(b~a,y)}, parallel = TRUE)
length(results)
results[[1]]

R> N <- 200
R> k <- 5
R> myData <- data.frame(a=1:N,b=round(runif(N),2))
R> myData$partition <- sample(rep(1:k, each = N/k,
+                             length.out = N), replace = TRUE)
R> MYDATA <- ore.push(myData)
R> head(MYDATA)
  a    b partition
1 1 0.89         2
2 2 0.31         4
3 3 0.39         5
4 4 0.66         3
5 5 0.01         1
6 6 0.12         4
R> results <- ore.groupApply(MYDATA, MYDATA$partition,
+                            function(y) {lm(b~a,y)}, parallel = TRUE)
R> length(results)
[1] 5
R> results[[1]]
 
Call:
lm(formula = b ~ a, data = y)
 
Coefficients:
(Intercept)            a  
   0.388795     0.001015