Arbitrary Data Transforms Using cdata

John Mount, Win-Vector LLC 11/22/2017

We have been writing a lot on higher-order data transforms lately:

What I want to do now is "write a bit more, so I finally feel I have been concise."

The cdata R package supplies general data transform operators.

We will end with a quick example, centered on pivoting/un-pivoting values to/from more than one column at the same time.

Suppose we had some sales data supplied as the following table:

SalesPerson Period BookingsWest BookingsEast
a 2017Q1 100 175
a 2017Q2 110 180
b 2017Q1 250 0
b 2017Q2 245 0

Suppose we are interested in adding a derived column: which region the salesperson made most of their bookings in.

## Loading required package: wrapr
d <- d  %.>% 
  dplyr::mutate(., BestRegion = ifelse(BookingsWest > BookingsEast, 
                                       ifelse(BookingsEast > BookingsWest, 

Our notional goal is (as part of a larger data processing plan) to reformat the data a thin/tall table or a RDF-triple like form. Further suppose we wanted to copy the derived column into every row of the transformed table (perhaps to make some other step involving this value easy).

We can use cdata::rowrecs_to_blocks() to do this quickly and easily.

First we design what is called a transform control table.

cT1 <- data.frame(Region = c("West", "East"),
                  Bookings = c("BookingsWest", "BookingsEast"),
                  BestRegion = c("BestRegion", "BestRegion"),
                  stringsAsFactors = FALSE)
##   Region     Bookings BestRegion
## 1   West BookingsWest BestRegion
## 2   East BookingsEast BestRegion

In a control table:

This control table is called "non trivial" as it does not correspond to a simple pivot/un-pivot (those tables all have two columns). The control table is a picture of of the mapping we want to perform.

An interesting fact is cdata::blocks_to_rowrecs(cT1, cT1, keyColumns = NULL) is a picture of the control table as a one-row table (and this one row table can be mapped back to the original control table by cdata::rowrecs_to_blocks(), these two operators work roughly as inverses of each other; though cdata::rowrecs_to_blocks() operates on rows and cdata::blocks_to_rowrecs() operates on groups of rows specified by the keying columns).

The mnemonic is:

Because pivot and un-pivot are fairly common needs cdata also supplies functions that pre-populate the controls tables for these operations (buildPivotControlTableD() and buildUnPivotControlTable()).

To design any transform you draw out the control table and then apply one of these operators (you can pretty much move from any block structure to any block structure by chaining two or more of these steps).

We can now use the control table to supply the same transform for each row.

d  %.>% 
                Quarter = substr(Period,5,6),
                Year = as.numeric(substr(Period,1,4)))  %.>% 
  dplyr::select(., -Period)  %.>% 
                    controlTable = cT1, 
                    columnsToCopy = c('SalesPerson', 
                                      'Quarter')) %.>% 
  arrange_se(., c('SalesPerson', 'Year', 
                  'Quarter', 'Region'))  %.>% 
SalesPerson Year Quarter Region Bookings BestRegion
a 2017 Q1 East 175 East
a 2017 Q1 West 100 East
a 2017 Q2 East 180 East
a 2017 Q2 West 110 East
b 2017 Q1 East 0 West
b 2017 Q1 West 250 West
b 2017 Q2 East 0 West
b 2017 Q2 West 245 West

Notice we were able to easily copy the extra BestRegion values into all the correct rows.

It can be hard to figure out how to specify such a transformation in terms of pivots and un-pivots. However, as we have said: by drawing control tables one can easily design and manage fairly arbitrary data transform sequences (often stepping through either a denormalized intermediate where all values per-instance are in a single row, or a thin intermediate like the triple-like structure we just moved into).