Azure ML studio is Data Scientists’ favorite tool that provides enough functionality for creating and maintaining models for predictive analytics. However, there could be a situation when the existing modules do not suffice for an experiment or, set of requirements. In these cases, ML studio provides the facility of extending the functionality of ML Studio through the R language by using the Execute R Script module. This can also be used in case when you already have your ML module written in R language and you want to import it to ML studio.

This module accepts multiple input datasets and it yields a single dataset as output. You can type an R script into the R Script parameter of the Execute R Script module.

Several R packages are available in addition to the standard packages of the base installation. Currently, it is not possible for you to install R packages directly into the ML studio through the GUI. However, we can install them into the individual workspaces via R code.

A list of the packages included in the current release is provided in the List of installed packages table below.

Listing all currently-installed packages

The list of installed packages can change. To get the complete list, include the following lines in the Execute R Script module send the list to the output dataset:

To view the output log, run the experiment, select the Execute R Script module, and click the View output log link near the bottom of the module parameter pane.  At this point, we support 400+ R packages out of MAML’s R Engine.

Now, to install & use R language packages which are not supported by the ML studio and are required in your code, we allow the following method.

Here are the key steps:

• Zip up the package(s) to be installed into your workspace for the experiment from R package repository on your machine.

Note: We might need to use multiple packages even in situations where we need only one external library in our code since it might have dependencies which are also not present by default in MAML.

• Zip all the zipped packages into another zip file so that all the required packages are bundled together and ready to go

• Click on +New at the bottom of the page and upload the zipped file created above as a dataset

• Verify that the file has been uploaded successfully

• Now, in your experiment add Execute R Script module if it is already not added and connect the dataset input port (if needed) and type/paste your R code which uses the library that we are going to install using the external zipped packages

  Note: There are 3 input ports on this module

• Drag and drop the uploaded zipped file which contains the packages to be installed

• Use the 3^rd input port in the Execute R Script module and connect the zipped file in the previous step

• In the R code just before using the external library which depends on the package(s), use the following code to install the package(s). In this example, I am using the library fpc.

#install package dependancies

install.packages("src/mclust.zip", lib = ".", repos = NULL, verbose = TRUE)

(success.mclust <- library("mclust", lib.loc = ".", logical.return = TRUE, verbose = TRUE))

install.packages("src/flexmix.zip", lib = ".", repos = NULL, verbose = TRUE)

(success.flexmix <- library("flexmix", lib.loc = ".", logical.return = TRUE, verbose = TRUE))

#Install actual package

install.packages("src/fpc.zip", lib = ".", repos = NULL, verbose = TRUE)

(success <- library("fpc", lib.loc = ".", logical.return = TRUE, verbose = TRUE))

#use library

library(fpc,lib.loc = ".")

• Post this run the experiment and use the intended library.

• Once the run is complete, we can look at the output of Execute R Script. Please note that there are two output ports. The first one

• Output port 1 is used to visualize and “Save as Dataset” if there is a dataset output while output port 2 outputs any standard output like verbose output and R plots. In the example here it shows that all the packages which were presented to Execute R Script module from the 3^rd input port inside of a zipped file were extracted to be used and placed into the path [“src”].

Additionally, in case you need to use your existing .R and .RData files in MAML, please use the same method as above of zip and upload. Additionally, in the R code inside of the Execute R Script module, please use the following section to provide the names of the files. The zipped file input via the 3^rd input port will extract the contents of the zipped file into the path [“SRC”] in the workspace sandbox.

https://channel9.msdn.com/Blogs/Windows-Azure/R-in-Azure-ML-Studio

**Note: Please take into account the legalities of using the R Language Packages while using this functionality.

In this blog, we are going to learn how to create reports on the fly using report builder 3.0 and also how to add parameters to the reports created using this method.

Important Note: Please note that in this approach we haven’t had to modify the underlying MDX query at all.

Assumptions

• The cube is well formed based on MS guidelines. The Data model conforms to standards and there are no glitches
• The attribute relationships and Hierarchies have been made properly.
• This demo has been made using the ADVENTUREWORKSDW database on a cube built for INTERNET SALES related measures groups

Some MDX Terms explained:

• SET – A set is an accumulation of members of the same kind
• MEMBER – A member represents a particular value in the cube
• STRTOSET – Used when we want to represent multiple members from a dimension/measure. The members could be from the same column/attribute or, multiple attributes.
• When we use STRTOSET in the FROM clause like the following, we mean to ask the engine to use the current set of members coming in from a MULTI-value parameter.

SELECT NON EMPTY { [Measures].[Sales Amount] } ON COLUMNS,  NON EMPTY { ([Order
Date].[Date Key].[Date Key].ALLMEMBERS * [Dim SalesTerritory].[Sales Territory Key].[Sales Territory Key].ALLMEMBERS
*  [DimCustomer].[Customer Key].[Customer Key].ALLMEMBERS )

 } DIMENSION PROPERTIES MEMBER_CAPTION, MEMBER_UNIQUE_NAME
ON ROWS FROM

 (

 SELECT

 (

 STRTOSET(@DimSalesTerritorySalesTerritoryKey,
CONSTRAINED) ) ON
COLUMNS

 FROM ( SELECT ( STRTOMEMBER(@FromOrderDateFullDateAlternateKey, CONSTRAINED) :

 STRTOMEMBER(@ToOrderDateFullDateAlternateKey, CONSTRAINED) ) ON COLUMNS

 FROM [Adventure Works DW]

 )

 ) CELL PROPERTIES VALUE,

 BACK_COLOR,

 FORE_COLOR,

 FORMATTED_VALUE,

 FORMAT_STRING, FONT_NAME,
FONT_SIZE, FONT_FLAGS

• STRTOMEMBER – This function converts a STRING
  value into a valid member type.

