Introduction to data.tree

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Introduction

Trees

Trees are ubiquitous in mathematics, computer science, data sciences, finance, and in many other attributes. Trees are especially useful when we are facing hierarchical data. For example, trees are used:

For more details, see the applications vignette by typing vignette("applications", package = "data.tree")

Trees in R

Tree-like structures are already used in R. For example, environments can be seen as nodes in a tree. And CRAN provides numerous packages that deal with tree-like structures, especially in the area of decision theory. Yet, there is no general purpose hierarchical data structure that could be used as conveniently and generically as, say, data.frame.

As a result, people often try to resolve hierarchical problems in a tabular fashion, for instance with data.frames. But often, hierarchies don't marry with tables, and various workarounds are usually required.

Trees in data.tree

This package offers an alternative. The data.tree package lets you create hierarchies, called data.tree structures. The building block of theses structures are Node objects. The package provides basic traversal, search, and sort operations, and an infrastructure for recursive tree programming. You can decorate Nodes with your own attributes and methods, so as to extend the package to your needs.

The package also provides convenience methods for neatly printing and plotting trees. It supports conversion from and to data.frames, lists, and other tree structures such as dendrogram, phylo objects from the ape package, igraph, and other packages.

Technically, data.tree structures are bi-directional, ordered trees. Bi-directional means that you can navigate from parent to children and vice versa. Ordered means that the sort order of the children of a parent node is well-defined.

data.tree basics

Definitions

Tree creation

There are different ways to create a data.tree structure. For example, you can create a tree programmatically, by conversion from other R objects, or from a file.

Create a tree programmatically

Let's start by creating a tree programmatically. We do this by creating Node objects, and linking them together so as to define the parent-child relationships.

In this example, we are looking at a company, Acme Inc., and the tree reflects its organisational structure. The root (level 1) is the company. On level 2, the nodes represent departments, and the leaves of the tree represent projects that the company is considering for next year:

library(data.tree)

acme <- Node$new("Acme Inc.")
  accounting <- acme$AddChild("Accounting")
    software <- accounting$AddChild("New Software")
    standards <- accounting$AddChild("New Accounting Standards")
  research <- acme$AddChild("Research")
    newProductLine <- research$AddChild("New Product Line")
    newLabs <- research$AddChild("New Labs")
  it <- acme$AddChild("IT")
    outsource <- it$AddChild("Outsource")
    agile <- it$AddChild("Go agile")
    goToR <- it$AddChild("Switch to R")

print(acme)

As you can see from the previous example, each Node is identified by its name, i.e. the argument you pass into the Node$new(name) constructor. The name needs to be unique among siblings, such that paths to Nodes are unambiguous.

Node inherits from R6 reference class. This has the following implications:

  1. You can call methods on a Node in OO style, e.g. acme$Get("name")
  2. Node exhibits reference semantics. Thus, multiple variables in R can point to the same Node, and modifying a Node will modify it for all referencing variables. In the above code example, both acme$IT and it reference the same object. This is different from the value semantics, which is much more widely used in R.

Create a tree from a data.frame

Creating a tree programmatically is useful especially in the context of algorithms. However, most times you will create a tree by conversion. This could be by conversion from a nested list-of-lists, by conversion from another R tree-structure (e.g. an ape phylo), or by conversion from a data.frame. For more details on all the options, type ?as.Node and refer to the See Also section.

One of the most common conversions is the one from a data.frame in table format. The following code illustrates this. We load the GNI2014 data from the treemap package. This data.frame is in table format, meaning that each row will represent a leaf in the data.tree structure:

library(treemap)
data(GNI2014)
head(GNI2014)

Let's convert that into a data.tree structure! We start by defining a pathString. The pathString describes the hierarchy by defining a path from the root to each leaf. In this example, the hierarchy comes very naturally:

GNI2014$pathString <- paste("world", 
                            GNI2014$continent, 
                            GNI2014$country, 
                            sep = "/")

Once our pathString is defined, conversion to Node is very easy:

population <- as.Node(GNI2014)
print(population, "iso3", "population", "GNI", limit = 20)

This is a simple example, and more options are available. Type ?FromDataFrameTable for all the details.

