For visualising hierarchical data a treemap can also work well. For
example, using the treemap package:
n <- 1000
library(data.table)
library(treemap)
dta <- data.table(
level1 = sample(LETTERS[1:5], n, replace = TRUE),
level2 = sample(letters[1:5], n, replace = TRUE),
level3 = sample(1:
Hi Richard,
Thinking about this, you might also find intersectDiagram, also in
plotrix, to be useful.
Jim
On Mon, May 30, 2022 at 4:37 PM Jim Lemon wrote:
>
> Hi Richard,
> Some years ago I had a try at illustrating Multiple Causes of Death
> (MCoD) data. I settled on what is sometimes called a
Hi Richard,
Some years ago I had a try at illustrating Multiple Causes of Death
(MCoD) data. I settled on what is sometimes called a "sizetree". You
can see some examples in the sizetree function help page in "plotrix".
Unfortunately I can't use the original data as it was confidential.
Jim
On Mo
Really this depends on the analysis you want to perform.
In the past, I have used a super/sub two-column format as a compact,
non-redundant representation for data entry, and after applying a recursive
algorithm to convert this to a super/sub/level/id table where _all_ sub
components have (dupl
There is a kind of data I run into fairly often
which I have never known how to represent in R,
and nothing I've tried really satisfies me.
Consider for example
...
- injuries
...
- injuries to limbs
...
- injuries to extremities
...
- injuries to hands
- i
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