Steps to create the report (with screenshots)

Click on New Report from the menu on the left hand side.

Now, Select Tablix (Table or, Matrix Wizard).

Create a new data source or, use a shared Datasource.

 Select the following option for using a connection to SSAS.

Then build the connection string.

Post building the connection
string, drag and drop your Measures and Dimension attributes into the right
side pane.

It will look something like the following.

Now, add filters to filter your result row-set on the top pane.

Add more filters if needed.

Select the check box located at the right corner of the parameter row.

Arrange fields by dragging and dropping fields from the list of fields.

Now, you may want to look for options to decide the function that will be used for the Values field.

Select the LAYOUT.

It should look something like the following once all selections have been made.

In case you want to use a CALENDAR control on the DATE filters, here is what you can do.

Go ahead and modify the type of the Report Parameters which are automatically added when you add a DATE RANGE filter in the design window.

Now, set the AVAILABLE VALUES section to NONE

As well as, the Default VALUE section.

Do, the same for both the FROMDATE & TODATE parameters.

Now, come back to the DATASET and open PROPERTIES.

You will see that by default the Query Parameters are mapped to Report Parameters of the same name.

Now, let’s modify the value by providing an expression here as follows in both FROM DATE and TO DATE:

="[Order Date].[Full Date Alternate Key].&[" + Format(Parameters!FromOrderDateFullDateAlternateKey.Value, "yyyy-MM-dd") + "T00:00:00]"

Note:

• We are using this expression to convert the date format supplied by the calendar control which will look like ‘2/13/2005’ to the
  stored value in the FULLDATEALTERNATEKEY column/attribute of the DATE dimension
  of the cube i.e. ‘2005-02-13T00:00:00’.
• Here "[Order Date].[Full Date Alternate Key].&[" + has been added to fully qualify the member name
• FORMAT function has been used to format the date coming from the calendar control into the member format from the cube
• And  "T00:00:00]" has been concatenated to the expression to convert the value so far from string to date

Repeat the above for TO DATE as well.

Now, your DATASET Properties -> Parameters should look like this:

Now, your report viewer report is ready to run.

Please click on the RUN button on the top LEFT. Choose the dates in the Calendar controls.

Note: Throws the error about the CONSTRAINED flag if you happen to select a date beyond the available limit in the DATE dimension.

An error has occurred during report processing.
(rsProcessingAborted)

Query execution failed for dataset 'DimSalesTerritorySalesTerritoryKey'.
(rsErrorExecutingCommand)

Query (1, 588) The restrictions imposed by the CONSTRAINED
flag in the STRTOMEMBER function were violated.

To avoid hitting this issue we can use the following expression in the DATASET -> PROPERTIES -> PARAMETERS.

=IIF((Parameters!FromOrderDateFullDateAlternateKey.Value)<= Today(),("[Order Date].[Full Date Alternate Key].&[" +
Format(Parameters!FromOrderDateFullDateAlternateKey.Value, "yyyy-MM-dd") + "T00:00:00]"),"[Order Date].[Full Date Alternate
Key].&[2008-03-21T00:00:00]")

Here the highlighted value is the last valid date key value in the Date dimension.

Moving on….

After selecting a value(s) for the 3^rd parameter, once we click on View Report, we can see the following.

How to handle non-date parameters (Sales Territory Key in my case)

For parameters other than Dates, we need to provide AVAILABLE value as follows (when you add a filter, this is added by default):

This is because when the Query parameter is equated with the report parameter, it is by default equated with this value.

SELECT NON
EMPTY { [Measures].[Sales Amount] } ON COLUMNS,

 NON EMPTY { ([Order Date].[Date Key].[Date Key].ALLMEMBERS *

 [Dim Sales Territory].[Sales Territory
Key].[Sales Territory Key].ALLMEMBERS *

 [Dim Customer].[Customer Key].[Customer Key].ALLMEMBERS )

 }

 DIMENSION PROPERTIES MEMBER_CAPTION,
MEMBER_UNIQUE_NAME ON
ROWS FROM

 (

 SELECT

 (

 STRTOSET(@DimSalesTerritorySalesTerritoryKey, CONSTRAINED)
) ON COLUMNS

 FROM ( SELECT ( STRTOMEMBER(@FromOrderDateFullDateAlternateKey,
CONSTRAINED) :

 STRTOMEMBER(@ToOrderDateFullDateAlternateKey,
CONSTRAINED) ) ON
COLUMNS

 FROM
[Adventure Works DW]

 )

 ) CELL PROPERTIES VALUE,

 BACK_COLOR,

 FORE_COLOR,

 FORMATTED_VALUE,

 FORMAT_STRING,
FONT_NAME, FONT_SIZE,
FONT_FLAGS

Another thing to note here is that the PARAMETERS should be in the correct SEQUENCE everywhere.

We can use the following method (right click) to move a filter UP/DOWN to make it in the correct sequence.

Also, the sequence should be the same everywhere i.e. in the Parameters tab of the DATASET properties as well as the report itself.

Conclusion

Report builder is a very efficient tool for realizing self-service BI reporting. For using this tool to generate reports based on multi-dimensional cubes on the fly, one does not need to possess knowledge of MDX/DAX or, any other multi-dimensional query language. This tool can be downloaded freely from the following location.

https://www.microsoft.com/en-in/download/details.aspx?id=6116