Example 9-2 Using the ore.doEval Function

In this example, RandomRedDots gets a function that has an argument and that returns a data.frame object that has two columns and that plots 50 random normal values. The example then calls ore.doEval function and passes it the RandomRedDots function object. The image is displayed at the client, but it is generated by the database server R engine that runs the RandomRedDots function.

%r

res <- ore.doEval(FUN.NAME="myRandomRedDots", divisor = 50,
                     FUN.VALUE= data.frame(id = 1, val = 1), ore.graphics=FALSE)
class(res)

The result is:

'ore.frame'

Listing for This Example

R> RandomRedDots <- function(divisor = 100){
+   id<- 1:10
+   plot(1:100, rnorm(100), pch = 21, bg = "red", cex = 2 )
+   data.frame(id=id, val=id / divisor)
+ }
R> ore.doEval(RandomRedDots)
   id  val
1   1 0.01
2   2 0.02
3   3 0.03
4   4 0.04
5   5 0.05
6   6 0.06
7   7 0.07
8   8 0.08
9   9 0.09
10 10 0.10

Figure 9-1 Display of Random Red Dots

Description of "Figure 9-1 Display of Random Red Dots"

Example 9-3 Using the ore.doEval Function with an Optional Argument

You can provide arguments to the input function as optional arguments to the doEval function. This example calls the doEval function with an optional argument that overrides the divisor argument of the RandomRedDots function.

ore.doEval(RandomRedDots, divisor = 50)

Listing for This Example

R> ore.doEval(RandomRedDots, divisor = 50)
   id  val
1   1 0.02
2   2 0.04
3   3 0.06
4   4 0.08
5   5 0.10
6   6 0.12
7   7 0.14
8   8 0.16
9   9 0.18
10 10 0.20
# The graph displayed by the plot function is not shown.

Example 9-4 Using the ore.doEval Function with the FUN.NAME Argument

If the input function is stored in the OML4R script repository, then you can invoke the ore.doEval function with the FUN.NAME argument. This example first calls ore.scriptDrop to ensure that the script repository does not contain a script with the name myRandomRedDots. The example adds the RandomRedDots function from Example 9-2 to the repository under the name myRandomRedDots. This example calls the ore.doEval function and specifies myRandomRedDots. The result is assigned to the variable res.

The return value of the RandomRedDots function is a data.frame but in this example the ore.doEval function returns an ore.object object. To get back the data.frame object, the example calls ore.pull to pull the result to the client R session.

ore.scriptDrop("myRandomRedDots")
ore.scriptCreate("myRandomRedDots", RandomRedDots)
res <- ore.doEval(FUN.NAME = "myRandomRedDots", divisor = 50)
class(res)
res.local <- ore.pull(res)
class(res.local)

Listing for This Example

R> ore.scriptDrop("myRandomRedDots")
R> ore.scriptCreate("myRandomRedDots", RandomRedDots)
R> res <- ore.doEval(FUN.NAME = "myRandomRedDots", divisor = 50)
R> class(res)
[1] "ore.object"
attr(,"package")
[1] "OREembed"
R> res.local <- ore.pull(res)
R> class(res.local)
[1] "data.frame"

Example 9-5 Using the ore.doEval Function with the FUN.VALUE Argument

To have the doEval function return an ore.frame object instead of an ore.object, use the argument FUN.VALUE to specify the structure of the result, as shown in this example.

%r

res <- ore.doEval(FUN.NAME="myRandomRedDots", divisor = 50,
                     FUN.VALUE= data.frame(id = 1, val = 1), ore.graphics=FALSE)
class(res)

The ouput is similar to the following:

'ore.frame'

R> res.of <- ore.doEval(FUN.NAME="myRandomRedDots", divisor = 50,
+                        FUN.VALUE= data.frame(id = 1, val = 1), ore.graphics=FALSE)
R> class(res.of)
[1] "ore.frame"
attr(,"package")
[1] "OREbase"

Example 9-6 Using the doEval Function with the ore.connect Argument

This example demonstrates using the special optional argument ore.connect to connect to the database in the embedded R function, which enables the use of objects stored in a datastore. The example creates the RandomRedDots2 function object, which is the same as the RandomRedDots function from Example 9-2 except the RandomRedDots2 function has an argument that takes the name of a datastore. The example creates the myVar variable and saves it in the datastore named datastore_1. The example then calls the doEval function and passes it the name of the datastore and passes the ore.connect control argument set to TRUE.

