Introduction to data. tree

Again, note that we always call such methods on the root Node of a data.tree structure, or on the root Node of a subtree: acmedf <- as.data.frame(acme)

And, finally, we can also put attributes of our nodes in a column, based on a type discriminator. Database browser This sounds more complicated then what it is. Database help Consider the default discriminator, level: ToDataFrameTypeCol(acme, ‘cost’) ## level_1 level_2 level_3 cost

Let’s look at a somewhat more advanced example. Database vendors First, let’s assume that for the outsourcing project, we have two separate possibilities: Outsourcing to India or outsourcing to Poland: acme$IT$Outsource$AddChild(“India”)

Now, with this slightly more complex tree structure, the level is not a usefuly discriminator anymore, because some projects are in level 3, while the new projects are in level 4. Database key For this reason, we introduce a type field on our node objects: A node type can be a company (root only), a department (Accounting, Research, and IT), a program (Oursource), and a project (the rest, i.e. all the leaves): acme$Set(type = c(‘company’, ‘department’, ‘project’, ‘project’, ‘department’, ‘project’, ‘project’, ‘department’, ‘program’, ‘project’, ‘project’, ‘project’, ‘project’))

We can now create a data.frame in which we have one column per distinct type value. Data recovery live cd Namely, a company column, a department column, a program column, and a project column. Data recovery clean room Note that the columns are not hardcoded, but derived dynamically from your data in the tree structure: ToDataFrameTypeCol(acme, type = ‘type’, prefix = NULL) ## company department program project

The data.tree package has been built to work with hierarchical data, to support visualization, to foster rapid prototyping, and for other applications where development time saved is more important than computing time lost. Database view Having said this, it becomes clear that big data and data.tree do not marry particularly well. Data recovery fort lauderdale Don’t expect R to build your data.tree structure with a few million Nodes during your cigarette break. Data recovery equipment tools Do not try to convert a gigabyte JSON document to a data.tree structure in a testthat test case.

• Creating a Node is relatively expensive. Data recovery lab CreateRegularTree(6, 6) creates a data.tree structure with 9331 Nodes. Data recovery online On an AWS c4.large instance, this takes about 2.5 seconds.

This is really what you would expect.


Database union data.tree builds on R6, i.e. reference objects. Data recovery houston There is an overhead in creating them, as your computer needs to manage the references they hold. 7m database soccer basketball However, performing operations that change your tree (e.g. Create database link Prune or Set) are often faster than value semantics, as your computer does not need to copy the entire object in memory.

Just to give you an order of magnitude: The following times are achieved on an AWS c4.large instance: system.time(tree <- CreateRegularTree(6, 6)) ## user system elapsed

With caching, you can save some time: system.time(tree$Get(function(node) Aggregate(tree, “leafId”, max, “maxLeafId”), traversal = “post-order”)) ## user system elapsed

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