Create a tree from a file

Often, trees are created from one of many file formats. When developing this package, We opted for a multi-step approach, meaning that you first import the file into one of the well-known R data structures. Then you convert these into a data.tree structure. For example, typical import patterns could be:

If you have a choice, we recommend you consider yaml format to store and share your hierarchies. It is concise, human-readable, and very easy to convert to a data.tree. An example is provided here for illustration. The data represents what platforms and OS versions a group of students use:

library(yaml)
yaml <- "
name: OS Students 2014/15
OS X:
  Yosemite:
    users: 16
  Leopard:
    users: 43
Linux:
  Debian:
    users: 27
  Ubuntu:
    users: 36
Windows:
  W7:
    users: 31
  W8:
    users: 32
  W10:
    users: 4
"

osList <- yaml.load(yaml)
osNode <- as.Node(osList)
print(osNode, "users")

Node methods

As seen above, a data.tree structure is composed of Node objects, and the entry point to a data.tree structure is always a Node, often the root Node of a tree.

There are different types of methods:

Actives Examples (aka Properties)

Actives look and feel like attributes, but they are dynamically evaluated. They are documented in the Node documentation, which is accessed by typing ?Node.

Remember our population example:

print(population, limit = 15)
population$isRoot
population$height
population$count
population$totalCount
population$attributes
population$attributesAll
population$averageBranchingFactor

The naming convention of the package is that attributes and actives are lower case, whereas methods are upper / CamelCase. RStudio and other IDEs work well with data.tree. If you have a Node, simply type myNode$ + SPACE to get a list of available attributes, actives and methods.

OO-Style Methods Examples

Examples of OO-Style methods

You will find more information on these examples below.

Get will traverse the tree and collect specific values for the Nodes it traverses:

sum(population$Get("population", filterFun = isLeaf))

Prune traverses the tree and keeps only the subtrees for which the pruneFun returns TRUE.

Prune(population, pruneFun = function(x) !x$isLeaf || x$population > 1000000)

Note that the Prune function has side-effects, as it acts on the original population object. The population sum is now smaller:

sum(population$Get("population", filterFun = isLeaf), na.rm = TRUE)

Traditional R Methods

popClone <- Clone(acme)

Traditional S3 generics are available especially for conversion:

as.data.frame(acme)

Though there is also a more specialised non-generic version:

ToDataFrameNetwork(acme)

Climbing a tree (tree navigation)

To climb a tree means to navigate to a specific Node in the data.tree structure.

Navigation by path

The most natural form of climbing a tree is to climb by path:

acme$IT$Outsource
acme$Research$`New Labs`

Navigation by position

However, there is a number of other ways to get to a specific Node. We can access the children of a Node directly through Node$children:

acme$children[[1]]$children[[2]]$name

Navigation by attributes

Furthermore, we can not only navigate by name, but also by other attributes. This is achieved with the Climb method. The name of each ... argument designates the field, and the value matches against Nodes. Each argument refers to the subsequent level to climb. In this example, Climb takes acme's child at position 1 (i.e. Accounting), then it takes Accounting's child called New Software:

acme$Climb(position = 1, name = "New Software")$path

As a shortcut, you can climb multiple levels with a single argument:

tree <- CreateRegularTree(5, 5)
tree$Climb(position = c(2, 3, 4))$path

Finally, you can even combine. The following example starts on the root, then looks for child at position 2, then for its child at position 3. Next, we move to the child having name = "1.2.3.4", and finally its child having name "1.2.3.4.5":

tree$Climb(position = c(2, 3), name = c("1.2.3.4", "1.2.3.4.5"))$path

Custom attributes

Just as with, say, a list, we can add any custom field to any Node in a data.tree structure. Let's go back to our acme company:

acme

We now add costs and probabilities to the projects in each department:

acme$Accounting$`New Software`$cost <- 1000000
acme$Accounting$`New Accounting Standards`$cost <- 500000
acme$Research$`New Product Line`$cost <- 2000000
acme$Research$`New Labs`$cost <- 750000
acme$IT$Outsource$cost <- 400000
acme$IT$`Go agile`$cost <- 250000
acme$IT$`Switch to R`$cost <- 50000

acme$Accounting$`New Software`$p <- 0.5
acme$Accounting$`New Accounting Standards`$p <- 0.75
acme$Research$`New Product Line`$p <- 0.25
acme$Research$`New Labs`$p <- 0.9
acme$IT$Outsource$p <- 0.2
acme$IT$`Go agile`$p <- 0.05
acme$IT$`Switch to R`$p <- 1
print(acme, "cost", "p")

Note that there is a list of reserved names you cannot use as Node attributes:

NODE_RESERVED_NAMES_CONST

Custom attributes in constructor

An alternative, often convenient way to assign custom attributes is in the constructor, or in the Node$AddChild method:

birds <- Node$new("Aves", vulgo = "Bird")
birds$AddChild("Neognathae", vulgo = "New Jaws", species = 10000)
birds$AddChild("Palaeognathae", vulgo = "Old Jaws", species = 60)
print(birds, "vulgo", "species")

Custom attributes as function

Nothing stops you from setting a function as a field. This calculates a value dynamically, i.e. whenever a field is accessed in tree traversal. For example, you can add a new Node to your structure, and the function will reflect this. Think of this as a hierarchical spreadsheet, in which you can set formulas into cells.

Consider the following example:

birds$species <- function(self) sum(sapply(self$children, function(x) x$species))
print(birds, "species")

data.tree maps the self argument to the Node at hand. Thus, you must name the argument self.

Now, let's assume we discover a new species. Then, the species on the root adjusts dynamically:

birds$Palaeognathae$species <- 61
print(birds, "species")

This, together with the Set method and recursion, becomes a very powerful tool, as we'll see later.

Printing

Basic Printing

Basic printing is easy, as you surely have noted in the previous sections. print displays a tree in a tree-grid view. On the left, you have the hierarchy. Then you have a column per variable you want to print:

print(acme, "cost", "p")

For more advanced printing, you have a few options.

Formatters

You can use formatters to output a variable in a certain way. You can use formatters in two ways:

Setting a formatter using the SetFormat method:

SetFormat(acme, "p", formatFun = FormatPercent)
SetFormat(acme, "cost", formatFun = function(x) FormatFixedDecimal(x, digits = 2))
print(acme, "cost", "p")

Printing using Get

Formatting with the Get method overwrites any formatters found along the path:

data.frame(cost = acme$Get("cost", format = function(x) FormatFixedDecimal(x, 2)),
           p = acme$Get("p", format = FormatPercent))

Plotting

plot

data.tree is mainly a data structure. As it is easy to convert data.tree structures to other formats, you have access to a large number of tools to plot a data.tree structure. For example, you can plot a data.tree structure as a dendrogram, as an ape tree, as a treeview, etc. Additionally, data.tree also provides its own plotting facility. It is built on GraphViz/DiagrammeR, and you can access these features via the plot and ToGraphViz functions. Note that DiagrammeR is not required to use data.tree, so plot only works if DiagrammeR is installed on your system. For example:

plot(acme)

acme

Styling

Similar to formatters for printing, you can style your tree and store the styling directly in the tree, for later use:

SetGraphStyle(acme, rankdir = "TB")
SetEdgeStyle(acme, arrowhead = "vee", color = "grey35", penwidth = 2)
SetNodeStyle(acme, style = "filled,rounded", shape = "box", fillcolor = "GreenYellow", 
            fontname = "helvetica", tooltip = GetDefaultTooltip)
SetNodeStyle(acme$IT, fillcolor = "LightBlue", penwidth = "5px")
plot(acme)

acme

For details on the styling attributes, see http://graphviz.org/Documentation.php .

Note that, by default, most Node style attributes will be inherited. Though, for example, label will not be inherited. However, inheritance can be avoided for all style attributes, as for the Accounting node in the following example:

SetNodeStyle(acme$Accounting, inherit = FALSE, fillcolor = "Thistle", 
             fontcolor = "Firebrick", tooltip = "This is the accounting department")
plot(acme)

acme

Use Do to set style on specific nodes:

Do(acme$leaves, function(node) SetNodeStyle(node, shape = "egg"))
plot(acme)

acme

Other Visualisations

However, there are also endless other possibilities to visualise data.tree structures. There are more examples in the applications vignette. Type vignette('applications', package = "data.tree").

Dendrogram

For example, using dendrogram:

plot(as.dendrogram(CreateRandomTree(nodes = 20)), center = TRUE)

igraph

Or, using igraph:

library(igraph, quietly = TRUE, warn.conflicts = FALSE, verbose = FALSE)
library(igraph)
plot(as.igraph(acme, directed = TRUE, direction = "climb"))

networkD3

Or, using networkD3: (you can actually touch these thingies and drag them around, don't be shy!)

library(networkD3)
acmeNetwork <- ToDataFrameNetwork(acme, "name")
simpleNetwork(acmeNetwork[-3], fontSize = 12)

