Data Wrangling in XDF files using ScaleR Functions


The RevoScaleR package provides a set of over one hundred portable, scalable, and distributable data analysis functions. In this article, we will see some examples of using ScaleR Functions to do Data Wrangling in XDF files. For all the following examples, we will be using input XDF files from the SampleData Directory in Microsoft R Server.

  • Create rollup/aggregate variables in the same dataset
  • Columnar operations like Min/Max of rows/columns
  • Roll ups and data consolidations
  • Merge more than 2 XDF Files
  • Merge two datasets, renaming primary key in one dataset
  • Using rowsPerBlock and blocksPerRead parameters in rxSummary

Create rollup/aggregate variables in the same dataset

In this example, we will see how to add summary information like Min,Max,Mean as columns to the original dataset using dplyrXdf. We will find out the summary information of CRSDepTime grouped by the DayOfWeek.

OUTPUT :

r14
 

 

 

 

 

Columnar operations like Min/Max of rows/columns

We will use rxDataStep function to find out the min/max of single column, multiple columns, set of rows, filtered rows. The output min/max is appended as a column to provide an output similar to the original dataset.

OUTPUT :
s1
 

 

 

 

 
s2
 

 

 

 

 
s3
 

 

 

 

 
s4
 

 

 

 

 

Roll ups and data consolidations

To count the number of rows in a table with group by operation on one of the columns, aggregate() function can be used. In the below example, we use the claims
data-set and group the type of claim and sum the cost for each type. aggregate() works only on data frame, so xdf needs to be converted into data frame type.

OUTPUT :

  RowNum   age car.age type cost number
1      1 17-20     0-3    A  289      8
2      2 17-20     4-7    A  282      8
3      3 17-20     8-9    A  133      4
4      4 17-20     10+    A  160      1
5      5 17-20     0-3    B  372     10
6      6 17-20     4-7    B  249     28

OUTPUT :

> print(res)
Group.1    x
1       A 6392
2       B 7062
3       C 6861
4       D 9015

Merge more than 2 xdf files

rxMerge() can be used to merge two or more .xdf files.
In this example, we will merge claims.xdf multiple times.

Data Source Information about claims.xdf:

File name: C:\Program Files\Microsoft SQL Server\130\R_SERVER\library\RevoScaleR\SampleData\claims.xdf 
Number of observations: 128 
Number of variables: 6 
Number of blocks: 1 
Compression type: zlib

To merge two xdf files, just supply the name of the two files to inData1 and inData2 parameters.

OUTPUT :

File merge progress at row: 128
File merge progress at row: 128
Time to merge data file: 0.016 seconds
RxXdfData Source
"claims_merged_twice.xdf"
fileSystem: 
 fileSystemType: native

More than two xdf files, can be merged by passing the list of the rxXdfData objects to inData1, keeping inData2 as NULL.

OUTPUT :

File merge progress at row: 128
File merge progress at row: 128
Time to merge data file: 0.017 seconds
File merge progress at row: 128
File merge progress at row: 256
File merge progress at row: 128
Time to merge data file: 0.018 seconds
RxXdfData Source
"multiple_claims_merged.xdf"
fileSystem: 
 fileSystemType: native

Check the number of observations using rxGetInfo():
File name: C:\Users\madraju.REDMOND\Documents\Teradata_Tests\DC_debug\multiple_claims_merged.xdf 
Number of observations: 384 
Number of variables: 6 
Number of blocks: 3 
Compression type: zlib

Merge two datasets, renaming primary key in one dataset

Here we have two xdf files with different key names, we will merge them renaming the key in one of the files.

OUTPUT :
r9

 

 

 

 

 

Using rowsPerBlock and blocksPerRead parameters in rxSummary

rowsPerBlock means the maximum number of rows per block in the byGroupOutFile which contains the summary result. blocksPerRead  is the number of blocks per read from input xdf data. if your xdf data is too large you can set blocksPerRead as a small one, suppose you have many blocks in your input xdf file.

OUTPUT :

r8
 

 

 

 

 

REFERENCES

RevoScaleR Functions

Comparison of Base R and ScaleR Functions

A simple Big Data analysis using the RevoScaleR package in Revolution R

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