knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
This note is about the design of data transforms using the
cdata package. The
cdata packages demonstrates the "coordinatized data" theory and includes an implementation of the "fluid data" methodology for general
cdata adheres to the so-called "Rule of Representation":
Fold knowledge into data, so program logic can be stupid and robust.
The design principle expressed by this rule is that it is much easier to reason about data than to try to reason about code, so using data to control your code is often a very good trade-off.
We showed in this article how
cdata takes a transform control table to specify how you want your data reshaped.
The question then becomes: how do you come up with the transform control table?
Let's discuss that using the example from the article: "plotting the
iris data faceted".
The goal is to produce the following graph with
In order to do this, one wants data that looks like the following:
Species is in a column so we can use it to choose colors.
flower_part is in a column so we can use it to facet.
iris data starts in the following format.
We call this form a row record because all the information about a single entity (a "record") lies in a single row.
When the information about an entity is distributed across several rows (in whatever shape), we call
that a block record. So the goal is to transform the row records in
iris into the desired block records
This new block record is partially keyed by the
column, which tells us which piece of a record a row corresponds to
(the petal information, or the sepal information). We could also
iris_id as a per-record key; this we are not
adding, as we do not need it for our graphing task. However, adding a
per-record id makes the transform invertible, as is shown
There are a great number of ways to achieve the above transform.
We are going to concentrate on the
cdata methodology. We want to move data from an "all of the record is in one row"
format to "the meaningful record unit is a block across several rows" format.
cdata this means we want to perform a
transform. To do this we start by labeling the roles of different portion of
the block oriented data example. In particular we identify:
Species, but often a per-record index or key).
data.frame. These will go where values are currently in the block record data.
We show this labeling below.
Notice we have marked the measurements
1.4, 0.2, 5.1, 3.5 as "column names", not values. That is because
we must show which columns in the original data frame these values are coming from.
This annotated example record is the guide for building what we call the transform control table. We build up the transform control table following these rules:
R version of the above is specified as follows:
# get a small sample of irises iris <- head(iris, n = 3) # add a record id to iris iris$iris_id <- seq_len(nrow(iris)) knitr::kable(iris)
Specify the layout transform.
library("cdata") controlTable <- wrapr::qchar_frame( "flower_part", "Length" , "Width" | "Petal" , Petal.Length, Petal.Width | "Sepal" , Sepal.Length, Sepal.Width ) layout <- rowrecs_to_blocks_spec( controlTable, recordKeys = c("iris_id", "Species")) print(layout)
And we can now perform the transform.
iris %.>% knitr::kable(.) iris_aug <- iris %.>% layout iris_aug %.>% knitr::kable(.)
The data is now ready to plot using
ggplot2 as was shown here.
blocks_to_rowrecs transform is just as easy, as the
controlTable has the same
shape as the incoming record block (assuming the record partial key controlling column is the first column). All
one has to is get the reverse specification using
inv_layout <- t(layout) print(inv_layout) iris_aug %.>% inv_layout %.>% knitr::kable(.)
Notice in both cases that having examples of the before and after form of the transform is the guide to building the transform specification, that is, the transform control table. In practice: we highly recommend looking at your data, writing down what a single record on each side of the transform would look like, and then using that to fill out the control table on paper.
The exercise of designing a control table really opens your eyes to how data is moving in such transforms and exposes a lot of structure of data transforms. For example:
krows then the
rowrecs_to_blocks()direction could be implemented as
Some discussion of the nature of block records and row records in
cdata can be found here.
Some additional tutorials on
cdata data transforms can are given below:
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