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OLAP Operation in R

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OLAP Operation in R

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OLAP (Online Analytical Processing) is a very common way to analyze raw transaction data by aggregating along different combinations of dimensions.  This is a well-established field in Business Intelligence / Reporting.  In this post, I will highlight the key ideas in OLAP operation and illustrate how to do this in R.

Facts and Dimensions

The core part of OLAP is a so-called "multi-dimensional data model", which contains two types of tables; "Fact" table and "Dimension" table

A Fact table contains records each describe an instance of a transaction.  Each transaction records contains categorical attributes (which describes contextual aspects of the transaction, such as space, time, user) as well as numeric attributes (called "measures" which describes quantitative aspects of the transaction, such as no of items sold, dollar amount).

A Dimension table contain records that further elaborates the contextual attributes, such as user profile data, location details ... etc.

In a typical setting of Multi-dimensional model ...

  • Each fact table contains foreign keys that references the primary key of multiple dimension tables.  In the most simple form, it is called a STAR schema.
  • Dimension tables can contain foreign keys that references other dimensional tables.  This provides a sophisticated detail breakdown of the contextual aspects.  This is also called a SNOWFLAKE schema.
  • Also this is not a hard rule, Fact table tends to be independent of other Fact table and usually doesn't contain reference pointer among each other.
  • However, different Fact table usually share the same set of dimension tables.  This is also called GALAXY schema.
  • But it is a hard rule that Dimension table NEVER points / references Fact table

 A simple STAR schema is shown in following diagram.


Each dimension can also be hierarchical so that the analysis can be done at different degree of granularity.  For example, the time dimension can be broken down into days, weeks, months, quarter and annual; Similarly, location dimension can be broken down into countries, states, cities ... etc.

Here we first create a sales fact table that records each sales transaction.

# Setup the dimension tables

state_table <- 
  data.frame(key=c("CA", "NY", "WA", "ON", "QU"),
             name=c("California", "new York", "Washington", "Ontario", "Quebec"),
             country=c("USA", "USA", "USA", "Canada", "Canada"))

month_table <- 
  data.frame(key=1:12,
            desc=c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"),
            quarter=c("Q1","Q1","Q1","Q2","Q2","Q2","Q3","Q3","Q3","Q4","Q4","Q4"))

prod_table <- 
  data.frame(key=c("Printer", "Tablet", "Laptop"),
            price=c(225, 570, 1120))

# Function to generate the Sales table
gen_sales <- function(no_of_recs) {

  # Generate transaction data randomly
  loc <- sample(state_table$key, no_of_recs, 
                replace=T, prob=c(2,2,1,1,1))
  time_month <- sample(month_table$key, no_of_recs, replace=T)
  time_year <- sample(c(2012, 2013), no_of_recs, replace=T)
  prod <- sample(prod_table$key, no_of_recs, replace=T, prob=c(1, 3, 2))
  unit <- sample(c(1,2), no_of_recs, replace=T, prob=c(10, 3))
  amount <- unit*prod_table[prod,]$price

  sales <- data.frame(month=time_month,
                      year=time_year,
                      loc=loc,
                      prod=prod,
                      unit=unit,
                      amount=amount)

  # Sort the records by time order
  sales <- sales[order(sales$year, sales$month),]
  row.names(sales) <- NULL
  return(sales)
}

# Now create the sales fact table
sales_fact <- gen_sales(500)

# Look at a few records
head(sales_fact)

  month year loc   prod unit amount
1     1 2012  NY Laptop    1    225
2     1 2012  CA Laptop    2    450
3     1 2012  ON Tablet    2   2240
4     1 2012  NY Tablet    1   1120
5     1 2012  NY Tablet    2   2240
6     1 2012  CA Laptop    1    225


Multi-dimensional Cube

Now, we turn this fact table into a hypercube with multiple dimensions.  Each cell in the cube represents an aggregate value for a unique combination of each dimension.  



# Build up a cube
revenue_cube <- 
    tapply(sales_fact$amount, 
           sales_fact[,c("prod", "month", "year", "loc")], 
           FUN=function(x){return(sum(x))})

# Showing the cells of the cude
revenue_cube

, , year = 2012, loc = CA

         month
prod         1    2     3    4    5    6    7    8    9   10   11   12
  Laptop  1350  225   900  675  675   NA  675 1350   NA 1575  900 1350
  Printer   NA 2280    NA   NA 1140  570  570  570   NA  570 1710   NA
  Tablet  2240 4480 12320 3360 2240 4480 3360 3360 5600 2240 2240 3360

, , year = 2013, loc = CA

         month
prod         1    2    3    4    5    6    7    8    9   10   11   12
  Laptop   225  225  450  675  225  900  900  450  675  225  675 1125
  Printer   NA 1140   NA 1140  570   NA   NA  570   NA 1140 1710 1710
  Tablet  3360 3360 1120 4480 2240 1120 7840 3360 3360 1120 5600 4480