Another example, which at the same time shows conversion from csv:

fileName <- system.file("extdata", "useR15.csv", package="data.tree")
useRdf <- read.csv(fileName, stringsAsFactors = FALSE)
#define the hierarchy (Session/Room/Speaker)
useRdf$pathString <- paste("useR", useRdf$session, useRdf$room, useRdf$speaker, sep="|")
#convert to Node
useRtree <- as.Node(useRdf, pathDelimiter = "|")

#plot with networkD3
useRtreeList <- ToListExplicit(useRtree, unname = TRUE)
radialNetwork( useRtreeList)

Tree Conversion

In order to take advantage of the R eco-system, you can convert your data.tree structure to other oft-used data types. The general rule is that, for each target type, there is a one-does-it-all generics, and a few more specialised conversion functions. For example, in order to convert a data.tree to a data.frame, you can either use as.data.frame.Node, or ToDataFrameTree, ToDataFrameTable, or ToDataFrameNetwork. The documentation for all of these variations is accessible via ?as.data.frame.Node.

Converting to data.frame

As you saw just above, creating a data.frame is easy.

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)
as.data.frame(acme$IT)

The same can be achieved by using the more specialised method:

ToDataFrameTree(acme)

We can also add field values of the Nodes as columns to the data.frame:

ToDataFrameTree(acme, "level", "cost")

Note that it is not required that the field is set on each and every Node.

Other data frame conversions are:

ToDataFrameTable(acme, "pathString", "cost")
ToDataFrameNetwork(acme, "cost")

And, finally, we can also put attributes of our nodes in a column, based on a type discriminator. This sounds more complicated then what it is. Consider the default discriminator, level:

ToDataFrameTypeCol(acme, 'cost')

Let's look at a somewhat more advanced example. First, let's assume that for the outsourcing project, we have two separate possibilities: Outsourcing to India or outsourcing to Poland:

acme$IT$Outsource$AddChild("India")
acme$IT$Outsource$AddChild("Poland")

Now, with this slightly more complex tree structure, the level is not a usefully discriminator anymore, because some projects are in level 3, while the new projects are in level 4. 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'))

Our tree now looks like this:

print(acme, 'type')

We can now create a data.frame in which we have one column per distinct type value. Namely, a company column, a department column, a program column, and a project column. Note that the columns are not hardcoded, but derived dynamically from your data in the tree structure:

ToDataFrameTypeCol(acme, type = 'type', prefix = NULL)

Converting to List of Lists

List of lists are useful for various use cases:

data(acme)
str(as.list(acme$IT))
str(ToListExplicit(acme$IT, unname = FALSE, nameName = "id", childrenName = "dependencies"))

Converting to other objects

There are also conversions to igraph objects, to phylo / ape, to dendrogram, and others. For details, see ?as.phylo.Node, ?as.dendrogram.Node, ?as.igraph.Node.

Tree Traversal

Tree traversal is one of the core concepts of trees. See, for example, here: Tree Traversal on Wikipedia.

Get

The Get method traverses the tree and collects values from each node. It then returns a vector or a list, containing the collected values.

Additional features of the Get method are:

Traversal order

The Get method can traverse the tree in various ways. This is called traversal order.

Pre-Order

The default traversal mode is pre-order.

pre-order

This is what is used e.g. in print:

print(acme, "level")

Post-Order

The post-order traversal mode returns children first, returning parents only after all its children have been traversed and returned:

post-order

We can use it like this on the Get method:

acme$Get('level', traversal = "post-order")

This is useful if your parent's value depends on the children, as we'll see below.

Ancestor

This is a non-standard traversal mode that does not traverse the entire tree. Instead, the ancestor mode starts from a Node, then walks the tree along the path from parent to parent, up to the root.

data.frame(level = agile$Get('level', traversal = "ancestor"))

Filter and Prune

You can add a filter and/or a prune function to the Get method. These functions have to take a Node as an input, and return TRUE if the Node should be considered, and FALSE otherwise. The difference between the pruneFun and the filterFun is that filters act only on specific nodes, whereas if the pruneFun returns FALSE, then the entire sub-tree spanned by the Node is ignored.

For example:

acme$Get('name', pruneFun = function(x) x$position <= 2)

There are also some convenient filter functions available in the package, such as isLeaf, isRoot, isNotLeaf, etc.

acme$Get('name', filterFun = isLeaf)

Attributes

The attribute parameter determines what is collected. This is called attribute, but it should not be confused with R's concept of object attributes (e.g. ?attributes). In this context, an attribute can be either:

Throughout this document, we refer to attribute in this sense.

