Fully General Record Transforms with cdata

One of the design goals of the cdata R package is that very powerful and arbitrary record transforms should be convenient and take only one or two steps. In fact it is the goal to take just about any record shape to any other in two steps: first convert to row-records, then re-block the data into arbitrary record shapes (please see here and here for the concepts).

But as with all general ideas, it is much easier to see what we mean by the above with a concrete example.

Let’s consider the following artificial (but simple) example. Suppose we have the following data.

library("cdata")

data <- build_frame(
  'record_id', 'row',  'col1', 'col2', 'col3' |
  1,           'row1',  1,      2,      3     |
  1,           'row2',  4,      5,      6     |
  1,           'row3',  7,      8,      9     |
  2,           'row1',  11,     12,     13    |
  2,           'row2',  14,     15,     16    |
  2,           'row3',  17,     18,     19    )

knitr::kable(data)
record_id row col1 col2 col3
1 row1 1 2 3
1 row2 4 5 6
1 row3 7 8 9
2 row1 11 12 13
2 row2 14 15 16
2 row3 17 18 19

In the above the records are the triples of rows with matching record_id and the different rows within the record are identified by the value in the row column. So The data items are named by the triplet record_id, row and renaming column name (col1, col2, or col2). This sort of naming of values is essentially Codd’s "guaranteed access rule".

Suppose we want to transpose each of the records- swapping the row and column notions. With cdata this is easy. First you design a transform to flatten each complex record into a single wide row (using the design steps taught here). Essentially that is just specifying the following control variables. We define how to identify records (the key columns) and the structure of the records (giving the interior of the record arbitrary names we will re-use later).

keyColumns = 'record_id'

incoming_shape <- qchar_frame(
  row,  col1, col2, col3 |
  row1, v11,  v12,  v13  |
  row2, v21,  v22,  v23  |
  row3, v31,  v32,  v33  )

And we specify (using the same principles) the desired final record shape, re-using the interior names from the first step to show where values are to be mapped.

outgoing_shape <- qchar_frame(
  column, row1, row2, row3 |
  col1,   v11,  v21,  v31  |
  col2,   v12,  v22,  v32  |
  col3,   v13,  v23,  v33  )

Once you have done this the conversion is accomplished in two function calls.

rowrecs <- blocks_to_rowrecs(
  data,
  keyColumns = keyColumns,
  controlTable = incoming_shape)
transformed <- rowrecs_to_blocks(
  rowrecs,
  controlTable = outgoing_shape,
  columnsToCopy = keyColumns)

knitr::kable(transformed)
record_id column row1 row2 row3
1 col1 1 4 7
1 col2 2 5 8
1 col3 3 6 9
2 col1 11 14 17
2 col2 12 15 18
2 col3 13 16 19

And the transform is done, each record has been transposed. The principle is "draw a picture." First we draw a picture of the block record structure we have, and then we draw a picture of the block record structure we want. The intermediate form (rowrecs) is a special form where the concepts of records and rows exactly agree. In this form each record is one exactly row and each row is exactly one record. This data looks like the following.

knitr::kable(rowrecs)
record_id v11 v21 v31 v12 v22 v32 v13 v23 v33
1 1 4 7 2 5 8 3 6 9
2 11 14 17 12 15 18 13 16 19

We have complete freedom to re-name columns and record-piece labels (the labels that tell us which portion of a block-record each row fits into).

In the development version of cdata (1.0.5 or newer, install instructions here) we can make things even easier and use a conveneince function that combines these steps.

t2 <- convert_records(
  data,
  keyColumns = keyColumns,
  incoming_shape = incoming_shape,
  outgoing_shape = outgoing_shape)

knitr::kable(t2)
record_id column row1 row2 row3
1 col1 1 4 7
1 col2 2 5 8
1 col3 3 6 9
2 col1 11 14 17
2 col2 12 15 18
2 col3 13 16 19

Also these conversions can also be translated into rquery operators, and therefore saved to be run either in memory or directly on a database.

table_desciption <- rquery::local_td(data)
ops <- table_desciption %.>%
  convert_records(
    .,
    keyColumns = keyColumns,
    incoming_shape = incoming_shape,
    outgoing_shape = outgoing_shape)

cat(format(ops))
#> table(data; 
#>   record_id,
#>   row,
#>   col1,
#>   col2,
#>   col3) %.>%
#>  non_sql_node(., blocks_to_rowrecs(.)) %.>%
#>  non_sql_node(., rowrecs_to_blocks(.))

rquery::column_names(ops)
#> [1] "record_id" "column"    "row1"      "row2"      "row3"

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