, , year = 2012, loc = NY

         month
prod         1     2    3    4    5    6    7    8    9   10   11   12
  Laptop   450   450   NA   NA  675  450  675   NA  225  225   NA  450
  Printer   NA  2280   NA 2850  570   NA   NA 1710 1140   NA  570   NA
  Tablet  3360 13440 2240 2240 2240 5600 5600 3360 4480 3360 4480 3360

, , year = 2013, loc = NY

.....

dimnames(revenue_cube)

$prod
[1] "Laptop"  "Printer" "Tablet" 

$month
 [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10" "11" "12"

$year
[1] "2012" "2013"

$loc
[1] "CA" "NY" "ON" "QU" "WA"


OLAP Operations

Here are some common operations of OLAP

  • Slice
  • Dice
  • Rollup
  • Drilldown
  • Pivot

"Slice" is about fixing certain dimensions to analyze the remaining dimensions.  For example, we can focus in the sales happening in "2012", "Jan", or we can focus in the sales happening in "2012", "Jan", "Tablet".

# Slice
# cube data in Jan, 2012
revenue_cube[, "1", "2012",]

         loc
prod        CA   NY   ON   QU   WA
  Laptop  1350  450   NA  225  225
  Printer   NA   NA   NA 1140   NA
  Tablet  2240 3360 5600 1120 2240
 
# cube data in Jan, 2012
revenue_cube["Tablet", "1", "2012",]

  CA   NY   ON   QU   WA 
2240 3360 5600 1120 2240 


 "Dice" is about limited each dimension to a certain range of values, while keeping the number of dimensions the same in the resulting cube.  For example, we can focus in sales happening in [Jan/ Feb/Mar, Laptop/Tablet, CA/NY].

revenue_cube[c("Tablet","Laptop"), 
             c("1","2","3"), 
             ,
             c("CA","NY")]
, , year = 2012, loc = CA

        month
prod        1    2     3
  Tablet 2240 4480 12320
  Laptop 1350  225   900

, , year = 2013, loc = CA

        month
prod        1    2    3
  Tablet 3360 3360 1120
  Laptop  225  225  450

, , year = 2012, loc = NY

        month
prod        1     2    3
  Tablet 3360 13440 2240
  Laptop  450   450   NA

, , year = 2013, loc = NY

        month
prod        1    2    3
  Tablet 3360 4480 6720
  Laptop  450   NA  225


"Rollup" is about applying an aggregation function to collapse a number of dimensions.  For example, we want to focus in the annual revenue for each product and collapse the location dimension (ie: we don't care where we sold our product). 

apply(revenue_cube, c("year", "prod"),
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
      prod
year   Laptop Printer Tablet
  2012  22275   31350 179200
  2013  25200   33060 166880
 


"Drilldown" is the reverse of "rollup" and applying an aggregation function to a finer level of granularity.  For example, we want to focus in the annual and monthly revenue for each product and collapse the location dimension (ie: we don't care where we sold our product).

apply(revenue_cube, c("year", "month", "prod"), 
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
, , prod = Laptop

      month
year      1    2    3    4    5    6    7    8    9   10   11   12
  2012 2250 2475 1575 1575 2250 1800 1575 1800  900 2250 1350 2475
  2013 2250  900 1575 1575 2250 2475 2025 1800 2025 2250 3825 2250

, , prod = Printer

      month
year      1    2    3    4    5    6    7    8    9   10   11   12
  2012 1140 5700  570 3990 4560 2850 1140 2850 2850 1710 3420  570
  2013 1140 4560 3420 4560 2850 1140  570 3420 1140 3420 3990 2850

, , prod = Tablet

      month
year       1     2     3     4     5     6     7     8     9    10    11    12
  2012 14560 23520 17920 12320 10080 14560 13440 15680 25760 12320 11200  7840
  2013  8960 11200 10080  7840 14560 10080 29120 15680 15680  8960 12320 22400


"Pivot" is about analyzing the combination of a pair of selected dimensions.  For example, we want to analyze the revenue by year and month.  Or we want to analyze the revenue by product and location.

apply(revenue_cube, c("year", "month"), 
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
      month
year       1     2     3     4     5     6     7     8     9    10    11    12
  2012 17950 31695 20065 17885 16890 19210 16155 20330 29510 16280 15970 10885
  2013 12350 16660 15075 13975 19660 13695 31715 20900 18845 14630 20135 27500
 

apply(revenue_cube, c("prod", "loc"),
      FUN=function(x) {return(sum(x, na.rm=TRUE))})
         loc
prod         CA     NY    ON    QU    WA
  Laptop  16425   9450  7650  7425  6525
  Printer 15390  19950  7980 10830 10260
  Tablet  90720 117600 45920 34720 57120


I hope you can get a taste of the richness of data processing model in R.

However, since R is doing all the processing in RAM.  This requires your data to be small enough so it can fit into the local memory in a single machine.

Check out the Exaptive data application Studio. Technology agnostic. No glue code. Use what you know and rely on the community for what you don't. Try the community version.

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Published at DZone with permission of Ricky Ho, DZone MVB. See the original article here.

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