Field

acme$Get('name')

Method

You can pass a standard R function to the Get method (and thus to print, as.data.frame, etc.). The only requirement this function must satisfy is that its first argument be of class Node. Subsequent arguments can be added through the ellipsis (...). For example:

ExpectedCost <- function(node, adjustmentFactor = 1) {
  return ( node$cost * node$p * adjustmentFactor)
}

acme$Get(ExpectedCost, adjustmentFactor = 0.9, filterFun = isLeaf)

Using recursion

Recursion comes naturally with data.tree, and it is one of its core strengths:

Cost <- function(node) {
  result <- node$cost
  if(length(result) == 0) result <- sum(sapply(node$children, Cost))
  return (result)
}

print(acme, "p", cost = Cost)

There is a built-in function that would make this example even simpler: Aggregate. It is explained below.

Do

Do is similar to Get in that it also traverses a tree in a specific traversal order. However, instead of fetching an attribute, it will (surprise!) do something, namely run a function. For example, we can tell the Do method to assign a value to each Node it traverses. This is especially useful if the attribute parameter is a function, as in the previous examples. For instance, we can store the aggregated cost for later use and printing:

acme$Do(function(node) node$cost <- Cost(node), filterFun = isNotLeaf)
print(acme, "p", "cost")

Set

The Set method is the counterpart to the Get method. The Set method takes a vector or a single value as an input, and traverses the tree in a certain order. Each Node is assigned a value from the vector, one after the other, recycling.

Assigning values

acme$Set(id = 1:acme$totalCount)

print(acme, "id")

The Set method can take multiple vectors as an input, and, optionally, you can define the name of the attribute. Finally, just as for the Get method, the traversal order is important for the Set.

secretaries <- c(3, 2, 8)
employees <- c(52, 43, 51)
acme$Set(secretaries, 
         emps = employees,
         filterFun = function(x) x$level == 2)
print(acme, "emps", "secretaries", "id")

Deleting attributes

The Set method can also be used to assign a single value directly to all Nodes traversed. For example, to remove the avgExpectedCost, we assign NULL on each node, using the fact that the Set recycles:

acme$Set(avgExpectedCost = NULL)

However, note that setting a field to NULL will not make it gone for good. You will still see it:

acme$attributesAll

In order remove it completely, you can use the RemoveAttribute method:

acme$Do(function(node) node$RemoveAttribute("avgExpectedCost"))

Using Set and function assignment

Earlier, we saw that we can add a function dynamically to a Node. We can, of course, also do this via the Set method

acme$Set(cost = c(function(self) sum(sapply(self$children, 
                                            function(child) GetAttribute(child, "cost")))), 
         filterFun = isNotLeaf)
print(acme, "cost")
acme$IT$AddChild("Paperless", cost = 240000)
print(acme, "cost")

Traverse and explicit traversal

Previously, we have used the Get, Set and Do methods in their OO-style version. This is often very convenient for quick access to variables. However, sometimes you want to re-use the same traversal for multiple sequential operations. For this, you can use what is called explicit traversal. It works like so:

traversal <- Traverse(acme, traversal = "post-order", filterFun = function(x) x$level == 2)
Set(traversal, floor = c(1, 2, 3))
Do(traversal, function(x) {
    if (x$floor <= 2) {
      x$extension <- "044"
    } else {
      x$extension <- "043"
    }
  })
Get(traversal, "extension")

Advanced Features

Aggregate

The Aggregate method provides a shorthand for the oft-used case when a parent is the aggregate of its child values, as seen in the previous example. Aggregate calls a function recursively on children. If a child holds the attribute, that value is returned. Otherwise, the attribute is collected from all children, and aggregated using the aggFun. For example:

Aggregate(node = acme, attribute = "cost", aggFun = sum)

We can also use this in the Get method, of course:

acme$Get(Aggregate, "cost", sum)

Note, however, that this is not very efficient: Aggregate will be called twice on, say, IT: Once when the traversal passes IT itself, the second time recursively when Aggregate is called on the root. For this reason, we have the option to store/cache the calculated value along the way. For one thing, this is a convenient way to save an additional Set call in case we want to store the aggregated value. Additionally, it speeds up calculation because Aggregate on an ancestor will use a cached value on a descendant:

acme$Do(function(node) node$cost <- Aggregate(node, attribute = "cost", aggFun = sum), traversal = "post-order")
print(acme, "cost")