%r

RandomRedDots2 <- function(divisor = 100, dsname = "ds-1"){
  id <- 1:10
  plot(1:100, rnorm(100), pch = 21, bg = "red", cex = 2 )
  ore.load(dsname) # Contains the numeric variable myVar.
  data.frame(id = id, val = id / divisor, num = myVar)
}

myVar <- 5
ore.save(myVar, name = "ds-1", overwrite=TRUE)
ore.doEval(RandomRedDots2, dsname="ds-1", ore.connect=TRUE)

The output is similar to the following:

   id  val num
1   1 0.01   5
2   2 0.02   5
3   3 0.03   5
4   4 0.04   5
5   5 0.05   5
6   6 0.06   5
7   7 0.07   5
8   8 0.08   5
9   9 0.09   5
10 10 0.10   5

Listing for This Example

R> RandomRedDots2 <- function(divisor = 100, datastore.name = "myDatastore"){
+   id <- 1:10
+   plot(1:100, rnorm(100), pch = 21, bg = "red", cex = 2 )
+   ore.load(datastore.name) # Contains the numeric variable myVar.
+   data.frame(id = id, val = id / divisor, num = myVar)
+ }

R>myVar <- 5
R>ore.save(myVar, name = "ds-1", overwrite=TRUE)
R> ore.doEval(RandomRedDots2, datastore.name = "datastore_1", ore.connect = TRUE)
   id  val num
1   1 0.01   5
2   2 0.02   5
3   3 0.03   5
4   4 0.04   5
5   5 0.05   5
6   6 0.06   5
7   7 0.07   5
8   8 0.08   5
9   9 0.09   5
10 10 0.10   5
# The graph displayed by the plot function is not shown.

Example 9-7 Using the ora.type Attribute

This example demonstrates using the ora.type attribute to specify database data types of CLOB and BLOB for columns in the data.frame object specified by the FUN.VALUE argument.

%r

# NOTE FROM SL: I added spaces between each example

eval1 <- ore.doEval

eval2 <-
  ore.doEval(function()
	           data.frame(x = "Hello, world", stringsAsFactors = FALSE))
	           
eval3 <-
  ore.doEval(function()
	           data.frame(x = "Hello, world", stringsAsFactors = FALSE),
	           FUN.VALUE = data.frame(x = character(), stringsAsFactors = FALSE))
	           
out.df <- data.frame(x = character(), y = raw(), stringsAsFactors = FALSE)
attr(out.df$x, "ora.type") <- "clob"
attr(out.df$y, "ora.type") <- "blob"

eval4 <- 
  ore.doEval(function() {
	           res <- data.frame(x = "Hello, world",stringsAsFactors = FALSE)
	           res$y[[1L]] <- charToRaw("Hello, world")
	           res},
	           FUN.VALUE = out.df)
	           
eval1
class(eval1)  # ore.object
eval2
class(eval2)  # ore.object
eval3
class(eval3)  # ore.frame
eval4$x
rawToChar(ore.pull(eval4$y))

The output is similar to the following:

Listing for This Example

R> eval1 <- ore.doEval(function() "Hello, world")
R> eval2 <- 
+    ore.doEval(function()
+               data.frame(x = "Hello, world", stringsAsFactors = FALSE))
R> eval3 <-
+    ore.doEval(function()
+               data.frame(x = "Hello, world", stringsAsFactors = FALSE),
+               FUN.VALUE =
+               data.frame(x = character(), stringsAsFactors = FALSE))
R> out.df <- data.frame(x = character(), y = raw(), stringsAsFactors = FALSE)
R> attr(out.df$x, "ora.type") <- "clob"
R> attr(out.df$y, "ora.type") <- "blob"
R> eval4 <- 
+    ore.doEval(function() {
+               res <- data.frame(x = "Hello, world",stringsAsFactors = FALSE)
+               res$y[[1L]] <- charToRaw("Hello, world")
+               res},
+               FUN.VALUE = out.df)
R> eval1
[1] "Hello, world"
R> class(eval1)
[1] "ore.object"
attr(,"package")
[1] "OREembed"
R> eval2
             x
1 Hello, world
R> class(eval2)
[1] "ore.object"
attr(,"package")
[1] "OREembed"
R> eval3
             x
1 Hello, world
Warning message:
ORE object has no unique key - using random order 
R> class(eval3)
[1] "ore.frame"
attr(,"package")
[1] "OREbase"
R> eval4$x                              
[1] "Hello, world"
Warning message:
ORE object has no unique key - using random order 
R> rawToChar(ore.pull(eval4$y))
[1] "Hello, world"
Warning message:
ORE object has no unique key - using random order