As of cdata version 1.0.8 cdata implements an operator notation for data transform.

The idea is simple, yet powerful.

First let’s start with some data.

d <- wrapr::build_frame(
  "id", "measure", "value" |
    1   , "AUC"    , 0.7     |
    1   , "R2"     , 0.4     |
    2   , "AUC"    , 0.8     |
    2   , "R2"     , 0.5     )

knitr::kable(d)

In the above data we have two measurements each for two individuals (individuals identified by the "id" column). Using cdata‘s new_record_spec() method we can capture a description of this record structure.

library("cdata")

record_spec <- new_record_spec(
  wrapr::build_frame(
    "measure", "value" |
    "AUC"    , "AUC" |
    "R2"     , "R2"  ),
  recordKeys = "id")

print(record_spec)

## $controlTable
##   measure value
## 1     AUC   AUC
## 2      R2    R2
## 
## $recordKeys
## [1] "id"
## 
## $controlTableKeys
## [1] "measure"
## 
## attr(,"class")
## [1] "cdata_record_spec"

Once we have this specification we can transform the data using operator notation.

We can collect the record blocks into rows by a "division" (or aggregation/projection) step.

knitr::kable(d)

d2 <- d %//% record_spec

knitr::kable(d2)

We can expand record rows into blocks by a "multiplication" (or join) step.

knitr::kable(d2)

d3 <- d2 %**% record_spec

knitr::kable(d3)

And that is truly fluid data manipulation.

This article can be found in a vignette here.