Cumulate

In its simplest form, the Cumulate function just sums up an attribute value along siblings, taking into consideration all siblings before the Node on which Cumulate is called:

Cumulate(acme$IT$`Go agile`, "cost", sum)

Or, to find the minimum cost among siblings:

Cumulate(acme$IT$`Go agile`, "cost", min)

This can be useful in combination with traversal, e.g. to calculate a running sum among siblings. Specifically, the cacheAttribute lets you store the running sum in a field. This not only speeds up calculation, but lets you re-use the calculated values later:

acme$Do(function(node) node$cumCost <- Cumulate(node, 
                                                attribute = "cost", 
                                                aggFun = sum))
print(acme, "cost", "cumCost")

Clone

As stated above, Nodes exhibit reference semantics. If you call, say, Set, then this changes the Nodes in the tree. The changes will be visible for all variables having a reference on the data.tree structure. As a consequence, you might want to "save away" the current state of a structure. To do this, you can Clone an entire tree:

acmeClone <- Clone(acme)
acmeClone$name <- "New Acme"
# acmeClone does not point to the same reference object anymore:
acme$name == acmeClone$name

Sort

With the Sort method, you can sort an entire tree, a sub-tree, or children of a specific Node. The method will sort recursively and sort children with respect to a child attribute. As explained earlier, the child attribute can be a function or a method.

Sort(acme, "name")
acme
Sort(acme, Aggregate, "cost", sum, decreasing = TRUE, recursive = TRUE)
print(acme, "cost", aggCost = acme$Get(Aggregate, "cost", sum))

Prune

You can prune sub-trees out of a tree, by that removing an entire sub-tree from a tree. There are two variations of this: temporary pruning, e.g. just for printing: This is the pruneFun parameter, e.g. in Get side effect or permanent pruning, meaning that you modify your data.tree structure for good. This is achieved with the Prune method.

Consider the following example of permanent pruning:

acme$Do(function(x) x$cost <- Aggregate(x, "cost", sum))
Prune(acme, function(x) x$cost > 700000)
print(acme, "cost")

Performance Considerations

CPU

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. Having said this, it becomes clear that big data and data.tree do not marry particularly well. Don't expect R to build your data.tree structure with a few million Nodes during your cigarette break. Do not try to convert a gigabyte JSON document to a data.tree structure in a testthat test case.

However, if you are respecting the following guidelines, I promise that you and your Nodes will have a lot of fun together. So here it goes:

  1. Creating a Node is relatively expensive. CreateRegularTree(6, 6) creates a data.tree structure with 9331 Nodes. On an AWS c4.large instance, this takes about 2.5 seconds.
  2. Clone is similar to Node creation, with an extra penalty of about 50%.
  3. Traversing (Traverse, Get, Set and Do) is relatively cheap.

This is really what you would expect. data.tree builds on R6, i.e. reference objects. There is an overhead in creating them, as your computer needs to manage the references they hold. However, performing operations that change your tree (e.g. 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))
c(user = 2.499, system = 0.009, elapsed = 2.506)
system.time(tree <- Clone(tree))
c(user = 3.704, system = 0.023, elapsed = 3.726)
system.time(traversal <- Traverse(tree))
c(user = 0.096, system = 0.000, elapsed = 0.097)
system.time(Set(traversal, id = 1:tree$totalCount))
c(user = 0.205, system = 0.000, elapsed = 0.204)
system.time(ids <- Get(traversal, "id"))
c(user = 0.569, system = 0.000, elapsed = 0.569)
leaves <- Traverse(tree, filterFun = isLeaf)
Set(leaves, leafId = 1:length(leaves))
system.time(Get(traversal, function(node) Aggregate(node, "leafId", max)))
c(user = 1.418, system = 0.000, elapsed = 1.417)

With caching, you can save some time:

system.time(tree$Get(function(node) Aggregate(tree, "leafId", max, "maxLeafId"), traversal = "post-order"))
c(user = 0.69, system = 0.00, elapsed = 0.69)

Memory

data.tree structures have a relatively large memory footprint. However, for every-day applications using modern computers, this will not normally have an impact on your work except when saving a data.tree structure to disk.

For an explanation why that is the case, you might want to read this answer on Stack Overflow.

Depending on your development environment, you might want to turn off the option to save the workspace to .RData on exit.



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data.tree documentation built on Aug. 3, 2020, 5:12 p.m.